Artificial Intelligence in Europe

Norway Outlook for 2019 and Beyond

How 277 Major Companies Benefit from AI

REPORT COMMISSIONED BY MICROSOFT AND CONDUCTED BY EY Contents

Preface Foreword 06 Executive Summary - ‘At a Glance’ ...... 08

Setting the Scene About this Report ...... 10 Rich Data ...... 13 Executive Perspective ...... 14 Participating Companies ...... 16 Bits & Bytes ...... 18 Follow the Money ...... 20 Case Study ...... 22 Experts ...... 23

Role of AI in European Business A Strategic Agenda ...... 27 Among Friends ...... 28 Push or Pull ...... 29 Ready, Set...... 30 AI Maturity Curve ...... 32 State Your Business 34 Case Study 36

Business Benefits and Risks Another World ...... 38 AI Here, There, Everywhere 40 Case Study ...... 41 Use it or Lose it ...... 42 Making AI simple ...... 44 Case Study ...... 47 Sector Benefits Landscape ...... 48 Risky Business ...... 51

Learn from the Leaders Capabilities ...... 53 AI Competency Model ...... 55 Advanced Analytics ...... 56 Data Management 58 AI Leadership 60 Open Culture ...... 62 Emerging Technology 64

Disclaimer Agile Development ...... 66 This report has been prepared by Ernst & Young LLP in accordance with This report does not constitute a recommendation or endorsement by Ernst External Alliances ...... 68 an engagement agreement for professional services with Microsoft. Ernst & Young LLP or Microsoft to invest in, sell, or otherwise use any of the mar- Emotional Intelligence ...... 70 & Young LLP’s obligations to Microsoft are governed by that engagement kets or companies referred to in it. To the fullest extent permitted by law, agreement. This disclaimer applies to all other parties. Microsoft and Ernst & Young LLP and its members, employees and agents, do Case Study 73 not accept or assume any responsibility or liability in respect of this report, or This report has been prepared for general informational purposes only and decisions based on it, to any reader of the report. Should such readers choose What’s next for you? is not intended to be relied upon as accounting, tax, or other professional to rely on this report, then they do so at their own risk. advice. Refer to your advisors for specific advice. Ernst & Young LLP and Mi- How to Get Started 74 crosoft accept no responsibility to update this report in light of subsequent ©2018 EY LLP Limited All Rights Reserved. Who to Contact from Microsoft ...... 77 events or for any other reason. Contributors from EY ...... 79

2 3 AI is clearly an important business priority for many Norwegian companies. Combined with an already-strong economy and digital maturity, Norway is in a fantastic position to create exciting new opportunities with AI.

— Harry Shum, Executive Vice President of Microsoft AI and Research Group

4 5 Preface Preface

Foreword

Human Ingenuity Get inspired by your peers

The printing press, the automobile & the Internet are just a few technolog- We are currently witnessing a comprehensive digitalization of our society. ical achievements that have advanced our world. All were driven by human New technological advances are changing how we live and work. Artificial ingenuity: our innate creativity that inspires us to learn, imagine & explore. Intelligence has the potential to expand human capacity and give us the This spirit is what pushes us to challenge the boundaries of the possible to super powers we need to solve the greatest challenges of our times. But go ever forward. such powers must be mastered responsibly. Innovation and ethics go hand in hand. Today, AI is helping to amplify our human ingenuity, opening up exciting new possibilities for how intelligent technology can shape our world. At At Microsoft, we want to develop technology that empower humans, Microsoft, our goal is to democratize access to AI for everyone through in- rather than replace them. We challenge business leaders to look beyond novative & powerful platforms, & above all, we’re focused on ensuring that automating to cut costs and seek out opportunities to drive growth and our AI tools & technologies are deployed responsibly & earn people’s trust. innovation. Still, AI will significantly change the labor market, and continuous learning is a key premise to ensure the necessary skills for the future. And yet, we realize that AI is one of the lesser understood modern tech- Collaboration between the public and private sector, academia and policy nological break-throughs. Many questions remain. How are companies makers is more important than ever before. applying this technology to empower employees, engage with customers, transform their business and optimize their operations? Where are they Twenty-one Norwegian companies have participated in this extensive sur- seeing benefits, and what are their blockers? vey about AI. Their experiences create a framework to better understand the technology and its potential to empower people and organizations To provide answers, Microsoft commissioned this study to understand the in new and more impactful ways. We hope the report will both help and AI strategy of major companies across 7 sectors & 15 countries in Europe. inspire you to see how AI can develop and prepare your business for the It examines these companies’ readiness to adopt AI, how they rate the im- future. pact and benefits from AI implementations, and what they perceive as risks & keys to success.

We hope you find these insights inspirational for your own journey toward adopting AI & realizing its benefits for amplifying human ingenuity.

Vahé Torossian Kimberly Lein-Mathisen President, Microsoft Western Europe General Manager, Microsoft Norway

6 7 Preface Preface

At a Glance

Noticeable potential for AI in many Norwegian companies beginning to explore possibilities with AI corporate functions When looking across the 21 companies that have participated in the The most widely reported adoption of study in Norway, it is clear that they are actively exploring and pursuing AI (47%) was in the IT/Technology func- possibilities with AI, with their current maturity level roughly on level tion, followed by R&D with 36%, and with their European peers. More than half of the companies report that customer service with 24%. Interestingly, AI is currently considered an important topic at the C-suite level and several functions are hardly using AI at 81% report that AI is as important as other digital priorities, if not more all; most notably, the procurement func- important. Expected impact is high as well: the vast majority of compa- While the hype of artificial intelligence Most impact expected from ‘opti- is almost as much as AI is expected to tion, where only 4% of the companies nies from Norway report expecting AI to create some degree of impact (AI) and its potential role as a driver of mizing operations’, with ‘engaging impact the core of these companies’ cur- currently use AI, followed by HR with 7% across all business areas – core, adjacent and new. transformational change to businesses customers’ as a close second rent business with 65% expecting AI to and product management with 9%. This and industries is pervasive, there are have a high or a very high impact on the 89% of the respondents expect AI to is perhaps surprising, given the many limited insights into what companies core business. With AI presumably push- generate business benefits by optimiz- use cases and applicable solutions in are actually doing to reap its benefits. ing companies into totally new domains ing their companies’ operations in the these functional areas. This report aims at getting a deeper future. This is followed by 74% that ex- in the future, it is perhaps not surprising understanding of how companies cur- that AI is receiving attention as a key pect AI to be key to engaging custom- 8 key capabilities that are most im- rently manage their AI activities, and topic for executive management. ers by enhancing the user experience, portant ‘to get AI right’ What sets the most ‘AI mature’ how they address the current challeng- tailoring content, increasing response When asking the respondents to rank es and opportunities ahead. speed, adding sentiment, creating Very few of the 277 companies con- companies apart? the importance of 8 capabilities to ena- experiences, anticipating needs, etc. sider themselves “advanced” with AI ble AI in their businesses, ‘advanced ana- To get to the heart of this agenda, we Despite the apparent sizable impact that They expect AI will be beneficial in ‘empowering employees’ (76% lytics’ and ‘data management’ emerged received input from AI leaders in 277 C-suite respondents scored ‘engaging companies expect from AI, only a very of ‘more mature’ companies* vs. 42% of ‘less mature’ companies)*. as the most important. ‘AI leadership’ companies, across 7 sectors and 15 customers’ highest of the AI benefit small proportion of companies, consti- and having an ‘open culture’ followed. countries in Europe, via surveys and/or areas. Noticeably, 100% of the most ad- tuting 4% of the total sample, self-re- They report using a combination of structured and unstructured interviews. Below is the brief summary vanced* companies expect AI will help port that AI is actively contributing to data for AI (65% of ‘more mature’ companies vs. 15% of ‘less mature’ When self-assessing the capabilities of what they had to say. them engage customers, compared to ‘many processes in the company and companies), and data from both internal and external sources (68% where the companies are least com- only 63% of the less mature companies. enabling quite advanced tasks today’ of ‘more mature’ companies vs. 16% of ‘less mature’ companies). petent, they point to emotional intelli- AI is a “hot topic” - but more so on Using AI to ‘transform products and (referred to as ‘most advanced’ in this gence and AI leadership - defined as the C-level than in daily operations services’ comes out slightly lower with report). They expect AI will help them ‘engage customers’ (85% of ‘more (lack of) ability to lead an AI transfor- 71% of the companies respond that 65%, and ‘empowering employees’ mature’ companies vs. 59% of ‘less mature’ companies). mation by articulating a vision, setting AI is considered an important topic the lowest with 60% of the companies Another 28% are in the ‘released’ stage goals and securing broad buy-in across on the executive management level. expecting AI-generated benefits in where they have put AI selectively to ac- They see AI predominately being driven from a combination of the organization. This is significantly higher than on that area. tive use in one or a few processes in the technology push and business pull (61% of ‘more mature’ compa- the non-managerial / employee level company. The majority, 51% of companies vs. 32% of ‘less mature’ companies). To summarize, the challenge ahead where AI is only considered an impor- AI is expected to impact entirely nies, are still only planning for AI or are appears to be as much about culture and tant topic in 28% of the companies. new business areas in the future in early stage pilots. 7% of companies leadership as it is about data, analytics, * ‘More mature’ defined as companies that self-ranked as 4 or 5 on the maturity Interestingly, Board of Directors also are self-rated as least mature, indicating 57% of the companies expect AI to and technology. 5-scale, and ‘less mature’ defined as companies that self-ranked as 1 or 2. came out lower with ‘only’ 38% of have a high impact or a very high im- that they are not yet thinking about AI at respondees reporting that AI is impor- pact on business areas that are “entirely this stage. tant to their board. unknown to the company today”. This

Percentage of companies Share of companies that use that are still only in the Only 71% 57% acquisitions as a way to 80% 4% planning or piloting stages: of the companies of the companies obtain AI capabilities: of the most mature

of the companies are actively respond that AI is considered expect AI to have a high impact companies expect that AI using AI in ‘many processes ‘an important topic’ on the on ‘business areas that are will be beneficial by and to enable advanced tasks’ 61% executive management level entirely unknown today’ 10% only ‘empowering employees’

8 9 Setting the Scene Setting the Scene

About this Report What’s new?

Artificial Intelligence (AI) is not new. in AI, what they are investing in, and efit areas, how mature companies are Straight from the executives Contributions from open-minded and collaborative companies It has existed for decades: processing how they are managing the compli- in terms of adoption, and examining Where this report and extensive da- voice to text or language translation; cated process of adopting this new self-reported competence levels re- We are extremely thankful for the time taset adds new insights is primarily AI is one of the most powerful real-time traffic navigation; dynami- technology and deriving value across garding the capabilities required to into how leading companies are ap- and effort the many executives have technologies, driving digital cally serving targeted advertisements business opportunities. succeed when implementing AI. proaching AI on a very practical level. put into participating in interviews and based on personal data and browsing We hear straight from executives how providing data for this study. We’re par- transformation and new value history; predicting trends and guiding Perspectives, experiences, self- From the aggregate dataset we have their companies are addressing cur- ticularly appreciative of their willing- creation in the energy and investment decisions in financial in- assessment, and benchmarks been able to determine some bench- ness to openly share experiences and rent challenges, and how they apply AI maritime industries. stitutions. The current developments From new surveys, interviews and case marks across the covered markets, to unlock new value pockets. provide their perspectives on where have been fueled by an exponential studies gathered from approximately which we compare the individual the future is heading within AI. rise in computing power, increasing 277 companies, we provide a snapshot country with throughout the report. Based on the many interviews con- — Kongsberg Gruppen accessibility and sophistication of pow- of the current state of AI in 15 European The report also covers a full spectrum ducted, this report reveals some clear While this indicate a general interest in Technology group erful algorithms, and an explosion in markets. This includes analyzing AI’s of industry groups which tend to reveal excitement and immense potential for the AI topic, it also speaks to the in- the volume and detail of data available relative importance on the strategic interesting insights. using AI to bring new, improved prod- creasingly collaborative approach to feed AI’s capabilities. agenda, its expected impact and ben- ucts and services to market, create many leading companies are taking exceptional experiences for customers when entering new technology do- Reality vs. hype and employees, and create ways to mains and embarking on journeys into unknown territories. Only recently started to see more operate that enhance performance widespread, scaled adoption of AI across the board. across sectors, value chains and eco- systems. Yet AI technology is quickly We learned that regardless of which approaching a point where it is be- use cases the companies pursue and coming a critical element in enabling the role that AI currently has, taking a companies across sectors to drive strategic outlook to assess the implica- revenue, increase profits and remain tions for the business and responding In the long term AI has the potential to competitive. accordingly are increasingly seen as be transformative to the sector, we have crucial for any executive agenda. We hear many people in numerous only just started. companies talk about AI. While the hype is pervasive, not a lot of people — DNB fully understand its technological Financial services group potential, where it can create value or how to get started. This report aims at providing a practical understanding of why European companies are investing

10 11 Artificial intelligence in Europe Setting the Scene

Rich Data Which sources of information is the study based on? By shifting focus towards the more complex and spending less time on simple, mundane This report combines multiple sources We also present case studies of specific Recognizing and mitigating of data to answer the questions of why, companies, both local and internation- potential survey and interview bias where and how AI is currently being used al, to provide an understanding of what In terms of methodology, this report tasks, we can be real advisors, shifting focus in business. It provides an inside view they are doing with AI and why, draw- follows robust research design and across markets and sectors. It combines ing on lessons learned and obstacles to protocol. Doing so minimizes potential away from high volume routine tasks that local and pan-European views, and adds overcome when putting AI to use for bias, but does not eliminate it, as it value through a quantitative perspective specific use cases and to derive value is inevitable in market research. One on how advanced companies are with on a strategic level. potential type is social desirability create little value. AI, and a qualitative perspective on how and conformity bias, as the topic of to develop the skills required to succeed Proprietary AI investment data AI receives lots of media and political with AI. We have received input from We have supplemented the prima- attention. Response bias, including over 300 people from 277 participating ry source input from the companies extreme responding, cultural bias, and companies. This has resulted in a range of with acquisition data from numerous acquiescence bias (“yea-saying”), are — DNB Financial services group interviews and case studies as well as 269 sources, to take the pulse of the AI potential factors as we ask respondents company responses to our survey. investment market in Europe. These to self-report on their respective com- insights help provide a picture of the panies’ experience. Therefore, while Extensive online survey data from wider European AI ecosystem and its this report follows best practices, some business leaders in 269 companies development. bias is possible. We have surveyed people with a leading role in managing the AI agenda in all the AI expert perspectives Nonetheless, with the combination of companies that have contributed to the With this wider understanding of AI extensive survey data, interview data, study. This gives us an aggregate dataset start-up acquisitions, partnerships, and investment data, and expert perspec- that enables a perspective for each mar- investment funding, we outline how tives, we believe the report provides a ket and each sector, as well as compara- investments in AI are skyrocketing, solid foundation for an indispensable tive insights for the respective company where AI investment is taking place view of executive experience with – Machine learning should be considered a craft, types, sectors, and countries in Europe. geographically, and which sectors are and future plans for – AI in business. making bets. As we are on the cusp of not a science. Acquiring experienced staff is Qualitative in-depth interviews with widespread change driven by AI, we senior business executives also reached out to AI experts from In addition, we conducted deep-dive academia for an outlook of AI technol- required to be able to be on top of things, and interviews to gain deeper, qualitative ogies going mainstream, and to gain insights into how AI is affecting the ex- an understanding of the macro scale thus succeed with an AI strategy. ecutive agenda. Through conversations of business effects that they expect will with business leaders, we report on materialize when looking into a distant where they expect AI will have an impact, future. how important AI is to their current and future business strategies, what benefits — Sbanken Bank they hope to realize from implementing AI, and which capabilities they believe are key to advance AI maturity in their companies.

12 13 Setting the Scene Setting the Scene

Executive Perspective Large group of respondents Surveyed companies are well represented across Who are the respondents that have contributed to the study? with a specific AI/digital role each of the 15 European markets Organizational function of respondents Number of online surveyed companies per country in the online survey

The data approach used allows us to Functional diversity A combined annual revenue identify trends across industries and The respondents cover very different of $1.9 trillion countries based on input from various functions, of which the most common Participants come from both major Netherlands functional business areas. Consequent- are designated AI/digital department, listed companies, privately held com- 67 ly, we have captured a range of in- followed by IT, and strategy/general panies, and in some case relatively small sights, learnings, and perspectives from 60 Ireland management functions. This functional companies. In totality, they represent a Norway both strategic and technical points of diversity increases the breadth of the combined revenue of approximately view. report, with insights and perspectives $1.9 trillion. Despite covering a signifi- Sweden 22

covering widely different aspects of AI. cant part of total European business, our 20 Respondents predominantly in 45 21 selection criteria have also favored more Denmark 20 senior level positions Surveyed companies span niche oriented companies with extensive 39 Switzerland To ensure that these insights and per- multiple sectors AI experience and capabilities. 25 27 26 20 spectives are relevant at the executive The participating companies are spread 269 level, we surveyed and interviewed fairly evenly across seven sectors, with online survey Primarily listed companies 21 20 high-ranking officers with a responsi- the majority of companies belonging companies Italy represented in Norwegian data Austria in total bility for driving the AI agenda in their to Industrial Products & Manufactur- 5 The vast majority of respondents respective companies. With 60% of ing, followed by Financial Services, and 22 in Norway are major listed compa- 22 respondents being either part of top Transportation, Energy & Construction. nies or companies privately held 15 Finland management or the executive man- 21 Services and Life Science are represented 20 by foundations. All the participat- agement team, their input is likely well to a lesser extent. Portugal

ing companies in Norway had a Other attuned to the general perspective and United Kingdom, France & Germany overall strategic direction of the com- combined total annual revenue of Digital/AI Luxenbourg & Belgium panies they represent. over $60 billion in 2017. Spain Admin & Finance General IT/Technology General & Business Development R&D & Product Management Customer Service & Marketing General Management, Strategy More than 300 participants Majority hold a top management or executive position Number of participants interviewed Organisational level of person participating in the study and/or online surveyed in the study Seven major sectors covered in the study Representation of participating companies per sector category

26 of +300 0% are Norwegian companies C-suite/Executive 27% + 29% 9% 21% 17% 7%

Top Management Life Science Industrial Products Finance Services (non-executive) 33% Pharmaceutical, Healthcare, Manufacturing, Banking, Insurance, Professional Services, Biotech Materials, Equipment Investments Hospitality, Public Services, Membership Organization 66%

Management Level 37% 13% 16% 17%

Employee (non-managerial level) 3% 5% CPR TMT Infrastructure Consumer Products Technology, Transportation, Energy, & Retail Media/Entertainment & Telecom Construction, Real Estate 15 European markets Norway 15 European markets Norway

14 15 Setting the Scene Setting the Scene

Indie Campers, Intesa Sanpaolo, ISDIN, ISS, Jansen AG, Julius Baer, 277 Companies Katoen Natie, KBC Group, Kemira, Kingspan Group, KLP Banken, Komplett, Kongsberg Gruppen, LafargeHolcim, LanguageWire, LEGO, LEO Pharma, Lerøy Seafood, Liga Portugal, L’Occitane, Lonza, A.P. Moller - Maersk, Acciona, Adamant-Namiki of Europe, Aegon, L’Oreal, Lusíadas Saúde, Luz Saúde, Länsförsäkringar, MAPFRE, Aena, Ageas, Agfa-Gevaert, Agrifirm Group, Ahlstrom-Munksjö, Merkur Versicherung, Metall Zug , Metro, Metso, M-Files, Millicom, AIB, AkzoNobel, Almirall, Alpro, ALSA, Amadeus, AMAG, Ambea, Mota-Engil, Mutua Madrileña Automovilista, Møller Mobility Group, APM Terminals, Aprila Bank, Arcelor Mittal, Ardagh Group, Neste, NH Hotel Group, Nilfisk, Nokia Corporation, NorgesGruppen, Arval BNP Paribas Group, Asiakastieto Group, Assa Abloy, Norstat, Novabase, Novartis, Novo Nordisk, Novozymes, Assicurazioni Generali, Atea, Audi, Austrian Airlines, Austrian Now TV, OBI, Oesterreichische Nationalbank, OP Financial Group, Federal Computing Centre, Autogrill, BAM Group, Barco, BASF, Opportunity Network, Orion, Paddy Power Betfair, Peltarion, BAWAG P.S.K, Baxter, BBVA, Besix, Bolloré, BTG, BUWOG, C&C Pernod Ricard,PFA, Philips, Planeta DeAgostini, Poste Italiane, Group, Campbells International, Capio, Carmeuse, Carnival Posti, PostNord, Proximus, Pöyry, Rabobank, Raiffeisen Software, UK, CEiiA, Cermaq, Chr. Hansen, Cirsa, City of Amsterdam, Raiffeisen Switzerland, Ramada Investimentos SA, Randstad, Rexel, Colruyt Group, Com Hem, Combient, Comifar Distribuzione, ROCKWOOL Group, Room Mate Hotels, Royal College of Surgeons in Constitutional Court of Austria, Coolblue, COOP Nederland, Ireland, S Group, Saipem, Saint Gobain, Sakthi Portugal, Salsa, Saxo Bank, Cosentino Group, Costa Crociere, Credit Suisse, Crédito Agrícola, Sbanken, SBB Swiss Federal Railways, Schindler, SEB, SGS, DAF Trucks, Danfoss, Danske Bank, Dawn Meats, DFDS, DNA, Siemens Mobility, SimCorp, Skandia, Solvay, Sonae, Sonae Arauco, DNB, DSM, DSV, Dümmen Orange, Dynamic ID, DAA, Edison, SpareBank 1 SMN, SpareBank 1 Østlandet, Sportmaster, Statkraft, EDP - Energias de Portugal, Egmont, EQT, Ericsson, Erste Group Stedin, Steyr Mannlicher, Stora Enso, Styria Marketing Services, Suomen Bank, ESB, ESIM Chemicals, Esprinet, Europac, Fazer, FDJ, Terveystalo, Swedbank, Swisscom, Taylor Wimpey, TDC, Teamwork, Federal Office of Meteorology and Climatology MeteoSwiss, Ferrovial, Telefónica, Telekom Austria, Telenor Global Shared Services, Telia, Fexco, Finnair, Fortum, Galp, Geberit, Genalice, Generali Versicherung, Tesco, Tetra Pak, The Navigator Company, TIM, Tine, Tokmanni , GetVisibility, Gjensidige Forsikring, Glen Dimplex Group, Globalia, TomTom, Tryg, TTS Group, TVH, Ubimet, UDG Healthcare, UniCredit, GN Store Nord, GrandVision, Grupo Antolin, Grupo Ascendum, Unilin, UPM, Vaisala, Valmet, Valora Group, Van Lanschot, Vattenfall, Grupo Codere Cablecom, Grupo Juliá, Grupo Nabeiro – Delta Cafés, Version 1, Visana, Vodafone Automotive, VodafoneZiggo, Voestalpine Grupo Pestana, Grupo Visabeira, GSK, GAA, H. Lundbeck, Hafslund, High Performance Metals, WABCO, WALTER GROUP, Western Bulk, Handelsbanken, Hera, Hostelworld, Husqvarna, IKEA Group, William Demant, Wind Tre, WIT Software, Wolters Kluwer, Zurich Ilmarinen Mutual Pension Insurance Company, Implenia, Impresa, Airport, Zurich Insurance, Öhman, Ørsted, Österreichische Post.

Norweigan companies All companies, excluding Norweigan companies Note: Of all contributing companies, 14 chose to be anonymous, 0 of them being from Norway

16 17 Setting the Scene Setting the Scene

Companies are using a mix of Data Sources and Storage Companies are using a combination Solution: How are you primarily dealing with the computing demands Bits and Bytes of on-premise and cloud solutions needed for AI? Data Source: 1.Are you currently using unstructured or structured data Companies are increasingly using types in your AI process? 2.Are you currently using internal or external What technologies and data solutions are within the scope of the study? cloud-based AI solutions for both data sources in your AI process? storage and on-demand computing power - 83% of companies reporting using Cloud technology to some ex- AI can be defined as the ability of a not in common use by companies in While companies historically have tent to enable their AI capabilities. Key machine to perform cognitive func- Europe. Companies surveyed are cur- primarily have used internal data for benefits of cloud solutions mentioned tions which are normally associated rently focused on narrower and more supervised Machine Learning, many by many respondents are the flexibility with humans. This includes reasoning, specific use-cases that support existing have begun exploring the possibility of to swiftly scale systems up and down learning, problem solving, and in some business. These efforts will undoubt- combining internal and external data- to accommodate changing demand, a Solution cases even exercising human behavior edly help companies build capabilities sets in order to produce even deeper variable cost structure, and access to 27% 17% 56% such as creativity. that are necessary to deploy more insights. larger data sets. However, many com- In Cloud On premise Both advanced AI solutions in the future. panies are still relying on on-premise Advanced AI applications are not Machine Learning and Smart Robot- solutions, not least due to existing data yet widespread Machine Learning ics were found to be the most useful. infrastructure. AI holds the potential to transform The most commonly used AI technol- It is not clear from the study if this is because they are simply the most com- business in a radical way given its wide ogy among the surveyed companies Machine learning most used for mon starting points before deploying variety of use. Quite simply, business is Machine Learning. This is inarguably Norwegian companies leaders need to understand AI in order due to its wide-ranging applicabili- more advanced technologies, or if they The vast majority of Norwegian to grasp the opportunities and threats ty, making it relevant for a variety of also longer term hold the most wide companies report that machine the technologies pose. use-cases across the value chain. Of the and significant application potential. learning is the AI technology different types of Machine Learning, that they use most, well above 32% 7% 43% While companies acknowledge the the most common is supervised Ma- the European aggregate. With a Structured Unstructured Both significant potential of broader, more chine Learning, where software is fed considerably lower adoption rate, advanced AI technologies such as structured data and finds patterns that machine learning is followed by computer vision, speech recognition, can be used to understand and inter- Data Source neural networks and deep learn- and virtual agents, they are currently pret new observations. ing (38%), smart robotics (33%), and text analysis (33%).

A broad definition of technologies are included in this AI definition 38% 3% 44% Technologies included in the definition of AI used in this study Internal External Both

Text Analysis Computational analysis of texts, making it readable by other AI or Machine Learning and Smart Robotics found to be the most useful Natural Language Processing computer systems. Biometrics Computer interpretation, under- Analysis of human physical and Which of the following technologies have you found to be most useful in your company’s deployment of AI? standing, and generation of written emotional characteristics – used natural human language. also for identification and access control.

77% 44% 40% 39% 39% 26% 21% 20% 6% Virtual Agents Computer-generated virtual personas Machine Learning A computer’s ability to ‘learn’ that can be used to interact with people 95% 33% 24% 38% 33% 24% 5% 14% 5% in both B2C, C2B, and B2B contexts. from data, either supervised or non-supervised.

Speech Recognition Neural Networks and Deep Learning Enables computers to interpret spo- Machines emulating the human brain, ken language and to transform it into enabling AI models to learn like humans. written text or to treat it as commands Machine Smart robotics Natural Neural Text analysis Virtual agents Speech Computer Biometrics for a computer. learning language networks and recognition vision Computer Vision processing deep learning Gives computers the ability to Smart Robotics “see” images similar to how humans see. The combination of AI and robots to Affirmative responses, 15 European markets Affirmative responses, Norway perform advanced tasks compared to traditional non-intelligent robots. Note: Remaining percent ‘Don’t know’ responses

18 19 Setting the Scene Setting the Scene

acquisitions, and is also much in line TMT most active, behind private Follow the Money with what we’re seeing when compar- Over $30 million invested in AI equity and venture capital ing with the US and Asia. start-ups in Norway in the past Investments into AI companies per sector, How much is invested in AI in Europe? Finland decade mUSD (accumulated 2008-2018)* $24M Investment activity concentrated in In Norway, there were only five Norway 21 deals major European markets transactions over the past dec- $30M It comes as no surprise that a lot of ade involving companies working A few big AI transactions 5 deals influencing the overall picture investment activity is in the UK, France, with AI. Of these, 3 reported deal and Germany, having attracted 87% of value totaling $30 million, imply- Company AI investments in mUSD and $254M transaction volume per market 73 deals investment in AI companies over the ing the actual amount is higher. past decade. The UK leads significantly A large portion of this amount $7,453M (accumulated 2008-2018) Sweden 1,027 deals in this regard, with 533 of the total was the $25 million investment Private Equity / 1,362 AI transactions in Europe. From in Arundo Analytics. Interesting- Venture Capital** an investment perspective, it is also ly, the small amount of deals in $330M* 21 deals worth noting that in April 2018, the EU start-ups in Norway is somewhat committed to a 70% increase in invest- compensated for by Norwegian Denmark $7262M ment in European AI by 2020, suggest- investors, mainly venture capital Ireland 533 deals ing further growth and potential in the firms, involved with 11 transac- United Kingdom The Netherlands region. tions in AI start-ups outside of $39M $43M 37 deals 45 deals Norway.

$110M 14 deals $520M Belgium and 140 deals Luxembourg Germany Steady increase in European AI investment AI companies invested into, transaction volume, Europe (from 2008-2018)** $1,843M 220 deals $107M TMT 31 deals $75M 17 deals $1357M Switzerland 165 deals Number of $494M Austria transactions 17 deals France Industrial Products

$3M 450 8 deals $368M 398 Portugal $47M 12 deals 29 deals 400 Infrastructure

European Norway $131M Italy 350 327 markets 79 deals Total $254M UK bubble size not represenative 300 investment 21 deals $10.5bn Life Science *Universal Robots acquired for $285M v 250 228

The acquisition data from numerous alone. This trend is on track to con- tive investors and acquirers of AI than $70M corporates, accounting for 75% of deal 200 41 deals sources enabled us to explore the tinue, with an exponential increase Finance European AI ecosystem and gain in- in interest in AI driving more large volume in the last 10 years. This is an 148 150 sights into investment activity. companies to invest in AI or acquire AI indication that AI companies are in the early stages of high risk/high growth capabilities from innovative start-ups. 88 $38M dynamics. It also indicates that, for 100 An exponential increase in AI in- Of the 15 markets surveyed, some in- 64 10 deals vestment over the past decade clude one or two transactions that are large corporates, acquiring or invest- CPR ing in external AI businesses in order 50 29 27 Looking at AI transaction activity significantly large deals. 14 11 to obtain AI capabilities is relatively across Europe, there has been a steep $22M limited. This is confirmed by our survey 0 consistent growth trend over the past Majority of investments in AI from 14 deals results where only 10% of companies 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Services 10 years, totaling 1,334 transactions private equity and venture capital are seeking to obtain needed AI capa- involving AI by 2017 – with a six-fold Private equity (PE) and venture capital bilities through external investment or increase in activity in the last 5 years (VC) firms are significantly more -ac Europe **Including governmental investment

Note: Several transactions in the dataset did not have publically disclosed deal values, suggesting that actual total values are higher than what’s shown above *For all of Europe, 34 countries (not just the 15 markets focused on in this report)

20 21 Artificial intelligence in Europe ( Case Study ) Setting the Scene

Expert Perspective Norstat What does the future look like according to AI analysts?

Four years ago, Norstat initiated a automated processes operations be- However, compared to other players in data collection process on survey re- come less labour intensive and easier to the market Norstat started the journey spondents and is now starting to reap scale, while the market insight provided of data collection quite early. As a re- the benefits. The data is fed through becomes more robust, for example by sult, many of the processes that used to intelligent algorithms, allowing Norstat removing noise from respondents who be manual are now automated, leading to optimize operations by automating complete surveys too quickly. to a significant reduction in operational quality control. This creates costs. Norstat recognizes the higher value for Norstat’s advantage that lies in the We also spoke to a range of leading AI Agile culture enables AI task is to educate and improve un- customers by increasing amount - and quality - of experts from business and academia Culture was a recurring theme as well. derstanding, from C-suite leadership market insight accuracy and data that has been captured to gain insights into the kind of change It can either stifle forward momentum teams to employees at the coal face. quality. Investing in automation requires signif- over the past few years as few which we are on the cusp, and the in organizations, or be the silver bullet This also ties in with the importance of icant patience, as it takes time to collect other players have acquired role AI is expected to play as part of a that enables the potential of AI to be partnering to get started and access AI is used to identify patterns similar datasets in terms of enough data to get good predictions. broader transformational wave. realized from top to bottom. the expertise needed to use AI. While in survey responses and to volume and quality. The com- partnering and collaborating solves calculate the probability pany does not therefore see AI is entering the mainstream Some of the experts even argue that the perennial AI challenge concerning of receiving a high quality Big Data and AI as a major and here to stay it’s not only technical skills that hold the scarcity of talent, the significant response. By comparing sector disruptor, but rather a One thing was clear from the experts up AI projects, it’s also the need for a cost and substantial benefit that can respondent metrics to a large dataset tool to improve quality. we spoke to: as far as the peaks and culture of experimentation. be gained from AI means that organ- of historic responses, Norstat is able to Investing in automation requires signif- troughs of hype and technological izations also need to be cognizant of automatically remove low-quality re- icant patience, as it takes time to collect leaps surrounding AI go, there is no Companies that are more natively building capabilities in-house for the spondents from the database. Via such enough data to get good predictions. doubt that we are living through a digital or have gone down that road long-term. particularly prominent peak, with no understand the value of experimenting indication that the buzz nor the po- and iterating. They don’t think in tra- Finally, as AI develops, we are also tential will fade away any time soon. In ditional terms of committing to year- going to see innovation and expertise a world increasingly dominated, dis- long projects that need to produce spreading outside of the dominant What next? rupted and driven by innovative tech specific outputs, but rather to explore clusters of the likes of Silicon Valley,

powerhouses, large and small, it is no and test ideas before scaling. as governments, businesses and uni- Norstat will continue to increase the application of automated Norstat is a privately held high-quality data solutions provider in understatement to suggest that AI will versities increasingly invest in building analysis in the future, foreseeing a gap that will form between the research industry. The company uses well-grounded research be a chief protagonist in the change When it comes to AI, knowledge, resources and capabilities. those players who work based on predictions and those who use methods to collect reliable data about any desired topic or target transcending all elements of business knowledge is power group, to help customers make the right decisions based on mass-targeting and human processing methods. The company is in in what has been labelled the Fourth Expert opinion also seemed unani- reliable market insight. Norstat conducts data collection in 19 the process of launching a user platform that is structured so that Industrial Revolution. mous in that most people not directly countries and has operations in 12 countries across Europe, with a AI can help more precisely target respondents and obtain higher hit-rates based on previous behavior, for example by identifying involved with AI must still have quite a revenue in 2017 of over 400 million NOK. Business-minded people will the most appropriate type of incentives for responding. basic understanding of what AI is and drive the transformation what it can actually do. Therefore, the The AI experts confirmed some of the key ingredients necessary for AI in organizations: a combination of domain and technical expertise, the appropriate technology, the right talent, and lots and lots of data. While letting tech-savvy individuals drive innovation Farmers and growers are still reasonably We maintain a live dataset – however our AI is somewhat a buzz word, however building is great for building understanding, conventional, with an average age of 55 years. The true transformation will not come until largest investment was to initiate the collection Artificial Intelligence basically means building chances are that this will change significantly in the business people start suggesting prob- of the data in the first place. It is important to algorithms that allows us to act on all the data lems for AI to solve - not the other way future. It could just be that technology companies have a vision driving this. we have collected. It requires patience to get round. will become the disruptors of our market. this right. — Royal Agrifirm Group Agricultural cooperative

22 23 Setting the Scene Setting the Scene

We believe that every organization is going to have to write their own AI manifesto: what they believe about AI, how they’re going From the Horse’s Mouth* to use or not use data, how they’re going to publish data, and *From the highest authority make the consumers of their products and services aware of that. The creation of those manifestos is going to become a gateway to the success of AI.

— Norm Judah, Chief Technology Officer of Worldwide Services at Microsoft

The full extent of the AI story remains in its early stages. What we do know is that big data, computing power and connectivity If you have a ton of data, and your problem is one of classifying pat- are changing the industrial landscape. The opportunity rests in terns (like speech recognition or object identification), AI may well accelerating the digitization of businesses, making them more be able to help. But let’s be realistic, too: AI is still nowhere near as data driven by building applications that deliver machine-assist- flexible and versatile as human beings; if you need a machine to ed insights. read, or react dynamically, on the fly, to some kind of ever changing problem, the technology you seek may not yet exist. Intelligence is a — Mona Vernon, CTO, Thomson Reuters Labs really hard problem.

— Gary Marcus, Founder & CEO, Geometric Intelligence [acquired by Uber] professor, NYU, contributor to The New Yorker and The New York Times In some cases, there is too much hype, but paradoxically, the potential opportunities and benefits of AI are still, if anything, under-hyped. Often, the impact of new technologies is overes- AI is a general purpose technology, so will eventually affect all in- timated in the short term and underestimated in the long term, dustries. However, this impact can be slowed by the lack of data in and while there is a lot of noise regarding AI, there’s been a lack particular industries. There’s also more innovative cultures inside of in-depth discussion and analysis of how it’s actually going to different organizations, that can either drive adoption or prevent it. transform businesses. — Marc Warner, CEO, ASI Data Science — Nigel Duffy, Global AI Innovation Leader, EY

24 25 Artificial intelligence in Europe Role of AI in European Business

A Strategic Agenda Where is the AI conversation currently taking place?

A good starting point to understand Active C-suite and Board of Direc- both pertain to job insecurity and to how large European companies are tors involvement the fact that AI is still a highly abstract handling AI is to look at who in the In 71% of the companies surveyed, AI topic for many when it comes to prov- organization is driving the AI agenda, is already an important topic on the ing day-to-day business value. whether it be the Board, the C-suite, C-suite agenda and across various managers, or employees. roles - from cost-focused CFOs looking Role of AI AI an important topic among for efficiency through automation, to Executives in Norwegian AI is particularly relevant at CDOs with customer-oriented ambi- companies higher organizational levels tions as part of wider digitalization From driving strategic considerations efforts, to the CTOs who is often still In Norway, 57% of respondents at the Board level to being a topic of in- responsible for a type of AI Center of report that AI is an important top- terest or concern at the employee level, Excellence. ic on the Executive level’s agenda. in European the results are clear: AI is important Yet, compared to the European and resides across all levels at many of Companies more advanced in AI tend aggregate, Norwegian compa- the organizations we interviewed. to have stronger involvement of the nies report a lower result for each C-suite and the Boards of Directors hierarchical level. One possibility Only a few companies stated that AI is than the rest. They focus less on the for this is that the question asks not currently an important topic at any technology itself and more on the busi- respondents to select all that level of the organization - while the ness problems that AI can addresses. applied, suggesting AI could be Business vast majority of companies view AI as concentrated at particular levels generally important regardless of how Relatively speaking, the AI topic seems rather than dispersed throughout advanced they are, or how much AI is to not yet having reached the same the organization. being considered for deployment in level of importance at the non-mana- the near future. gerial level (employees) than at the top. Speculating about the reason, it could There is a lot of hype surrounding AI at the moment, and few doubt its potential. We examine how important is AI compared to AI is an important topic on the C-suite level in particular other digital priorities and where AI fits on the strategic agenda. On what hierarchical levels in your company is AI an important topic? AI is in particular an important topic at the Execu- tive Management level We look at the impact of AI on the company’s core business, as STRATEGIC LEVEL Board 29% well as adjacent and new areas of business. of Directors 38% level

Executive 57% Management 71% We also examine the current AI maturity levels across sectors and level markets, the potential drivers for deploying AI, and where AI is 52% Managerial 56% applied within organizations, across customer-facing functions, level

Employee 19% operations, product development, and internal business support. (non managerial 28% level)

OPERATIONAL LEVEL

Affirmative responses, 15 European markets Affirmative responses, Norway

26 27 Role of AI in European Business Role of AI in European Business

Among Friends Push or Pull What is the importance of AI against other digital priorities? How is AI predominately deployed into the organizations?

To understand the drivers behind Both business and IT drive AI advancement in Norway In a business era driven by innovation The participating companies are gen- AI seen as slightly more impor- the adoption and deployment of AI Among Norwegian companies surveyed, AI deployment is cus- and tech-led disruption, AI is obviously erally in the process of understanding tant vs. other digital priorities in the companies, we took a closer tomarily driven by both pull from the business needs as well as not the sole priority. the potential of existing data, includ- Norway look at how AI is approached in a top push from IT’s capabilities and innovations. In addition, 43% of ing to what extent it can be used, Many companies in Norway down-bottom up management con- companies in Norway manage AI using a top-down approach as AI as a digital priority what it can be used for, and how to are engaging in successful pilot text, and from a functional tech- vs. opposed to 29% using bottom-up and 24% using both approach- capture and leverage it. When asked on a scale of 1 to 5 how projects and Proofs of Concept, business driven dynamic. es simultaneously. The interviews confirmed that Norwegian important AI is to the business relative or have AI initiatives that are re- companies lean towards a more centralized approach to AI, for Furthermore, many of the companies to other digital priorities, the majority leased into production. When it AI driven from a combination of instance through a center of excellence in charge of training busi- are focused on building the appropri- of respondents told us that it is about comes to their prioritization, re- technology push and business pull ness units on AI and digital matters. ate data infrastructures or modern- equal. Very few organizations said it spondents in Norway on average The contributing companies are quite izing legacy systems as a top digital was their most important digital priori- consider AI slightly more impor- evenly split across deploying AI as a priority, both being prerequisites ty, or not formalized as a digital priority tant than other digital priorities, top down process, as a bottom up, or for introducing AI into the company. AI deployed and managed in a balanced way at all, with the spread of responses a ranking marginally above the as a combination of the two. However, Considering that AI is heavily reliant How would you characterize the way AI is being managed in your com- leaning slightly towards the upper end European aggregate. However, when looking at the self-reported most on data as its fuel, this development pany? How would you characterize the way AI is being deployed in your of the importance spectrum. only 5% of the companies report advanced companies, they are more suggests that the foundations are company? AI to be the most important digi- top down than bottom up in their ap- being laid for further AI integration in This slant is likely to increase as many tal priority. Respondents are also proach. It was clear from speaking with the years to come. companies expect AI to become more focusing on collecting and storing them, that this is partly a result of AI important, as the technology develops the right data and building their being increasingly important enabler and use-cases become more clear to in the company, and playing an in- general digital strategy. These Top Down Bottom up Both companies. results suggest that, although AI creasingly significant role in the overall is not the highest digital priority, strategy. it is gaining importance and companies are taking the steps nec- AI driven from a combination of essary to move their AI initiatives technology push and business pull forward. According to a large part of the companies. and despite still being a techni- cally complex thing that requires many 34% 43% 29% 29% 28% 24% specially skilled employees, AI is most AI is seen as one of many digital priorities - but rarely the most important The majority consider AI to be important often deployed as a combination of How important is AI relative to your company’s other digital priorities? business pull and technology push.

This resonates well with one of the Business Pull IT Push Both Avg. Score most consistent inputs from the execu- 44% 38% 38% tives on the most sought after AI pro- Deployment Approach 28% 14% files which centered in on the hybrid 9% 12% 3.2 3.1 5% 5% 7% profile that understand the business needs and the ability to match them to the technological possibilities. 1 2 3 4 5 24% 24% 23% 14% 45% 52%

Not important Important Most important AI is not formalised AI is one of many AI is the most important as a digital priority digital priorities digital priority

15 European markets Norway

15 European markets Norway Note: Remaining percent ‘Don’t know’ responses Note: Remaining percent ‘Don’t know’ responses

28 29 Role of AI in European Business Role of AI in European Business

TMT sector with largest percentage of companies that are either released or advanced Ready, Set... How would you describe your company’s general AI maturity? Sectors arranged by maturity based on Advanced and Released What is the maturity of AI in different sectors?

TMT 2% 10% 40% 45% 2%

While working with AI should be con- the structure of existing data, collec- Services 5%6% 22% 27% 27% 18% sidered a continuous journey, the AI tion of new data, and data access in maturity of surveyed companies pro- general. However, the trend is clear: vides a tangible indication of the level AI maturity is on the rise as adoption of Finance 4% 22% 34% 36% 4% of advancement of current initiatives. key technologies accelerates and internal capabilities grow. Multiple use cases, limited scalabil- Infrastructure 5%9% 21%17% 32% 46% 28% ity and advanced use The vast majority of European busi- The majority of companies have begun nesses are currently either conducting exploring use-cases, while some com- pilot projects to test selected use- Industrial Products 4% 21%25% 53%44% 21% panies have made early investments cases, or have commenced implement- with the intention of taking a leading ing AI in the business. When talking position in AI. The levels of advance- with executives, it is evident that many Life Science 4%7% 25% 45% 49% 34% 17% 4% ment also vary in that some companies companies are struggling with how to are focusing on narrow use-cases to integrate pilot projects into daily op- support their existing business, while erations. CPR 25% 29% 29% 9% 9% others are taking an explorative approach. Among the small group of Clear sector patterns, with TMT, Services, and Finance on top companies with no or only little AI 0% 20% 40% 60% 80% 100% activity to date, several respond that Companies currently leading the way in they are planning to drastically ramp terms of AI maturity are in TMT, Servic- None Planned Piloting Released Advanced up efforts soon. es & Hospitality, and Financial Services. Companies in those sectors gravitate Our main focus for some Technology immaturity and internal towards grading their AI maturity as time was just to collect data data quality are key obstacles ‘Released’ (AI in active use, though and make sure that we are Many companies that have already selectively or not with very advanced This indicates slower technology ‘Advanced’ stage of AI maturity. collecting the relevant data. implemented AI initiatives in their tasks), or ‘Advanced’ (AI actively con- adoption lead times in these slight- Several companies in both Consumer tributing to many processes and en- Now we are improving its businesses are seeing tangible benefits. ly more conservative sectors. Yet, Products & Retail and Services & Hos- Consequently, many of them are ex- abling advanced tasks). A logical ex- with 74% of companies being in the pitality cite the challenges of knowing relevance and quality. The ploring more use-cases and structuring planation for the maturity in TMT and ‘Piloting’ or ‘Released’ phases, the what relevant AI technologies are avail- next step in our maturity their learnings from previous AI pro- Finance is their tendency to be digitally Infrastructure sector also seems to able, utilizing unstructured data, as well process, that we are now jects into a modus operandi that can advanced and more savvy with analyt- be evolving onto more advanced AI as affording the payback period where ics, favoring these companies to pro- entering, is using machine speed up new initiatives. maturity. there may be large upfront costs and gress beyond piloting by having data undetermined returns on investment. learning and AI technology Meanwhile, a substantial number of science capabilities in place to evolve Life science and CPR have fewest to create new services and companies have intentionally chosen to towards more advanced AI stages. released projects add value to our existing take a ‘follower’ position, reporting the Consumer Products & Retail compa- Infrastructure and IP with relatively products. perceived immaturity of AI technolo- nies have a broad spread in terms of AI gies as a key reason. Another reported many projects in ‘piloting’ phase maturity, where 25% state they have obstacle to rolling out broader AI The Infrastructure and Industrial Prod- no plans at present for how and when — Husqvarna initiatives are rooted in data and data ucts sectors both stand out as having to use AI – much higher than other Consumer equipment infrastructure, where companies have no companies responding that they are sectors – while others in the same company separate projects aimed at improving ‘Advanced’ in AI at this stage. sector are already at the ‘Released’ or

30 31 Role of Ai in European Business Role of Ai in European Business

AI Maturity Curve Companies in Norway have a significant number of early stage AI pilots In terms of AI maturity, companies in Norway are on par with the European aggregate. A significant Majority of companies are in the ‘Piloting’ or ‘Released’ stage share of Norwegian companies surveyed are planning (19%), conducting pilot projects (47%), or have begun releasing AI applications for use in their daily operations (29%). On the contrary, none of the We asked companies to self-report their current AI maturity level, grading themselves at None, companies we spoke to felt they have reached an advanced stage with AI and 5% report they have Planned, Piloting, Released, or Advanced - as defined below. not started thinking of AI yet. These results suggest that even though AI seems to be ramping up, there is still some work to be done before reaching full maturity.

0 / 21 LEVEL OF MATURITY (0%)

Advanced AI is actively contributing to many processes in the company and is enabling quite advanced tasks 6 / 21 (29%)

Released AI is put to active use in one or a few processes in the company, but still quite selectively, and/or 10 / 21 not enabling very advanced tasks (47%)

Piloting AI is put to active use, but still only in early stage pilots 4 / 21 (19%)

1 / 21 Planned (5%) AI is being planned, but not yet put to active use, not even in early stage pilots

None Not yet thinking about AI

20 / 269 59 / 269 106 / 269 74/ 269 10 / 269 (7%) (22%) (39%) (28%) (4%)

15 European markets Norway

32 33 Role of AI in European Business Role of AI in European Business

State your Business Where is AI currently deployed across the companies’ value chains?

Looking at the business functions that towards taking an experimental, agile AI technologies in the future. which in the case of HR include talent most commonly use AI provides a approach which is key to AI; and the Operations and back-end functions acquisition (avoiding human bias), good indication of where companies R&D function often sits on significant use AI to increase efficiency by au- onboarding (Q&A), performance eval- AI is important for us and are placing their bets. These functions amounts of useful data leading to high tomating processes and informing uation (analyzing data), etc. but rather almost all other banks. are driving the company AI agenda, potential use-cases. decision-making. The key enabler is seems to be a result of prioritizing other influencing the future direction of the data infrastructure, and many com- functions and priorities first. While many focus on RPA company’s AI efforts. Online customer interactions panies – currently limited by legacy today, this will move to more generating front-end data systems and processes that impede cognitive services in the Many AI in R&D and IT/Digital capture and retrieval of data – need to AI mostly applied in IT, Tech & Digital and Administration Customer-facing, commercial functions future. The focal point in the functions such as Marketing, Sales and Customer upgrade their infrastructure. & Finance in Norway medium to long term should On top of an expected high prevalence Service are also heavier users of AI, Among companies surveyed in Norway, usage spans 11 out of the 13 business of AI within IT departments, AI is also partly driven by their digitization levels. Limited use in HR and Procurement functions presented. The distribution of AI usage across business functions be areas where we find commonly used within R&D functions. Although AI is generally adopted more There are several functions where AI is within companies surveyed in Norway is concentrated in two areas, with highest client value or large process This primarily comes down to three slowly in customer facing interactions hardly in use among the participating usage in IT, Technology & Digital (52%), followed by Administration & Finance improvements by using AI. factors: employees in R&D are often than in back-end functions, the abun- companies. This includes people- (29%). On the contrary, R&D & Product Development (14%) falls significantly engineers who tend to have a good dance of data from increased use of ‘intensive’ functions such as HR and below European aggregate. The lower application of AI in R&D & Product devel- understanding and appreciation of AI; online channels is expected to make Procurement. This is not due to lack opment could be explained by the lack of Life Science companies in the Norwe- — Sbanken the R&D function is often already wired these functions obvious candidates for of potentially valuable AI use-cases, gian sample. Bank

AI most commonly applied in IT & R&D functions Which of your company’s business functions currently use AI?

6% 4% 7% 14% 36% 9% 4% 12% 23% 47% 22% 19% 24%

52% 29% 24% 19% 14% 14% 10% 10% 10% 5% 5% 0% 0% General Management General HR Finance / Admin R&D & Product Development Management Product Procurement Manufacturing Operations / Logistics IT / Technology / Digital Marketing Sales Service Customer Strategy

Group Product Operations Commercial

Affirmative responses, 15 European markets Affirmative responses,Norway

34 35 Artificial intelligence in Europe ( Case Study ) Artificial Intelligence in Europe

Western Bulk

Western Bulk is in the business of char- chartering decisions. The question not complex, using AI to automate The European tering and operating dry bulk vessels, is not if a move towards data-driven manual copy-paste tasks has allowed providing seaborne transportation ser- processes will take place, but how long Western Bulk to be “less typing and vices to its customers. This is a highly it will take to get there. Today, Western more brains,” empowering employees competitive and complex industry, and Bulk uses AI to increase the efficiency to focus their time on higher-yielding in order to position themselves for the of internal processes, from using AI tasks. future, the company is evalu- Western Bulk is currently AIBusiness Landscape ating opportunities of only exploiting off-the-shelf data-driven technology, Using AI to automate manual copy-paste AI tools, but sees in-house where AI is becoming in- development as important to creasingly strategically im- tasks has allowed Western Bulk to be win in the market going for- portant. The industry remains “less typing and more brains,” empow- ward. With risk management quite reliant on human input ering employees to focus their time on at the heart of their opera- and knowledge, and has tions, Western Bulk’s Charter- Benefits and higher-yielding tasks. been relatively slow to adopt ing Managers face complex intelligent system integra- decision-making and are tions to drive data collection constantly exposed to risk. for decision-making purposes. on emails to identify and categorize The goal therefore is to use AI to bet- However, there is a lot to explore in cargos and tonnage, to simple tasks in ter understand the risks involved, and areas such as market intelligence, risk accounting like reading travel receipts. allow Managers to make data-driven management, vessel operations and Although the current application is rather than gut-based decisions. Risks

As a number of industries are beginning to reap the benefits of What next? AI, we investigate what AI is actually doing for businesses today Western Bulk is a privately held global operator of dry bulk vessels Western Bulk is already benefiting from AI in administration and what is expected in the future. headquartered in Oslo, with offices in Casablanca, Singapore, and finance, but the vision for the future is that AI can be used Seattle, and Santiago. Western Bulk combines a solid shipping to conduct more complex operations. In the relatively complex experience with an asset-light, decentralized, trading-oriented operation of vessels, Western Bulk believes that AI can help business model. Risk management is a key part of Western Bulk’s identify problems on voyages and suggest correct measures. We takeWe looka look at at how how big important an impact the executives digital transformation expect AI will have in DNA, and fundamentally important for all operations. Western However, this requires a shift to a more data-driven modus terms of driving growth or causing disruption in their industry, Bulk’s employees operate primarily out of Oslo and Singapore, and operandi, which today is characterized by manual handling and agenda is on the highest executive level vis-à-vis other the company’s revenue in 2017 was 827 million NOK. industry know-how. Western Bulk envisions a more data driven – and examine AI’s basic and more advanced uses - highlighting and by extension, more transparent – sector in the future. strategic priorities. examples of these functionalities in operational mode. We dig deeper to understand whether digitalization is primarily a keyWe lever also to present improving a strategic and sustaining approach the to current understanding core busi AI’s- four ness,benefit or rather domains a lever forfrom building a business tomorrow’s perspective, business summarizing focusing the One of the biggest challenges with AI is finding We need to move away from gut-feeling value executives expect to generate by using AI, and touching on the right balance of features that are important decisions and to become more data-driven. AI on adjacent or even entirely new business areas. for the business today, and the features that will be an important tool to achieve this. what business leaders see as the most prevalent business risks. we think will be important to the business in the future. We need to exploit and explore. And we summarize how progressed the companies are on Balancing present with future thinking is their overall digital transformation maturity journey. always difficult.

36 37 Business Benefits and Risks Business Benefits and Risks

Another World What is the expected impact from AI within the next 5 years? One of the most important success factors we’ve had Of the surveyed companies, 81% believe tors, more than 30% expect the indus- Countries expect different impact Norwegian companies in the top third for expected is that we’ve been able to that AI will have a high or significant try to be disrupted. from AI impact from AI balance what the technology impact on their industry within the next When approaching impact from a At 48%, companies in Norway are among the top third across Europe five years. Digging deeper into the data, Limited sync of maturity and is in terms of possibilities country perspective, the tendency when it comes to expecting AI to have a significant impact on their in- many of these companies expect AI to expected impact and limitations with what remains; very high expectations across dustry in the future. Yet, Norway has a significant proportion (19%) ex- fundamentally change their competitive The biggest disparity is within Finance, the board. Portugal stands out with pecting only limited or some impact. One possible explanation is that is relevant for us and our landscape, driven by increasing risk of specifically Pension and Insurance, most ‘high’ impact responses. many companies in the Finance sector have high impact expectations, customers, allowing us to competition, including from new types where ambitious companies are mak- while companies in Industrial Products & Manufacturing have rather of start-ups and companies from adja- develop and implement ing significant investments in building In the opposite end of the expected low expectations. According to the respondents, some of the ways in cent industries. The majority of compa- solutions of value. data infrastructure and AI capabili- impact scale, Ireland, Austria, and which AI will disrupt industries relate to personalization of customer nies also believe that AI will play a key ties, while others are taking a waiting Spain, in that order, are the countries content, intelligent decision making or radical automation. role in their efforts to continuously cut stance, and will jump on the AI train where most companies expect only — Sparebank 1 Østlandet costs to stay competitive. when the technology is more mature. ‘some’ impact from AI or less. Bank Strongholds and premiums to change as AI gains ground Many companies expect competition Services the sector with the highest expected impact from AI High expected impact from AI consistently across countries to intensify due to the ‘winner takes all’ How much impact do you expect AI will have on your industry How much impact do you expect AI will have on your industry dynamic often associated with the mas- within the next 5 years? within the next 5 years? Portugal has the highest share of companies sive scale that AI and digital can create. expecting ‘significant They also expect significant impact on impact’ from AI their products, increasingly in the form of new services, and they believe the UK, France & Germany Belgium & Luxemburg Portugal Sweden Austria Italy Norway Netherlands Denmark Spain Ireland Switzerland Finland Infrastructure Life Science Services Finance Industrial Industrial Products TMT speed of developing new products and CPR taking them to market will drastically 100% 5 100% 5 decrease - making current competitive Significant impact 9% Significant impact strongholds less viable in the long-term. AI will disrupt the industry, re- AI will disrupt the industry, resulting in entirely new products, sulting in entirely new products, 25% 29% 33% services, and business models 33% services, and business models This is particularly clear in R&D intensive 33% 35% sectors such as Pharma, where big data- 80% 42% 40% 40% 4 80% 40%43% 40% 40% 40% 4 43% 45% sets and intelligent algorithms to speed High impact 48% High impact 55% up the drug discovery process (10x 50% 64% mentioned as realistic) can impact the dynamics towards existing peers, while new AI based entrants (e.g., intelligent 60% 3 60% 3 devices) can influence how premiums Some impact Some impact are distributed in future value chains. AI will create significant industry AI will create significant change to the industry, but key 33% 77% 25% change to the industry, but key 44% structures will remain as is 42% 57% 65% structures will remain as is Across sectors, executives expects 46% 30% 35% significant impact 40% 2 40% 2 45% 33% Limited impact 48% Limited impact Services comes out on top in the ‘High 47% 41% 50% Impact’ category, but all sectors expect 39% 54% a significant degree of impact from AI. 18% 35% An overwhelming share also anticipate that AI will result in entirely new prod- 20% 1 20% 29% 1 15% 35% ucts, services, and business models. No impact No impact 24% AI will not recognizably change 14% 22% AI will not recognizably change 21% 10% 17% products, services and business products, services and business Companies from Industrial Products 9% 18% models in the industry 5% 14% 5% 5% 14% 5% 14% models in the industry 11% 12% and CPR expect relatively least ‘high’ 3% 4% 5% 5% 5% 5% 5% 5% 5% 4% 3% impact from AI, but even in these sec- 0% 3% Don’t know 0% Don’t know

15 European markets Norway

38 39 Business Benefits and Risks ( Case Study ) Artificial Intelligence in Europe

AI Here, There, Everywhere What is the proximity of AI’s future impact to core business? TINE

With its roots as a traditional coopera- customer and partnership opportuni- to TINE products in order to drive further Companies expect impact across all horizons tive providing a large share of Norway’s ties they have not yet tapped into, in sales. In the back office, TINE is leveraging To what degree do you expect AI will create impact for your company within each of the following areas? dairy products, TINE’s focus in the addition to driving efficiencies within robotics within its supply chain to forecast increasingly digital consumer environ- internal operations. The rapid growth production from farmers and interpret ment has been to identi- useful data from vendors. fy growth opportunities

Avg. Score and leverage technolo- Not being a technology Core Business gies that will enable new company itself, TINE is aware 43% 29% 33% 28% 37% business models and of the need to strengthen Primary areas of TINE realizes how AI will play a big role in 4.1 4.0 allow the company to this competence through the company’s 10% 14% helping them develop new and untraditional current business 4% take advantage of these both partnerships and inter- 1% approaches to their business in the future. 0% 0% new areas of growth. In nal development if it expects order to better align the to take advantage of new company’s efforts to lev- business opportunities using Adjacent Business 33% 33% erage new technologies, AI. The company is focus- 31% 29% Business areas on 28% 22% TINE recently created a sing heavily on building this the edge of the 3.8 3.7 10% of e-commerce presents opportuni- company’s core 7% department called Radical Innovation, insight and knowledge within the organi- business 1% which focuses on business development ties for TINE to leverage personalized zation to become a more attractive partner 0% and research opportunities in both tra- shopping experiences that will expand for collaboration in the marketplace. ditional and digital contexts. its reach to customers as well as in- New Business telligently link online recipes directly 38% 33% Business areas 32% Currently, AI assists TINE in identifying 19% 22% 24% 4.0 3.8 entirely new to 10% 10% the company 3%

0% 1 2 3 4 5 What next

Not at all To some degree To a very high degree TINE is Norway’s largest producer, distributor and exporter The next steps for TINE in the AI space revolve around predicting of dairy products with 11,400 members (owners) and 9,000 consumer choices and forecasting demand well ahead of prod- cooperative farms. The company is the market leader across ucts going to market. By linking trends with purchase patterns, 15 European markets Norway Note: Remaining percent ‘Don’t know’ responses all dairy products in Norway, and has a large presence in TINE is looking to predict consumer demands, such as which new international markets including the US, the UK and other flavors consumers will likely demand a year in advance. Another Scandinavian countries. TINE employs more than 5,000 people future initiative is working with digital assistants that help con- Many of the participating companies Europe are more split and contain more Norwegian companies expect and generated a revenue of over 22 million NOK in 2017. TINE is sumers with their grocery shopping by identifying needs, prefer- are expansive, with diversified business “Don’t Know” responses than for “Core” high impact from core to new also currently involved in the production of food products such ences and purchasing patterns, which will also help TINE better units offering a range of products and – perhaps because there is an inherent as juice, ice cream and fresh convenience dishes. At least 60% of companies sur- understand the market and adapt to the diversity of consumer services. We questioned where they challenge in making predictions about veyed in Norway expect AI to demands. expect AI to have an impact - in their AI’s impact on new business areas have a high or very high impact core, adjacent and/or new business. where business results are not yet real- across core, adjacent and new ized, and where the role of current and business areas. Specifically, 43% of AI will impact across the board, but upcoming AI technology is not clear. companies in Norway expect AI to less consensus on timelines have a very high degree of impact Yet, interestingly 32% feel confident AI Companies expect AI to have a relative- on primary areas of the compa- will impact areas that are “entirely new ly equal impact on core, adjacent and ny’s current business, above the to the company.” This is not far behind There will be major changes in the grocery We need to develop partnerships and you new areas of their business. In inter- 37% European aggregate. Some the 37% of respondents who expect a industry; the internet will have a much need knowledge to become a good partner, views, they say impact depends on the respondents anticipate AI to have very high degree of impact on the core timeline, for instance AI impacting the a bigger impact on the core busi- more prominent role. We are confident that both within IT and business development. areas of the current business. core business now, but adjacent and ness in the short to medium term the technology will make it economically new business later on. The range of an- and on new business models and profitable for the grocery industry to engage swers for “Adjacent” and “New” across products in the longer term. in e-commerce.

40 41 Business Benefits and Risks Business Benefits and Risks

Use It or Lose It Predict AI will help make our products more reliable and allow real predictive actions based on various data sources. How is AI put to use in companies today? Anticipate events and outcomes ― Siemens (Mobility Division) Mobility solutions company

AI enables a wide range of uses, broadly tasks can be done without human inter- Prescriptions’ potential is big split into personalizing, automating, vention, a substantial number of com- Prescription is the laggard among the predicting, prescribing and generating panies are currently in the process of five AI uses, with current use-cases insights. We asked companies how training chatbots to transform the way typically being early stage, such as relevant each was to their business and information is acquired. suggestion engines and decision rec- found a significant degree of variance in Automate AI will currently be used to handle simple processes and ommendations for salespeople and terms of what executives expect to use Generating insights to make in- solutions, which allows the bank to focus efforts on key tasks, advisors. AI for advanced prescription Handle tasks without AI technologies for. formed decisions those that have a real impact on customers’ lives. But in the such as complex decision making lies in human intervention Focusing on generating insights based the future, as it requires collecting large future, it could also automate more complex processes. Prediction is the top use on internal and external data, 58% of amounts of data and understanding With 74% of companies seeing predic- companies view AI as a way to make which variables are significant, including ― Sparebank 1 Østlandet Bank tion as a relevant use of AI, this func- better decisions. This requires a sophis- some that are difficult to digitize. tionality, which includes churn analysis, ticated data infrastructure. Companies predictive analysis, and predictive reliant on R&D are using AI to speed up maintenance, comes out as the top the process of analyzing data for new Companies in Norway expect use. Companies with a large customer product development and to inform AI to optimize operations base use churn analysis to identify and future research. At least 50% of respondents in proactively engage customers with exit Norway consider three of the five Insights In all areas where a lot of data is reviewed and processed and potential. Sales teams use predictive Personalization is becoming a com- main uses of AI relevant for their has to be understood and assessed by people – we believe that analysis to identify leads with the high- mon feature company. The most common uses Identify and understand est likelihood of conversion. Companies Among the surveyed companies, 44% of AI are to automate and predict, patterns and trends in those areas AI can help us a lot to improve our efficiency. that sell or use advanced costly machin- are using AI to personalize the user followed by generating insights. ery use predictive maintenance to save experience, for instance by tailoring Current use-cases highlighted by ― Handelsbanken Bank money through decreased downtime. content to individual interactions as an respondents include automation effective way of driving mass-person- of customer service, as well as Intelligent automation for effec- alization. Next steps in personalization prediction of customer behavior tively dealing with routine tasks include chatbots and virtual assistants, and asset maintenance. Smart automation is seen as widely ap- where some companies already have plicable by 74% of companies surveyed. fully automated customer front-end We can provide a more personalized service to our guests, With estimates that 20-30% of current solutions in place. Personalize both before check-in, during the stay and after check-out. Tailor content and Content personalization and recommendations will further user-experience Prediction and automation relevant to most companies improve customer engagement. What are the relevant uses of AI in your company? ― Grupo Pestana Hotel chain

74% 72% 58% 44% 24%

71% 81% 57% 48% 14%

Prescribe We use Natural Language Processing to group customer inquiries and suggest which of our 300+ templates we should Suggest solutions to use in response. Our employees only need to confirm the defined problems To predict To automate To generate insights To personalize To prescribe choice or tweak it slightly. This dramatically lowers the time it takes to respond. Affirmative responses, 15 European markets Affirmative responses,Norway ― PFA Pensions and insurance company 42 43 Business Benefits and Risks Business Benefits and Risks

Making AI Simple Improved production and efficiency ented sectors, AI enables provision of through optimized operations new services via multilingual cognitive What is a good framework to map the potential benefits from AI? While digital transformation in general tools, geo-location suites, sentiment is based on customer engagement, op- analysis, cognitive robotic advisory ca- timizing operations is what companies pabilities, personalized service agents first look to when putting AI to use. It and more to transcend the sectors to draws on multiple levers such as: intelli- a new level of value-add -with signifi- The contributing companies generally text, voice, and images; and ‘interact- gent prediction, e.g., identifying chron- cantly increased scale and reach in real expect to benefit in all four key doing’ with employees, customers and ic diseases, anticipating non-perform- time. mains as outlined in Microsoft’s Digi- other stakeholders in natural ways. ing products, or adaptive modelling tal Transformation framework: opti- to flag corrective actions; operational Enabling employees to be more mizing operations; engaging customers; Applying AI to these domains can be efficiency, e.g., optimizing forecasting efficient and capable transforming products and services; transformational to a business, ulti- and order-to-fulfilment flows across Across sectors, numerous AI use-cases and enabling employees. Each domain mately changing the landscape of the the value chain, or processing huge focus on increasing employee produc- draws on underlying AI functionali- business itself and the industries and sets of documents in a fraction of the tivity or serve to enhance the human ties – ‘reasoning’ through learning and eco-systems in which it operates. time; and deep insights, e.g., detecting ingenuity and the ability to fulfil a forming conclusions with imperfect anomalies to surface irregularities such given function. AI helps employees in data; ‘understanding’ through inter- Let’s look in more detail at what that as fraud, or identifying new pockets of B2C companies expand organizational preting the meaning of data including entails. opportunity before competitors do. knowledge by analyzing vast customer behavior datasets in order to adapt Engaging customers more effec- online and offline store layouts, driving tively through AI conversion and sales. Customer per- After optimized operations, companies sonalization is used at scale, powered look to customer engagement as the by AI solutions that reveal real-time Artificial Intelligence impacts business in four benefit domains domain in which to seek most business customer insights, identifying the best Companies must consider how they approach the benefit domains in their AI strategy formulation benefits. Early examples of AI appli- next actions for up-sell and cross-sell cations in the customer engagement opportunities, as well as predictive space involve levers such as conversa- models that obtain a 360-degree view tional agents, e.g., bots providing per- of the customer by integrating cus- sonal recommendations and transac- tomer data and sentiment to generate tional advice; personal assistants, e.g., targeted offers. guiding decision-making, shortening conversion cycles; and self-service, e.g., options to help customers reduce time Engage your customers Transform your to resolution. E.g., provide customers products & services advice, shorten conver- E.g., speed up prod- Staying ahead of the competition by sion cycles, and reduce uct innovation cycles, transforming products and services time to resolution Artificial enable new value add services, and provide Transforming products and services, Intelligence real time support and enabling employees, came out on the same level, slightly below the two benefit other domains when it comes to where AI and digital transformation requires domains companies expect to generate future business benefits. us to rethink our ways of working and producing products. AI will both improve Transforming products and services, existing processes as well as enable ultimately giving rise to entirely new completely new products, services and business models, is mostly favored in Enable employees Optimize your R&D-heavy sectors where companies business models. E.g., increase employee operations consider AI and advanced analytics as efficiency through pre- E.g., improve plan- levers to speed up the product innova- dictions, enabled sup- ning and reduce costs — Kongsberg Gruppen tion and discovery process. In B2C-ori- port, and automation of through intelligent Technology group repetitive tasks prediction, operational efficiency, and deep insights, predictive maintenance

44 45 Business Benefits and Risks ( Case Study ) Artificial Intelligence in Europe

Where Value Hides What benefits do business leaders particularly expect from AI? A.P. Moller – Maersk

Respondents were asked to assess the Fewer expect products and services Companies in Norway expect There is no doubt that AI has the po- and services and improve existing Treating AI as a distinct part of wider potential of AI within each of the four and employee engagement AI to optimize operations tential to transform Transportation & products and services); enhanced digital initiatives, Maersk established benefit domains. Although executives speak of the po- Among Norwegian companies Logistics, giving rise to a new class of customer experience (service delivery, an in-house software development and

tential in making sense of existing and surveyed, 95% expect AI to opti- intelligent logistics assets and opera- issue resolution, empowering custom- innovation unit, consisting of 100 em- Optimizing operations and engag- new sources of data to introduce high- mize their operations in ways such tional models. Data science ployees and growing. The aim ing customers to deliver most value er margin services to product portfo- as automating tasks, or forecast- is not new to A.P. Moller – is to deliver AI products and Among all companies surveyed, 89% lios, expedite new product developing capacity, well above the 74% Maersk, yet only recently has solutions rooted in the group expect AI to prove beneficial in opti- ment, and introduce innovative new in Europe. At 89%, companies in AI become a part of Maersk’s As a designated new discipline posi- business strategy, building on mizing operations, with use-cases most offerings, only 65% expect AI to help Norway believe AI will benefit core strategy as a functional well-defined use-cases with highlighted by executives being mon- transform products and services. customer engagement, for in- technology with tangible tioned close to the core of group strat- deep sponsorship from the itoring results, predicting trends, and stance by automatically identify- applications. As a designated egy, Maersk is developing AI capabili- business, thereby avoiding the prescribing future solutions. A lot of Even fewer (60%) expect AI to provide ing customer needs and tailoring new discipline positioned ties as part of a broader transformation trap of living separately from focus is given to intelligent automation, benefit from empowering employees offerings; and 67% expect AI to close to the core of group of the business. the business and not adding such as making compliance cheaper to improve productivity, enable inno- enable employees by freeing up strategy, Maersk is develop- value. and more robust, improving risk analy- vation, support problem solving, etc. their time and thus allowing them ing AI capabilities as part of a Maersk’s early investment in sis, optimizing supply chains, providing to focus in more interesting and broader transformation of the agile transformation and peo- predictive maintenance capabilities, What we did hear overwhelmingly, value adding tasks, or by pro- business. ple capabilities has resulted in and more. however, was the importance of bring- viding personalized and tailored Maersk takes a broad view of er-facing employees); and operational the organizational structure ing all employees along on the com- training. Lastly, 57% of Norwegian AI, applying intelligent technology to efficiencies (for example via network and concentration of talent necessary Not surprisingly, the ability to struc- pany’s AI journey. This involves getting companies expect AI to contrib- three main areas: product offerings, optimization). to drive AI forwards in a large global ture repeatable processes and reduce internal buy-in that AI will be a force ute in transforming products and (using AI to develop new products organization. human error and bottlenecks is some- for good, generating excitement about services for instance by providing thing most executives can get behind working with intelligent technologies, real-time insights. from a cost-saving perspective. and making existing jobs easier and 74% of companies surveyed expect AI more engaging. to help them engage customers and What next? enhance the user experience, including tailoring content, increasing response speed, adding sentiment, creating A.P. Moller – Maersk is a Danish conglomerate with activities Maersk is developing a platform to leverage company data to experiences, and anticipating needs. in two sectors: Transport & Logistics and Energy. Maersk is develop products, partly by optimizing the company’s data the largest company in Denmark, and the world’s largest architecture to ensure faster development. To meet these operator of container ships and supply vessels. The company demands, Maersk is changing its approach to attracting talent. has approximately 88,000 employees, a fleet of more than 1,100 AI also requires an entire new skillset among company leaders, Most companies expect to generate benefit from optimizing operations vessels, and subsidiaries and offices in 130 countries. Its 2017 transforming them into AI leaders who are deeply engaged in What business benefit do you expect AI to generate? revenue was $31 billion. implementing AI in the business.

89% 74% 65% 60%

95% 71% 57% 67%

At Maersk, we build things that have deep There’s good awareness about what AI can business sponsorship and add value. bring to the shipping industry. In the future,

Optimizing operations Engaging customers Transforming products & services Empowering employees Maersk won’t just be a shipping company, but an integrated forwarder of logistics. Affirmative responses, 15 European markets Affirmative responses,Norway

46 47 Business Benefits and Risks Business Benefits and Risks

Sector Benefits Landscape Front to Back We asked companies across sectors what business benefit they expect AI to generate What are the expected benefits by sector? across Engaging customers, optimising operations, Empowering employees, and Transforming products & services Executives surveyed and interviewed wearables, paving the way to valuable TMT expects AI to increase engage- in the various sectors recognize the data collection and even entirely new ment, insights, and connectivity distinct benefits of AI, speaking about business models. The focus in many Telecom, Media the myriad of ways they see AI trans- and Technology companies seem to forming their businesses and indus- Engaging customers in new ways in be on using AI to reduce costs of re- Engaging customers Optimising Transforming Empowering tries. Although there are clear patterns Consumer Products and Retail taining and growing customer bases. operations products & services employees to discern, executives from different The Consumer Products and Retail AI is projected to help build seamless sectors often speak to different ben- companies we spoke to rank lowest in experiences across devices, predicting Tailoring content, increasing Automating processes, Adding data services, gener- Improving productivity, en- response speed, adding sen- monitoring results, pre- ating new business models, abling innovation, exploring efit areas from which they particularly terms of expecting benefits from AI, churn, and automating customer ser- timent, creating experiences, dicting trends, prescribing extending reach, etc. new capabilities, supporting hope to capitalize from. pulled down by only 44% expecting vice capabilities to solve some of the anticipating needs, etc. solutions, etc. problem solving, etc. benefits from AI to empower employ- sector’s longstanding challenges while Services companies expect the most ees. However, with multilingual cogni- bringing down costs. Life Science benefits from AI tive tools and being able to bring tar- Pharmaceutical, Services companies reported the high- geted, tailored offerings to customers, AI to revolutionize Financial Servic- 71% 71% 88% Healthcare, 54% Biotech est expected benefits across all four many spoke of the potential to engage es firms domains, expecting significant value customers, and of using AI for crucial Finance companies reported some of from AI through engaging customers activities such as understanding brand the highest expectations for AI benefits CPR and empowering employees, for ex- performance and sentiment analysis. across the four domains, which can Consumer ample via improving resource and skills explain the sector’s current frontrunner Products 78% 78% 58% 44% allocation across their large human Virtually all Industrial Products and & Retail when it comes to current AI maturity. capital pools. (Note: the Services sam- Infrastructure companies look to From using machine learning to detect ple is the smallest of all sectors.) optimize operations fraud and automation to streamlining Companies from the Infrastructure and KYC efforts in the back office, and to Industrial Products Expedited drug discovery and dis- Industrial Products & Manufacturing reducing compliance and regulatory Manufacturing, ease prediction in Life Science Materials, sectors top the list at 96% respectively costs via technologies that digest vast 61% 70% 56% Equipment 96% Executives in Life Science are among in terms of expecting efficiency gains quantities of legal documents, banks those most excited about benefits through AI optimized operations. The and other financial institutions are pertaining to transforming products heavy focus on equipment, complex looking to provide higher quality ser-

TMT and services. Many see AI leveraging supply chains and materials means vice at faster speeds and lower costs. Technology, existing internal and external datasets there is ample scope for intelligent Similarly, mortgage applications can be

Media/Entertainment 81% 52% 88% 64% to speed up the drug discovery process optimization. Yet, there is a relatively approved in a matter of minutes, and & Telecom and enable the transition towards pre- small focus on engaging customers investment decisions can be guided cision medicine. and empowering employees. This is by robo-traders to transform products likely due to the frequent B2B nature of and engage customers in the front Finance Banking, Deep learning with huge datasets is these businesses, and the potential for office. Insurance, also expected to assist with disease automated machinery to play an ev- 67% 73% 78% 84% Investments prediction. Customers can be engaged er-growing role in the industrial sector. using new health-oriented IoT-related

Infrastructure Transportation, Energy, Construction, 70% 74% 96% 54% Real Estate

As a railway company, we have significant physical assets that need to be maintained. With Services AI we see significant opportunities, like automatically detecting faults in railway tracks and Professional Services, Hospitality, Public predicting maintenance needs. This improves not only efficiency but also security. 78% 78% Services, Membership 83% 83% Organization — SBB Swiss Federal Railways Affirmative responses by sector Railway company

48 49 Artificial Intelligence in Europe Business Benefits and Risks

Risky Business? What do business leaders need to pay attention to when implementing AI?

There are inevitable concerns about need to take advantage of solutions employees, rather than replace them Today AI is often somewhat a ‘black box’, the business risks associated with AI, in accordance with everything from altogether, allowing for more peo- as many of the applications of the rel- GDPR to cybersecurity concerns. For ple-oriented or creative work. There is however we need to know why AI came up atively new technology are still in their the latter, the lack of clarity around AI also a larger task in training employees early development while receiving sig- regulation can slow down scaled im- to work together with AI, usually a nificant media and political attention. plementation as business leaders worry challenge and risk in itself. with a certain conclusion, such as telling a However, from what business leaders about investing in solutions when the tell us, they are balancing their excite- rulebook is still being written. Many Seeing the wood for trees customer why their loan application was ment about AI’s potential with some first movers within our AI report feel A further dominant risk articulated by healthy reflections on key business they need to write the rules themselves several surveyed business leaders is risks, not at least the risk of investing and hope for the best. about feeling information overload. AI declined or similar. Trust in AI is lacking, so in a technology that may not prove its can help make sense of huge quantities commercial value if not done correctly. Concern with the human in the new of data, but setting up AI and learning there is a risk that customers remain on the machine age to use it effectively requires feeding Broad concern with regulatory A prevailing risk many companies were the technology the right data and landscape also concerned with was impact on working out what is useful versus what fence with regards to the application of AI. Over half of all companies surveyed ex- personnel. The need for employees is noise. A further element in the risk of pressed concern regarding regulatory across the organization to buy in and overload is understanding the different requirements. This concern can broadly adapt to working with AI touches on all AI technologies and solutions available be split into compliance with existing industries and markets. The instinctual and making sense of technological as requirements and navigating the nas- fear of job losses among personnel is well as market developments to know — KLP Banken Bank cent, often ill-defined regulatory land- one that needs to be managed as AI where to make strategic use of AI. scape for AI. For the former, companies will often transform the daily tasks of

Top 3 business risks in Norway

Regulatory Upkeep of Information 1 Requirements 2 the System 3 Overload

62% 33% 24%

We are not looking to develop a separate AI More than half of Norwegian compa- The pace of change in AI technology Information overload may occur when nies expressed concern about regula- is very rapid. Companies fear that a trying to figure out what is useful in strategy; AI is part of our toolbox, applied to tory requirements, and in particular, technology they invest in now will be vast amounts of data, identifying in- the need for clear guidelines and regu- outdated before it has achieved its sights or establishing predictive trends. lations regarding AI. Without such clar- expected ROI. Additionally, as with Working with such large amounts of solve various problems and challenges. We do ity, investment in AI can be perceived any system, it requires planned and data requires consideration for the us- as risky for companies because they unplanned maintenance, including ers looking for understandable, action- not consider the use of AI as an end in itself. may invest in something allowed at the updating its servers and the data it able results. As companies implement time that may not be later on. Norway uses. Companies tell us it is difficult to AI, they are considering both the tech- shares this concern with other Europe- demonstrate business cases with small nical aspects of working with very large an countries surveyed: for almost all of pilot tests, and therefore, hard to know datasets and the human aspects of them, regulatory requirements was one where to invest initially and over time. interpreting the results in a real-world, — DNB Financial services group of the top three risks. business context.

Note: Affirmative responses, Norway. The respondents were asked to select all that applied of the following response options included: Diffusion of 50 51 resources, Loss of control, Upkeep of the system, Information overload, Regulatory requirements, Impact on personnel. Artificial Intelligence in Europe Learn fom the Leaders

Capabilities. How? What competencies are required to get AI right?

This section explores the necessary As the majority of companies we spoke eight capabilities to develop AI matu- to are looking to supplement their 8 capabilities rity, realize tangible business benefits, in-house skills with external partners 1. Advanced Analytics and minimize risk. As exhibited in the when building their AI solutions, par- Obtaining and deploying specialized chart on the following page, we asked ticularly for pilot projects, it is not due data science skills to work with AI by the companies to rank the importance to a general lack of relevance. attracting talent and working with external parties of these capabilities in terms of incor- Learn from porating AI into their business, as well Bringing behavioral science into play 2. Data Management as to self-assess how competent their via Emotional Intelligence to build Capturing, storing, structuring, companies are with regards to each AI solutions that understand and mimic labeling, accessing and enabling capability. human behavior, and make it easier for understanding data to build the humans to interact with the technolo- foundation and infrastructure to work with AI technologies The human element and technology gy, is seen as the relatively least important AI enabling capability. An explana- the Leaders Some of the eight capabilities center 3. AI Leadership around human elements: AI Leader- tion for this could be that the technical The ability to lead a transformation ship; Open Culture; Agile Development; skills are still so relatively complex for that leverages AI technology to set companies to grasp and establish, that defined goals, capture business Emotional Intelligence. Others are value and achieve broadly based more technology oriented: Advanced more advanced human cognitive skills internal and external buy-in by the Analytics; Data Management; Emerg- become less of a priority at this stage. organization ing Tech; External Alliances. The promise of AI lies in creating business value. Noticeable sector deviation 4. Open Culture Ranking of key capabilities for real- As exhibited in the following chart Creating an open culture in which where business leaders are asked how people embrace change, work to izing AI potential break down silos, and collaborate competent their company is in relation We have identified the eight most recognized capabilities needed Advanced Analytics comes out on across the organization and with to the most important AI enabling external parties top as the most important AI enabling to successfully create value from AI, and assessed how compe- capabilities, the sector aggregate capability among the companies sur- scores land at or just above the medi- 5. Emerging Tech veyed. Data Management is second. tent companies are within each. an, with a fairly close spread. Sectors The organizational-wide capability AI Leadership is perceived as the third to continuously discover, explore that are more mature in using AI are most important capability. Open Cul- and materialize value from new those that report higher competency solutions, applications, and data ture refers to collaboration and the in Advanced Analytics - particularly platforms Perhaps more importantly, the executives we spoke with high- ability to embrace change and uncer- TMT (Telecom, Media/Entertainment & tainty. lighted the importance of these 8 competencies as those needed Technology), as well as Finance (includ- 6. Agile Development An experimental approach in which ing Banking, Investment & Insurance), Understanding how to deploy the collaborative, cross-functional teams to successfully create value from AI. and Life Sciences (including Healthcare work in short project cycles and right Emerging Technologies in a future & Pharma) all report lower competency iterative processes to effectively proven way is ranked fifth, followed by advance AI solutions in AI Leadership. A possibility is that in Agile Development, where self-organ- the pharmaceutical industry, AI chiefly ized teams are characterized by shorter 7. External Alliances resides in R&D, and has yet to affect project cycles, the ability to work with Entering into partnerships and the broader organization on the wider alliances with third party solution constantly evolving technology, and strategic level. providers, technical specialists, and transparency regarding success and business advisors to access technical failure that leads to wider buy-in and capabilities, best practices - and Companies intend to use various levers talent scaling. to obtain these AI capabilities. Compa- nies are relatively evenly split between 8. Emotional Intelligence Entering into External Partnerships using recruitment (60%), training Applying behavioral science ranks second to last in terms of im- capabilities to understand and mimic (56%), partnering (57%). Only 10% of portance, perhaps because it’s the human behavior, address human the companies use acquisition of teams needs, and enable ways to interact area that resonates most with existing or businesses as a way to fast track with technology and develop more capabilities and where business leaders human-like applications building much needed AI capabilities. perceive themselves most in control.

52 53 Learn fom the Leaders Learn fom the Leaders

AI Competency Model

Advanced Analytics and Data management considered most important AI capability TMT leads the other sectors in AI competency How competent is your company within these organizational capabilities? How competent is your company within these organizational capabilities? How important is each of the organizational capabilities for your success with AI?

Competency Importance

IndustrialLife ScienceProductsFinanceCPR Infrastructure Services TMT

15 European 15 European Norway markets markets Norway Advanced Advanced Analytics Analytics

Data Data Management Management

AI Leadership AI Leadership

Open Culture Open Culture

Emerging Tech Emerging Tech

Agile Agile Development Development

External External Alliances Alliances

Emotional Emotional Intelligence Intelligence

2,0 2,5 3,0 3,5 4,0 4,5 5,0 2,0 2,5 3,0 3,5 4,0 4,5

Note: ‘Don’t know’ answers not included in average score. Note: ‘Don’t know’ answers not included in average score. Average competency and importance for Norway and 15 European markets (1: lowest – 5: highest). Average competency by sector (1: lowest – 5: highest). Capabilities ranked according to highest importance in 15 European markets. 54 55 Learn from the Leaders Learn from the Leaders

Companies consider themselves moderately competent within Advanced Analytics How competent is your company within Advanced Analytics? 1. Advanced Analytics Avg. Score

4.5 4.4

33% 36% Importance 33% 27% 18% 15% 15% 14% Obtaining and deploying specialized data science, data engineer- 6% 4% 3.0 3.3 ing, data architecture and data visualization skills by training em- Competency ployees, attracting talent and co-creating with external partners 1 2 3 4 5

Not competent Moderately competent Highly competent Limited analytics skills beyond tra- Semi-autonomous examination of Significant data science activity using ditional business intelligence based data using sophisticated techniques techniques such as complex event primarily on historical data and tools to predict future events processing, neural networks, etc. The backbone of AI is made up of In other words, the longer you wait, the skilled, intelligent minds who are ca- harder it can be to get the right people. 15 European markets Norway Note: Remaining percent are ‘Don’t know’ responses pable of understanding business prob- Consequently, a ‘wait-and-see’ strategy lems at the granular level, and deploy- can be risky for companies that are AI ing AI to effectively solve or support followers due to the scarcity of talent, others in solving these problems. This which may prove impossible to attract Co-creating to compensate for blind requires technical data science and once the company is ready to make a spots - while avoiding the black box Advanced Analytics is the mathematical engineering skills, to more ambitious move into AI. The scarcity of available talent has led most important capability in What to learn hybrid profiles with sufficient business companies to increasingly co-create Norway acumen to decode problems and abil- While many companies struggle with from AI leaders: solutions with external partners who Across all markets surveyed, ity to tackle them using quantitative acquiring AI talent, we also experi- bring with them specialized know-how. Advanced Analytics is consid- methods. enced companies - even in traditional 1. Providing interesting However, executives very clearly point ered one of the most important industries such as Transportation and problems, good data, and a to the need for internal AI capabilities of the eight capabilities neces- A self-fulfilling talent prophecy Industrial Products - with AI teams of freedom to thrive in a non- in the receiving end to understand the sary for success with AI – on a +25 experienced data scientists hold- corporate environment is It is evident from the study that there real problems and evaluate the perfor- scale of 1 to 5, the average score ing Ph.D’s in mathematics, astrophysics, key to attracting talent. is a major lack of technical data skills to mance of external partners. in Norway is slightly above the etc., from high profile universities. Most meet the drastically rising demand for European average (4.5 vs. 4.4). 2. A wait-and-see follower often, these companies have been AI. As a result, the hunt for AI experts Companies find that AI solutions im- Despite the rating in terms of strategy can prove risky and first movers on AI and attracted senior has become extremely competitive, plemented by external parties become importance, 71% of the com- put companies in a talent practitioners tasked with building out and it is far from uncommon that func- black boxes unless the organization panies in Norway report to be scarcity trap. sizeable AI communities to work on the tional AI experts are paid higher sala- is capable of contributing and taking moderately competent or below most strategic business agendas. ries than their superiors are - in some over the solutions after delivery. Avoid- with Advanced Analytics (3.0 3. Training existing staff with cases leading to new HR policies to ing black boxes is a general concern average). This demonstrates deep business intrinsics is Hybrid profiles becoming the reflect evolving requirements. among executives. Consequently, in- the room for growth in getting key to make AI work - and hardest currency ternal data scientists must be able to companies ready for something effective when access to Telecom is advanced and Several business leaders state that the One of the most consistent inputs from decode and dissect AI applications to they consider to be important. talent is challenged. challenging technologically lack of AI talent is the greatest barrier the executives was the need for people explain of the underlying rationales. The companies interviewed to implementation within business op- with deep domain knowledge com- so we need to combine talk about their efforts to in- erations. Interestingly, companies that bined with strong technology profi- Such rationales are important in mak- crease their competency in this the best minds in deep have chosen an early adopter strategy ciency. This hybrid profile is essential to ing AI driven solutions creditable, and area, and in particular, mention domain competence with for AI have been successful in attract- identify relevant use-cases in the busi- greatly reduce the risk that an AI appli- challenges around finding the the best minds with deep ing senior professionals who again ness with possible AI solutions. cation draws wrong conclusions based needed skills and personnel, We recruit from abroad, have been able to build out sizeable AI knowledge in machine on false assumptions. and appropriate applications of from eastern Europe to the teams in their companies – based on Contrary to data scientists, software advanced analytics. learning and AI. So we are the premise that talents seek talent – engineers, and even data architects US, as it can be difficult working with talent on making AI recruitment a self-fulfilling that can be recruited externally, the to find the correct, fitting multiple layers. prophecy for these pioneering com- hybrid profile is often nurtured by resources locally. panies. training existing employees from the line of business and adding AI skills. To — Ericsson succeed however, a fundamental ap- — Aprila Bank Telecommunications preciation for technology is required. Bank company 56 57 Learn from the Leaders Learn from the Leaders

A significant share of companies consider themselves moderately to highly competent within Data Management 2. Data Management How competent is your company within Data Management? Avg. Score

4.3 4.4

Importance Capturing, storing, structuring, labeling, accessing and govern- 38% 38% 33% 31% 24% 19% ing data to build the foundation and infrastructure to work with 5% 8% 3% 3.2 3.2

AI technologies 0% Competency 1 2 3 4 5

Not competent Moderately competent Highly competent Limited discipline and method to en- Traditional DM with embedded busi- Agile governance, processing of data sure currency and quality of reference ness rules to validate, reconcile, and from disparate sources incl. external data across subjects and systems align data to corporate policy networks, fast query access, etc.

Companies tend to focus their AI Companies reported that they typically 15 European markets Norway Note: Remaining percent are ‘Don’t know’ responses efforts in areas where they already spend 2-3 years building the appropri- have relevant data. We found that the ate data infrastructure for AI, and many amount of available data varies sig- respondents with the most ambitious AI nificantly by sector but regardlessly, a visions are still spending the majority of use mostly structured data from internal Similarly, 60% of these self-rated most significant proportion of the time com- their time fine-tuning their infrastructure. data sources, a significant 80% of the advanced companies report use of hy- panies dedicate to AI is spent on data most advanced companies also use both brid architectures of on-premise and management related tasks. Data privacy regulations structured and unstructured data, and cloud based storage, while the less ad- What to learn an equivalant 80% use data from both vanced predominatly rely on Data infrastructure is not only a prereq- internal and external sources. on-premise platforms. from AI leaders: Data governance is no trivial task uisite for effectively working with AI, but One of the major hurdles companies is increasingly needed to comply with 1. Make sure that the value face regarding data is governance, data privacy regulations, which respond- of data is understood and particularly who ‘owns’ it, how data is ents see as a key risk. The recent imple- Data Management is the second most important capability in Norway prioritized throughout stored, how to access it, and who may mentation of GDPR in the EU has high- Norwegian companies rate Data Management as the second most important the organization. access it are all essential questions lighted the need to govern what data is of the eight capabilities necessary to succeed with AI (4.3 average on a scale when working with AI. Questions that being used for. AI-specific regulation is in of 1 to 5) – slightly below the European average (4.4). Further, the average 2. Engage the C-suite in used to be about efficiency suddenly many ways still immature, and AI leaders level of competence (3.2) in Data Management for Norwegian respondents defining data governance become highly strategical and com- find that a lack of clear guidelines can is considerably lower than the average level of importance. Among Norwe- and strategy - it is key to plex to respond to without rethinking limit their progress. gian respondents, 76% report to be moderately competent or above in Data getting AI right. governance structure and policy. Gov- Management, with only 5% considering themselves highly competent in this 3. Build your data structure ernance aside, the most common ob- Advanced companies (also) appreci- capability. This suggests that some companies have developed a Data Man- to embrace unstructured stacles to using data are organizational ate external and unstructered data agement foundation but are still midway before achieving the capability level data, also from external silos or legacy systems built for specific To build precise and useful AI solutions, that will fully back their AI systems. Introducing an adequate data govern- sources - advanced purposes, resulting in decentralized companies not only need a lot of data, ance structure and finding the right quantity and quality of data is essential companies indicate that storage that limits access. but also accurate data that is appro- according to many of the companies interviewed. you may soon need it. priately structured and labeled. Data is often reported to be in a state that it is An algorithm is as good as the data that supports it. You simply unusable, as it could lead to unde- can make bad analytics by having a bad algorithm but also sirable or unreliable outcomes. by using bad data. It is important to get those things in While most companies are preoccupied We need the whole company to think data, be data-driven and understand that data has order and have processes and people to make sure that the with cleaning, structuring and migrating value we need to put in our products. quality of data is driven and maintained. historical data, some have chosen to build new data structures from scratch to collect the correct data going forward. — Husqvarna — Vattenfall Interestingly, we found that while com- Consumer equipment company Energy company panies that are less mature in AI tend to

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A large proportion of companies consider themselves to have limited or no AI Leadership competency 3. AI Leadership How competent is your company within AI Leadership? Avg. Score

4.2 4.2

33% 32% Importance The ability to lead an AI transformation from top to bottom - by 29% 24% 23% 14% 14% 10% 10% 9% articulating a vision, setting goals and securing broad buy-in 3.0 2.9 across the organization Competency 1 2 3 4 5

Not competent Moderately competent Highly competent Limited strategic priority assigned Substantial resources deployed to AI, AI recognized as a key strategic pri- to AI activities and only vague fo- mandates assigned, and leadership ority, with strong C-suite sponsorship cus on AI in the management team articulation of an AI vision and high tolerance of uncertainty As with any corporate transformation, enough in itself for understanding how the foundation for successful deploy- AI is impacting the business. As AI tech- ment of AI is executive leadership nologies become increasingly complex, 15 European markets Norway Note: Remaining percent are ‘Don’t know’ responses buy-in and sponsorship. The C-suite leaders must be able to launch, sup- must be aligned in what they want port and, where necessary, challenge to achieve, and AI must be placed on relevant AI initiatives against strategic Accepting loss of control AI Leadership is a high priori- the strategic agenda to ensure that AI business imperatives. The disruptive As new technological opportunities ty for companies in Norway efforts are an integrated part of the potential that companies believe AI will foster innovative, dynamic business What to learn company’s overall strategic goals, that have also means that leaders should models, organizations will need to tear Norwegian companies consider from AI leaders: capital is allocated, and employee time anticipate and prepare for a broader down silos to become more agile and AI Leadership to be one of the is dedicated. change management exercise aimed collaborative. To achieve this change, most important capabilities to 1. The organizational at embracing the change from AI on it is paramount for leaders to create succeed with AI, (4.2 average transformation driven by AI Leadership among the lowest multiple levels. and convincingly articulate a vision so on a scale of 1 to 5). However, AI will be continuous - competency of all capabilities stakeholders understand the bigger Norwegian companies rate their this requires seeing AI as a competence with AI Leadership Given the relative importance of AI Significant variation in AI conversa- picture. process, not a project. the second lowest of the eight Leadership (avg. 4.2 across all sectors), tions from top to bottom capabilities (3.0 average). The 2. Leadership must it is interesting to see that business Interestingly, data revealed that AI is A general characteristic of this chal- European average (2.9) is also be accustomed to leaders self-assess their level of com- considered an “important topic” on the lenge is that leadership needs to accept similarly well below the im- AI technologies to Going from talking and petency as among the lowest of all C-suite level among 73% of the compa- that it will lose some control. Projects portance average rating. This understand how it will eight AI enabling capabilities, with an nies surveyed. However, less so on the will increasingly be explorative, bot- building to doing means that likely reflects that many of the affect the company. avg. competency of only 2.9; 66% of Board of Director level where it is only tom-up and have less certain out- you take it to the decision companies surveyed have gone respondents state that their companies considered an important topic in 38% comes, requiring leaders to be ready to through an initial digital trans- 3. Articulating a clear AI point where leadership has have moderate, little or no AI Leader- of companies, and even less so on the adjust the overall direction of the com- formation and are now starting vision is key to achieving to decide A or B, based on ship competency. Many executives are operational employee level with 28%. pany more frequently. Increasingly, AI to develop their AI leadership buy-in and motivating realizing that business acumen is not projects will rely on open source code an AI-generated result. From competencies. Furthermore, exploration of use-cases We observed in the interviews that and off-site cloud solutions, building an intellectual perspective, many of the companies inter- with uncertain outcomes. companies very rarely have AI capable on collaborative capabilities outside viewed referred to major tech it is easy to say that you leaders across the Board of Directors, the company. companies when assessing their will follow AI results. But Executive Management, and Functional AI position; with those reference Management layers. Senior AI leaders when the moment comes points, many companies consid- can sometimes be found on one of the where you choose between ered themselves not to be highly levels, but rarely with any speaking competent in AI Leadership. recommendations based leadership colleagues to challenge ide- on old methods and AI, if as. This leadership vacuum was often you choose AI, that is when pointed to as an issue from lower level AI experts. change truly happens.

— EQT Private equity group

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Most companies rate themselves moderately competent in Open Culture How competent is your company within creating an Open Culture? 4. Open Culture Avg. Score

4.0 3.9 57% 41% Importance 23% 19% 19% 18% 13% 5% Creating an open culture in which people embrace change from AI, 4% 3.2 3.2 navigate confidently in uncertainty and ambiguity, work to break 0% Competency down silos, and collaborate seamlessly across the organization 1 2 3 4 5

Not competent Moderately competent Highly competent Gravitation towards linear processes, Emerging transparency, participation, Truly collaborative and open approach, and organization of AI in separate empowerment, community building to with clear accountability across both silos within existing structures increase speed and agility internal and external domains New technologies have often disrupted drive a fundamental transformation

how work is conducted. AI is no differ- and increasingly assist in tasks previ- 15 European markets Norway Note: Remaining percent are ‘Don’t know’ responses ent. Establishing an open, collaborative ously performed by humans. culture to minimize resistance and enable human performance can prove Interestingly, the companies that efficient to prepare the organization self-rated as most advanced see a ken down in order to promote a culture for transition. However, this may be lower risk to personnel than the less where AI-teams work in conjunction Norway rates Open Culture difficult, as the magnitude of impact advanced (only 20% of advanced re- with the rest of the company to create competency among What to learn driven by AI can imply a fear of uncer- ported this risk as a concern vs. 43% value, circumventing needless complex- their highest from AI leaders: tainty, ambiguity, and a general resist- for the companies still in the “planning” ity and time-consuming processes. Open Culture is among the ance to change. phase). highest ranking capabilities in 1. Establish cross- Another issue relates to the concept of terms of competency in Norway, organizational projects to Risk to employees less of a concern Relatively small competency gap sharing data openly, when the value of where 19% of companies report foster collaboration and among most advanced companies the data largely remains unknown until With a relatively small gap between to be highly competent. Al- learning across functions. Companies reported that employees importance (avg. 3.9) and competency it has been treated, processed or com- though, on average the compe- generally have a positive attitude (avg. 3.2), creating an Open Culture is bined with other datasets. tency level is only slightly above 2. Ensure employee buy-in towards AI. Yet, one thing is having a one of the capabilities where business moderately competent (3.2.). by being open and clear You cannot only have data positive attitude in general, another is leaders feel most comfortable. Cooperation across the organization The rate of importance is com- about on-going projects scientists do it. They have a to retain an open attitude once new Many of the most advanced companies paratively slightly lower com- and desired outcomes. super important role but you technologies start impacting the way An obstacle mentioned by many re- that have been able to produce several pared to the other eight capa- work is done. spondents is the ability to work col- AI projects have also managed to es- bilities (4.0 average) – although 3. Ensure that governance also have to complement laboratively across the organization tablish links and cooperation across the still slightly above the European structures support them with designers. To achieve buy-in, business leaders despite AI most often being put to use organization. These cases indicate that average (3.9). Companies inter- collaboration through Because you need to find the must make the changes due to AI towards quite narrow use-cases. With the benefits of an open work culture far viewed highlight their efforts to projects co-owned by AI experts and business use cases where you apply tangible to reduce organizational un- benefit areas being limited to specific exceed the difficulties and associated establish a type of culture and certainty. However, companies expect domains or functions, it is often not risks. leadership that embraces AI and leaders. those types of technologies. a significant impact from AI which will seen as relevant to involve the organ- is willing to take on the chal- Even though it is the ization in a broad and collaborative An obvious obstacle to an open culture lenges that come with it. fantastic technology that approach on AI. is the fear of job losses with the intro- can bring fantastic results, duction of AI. According to respond- Furthermore, many companies have ents, the fear of workforce redundancy it has to be embedded in a had difficulties in carrying out effective has some merit, but the concern should design approach to meet the AI programs, which are closely mod- not overshadow the significant benefit customer needs and solve elled on the lean processes of startups. potential of AI. A pivotal task for com- It’s imperative to link up traditional analysts and The primary purpose of such programs pany leaders is to proactively articulate real problems. developers with AI competencies. Collaboration between is to enable brief, agile projects to a tangible vision for AI initiatives. This gauge the applicability of AI use-cases, will make it easier for employees to business developers, analysts and developers is key. — IKEA Group requiring a substantial change to com- understand the AI opportunities on a Furniture retail company pany culture. Silos between depart- personal level, and thereby embrace — Sparebank 1 SMN ments in the company have to be bro- the change ahead. Bank

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Emerging Technology is the AI-enabling capability with most ‘Moderately Competent’ replies 5. Emerging Technology How competent is your company within adopting Emerging Technology? Avg. Score

4.1 3.9

48% 42% 33% Importance 29% 13% 14% 12% 5% The organization-wide ability to continuously discover, de- 3% 3.6 3.3 ploy, and create value from intelligent solutions, applications, 0% Competency and data platforms 1 2 3 4 5

Not competent Moderately competent Highly competent Legacy oriented tech stack, typically Increasingly AI enabled tech stack with Tech stack optimized for AI across on-premise based and with limited on-demand cloud computing, agile hardware, interface, algorithms, future proofing of AI technologies software, scalable architecture, etc. architecture, people, services, etc.

15 European markets Norway Note: Remaining percent are ‘Don’t know’ responses Evidence of the rapid pace of techno- that being unable to quickly integrate wider digital puzzle, where dots need to logical change are plentiful in today’s innovative trends and cutting edge be connected across technologies. This digital world. What we have seen is that technology due to the burden of means success with established technol- there is a definite correlation between legacy systems, siloed business units, ogies, from cloud and SaaS platforms to The importance of execution Emerging Technology second companies that are ahead of the pack and complex governance processes getting the basics right with analytics, is Finally, this capability is also effective highest competency in Norway with AI and with the wider technolog- is proving a real challenge for their AI key to building on what is already there. execution. Many companies we sur- What to learn ical adoption. That AI benefits from adoption. veyed across Europe had developed In Norway, 95% of companies from AI leaders: being able to identify and implement Working with emerging technology also prosperous use cases supported by report to be at least above mod- emerging technology may seem intu- While there is some truth behind such relates to agile development and the abil- erately competent in Emerging robust concepts and AI applications - 1. Build a radar to pick up on itive and obvious, yet finding the right stereotypes, we also heard from several ity to trial, test and experiment in iterative, Technology. This capability is on paper. But technical limitations tend merging tech trends and formula is no trivial exercise. executives who are able to build radars short cycles. This kind of working culture the second highest ranking in to get in the way of implementation. connect them to market that pick up what’s happening in tech- allows companies to work with less stable, terms of competency in Norway. opportunities. How strong is your tech radar? nology domains and applications that untested technology. Enabling innovation Employees with limited technical ability In terms of importance, Norwe- With an average score of 3.3, the ability this continuous explorative process is requires an outlook from the very top often need upskilling to work with new gian companies rate Emerging 2. Look past the technology to explore and implement emerging serving them well to get an overview of of the organization that accommodates technology. IT and business may need Technology between moder- hype and remember the technology is an area where business workable AI solutions that could prove longer investment horizons and at times to work closely together and speak ately and highly important (4.1 business model - it may leaders perceive their companies to successful in production. uncertain financial returns. This is particu- each other’s languages to reach com- average) – above the European likely need to change in be most competent across the eight AI larly key when working with AI technology mon goals. In addition, organizations average (3.9). Respondents the not so distant future. enabling capability areas. Do you enable or hinder innovation? that, according to the executives, is often need to learn to move more quickly report to be on the lookout for Once companies are able to selectively not as mature as the digital solutions and nimbly in this space - whether to new and potentially disruptive 3. Cloud solutions can be One factor in working with emerging source new solutions from the outside deployed for other purposes. complete an acquisition of new tech, to technologies while assessing the helpful to engage with and rapidly developing technology to world, the challenge is then how to ensure compliance with IT standards, impact these may have for their multiple datasets across build a stack fit for AI is a well-calibrat- enable it. This can be a case of actively Not all that glitters is gold or simply to pair new tech with legacy company and industry. sources - increasingly a ed ‘radar’ by which large companies encouraging enablement, or at the Despite the need to explore and navigate systems. This ability is often also about priority to capture value pick up on the trends outside of their very least not hindering it. Many com- a tech sea characterized by uncertainty, a speed, not far from the development from new pockets. own walls. Many companies mention panies treat AI as a crucial piece of a recurring theme when interviewing exec- pace that characterizes the emerging utives is the importance of balancing ex- tech itself. citement with new technology and com- mitment to an innovative mindset, with one foot planted firmly on the ground. There is a risk associated with betting on the Seeing past the hype, remembering the AI can require significant investments up front, so it comes down to identifying the right cases correct technology, for example investing business model, and not wasting finite for the use of AI and spending a lot of time structuring the business cases. in the wrong technology due to having resources on every shiny object is also im- insufficient data to make correct decisions. portant. In other words, remembering as a leader that while experimenting is crucial, — Sparebank 1 SMN not all that glitters is gold. Bank — Aprila Bank Bank

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Companies seem relatively competent within Agile Development How competent is your company within Agile Development? 6. Agile Development Avg. Score

4.2 3.8

48% 37% 30% Importance 16% 28% 10% 14% 10% 6% An experimental approach in which collaborative, 3.7 3.2 cross-functional teams work in short, iterative project cycles to 0% Competency effectively progress AI solutions 1 2 3 4 5

Not competent Moderately competent Highly competent Linear approach without iteration Applied agile principles and method- Agile development fully deployed, with loops, and limited continuous plan- ologies, but only limited use outside people empowered to make quick deci- ning, testing, integration and feedback software development functions sions together across functions Considering that many AI technologies use-cases. Of the most advanced com- are still in their infancies, working with panies, 80% deploy AI into the organi- 15 European markets Norway Note: Remaining percent are ‘Don’t know’ responses them is far from plug and play. To over- zation via top down only or a via hybrid come this, many of the companies that of top down and bottom up. are successfully working with AI tend to take an agile, iterative approach to It varies whether these central units projects. Using this approach, these take a leading role in pushing the Agile development new to many Agile Development, highest companies greatly increase their ability agenda, or instead focus on gather- business departments competency in Norway What to learn to explore AI potential due to a dras- ing knowledge and experience from Most companies fully understand the Norway is the country among from AI leaders: tically reduced project cycle time and already existing efforts that are decen- need for agile development, but less those surveyed with the highest dynamic risk reduction. Short project tralized in the organization. reckon that they have the necessary reported level of competency in 1. Agile development is cycles result in project teams receiv- capabilities for it. Working in an agile Agile Development (3.7 average). effective in engaging ing constant feedback on what works Agility provides the opportunity for manner is very different from what Additionally, Agile Development people across functions, and what does not, to continuously informed changes of direction most organizations are used to. While has the highest level of compe- fostering collaboration, steer the direction of the project. This the department running an AI project Taking an iterative approach can also tency among the eight capabil- and bridging tech and creates a process centered on learning might be accustomed to following an help mitigate risks. Frequent feedback ities for Norwegian companies. business. and experimentation, helping to build loops allow the project team to better agile approach, the vast majority of In terms of importance, the internal knowledge and capabilities. identify, understand, and correct unde- project teams consist of people from capability rates between moder- 2. Iterative processes sired outcomes before the AI applica- other parts of the business. ately and highly important (4.2 promotes quick internal Most advanced companies deploy tion is put into production, potentially average) – above the European learning due to their top down or via a hybrid model doing harm. This flexibility does not Several IT and AI departments indi- average (3.8). Many of the com- frequent feedback loops. With an average competence level only apply to risks, as agile projects can cate that this collaboration can be panies in Norway talked about AI of 3.2, Agile Development is an area generally use continuing knowledge difficult, but still see it as pivotal to pilot projects being introduced 3. Fast experimentation with where companies are self-reported and experience to make informed drive value from the projects. Getting at least in some areas of their pilot projects and use- to be reasonably skilled. Quickly es- changes of direction and avoid the the business accustomed to working organization, which can explain case testing can quickly tablishing proof of concept is key to “black box” syndrome. in an agile manner is not easy, as it the higher competency level in show how to create value organizational buy-in, and many com- requires acceptance of new ways of Norway. through AI. governing and evaluating projects. It is important to work with panies report that an agile, iterative ap- Contrary to agile projects, ‘big bang’ proach helps them build evidence and projects are more destined to fail as AI in an agile way and start proof in a fraction of the time it takes they skip the learning process, and lack The outcome of agile projects is typ- with small steps, otherwise it for a more traditional project, the important feedback loop pivotal ically less predictable than for tradi- will drown in long and costly to developing good AI solutions. The tional projects, and for stakeholders to fully embrace an agile approach, projects. Our focus is on This has great significance, as they world of AI is simply too complex for We totally believe in an iterative, agile process where find that tangible proof of concept humans to foresee potential issues, and they have to accept this randomness proving that it creates value you have POCs, then train the bot enough, release it and instrumental in achieving buy-in and therefore an agile approach is better. and recognize the value of learning. program the next flows, instead of doing a big bang via proof of concept. understanding in the wider organization. Efforts to develop proof via agile solution that will probably fail as these things are not simply — Egmont development processes are often or- plug and play. Start small and then grow it. Media company chestrated by a central unit that collab- orates with business units to identify — Com Hem Telecommunications company 66 67 Learn from the Leaders Learn from the Leaders

Companies generally consider themselves moderately to highly competent forging External Alliances How competent is your company within building External Alliances? 7. External Alliances Avg. Score

4.1 3.7

48% 37% Importance Entering into partnerships and alliances with academia, solution 19% 28% 14% 18% 14% 12% providers, and AI specialists to access technical capabilities, best 5% 3% 3.5 3.2 practices and talent Competency 1 2 3 4 5

Not competent Moderately competent Highly competent External relations mostly based on Multiple strategic alliances, often be- Significant alliances with AI partners traditional sourcing of external ven- tween equal partners working collab- in open eco-systems enabling access dors providing specific AI services oratively to mutually benefit from AI to external assets and (big) data AI leaders are increasingly opening up To address one of the biggest prerequi- to create collaborative alliances with sites of working with AI, access to large external partners, enabling them to amounts of data, companies state that 15 European markets Norway Note: Remaining percent are ‘Don’t know’ responses tap into a significantly larger pool of they are increasingly looking to entering capabilities and talent, and to reduce into data partnerships where they either the time it takes to develop or deploy buy or exchange data with other parties. tioned as very practical issue with AI in working solutions. This is a way for companies to get hold general. This led some companies to of data that they are unable to capture prefer internal teams and individuals in What to learn from This trend seems to be the new modus themselves, or simply a way of quickly order to ensure that despite poor doc- operandi, unfolding across markets and increasing the size of their datasets. umentation, the knowledge about the AI leaders: sectors. It is also the capability with the code at least stays inhouse. 1. Make sure to have internal people in the receiving end before smallest gap between perceived impor- Others report that they look to pre-de- widely engaging with external partners. tance and competence level among the veloped, out-the-box algorithms, in participating companies. order to increase the speed of bringing External Alliances among 2. Academic partnerships are an increasingly sought after way quality solutions in to product. Norwegian companies highest to access innovative eco-systems, gain new insights, and Technology, data, and service competencies explore emerging AI opportunities. delivery partnerships Academia playing a more noticeable Companies in Norway consider Development of AI and delivery of re- role in collaborating with companies themselves to be on average 3. Partnerships can pose a challenge to many business lated projects are most often done with It is becoming increasingly common for above moderately competent in processes; consider involving key functions like legal early, a mix of internal and external stake- companies to enter into partnerships External Alliances (3.5). This capa- to ensure a productive partnership structure and effective holders. The rationale is multifaceted – with universities in order to position bility is among the third highest collaboration model. some companies are simply struggling themselves within AI and get access to ranking in terms of competency to obtain the needed talent, whereas crucial knowledge. in Norway. External Alliances is others see a partnership approach to be considered an important capabil- a faster, more flexible solution. These Companies also see this as a way of es- ity to succeed in AI by Norwegian external alliances typically come in two tablishing a pipeline of AI talent already companies (4.1 average) – above forms: being focused on technology familiar with their business and the prob- the European ratings. The results and technical AI know-how, or focused lems they face. Some of the more ambi- suggest that Norwegian compa- on strategy and business development. tious companies have a strategy of posi- nies see the value of collaborating tioning themselves within AI, comprised with outside experts and have We need to develop partnerships and you need knowledge of active conference participation and already gained some experience There is a lack of available talent, so not to become a good partner, both within IT and business multiple university partnerships in which from previous partnerships. Yet, every bank can have internal teams of data development. It will be essential to build up insight and they actively participate in developing they are in the early phases of courses and programs. deploying AI and are still trying scientists. As such there should be a good knowledge internally to become a good partner. We need to to figure out what to develop market for sector-specific off-the-shelf work widely with partners on multiple levels - both specialists Documentation of code is proving a internally and when to collaborate solutions. and larger partners who work more broadly. challenge - also to externals with third parties. The lack of code documentation for — KLP Banken self-learning algorithms was often men- — Tine Bank Dairy company

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Companies consider themselves least capable within Emotional Intelligence How competent is your company within applying Emotional Intelligence? 8. Emotional Intelligence Avg. Score

3.4 3.3

38% Importance 29% 29% 34% Applying behavioral science to understand and mimic human be- 14% 14% 15% 5% 5% havior, address needs, improve human-machine interactions, and 2% 2.5 2.4 Competency ultimately create more human near applications 1 2 3 4 5

Not competent Moderately competent Highly competent Limited experience with adopting Appreciation of behavioral science Human-centered approach, ethical mind- behavioral science into the AI devel- and cultivation of uniquely human set and values that engender trust and opment process, at best outsourced skills to increase value from AI human ingenuity beyond business effects

AI has for long focused on cognitive they are still at a relatively low maturity capabilities and skills within math- stage where more immediate require- 15 European markets Norway Note: Remaining percent are ‘Don’t know’ responses ematics, statistics and logical rea- ments such as Advanced Analytics, soning. Adding human emotion and Data Management and AI Leadership intelligence, these capabilities move are more relevant and prevalent. to a new, more complex level: the un- irony, anger, and frustration. This will Emotional Intelligence: even It is given that we will derstanding of human behavior, and However, when taking a deeper look obviously become more valuable as it the lowest importance rating is What to learn see the rise of AI. All the the ability to interact accordingly with at the companies that have assessed is increasingly applied in customer- above moderately important facing solutions with the ability to from AI leaders: big tech companies are technology. themselves to be ‘Advanced’ in terms Norwegian companies rate Emo - of general AI maturity - meaning that learn and improve. tional Intelligence above moder spending money on AI - 1. The most advanced Changing the way people interact AI is actively contributing to many ately important for their success companies are putting capabilities. They have Human centrism requires strong with technology processes and enabling quite advanced with AI (3.4 average), slightly emotional intelligence leadership explicit visions to master One of the limits of traditional AI has tasks in the company - it is interesting above the European average to use within their AI human thinking and been the inability to understand hu- to see that they perceive the Emotional While emotional intelligence holds (3.3). Even so, in the respective applications, despite its Intelligence capability as more im- great potential that could lead to early Norwegian and European sam behavior. That may or may man traits such as emotional state, for - relatively infant stage. instance exhibited in writing, physical portant with a score that is noticeable adopters gaining a competitive advan- ples, these ratings are lowest of not happen in the next condition, or tone of voice. With AI’s higher than the average score for all tage, long-term success is dependent the eight capabilities. Similarly, 2. Companies must develop five years but certainly cognitive intelligence capacities within companies. on not only technological develop- the competency with Emotional their behavioral science within a certain time frame. reach, machines are increasingly able ment, but also leadership. Intelligence is also the lowest of capabilities to mimick Many advanced companies perceive Norwegian and European averag human behavior and When you combine it with to sense, recognize, and decode hu- - man traits. this to be either ‘very’ or ‘highly’ Leaders must drive the transformation es (2.5 vs. 2.4.), which is for both translate it to technology. computing power, it will be important. Notably, these companies that will make humans comfortable below moderately competent. 3. Many have virtual inevitable. This holds the potential to fundamen- come from five different markets and a with intelligent technology, as a pre- The ability to adopt behavioral assistants, chat bots, and tally change the way people interact wide variety of industries, including Life requisite for harvesting its potential science in the tech development NLP a powerful way to with machines, making technology ca- Sciences, Financial Services, TMT, CPR, benefits. What the most advanced process is in its infant stages for — Skandia get started with building pable of handling more complex tasks and Services & Hospitality. companies have shown is that this most local companies, which for Pensions and insurance emotional intelligence and ultimately augmenting humans to transformation must augment human the most part are still developing company into AI solutions. an extent previously unachievable. Value in customer-facing ingenuity to become truly effective. their AI strategies. Notably, Emo - applications tional Intelligence is the capability Emotional Intelligence in its infancy The need for behavioral science to with the lowest share of Norwe - gian companies that report to be Except for advanced companies, survey understand human needs is expected moderately competent or above results indicate that companies view to increase with the integration of AI in (48%). the adoption of emotional intelligence smart devices, and in customer facing in AI processes as the least important applications such as chat bots, roboad- capability, and the one where they visories, customer inquiries processing, have the lowest competency. When etc. The most advanced companies’ AI asked to address why this is, companies technologies are beginning to decode across sectors and markets note that human emotions from text, such as

70 71 Artificial Intelligence in Europe ( Case Study ) Successful Value Creation

EQT

EQT’s AI journey started in 2015, with with addressing commercial challenges looking at a small subset of companies This is going to go insanely fast, so it is just fundamental questions about how to for a few companies, with others natu- that are introduced to EQT or found via drive long-term value from digital for rally following. research, to screening a global startup its investors. To address this, manage- ecosystem. It tracks multiple struc- a matter of hanging in there and identifying ment brought in talent from leading EQT believes that AI is a disruptive tured and unstructured data points, tech companies to drive change in- technology, and it wanted to leverage public and proprietary, for millions of which trains to follow. ternally and across portfolio companies, including web companies. traffic, media mentions, and Internal change was push- AI algorithms are deployed to establish financials. based and transformative. Focus was put on the Cloud, whether successful companies have a AI algorithms are deployed to — Tine Dairy company new tools, and decommis- unique ‘digital signature’ that can be establish whether successful sioning old ones. From a spotted early on, so that EQT can ap- companies have a unique fully outsourced model, EQT proach them proactively. ‘digital signature’ that can insourced key functions and be spotted early on, so that now owns the source code the fund can approach them itself. Experience, results, and proactively. The platform is overcoming bottlenecks helped build it. It started building a tool to rede- being used in venture capital and pri- its position as a trusted advisor. This fine the core of it business: investing. vate equity research, with predictive was used to drive organic pull-based Motherbrain, a platform that has been analytics under development. change in portfolio companies, starting live since 2016, allows switching from

What next? It’s about having the right mindset. It’s not EQT is a Swedish private equity group of 27 funds, with over €50 EQT is addressing data-driven challenges in investment decision that tomorrow everything will be different. billion in capital raised. Since its establishment in 1994, EQT has flow. Currently, data gathering and prediction are the most time invested in 210 companies, and exited over 100 investments. The intensive, however, there is a shift in commoditizing these. group has several investment strategies within private capital, Motherbrain’s AI algorithms are used to build predictive analytics It’s all about building up capabilities and real assets and credit. EQT has 540 employees in 14 countries with continual learning loops to enable dynamic, better quality across Europe, Asia and North America. Portfolio companies have predictions, allowing resources to refocus on judgement. It speeding up constantly. The power of combined total sales of over €19 billion and a combined total of is a lengthy process, as the entire investment flow has to be over 110,000 employees. considered, including identifying companies and comparing them technology in general is overestimated in the to successful companies that EQT looked at or invested in. short term and underestimated for the long- term and I think that’s the case with AI too. There are so many things you have to fix before To be successful with AI, you need data and even starting to think about AI. The way we talent. The missing component is the mental — VodafoneZiggo Telecommunications company see digital and companies that are successful state when you are prepared to try it out, in this space is that they understand their when the moment comes where you choose customers and their market in a very granular between recommendations based on old way. methods and AI. If you choose AI, that’s when change truly happens.

72 73 What’s Next for You? What’s Next for You?

4. Build a data strategy and technology stack purposefully fit-for-AI AI Fast Forward Training your AI products essentially requires significant data. Useful data. Valid data. Establishing a solid data strategy and practice in your organization to proficiently acquire data, identify data, clean data, measure data, and manage data will ultimately make your organization flourish with AI. Build How to get started and take AI to the next level? your AI resources around data engineers who organize the data, data scientists that investigates the data, software engineers who develop algorithms and implement applications. Make sure that your structure and governance harness the power of data, and that your technology stack across products, solutions, and applications nimbly enables your AI priorities. When doing so, remember that your business model is likely to change.

Learn more about how to build a flexible platform and portfolio of AI tools and next generation smart applications where your data lives - whether in the intelligent cloud or on-premise

1. Choose a step-by-step approach in getting familiar with AI Given the wide scope of AI and variations in use cases, it is key to start out by identifying what prob- 5. Beyond all, engender trust and enable human ingenuity lems to solve and what opportunities to pursue. High level prioritizing between engaging customers, When designed with people at the center, AI can extend companies’ capabilities, free up creative and optimizing operations, empowering employees and/or transforming products and services adds clar- strategic endeavors, and help achieve more. Humans are the real heroes of AI – design experiences ity, is helpful to structure the discussion on a strategic level, and ensures a step-change approach to that augment and unlock human potential. Opt for a “people first, technology second” approach. This taking the company to the next AI level. Identify the problems you aim for AI to solve, prioritize the entails designing AI for where and how people work, play and live, bridging emotional and cognitive value with business owners, and acknowledge the capability gaps to get there. You need to get on the intelligence, tailoring experiences to how people use technology, respecting differences, and cele- AI train, but do not jump on the AI wagon blindly. AI should serve your business plan, not vice versa. brating the diversity of how people engage, Thereby putting people first, reflects human values and promotes trust in AI solutions. Read more in the blog on Linkedin about “AI for businesses: Not if, but when and how” by Michel van der Bel, Microsoft President, EMEA Learn more in the Microsoft Trust Center and the book ‘The Future Computed’ by Brad Smith and Harry Shum from Microsoft on artificial intelligence and its role in society

2. Display executive leadership and approach AI from a position of strength Leadership comes from the top, also in the case of AI. For this to happen, executives must understand AI essentials and strategic perspectives, and they must communicate a clear AI ambition to the organiza- Designing for people tion. AI leaders must actively sponsor and mobilize AI adoption on all levels, from the Board and Exec- utive levels, through Management and the operational employees. Staying ahead in the accelerating AI At Microsoft we believe that, when designed with people at the center, AI can extend your capabilities, free you up race requires executives to make nimble, informed decisions about where and how to employ AI in their for more creative and strategic endeavors, and help you or your organization achieve more. business. When doing so, look to strongholds before bringing in the AI ‘twist’. Amplifying existing company strengths is an excellent way to catalyze motivation and internal support. The following principles guide the way we design and develop our products: Read more customer stories to see how others are using AI to transform their business, and learn from Microsoft Research on how AI is solving the most pressing challenges • Humans are the heroes. People first, technology second. Design experiences that augment and unlock human potential. • Know the context. Context defines meaning. Design for where and how people work, play, and live. • Balance EQ and IQ. Design experiences that bridge emotional and cognitive intelligence. • Evolve over time. Design for adaptation. Tailor experiences for how people use technology. • Honor societal values. Design to respect differences and celebrate a diversity of experiences. 3. Hire new skills ahead of the curve – or focus relentlessly on training existing talent Innovation is what creates tomorrow. A key challenge for putting AI to productive use and accelerate intended outcomes is the war for skills and talent. This not only relates to data scientists and software engineers, but also to skill sets and expe- Learn about our AI platform to innovate and accelerate with powerful tools and services that bring AI to every rience within human and behavioral science. Opting for a follower strategy and being late to the game developer. can prove risky, as talent seeks to go where talent is already. If aggressive poaching for insourcing talent Explore Intelligent applications where you can experience the intelligence built into Microsoft products and is difficult to embrace, then work bottom-up by training the engineers you already have on the new AI services you use every day. paradigm and collaboratively ride on the backs of the others. Regardless of strategy, focusing relentlessly on building required skills and talent is key to staying ahead and progressing along the learning curve. Learn about AI for business. Use AI to drive digital transformation with accelerators, solutions, and practices that empower your organization. Learn more in the Microsoft AI School about the open-source cognitive toolkit (previously known as CNTK) and how to help train deep learning algorithms

74 75 What’s Next for You?

Who to Contact from Microsoft

The team in Norway that can empower your company to achieve more with AI

Asu Deniz Terje Aas Christopher Frenning AI Evangelist, Data & AI Lead, Cloud and AI Lead, Microsoft Norway Microsoft Norway Microsoft Norway

[email protected] [email protected] [email protected]

Asu helps customers achieve top Terje leads Microsoft’s team of Data Christopher leads Microsoft Nor- notch innovation with the use of and AI solution specialists in Norway, way’s intelligent cloud business as Data Visualization, Advanced Ana- driving digital transformation in the the strategic leader and ambassa- lytics, Chat Bots, Machine Learning largest companies in Norway. By dor for the Azure business in the and Cognitive Services to achieve combining industry knowledge with Norwegian market. Before that, digital transformation. Enables technological capabilities, organi- two decades of experience scaling a customers in Healthcare, Utilities, zations are supported to create and tech startup to an international in- Transportation, Media and Retail capture value from their digital busi- dustry leader, architecting software verticals to unlock their potential ness journey. New business models solutions, leading development using Microsoft Data and Artificial across ecosystems helps Norwegian teams, enterprise software sales, Intelligence stack and Azure. She organizations to innovate, sometimes and international channel manage- advises Norwegian customers on making outsiders into collaborators. ment. Likes solving problems. He is their digital transformation, moving Currently Aas is enrolled in a master’s a technology afficionado that loves over to Azure or on Hybrid Cloud degree at Norwegian University of the speed of innovation possible in to increase operational efficiency, Science and Technology. the cloud. product transformation and/or empower employees.

76 77 What’s Next for You?

Contributors from EY

Team responsible for the Norwegian edition of the study ‘Artificial Intelligence Report: Outlook for 2019 and Beyond’

Thomas Holm Møller Dr. Ellen Czaika Ronny Seehuus Christian Mjaanes

Partner EY | Innovation, Analytics, Digital Partner, EY Partner, Technology Consult- Co-founder EY-Box Deputy Leader, EMEIA ing, EY Norway

[email protected] [email protected] [email protected] [email protected]

EY-Box is focused on digital Ellen holds a PhD in technol- Ronny Seehuus is EY’s Nor- Christian is Nordic lead for strategy, growth ventures, ogy, policy and management dic Data & Analytics leader. EY’s Technology Consulting innovation architecture from MIT. She has masters He has extensive experience practice that works with and tech-led transactions. degrees in applied statistics from management of analyt- emerging technologies to en- Thomas works with leading from the University of Ox- ics, technology, strategy and able new digital possibilities. companies to uncover plau- ford. Ellen advised this study digitization processes from a He has contributed to setting sible futures, launch new on research design, method- variety of sectors. Ronny has up interviews to capture businesses, and rewire their ology, and analysis. worked with consulting in the data about the companies’ core through data and digital business and technology in- use of AI. Christian holds an in the search for new profit Ellen is engaged in the EY terface the last twenty years. M.Sc. in artificial intelligence pools and business mod- EMEIA Center of Excellence Common themes for most of from NTNU and has worked els. He serves on the board on innovation, analytics, and the major projects he has been with numerous technologies for several entrepreneurial digital. She has worked with working on is strategy, analyt- mimicking intelligent human growth-stage businesses. global organizations and ics, information architecture, behaviors. His clients include start-ups, having recently organizational restructuring, both large global companies Thomas is responsible for the served as the head of R&D for and introduction of new tech- and smaller entrepreneurial AI study across 15 markets a precision Ag startup that nology. start-ups. in collaboration with central uses AI to assist farmers. and local EY strategy teams From an industry point-of- and AI specialists. view, Christian has worked mostly in finance and public sector/healthcare.

Based in Copenhagen. Based in Zürich. Based in Oslo Based in Oslo