INDEX

Aarhus 105 Wing project 238, 239 academies 204 X (Experimental Laboratory) 87, accelerator programs 202, 236, 237–9 16, 21 Altman, Sam 18 Accenture 115 Amabile, Teresa 85 Acierno, Kyle 215 Amazon 4, 18, 34, 35, 38, 95, 145, Adams, Douglas 67 147, 161, 198, 199, 201, 202, agile working methods 137, 156, 212, 225 180, 191–2 Lab 126 225 AI first 7, 161 Amazon Studios 37 Airbnb 27, 46 Amazon Web Services (AWS) Airbus 199 35–6 Alexa 37 Amazon Wind Power 36 Alibaba 18 Amidi, Saeed 148, 149 All Nippon Airways (ANA) 4, 231, Apple 4, 27 232–3, 234 iPhone 39, 47 ANA Avatar 232, 233, 235 iPods 39, 116 ANA Holdings 235 Arla Foods 192, 193 Avatar XPRIZECOPYRIGHTED 233 artificial MATERIAL intelligence 18, 112, 130 Alpha Go 18 Astro Teller 237 Alphabet 38, 236 augmented reality 129–30, 200, 221 Founders’ Letter 17 augmented reality glasses 199, 237 Get Weird Award 202 augmenting innovation 7–8, 10, Loon project 238 129–30, 131–61, 212, 213

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‘back to the present’ method 214–17 Cola 60 Baidu 4, 145 confirmation bias 25 Baird, John Logie 210 conscientiousness 72 BE better motivation 42–8, 52 coordination problem 78 Be My Eyes 45–6 corporate entrepreneurship 153 Bell, Alexander Graham 210 creative skills, toolsfor developing Beyond Meat 241 180 Bezos, Jeff 2, 215 CRISPR-Cas9 20, 110 biases 23–4, 26 Cruise, Tom 219 BIG (Business Innovation Growth) Cruise Automation 240 195 cultural hacks 163–214 Bing, Asger Trier 111 ‘culture of camaraderie’ 30 Bitcoin 108 Blackberry 241 Daimler 97 blockchains 130, 228 DARE2 119 brain DeepMind 236 biases 24–5 Deloitte 115 dopamine receptors, DevOps software development type–2 (D2R) 70 method 129 Branson, Richard 215, 231 Dietz, Doug 48, 49 Brin, Sergey 17 Digifunds194 ‘bromance cultures’ in startups 188 Digital Factories 86, 135–8 Bush, George W. 112 digital industrial transformation Business Innovation Growth (BIG) 139 195 DISC 72 ByteDance 1 DNB 187 DO better motivation 43, 46–7 CA Technologies 153, 154, 156 dopamine 50 Calico 236 dot-com bubble (2001) 116, 118 Caterpillar 225, 230 drones 110, 112, 238 CBS 94 Dropbox 203 centres of excellence 144–8 certainty effect 71 Eagleman, David 174 chatbots 147 e-books 34 Cisco 193 Echo 36–7 Clarke, Arthur C. economic climate 98, 116–22 2001: A Space Odyssey 210 Edison, Thomas 138 Fountains of Paradise, The 210 Edmondson, Amy 181

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Einstein, Albert 165 Ford Motors 96, 210 electric cars 62–3, 96, 239 Ford Smart Mobility 240 Eliasson, Olafur: Little Sun project forecasting dashboards 193 241 Fortune Magazine 217 Emerson 225, 230 Friis-Olivarius, Morten 178–9 employee capabilities 74 Fukabori, Akira 232 e-readers 34, 35 Fundamental 4s 42–8 Ericsson 115, 156 BE better motivation 42–8, 52 Silicon Valley Innovation DO better motivation Center 157 43, 46–7, 52 Erixon and Weigel: Innovation FEEL better motivation 43, Illusion, The 110 48, 49, 52 Eroom’s law 97 LOOK better motivation 43, 47, Experience Economy 127 49–51, 52 experience expeditions 16 external accelerators 148–51 Game of Thrones 94 extroversion 72 game theory 78 EY 185 Gartenberg, Claudine 30 Gartner 129 Facebook 1, 18, 43, 50, 51, 89, 161, GE 4, 36, 102, 138, 139, 141, 142–4 221 FastWorks 142 failure rate of startups 3 GE Beliefs 142 Fast Company 217 GE Digital 140 FastWorks method 142 GE Healthcare 48, 49 FEEL better motivation 43, 48, 49, Geek Squad 127 52 gene editing technologies 112 Feynman, Richard P. 123 General Electric Company 138 financial crisis see also GE (2001) 117 Gevalia 60 (2008) 117, 118 gig economy companies 107 fintech companies 55, 92, 109 Gillette 6 first movers 113 Dollar Shave Club 128 ‘fish bowls’ method 205 Gilmore 127 500 Startups 57, 150 Gittell, J. Hoffer 82 Florida, Richard 116 GM (General Motors) 240 Folgers 59, 60, 62 Go 18, 236 Food and Drug Administration Gompers, Paul 185–6 (FDA) 97 Goodhart’s law 88

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Gooding Jr., Cuba 219 Innovate Everywhere Challenge 193 Google 1, 4, 18, 27, 38, 56, 89, 90, innovation culture 15–21 116, 148, 160, 161, 180–3, 197 agents that strengthen 177–88 Project Aristotle 180–1, 182 processes that strengthen 189–200 see also Alphabet scenes that strengthen 170–6 Google DeepMind 236 innovation labs 214 Google Glass 202, 237 ‘innovation on the edges’ 213 Google Maps 103 innovation theatre 15–21 Google Search Engine 115 insurtech startup 55 Google Ventures (GV) 236 Intel 17, 83 Grail 37 internal accelerators 152–60 ‘Great Places to Work’ survey 30 International Space Station 218 Grundfos 4, 86, 115, 116, 133, 135, Internet of Things 133, 139 138, 144 investors and shareholders 80, digital factory 174 94–8 management 132 Ismail, Salim: Exponential Task Force 133–4 Organizations 27, 190 iSpace 215, 216 hackathons 16, 21, 115, 203 Istrate, Ioan 232 halo effect 25 iWatches 38 Hastings, Reed 94–5 HBO 94 Japan Aerospace Exploration healthcare startup 55 Agency (JAXA) 211, 235 Hewlett-Packard 116 Jerry Maguire (movie) 219 hindsight bias 25 Jobs, Steve 24 horizontal units 138–44 Johnson & Johnson 86 HubSpot 202–3 human brain 23 Kærgården 108 Kahnemann, Daniel 23, 26 IBM 116, 203–4, 227, 228, 230 Kajitani, Kevin 232 IIH Nordic 190–1 Kantrowitz, Arthur 210 IKEA 241 Karinthy, Frigyes: Chains 58 Space10 201, 225 Keurig 60 Impossible Foods 18–19, 21 Key Performance Indicators (KPIs) individual immune systems 8, 80, 81–9, 93, 98, 121, 137 69–74 Kickstarter 46 ING 299 Kindle 34, 35, 37, 225

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kindness 72 Made in Space 222, 223 King 1 Maersk 4, 56, 97, 227–30 Klarna 1 Mærsk Growth 174, 205, 226, Kloor, Dr Harry 232 229, 230 Knott, Annie Marie Knott: How massive transformative purpose Innovation Really Works 141 (MTP) 27, 28, 29, 31 Knudsen, Marianne Kjeldgaard Mattox, Matt 140 132, 134 Maxwell House 60 KPMG 196–7 McKinsey 117 ‘10,000 Stories-Challenge, MD Foods 108 The’ 197 measurement 84–5 Kursawe, Goetz 157 Microsoft 18, 87, 203, 241 Microsoft HoloLens 199 Law of Jante 49–50 Milgram, Stanley 58, 59 lean startup method 142 Millennials 28 legacy customers and suppliers 98, mini-accelerators 194 113–16 minimum viable product (MVP) legacy organizations 2 93, 142, 195 legacy structures, processes and mobile technology 130 tools 80, 89–93 Mobileye 241 legislation 98, 102–13 money as motivational factor 81 lifelong learning 73 Moore, Geoffrey 116 Lincoln, Abraham 13 Crossing the Chasm 113 LinkedIn 205, 241 Moore, Gordon 16–17 Lockheed Martin 225 Moore’s law 16, 18, 97 P-8+ Shooting Star plane’s 225 Mortensen, Anders 114, 182 Skunk Works 225 MR scanners 48, 49 LOOK better motivation 43, 47, Muse 61, 62, 63 49–51, 52 Musk, Elon 2, 18, 24, 215 Loon project 238 mutating innovation 209–42 Lowe’s 4, 229, 230 Myers-Briggs 72 Innovation Labs 217–20, 222, 223, 224, 229 Nadella, Satya 87, 203, 241 Lyft 107 narratives 26, 31, 220 Nel, Kyle 217, 218, 221, 222 Maagøe, Jens 196 Nescafe 60 machine-learning algorithms 193 Netflix 94–5

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Neurons Inc. 35 Payg 111 neuroticism (degree of worry) 72 PayPal 19, 148 Ng, Andrew 145 Pentium II processor 17 NNIT 195–6 persistence159 Norwegian Equinor (formerly personality profile 74 Statoil) 97 psychological barriers 74 ‘not-invented-here’ problem 86 Pine, Joe 119, 127 Pinterest 46 Obayashi Corporation 211 pizza rule for team composition 201 Objectives and Key Results (OKRs) plant-based burgers 18–19, 21 83–4 Plug and Play 57, 148, 149, 150, 151 Oculus 19–20 Pokémon Go 129–30, 221 Oculus Rift 221 pomodoro technique, 191 office environment 173 PostNord 109 open 171–6 PowerPoints 198, 199 omission bias 25 pramipexole 71 Onarheim, Balder 178–9 Prat, Andrea 30 Open AI 18 price-performance doubling time open office environment 171–6 17–18 openness (towards the conventional prisoner’s dilemma 78, 79 vs. new) 72 Procter & Gamble 56, 128 Opower 51 prototyping 115 optimism bias 24 proximity bias 24–5 optimizing innovation 6–7, 10, psychological safety 181 125–30, 212, 213 purpose, sense of, employee 29–30 Oracle 51 ‘purpose-camaraderie organizational immune system organizations’ 30 8, 77–98 ‘purpose-clarity organizations’ 30 Ørsted (formerly DONG Energy) PwC 115 97, 201 outcome bias 25 radical open innovation 231–5 Page, Larry 2 Ramsøy, Thomas and Nathan Furr: Palantir 19 Leading Transformation 218 Parkinson’s disease 71 RAND 78 Pascal, Blaise 198 Red Bull 60, 63 Pawlak, Laila 41, 119, 167, 168 reflexive criticism 86

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repetition suppression 174 ‘small world’ problem 58, 59 reverse engineering 217 smart contracts 228 reward systems 80, 81–9, 98, 121 Smart startups 55 Richter, MacKenzie 232 social recognition 50 Ries, Eric: Lean Startup, The 142 societal immune systems 8, 9, 98, Ringnes, Isabelle 183, 186, 187 101–22 risk profiles 67, 72 Softbank 18, 95 Rogers, Everett 113 solar energy111 Sopranos, The 94 Salesforce 89 South by South West 153 1-1-1 model 204 spillover effect 29 SAP 115, 116 Spotify 1, 95 Schmidt, Eric 81 Stage Gate model 195 Schumpeter 117, 119 Star Trek 233 science fiction strategies 217–24 Starbucks 61, 63 scientific management theories 85 Startupbootcamp 151 self-deception 24 status quo bias 25, 31 self-driving cars 38, 39, 62–3, 64, Stenmann, Henrik 190 96, 237, 239, 240 Stephens, Robert 127 self-driving taxis 19 Stilling, Sune 226–7 Serafeim, George 30 story, company 26, 27, 31 Shark Tank 157 Sun Microsystems 173 #ShesGotThis 183 ‘sunk-cost fallacy’ 26, 87 Shizuoka University 211 Suto, Jun 231 Sidewalk Labs 236 SWAT team 115 Silicon Valley 1, 15, 21, 114, 148, 184, 240 Talya, Akanksha Manik 140 Singularity University 199 Tata: ‘Dare to Try’ prize 202 Global Startup Program 57, 151 Taylor, Frederick 85 SingularityU Nordic 167 TDC 147 ‘six degrees of competition’ 58 TechStars 57, 150 ‘six degrees of separation’ 59 Telia 56 skunk works 214 Tencent 18 Slack 176 TENK (Technology Network for small and medium-sized enterprises Women) 183 (SMEs) 116, 118 Tesla 2, 27–8, 29, 44–5, 95, 96, 108

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Thiel, Peter 19 virtual reality glasses 19–20, Think About conference 127 234 thumb rules (heuristics) 23 vision 28 Thync 61, 62, 63 Vision Fund 95 TomTom 240 Toyota 97 waterfall method 192, 193 Toyota Motor Corp., 240 Watt, George 153 Toyota Research Institute-Advanced Waymo 19, 237 Development (TRIAD) Waze 103 239–40 Western Union 210 TradeLens 228, 229 Westworld 94 Trojan horse 136, 140, 146 WeWork 95 Trump, Donald 90 women CEOs 184 Tucker, Albert W. 78 working culture, positive Tversky, Amos 23, 26 30–1 Twitter 19 workshops 115 Tyson 241 World Trade Center attack 118 Uber 102–7, 109 Wright brothers’ plane 210 unicorn wave 1–2 Unilever 128, 194 X divisions 224–31 XPRIZE 231, 232–3, 234, 235 Verily 236 Virgin Galactic f 231 Y Combinator 18, 57, 151

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