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The Cxo Guide to Accelerating Growth at Scale with Modern AI

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The Cxo Guide to Accelerating Growth at Scale with Modern AI The CxO guide to accelerating growth at scale with modern AI Drive breakthrough returns on AI investments in the platform era 1 Abstract The opportunities and challenges of AI Disrupters in every industry have set new standards in customer Businesses are using AI to predict business outcomes, streamline experience, speed-to-market and innovation. The use of artificial operations, improve efficiency, protect against cyberthreats and fraud, intelligence (AI) has reached an inflection point where leading and discover new market opportunities. These predictions can help organizations are demonstrating groundbreaking results, reshaping leaders stay ahead of competitors and market fluctuations. the marketplace and setting themselves apart in their industries. Additionally, corporate officials face pressure to meet shareholder At the heart of AI are strategic enablers: automation, prediction expectations while making significant changes to processes, and optimization. Your organization’s ability to automate mundane technologies and organizations when implementing AI. tasks, predict outcomes and optimize your resources is vital to your growth. Indeed, high-growth companies are meeting Furthermore, there is board-level scrutiny of data and governance the business imperatives—creating superior customer experiences, related to AI models. The IBM Institute of Business Value released speeding product and service delivery, streamlining operations a C-suite Study¹ titled “Build Your Trust Advantage—Leadership and capitalizing on the ecosystem—as well as meeting compliance in the era of data and AI everywhere” that revealed that customers’ and risk management requirements at scale. trust in brand names and institutions is quickly eroding. Customers demand transparency of data associated with products and services, This paper explores: and they want assurances that any personal data will be kept safe and used fairly. – Characteristics of AI investments by high-growth leaders – Why you need a data and AI platform – What you should look for in a data and AI platform: automation, prediction and optimization – Benefits of building and scaling AI with trust and transparency By reading this paper, you will gain insights on how industry leaders are taking advantage of AI, the importance of the platform approach, and the benefits that a data and AI platform offers. Additionally, this guide highlights what actions you can take and explores the strategies that help your business succeed. “The governance of data and how we govern AI models—how they’re validated and used— are now board-level issues. So, too, is the ethical use of data.”¹ COO Banking Netherlands IBM Watson 2 High-growth leaders are winning with AI According to Forrester Research, high-growth leaders invest heavily Talent: Skills and staffing are vital to AI success. As shown in PwC’s in AI. More than 50 percent of respondents to a Forrester survey 2019 AI Predictions, you need an AI-ready workforce. This requires expect to see a greater than five times return on their investments continuous learning initiatives for reskilling and upskilling your talent. in AI.² To put this in perspective, consider that high-growth leaders Further, as job descriptions change, you must rethink an organizational who have invested $10 million can expect an ROI of $60 million. structure to help train your evolving workforce.⁴ Leaders also invest twice the data and analytics budget and 2.5 times more in AI and machine learning (ML) platforms compared Data: Data is the lifeblood of model performance. AI models are built to low-growth firms. on data, and having the right production data determines how well a model performs. This setup means that a platform needs to enable Forrester Research also found that firms investing in data scientists access to a continuous flow of data. It should also detect and mitigate with hard-core skills—such as the expertise to build predictive, ML, the inevitable drift in accuracy as your models encounter production deep learning, natural language processing (NLP), computer vision data different from data on which they were trained. And it should and other types of models—are growing faster than firms not making provide an auditable record of models and data used throughout these investments. the AI lifecycle. For certain AI applications, such as HR, sales lead scoring, Trust: Your team needs to be able to demonstrate how bias can be or expense management including fraud detection, organizations detected and mitigated in your AI models and to explain individual prefer to purchase packaged AI solutions, according to Forrester. outcomes. Your platform should also be able to track outcomes against business KPIs. And it should include embedded trust and explainability, – 46 percent purchase packaged solutions embedded with AI to help you scale and sustain your AI-related efforts. for certain applications – 20 percent develop their AI in house Integrate AI predictions and optimizations into applications rapidly In practice, you need a data and AI platform that make it easy A data and AI platform should also support integration of insights for you to buy, build, or both based on your business needs. from AI models into your modern apps. Most enterprises have already invested heavily in application development. A flexible, open data Requirements to look for when evaluating and AI platform can serve as a foundation for your application a data and AI platform development and business teams to build model operations (ModelOps). ModelOps can work seamlessly with DevOps to help Data science, a discipline that helps a business recognize meaningful increase the success of your modern applications with AI. patterns, predict outcomes and simplify decisions, is a key accelerator in the use of AI. Using new insights, patterns and other valuable Automate AI lifecycle management discoveries from data can empower your business to anticipate what Data science and AI investments have traditionally focused comes next and simplify decisions. You can optimize actions armed on using predictive analytics and ML to answer business questions with the right offers and approaches, seeking to achieve the best or automate a small set of processes. However, most leaders are possible outcomes based on chosen scenarios. This potential is why now looking to broaden the use of AI. This perspective means that you need a platform enabling you to put your ideas into action and take your platform should be designed to help operationalize and automate advantage of data science progressively. the management of models and tools across your business from end to end. So, what is a platform? A platform is “an infrastructure that promotes value-creating interactions among participants. The platform provides Automation helps your team refocus on high-value activities that an open environment for these interactions and sets governance take advantage of your core differentiations. Look for a platform conditions for them.”³ To succeed in modernizing your business with that can automate steps such as: AI, selecting the right platform is a strategic imperative. – Data preparation Build a foundation for tackling talent, data and trust – Feature engineering Your goal is to turn the process of prediction and optimization into – Selection of machine learning algorithms iterative innovation and intelligent workflows. To fulfill this promise – Hyperparameter optimization to choose and let AI thrive in your operations, your workforce needs simple the best possible ML model onboarding to a data and AI platform that can help automate development. What’s more, you need to find ways to leverage your This series of steps should be guided by an AI system that drives existing technology investments with your new platform instead towards the most promising step at each stage of the process. of adding one-off tools. This is using AI to build AI, and an example is AutoAI, a capability powered by IBM Research™.⁵ IBM Watson 3 Optimize decisions based on predictive outcomes Provide an ecosystem of open source and best-in-class Some high-growth leaders are skilled at tackling multiple use cases tools in any cloud and improving decision-making with AI. You can combine AI insights Your team is everywhere, and so is data. To take advantage of the and AI orchestration with human talent. To help your business see the innovation happening around the globe, you need to bring models highest ROI, a data and AI platform needs to use predictive outcomes to wherever your data is. Your data and AI platform must be open and use them to prescribe action. and support models and data running on multiple clouds, while benefiting from vibrant ecosystems. Your platform should also enable A modern data and AI platform should facilitate the workflow of you to mitigate the cost and risk of needing to move data, which selecting and editing data for your team’s optimization problem. Using can potentially cause regulatory or legal concerns. a natural language interface, the platform should enable your team members to run optimization models and create and share reports In addition, the platform should also provide the ability to jump-start with Gantt charts, schedules, resource plans, and supply and demand your AI projects with industry accelerators, and offer prebuilt apps with allocations. Having decision optimization as part of the data and AI predefined business terms and data science artifacts.⁷ platform simplifies application of prescriptive analytics to predictive outcomes.
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