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[REPORT PREVIEW] The AI Maturity Playbook: Five Pillars of Enterprise Success

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[REPORT PREVIEW] The AI Maturity Playbook: Five Pillars of Enterprise Success

  1. 1. The AI Maturity Playbook: Five Pillars of Enterprise Success DECEMBER 11, 2018 BY Susan Etlinger RESEARCH REPORT PREVIEW VERSION
  2. 2. 1 TABLE OF CONTENTS 2 EXECUTIVE SUMMARY 3 FOUR MACRO TRENDS AFFECTING AI SUCCESS 3 How We Interact: From Screens to Senses 4 How We Decide: From Business Rules to Probabilities 5 How We Innovate: From Data Analytics to Data Science to Data Engineering 6 How We Lead: From Expertise-Driven to Data-Driven 7 A MATURITY MODEL FOR ARTIFICIAL INTELLIGENCE 9 Strategy: From Optimization to Business Model Innovation 10 Data Science: From Specialty to Scale 12 Product and Service Development: From Reactive to Anticipatory 14 Organization and Culture: From Hierarchical to Dynamic 14 Ethics and Governance: From “The Wild West” to “Enterprise-Ready” 18 BUILDING YOUR PLAYBOOK 20 ENDNOTES 21 ABOUT US 22 METHODOLOGY 22 ACKNOWLEDGEMENTS 1
  3. 3. 2 Every day we see new stories — and new hype — about the capabilities and sheer momentum of artificial intelligence. Use cases abound: AI is as good as experts at detecting eye disease. It is changing everything from banking to fashion to education. Investment continues at a healthy clip; research firm IDC expects cognitive and AI spending to reach $52.2 billion by 2021.1 DARPA alone expects to invest $2B in AI over the next five years.2 But as we’ve seen with all significant technology shifts — from the Gutenberg Bible to electricity to the Internet — AI upends many of the assumptions, processes, and even cultures of the organizations and societies that implement it. Some of these changes are temporary, as we learn more and as the technologies mature, and some are more fundamental and longer lasting, but the clear message is that we are still in the earliest stages of the shift to intelligent systems.3 This report lays out a maturity model for AI adoption in the enterprise. It outlines four macro shifts that define the impact of AI in organizations and society and four stages of AI maturity based on how organizations approach business strategy, data science, product and service design, organization and culture, and ethics and governance. As with any significant technology shift, we’ll know AI has reached maturity when it no longer looks like magic but has become an integral part of our professional and personal lives. 2
  4. 4. 3 FOUR MACRO TRENDS AFFECTING AI SUCCESS Beyond use cases, investment levels, and the technology landscape, the natural questions business leaders tend to ask about AI are: “Who’s doing it well?” “What does maturity look like?” and “How do we move from one stage of maturity to the next?” The answers lie as much in external as internal factors. From an external standpoint, meaning factors that leaders cannot meaningfully influence, there are four fundamental shifts at play: 1. How we interact: from screens to senses; 2. How we decide: from business rules to probabilities; 3. How we innovate: from data analytics to data science to data engineering; and 4. How we lead: from expertise-driven to data-driven. 1. HOW WE INTERACT: FROM SCREENS TO SENSES One of the fundamental shifts that AI enables in the digital world is the shift from browser- or app-based interactions to interactions that make use of our senses — how humans most naturally relate to the world. Sensory interaction is significantly different from screen interaction; it frees people to communicate in whatever style they prefer but can be more challenging to design for.4 Today, vision, hearing/language, and touch- related technologies are commonplace; we think little of talking to a plastic cylinder on the dining table or touching screens to make them respond to our commands, but that was not always the case. The next frontier are technologies that aim to digitize our sense of smell and taste, which are actively being researched.5 Figure 1, below, describes some of the technologies in use or being explored today. Figure 1. From Screen to Sense-Based Interaction Models VISION Interpreting the objects and attributes of visual objects and images: photos, drawings, video HEARING Interpreting sounds or text and translating them into speech, text, or images, or from one language to another TOUCH Pinch-and-zoom, gestural interfaces (such as in AR/VR) that translate move- ment into meaning or commands SMELL The ability to translate smells into digital chemical information, and vice-versa TASTE The ability to translate taste into digital chemical information, and vice-versa Computer Vision Speech Recognition Natural Language Understanding Gesture-Based Communication Touch-Based Inputs Digitizing Olfaction Digitizing Taste Related Science/Technology
  5. 5. 4 AI has also expanded the possibilities for how people interact with applications. Designed thoughtfully, notifications, nudges, and other User Interface (UI) features render it unnecessary for people to open an app, make an update, and then close the app, which causes developers and UI designers to reconsider the flow of information between apps and the people who use them. Says Melissa Boxer, VP Product Management, Oracle Adaptive Intelligent Apps, “AI has forced us to rethink the user experience. We can't use apps in the traditional way, so we’re starting to build in a new user experience paradigm; for example, auto-filling fields in machine learning-driven drop-down menus. With recommended actions, the user interface can now surface information in a way that is meaningful. When should a user have to open up an application? Why should I have to open an app to input my expense report? So the paradigm shift is around when to notify or connect with the person. It’s driven us down an interesting path.” 2. HOW WE DECIDE: FROM BUSINESS RULES TO PROBABILITIES Unlike rules-based systems that work by means of “if/then” statements (for example, if checking account balance drops below $500, send an alert to the customer), artificial intelligence is based on statistics rather than rules. As a result, it is inherently probabilistic (an outcome has an 84% chance of occurring) rather than deterministic (if X, then Y). Although the math is usually hidden from us, we see probabilities in action every day; in the recommendation engines we use (people who like Agatha Christie also tend to like G. K. Chesterton), the social feeds we see on Twitter and Facebook (If I comment on a post by my friend Emily, I probably want to see more content from her), and so forth. While rules are critical for some types of applications where A always leads to B, AI, with its reliance on data and ability to learn, always involves a degree of uncertainty. One example is in fraud detection. If someone lives in California but suddenly her credit card processes transactions in Berlin, it could be fraudulent, or she might simply be traveling. In this situation, she can avoid having her credit card declined by filing a travel plan with her issuing bank, but the onus is on her as a customer. But if the bank used AI (and many are doing this for just this purpose), an algorithm might learn from her past behavior. Maybe she’s gone to Berlin the past three Octobers. Maybe she used that card to buy a plane ticket to Germany in the past 30 days. Maybe she logged in with two-factor 4
  6. 6. 5 authentication from Germany. Like humans, an algorithm has to take into account a multitude of implicit and explicit signals in order to come to a conclusion or make a decision. Of course, some probabilistic scenarios are fairly inconsequential. A customer is unlikely to sue Netflix for recommending a movie he doesn’t like, for example.6 But some probabilistic scenarios have life-and-death consequences. Probabilistic Decision-Making in the Global Intelligence Community Intelligence analysts are used to thinking in probabilities – in their case, the likelihood of events happening. They might evaluate, for example, events that have financial implications: currency valuations, changes in interest rate, and so forth. But to make a recommendation for action based on probabilities, they need justifications to build their case. Says Jana Eggers, CEO, Nara Logics, “Our customers have seen our AI algorithm indicate a higher probability than was expected for an adverse event. This happens when multiple streams of information can tell different stories. For example, banks, politicians, and corporations do not act in concert, and the confluence of their actions lead to different event probabilities than can be predicted with any stream alone.” “Our platform helps our customers conduct scenario planning, and our transparent AI helps build a case for reacting to a probabilistic event.” But, says Eggers, “The intelligence community needs reliable confidence levels so they can determine what action, if any, to take.” 3. HOW WE INNOVATE: FROM DATA ANALYTICS TO DATA SCIENCE TO DATA ENGINEERING One of the most striking shifts as AI matures is an evolution from data analytics to data science to what will eventually look more like data engineering — a future state in which we are not only able to collect, analyze, and learn from data, but in which self-learning algorithms, fed by clean and plentiful data, continuously inform our systems, products, and services. This is what Omar Tawakol, Founder and CEO of Voicera, calls “a compounding data advantage” — the data equivalent of a compound interest rate in which value accrues and is amplified over time. Before we can begin to expect anything approaching a compounding data advantage, however, there are a number of practical challenges to consider. As in any major technology shift, the most salient constraints are mature tools and people with the right expertise; in this case, AI toolkits and data scientists. And, because the tools and processes are still very new, data scientists must spend more time on the basics — namely, data preparation and data organization — than on more strategic tasks. This will change over time as the industry and technology matures and as data science becomes more integrated into the engineering process. AI will be a forcing function for a more data-centric organization.” — Omar Tawakol, Voicera
  7. 7. 6 4. HOW WE LEAD: FROM EXPERTISE- DRIVEN TO DATA-DRIVEN Organizations that are successfully implementing AI — no matter their size or industry — tend to agree on one thing: We are moving away from purely expertise-driven to more data-driven decision-making. This doesn’t obviate the need for expertise — it’s not a people versus machine scenario — but it does mean that business leaders need to become more comfortable with data-driven decision-making. “The one thing everybody has to come to grips with,” says Steve Stine, SVP AT&T Communications Transformation, “is that data will be foundational to all the things you will do. There will be more data available, so if you’re not doing something with it you've got a blind spot that may be catastrophic. You've got to think of this as a sequential process in which you have to invest. This doesn’t necessarily mean you need your own data science team, but you do need to learn more about what you are trying to do and the outcomes you’re looking for.” Voicera’s Omar Tawakol agrees: “Marketers need to get comfortable with driving strategy based on what data is telling them. They need to become data-driven versus HiPPO- [Highest-Paid Person’s Opinion] driven.” Tawakol maintains that paying attention to what the data tells you is critical, whether it’s a major decision or a tiny product feature. “We've had features where we thought they’d increase usage,” he says, “and they ended up decreasing usage. Whenever we get into a debate, we always leverage the data.” 6
  8. 8. 7 It’s important to recognize that, despite the investments and the hype, enterprise adoption of AI is still nascent. As of January 2018, Gartner found that only “one in 25 CIOs described themselves as having artificial intelligence in action in their organizations.”7 Furthermore, many of the challenges related to AI success—such as access to skilled employees and mature tools—are common across the industry. Based on interviews with a range of industry experts, Altimeter has found that organizations using AI technologies tend to fall into four stages of maturity (see Figure 2): • Phase 1: Exploring. The organization is exploring AI, engaging with experts, considering use cases, but has not as yet committed significant time or resources, either with external or internal experts, to map processes, make data accessible or fund AI-related initiatives. • Phase 2: Experimenting. The organization is actively experimenting with AI for a range of use cases, using either internal (employee) or external (service) resources, but these are generally seen as discrete projects rather than scalable and persistent implementations. • Phase 3: Formalizing. AI is becoming a formal part of corporate strategy, and data is now a core competency across the organization. Implementations are moving beyond optimization to more customer- and market-focused strategies. • Phase 4: Integrating. AI is part of the fabric of the company, embedded into processes, products and services across the organization, and is delivering value across the business. A MATURITY MODEL FOR ARTIFICIAL INTELLIGENCE
  9. 9. 8 Phase 1: EXPLORING. Phase 2: EXPERIMENTING. Phase 3: FORMALIZING. Phase 4: INTEGRATING. Strategy Objectives are undefined and no resource or budget has yet been allocated. Data is siloed, not in accessible, useful form; analytics are largely descriptive and retrospective. Business cases present, but no development is underway as yet. Seen as promising but unproven. Not yet seen as a priority at the C-level. Emerging under- standing of AI governance issues but no principles or processes present. Discrete proofs of concept focus on cost reduction, productivity improvement, and/or Robotic Process Automation (RPA). Organization has committed to data strategy and is moving from descriptive into predictive analytics. Organization has begun to use APIs and internal or external resources to perform proofs of concept and pilots. Organization may have a Chief Data Officer, but data science and AI projects are discrete rather than critical elements of an enduring product, service, or business Organization has identified and com- municated ethical principles for AI and is implementing pol- icies and processes to support them. An expected part of strategic planning, focused on custom- er experience. Data strategy is becoming a core competency but AI is not yet scaled across the organization. AI is becoming a critical part of product and service development. Clear understanding of and optimized relationship with data science and AI resources, whether they are external services and platforms, a larger ecosystem, or a combination. AI ethics and governance processes are formalized through- out the business. Integral to agile business and a critical component of digital transformation and competitive advantage. Organization benefits from a compounding data advantage. AI is a core development competency across the organization. Organization has a learning organization mindset; design thinking and experimentation are valued in the culture. AI ethics/governance is embedded in corporate practice and customer experience and is part of performance evaluations and incentive programs. Data Science Product & Service Development Organization & Culture Ethics & Governance Figure 2. Four Stages of Artificial Intelligence Maturity

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