The speed, volume and complexity of decisions – as well as the impact they have on customer experience – demand automated, real-time decision making. Digital decisioning is an emerging best practice for delivering business impact from AI, machine learning, and analytics. Digital decisioning is an approach that ensures your systems act intelligently on your behalf, making precise, consistent, real-time decisions at every customer touchpoint.
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The speed, volume and complexity of decisions – as well as the impact they have on customer experience – demand automated, real-time decision making. Digital decisioning is an emerging best practice for delivering business impact from AI, machine learning, and analytics. Digital decisioning is an approach that ensures your systems act intelligently on your behalf, making precise, consistent, real-time decisions at every customer touchpoint. Join Digital Decisioning expert James Taylor to learn how organizations are applying digital decisions to operationalize AI and machine learning to automate the customer-facing decisions essential for more profitable, customer-centric business decisions.
AI potential is huge AI will add $13T to the global economy over the next decade —Building the AI Powered Organization, HBR July-2019 97% of firms are investing in big data and artificial intelligence (AI) —2019 survey by New Vantage Partners Three-quarters of executives believe AI will enable their companies to move into new businesses. Almost 85% believe AI will allow their companies to obtain or sustain a competitive advantage. —Reshaping Business With Artificial Intelligence, MIT Sloan Management Review September 06, 2017 76% [believed AI] will “substantially transform” their companies within the next 3 years —Tom Davenport, The AI Advantage
AI results are disappointing Gartner's 2019 CIO survey points to the fact that, although 86% of respondents indicate that they either have AI on their radar, or have initiated projects, only 4% have projects currently deployed.
In a 2017 McKinsey survey with 3,000+ respondents, only 20% had adopted one AI technology in one part of their business
There’s a misconception that it’s always going to be better to let an algorithm determine a solution, but that won’t always be the case. AI isn’t a good fit for every sort of problem. —Building the AI Powered Organization, HBR July 2019 The gap between ambition and execution is large at most companies… only about one in five companies has incorporated AI in some offerings or processes. Only one in 20 companies has extensively incorporated AI in offerings or processes. Across all organizations, only 14% of respondents believe that AI is currently having a large effect on their organization’s offerings. —Susan Athey, Economics of Technology Professor at Stanford Graduate School of Business, quoted in MIT Sloan Management Review September 06, 2017 There are relatively few examples of radical transformation with cognitive technologies actually succeeding, and many examples of “low hanging fruit” being successfully picked —Tom Davenport AI Advantage Many organizations’ efforts with [AI] are falling short. Most firms have run only adhoc pilots of are applying AI in just a single business process… Firms struggle to move from the pilots to companywide programs —Building the AI Powered Organization, HBR July 2019
Or as Forrester put it, digital decisioning
Analytics not valuable until changing the business Enterprises waste time and money on unactionable analytics Digital decisioning can stop this insanity It is the highest-value next step for a successful digital transformation
Forrester Recommendations: Institute a culture of digital decisions-first design thinking Think of digital decisioning as the nexus of business rules, data, analytics, and machine learning models. One smarter, automated decision can be worth millions in terms of customer acquisition, retention, and/or operational efficiency
Experience is that AI projects must be focused on a specific decision from the beginning. You cannot simply develop an AI and then try to figure out how to improve a decision with it. Because core AI algorithms are decision-making, AI is often presented as “replacing” other decision-making technologies like business rules, data mining or optimization. However, not all problems are best solved by AI and machine learning. Adopt a business decision-centric approach to AI – one that puts business decisions first. This means focusing on decision-making and changing how you define AI requirements to bring more business knowledge into AI projects. Consider AI as one of a set of decision-making technologies not a standalone technology stack. Specifically, it must be combined with expert knowledge – business rules – to be effective
Decisions matter Especially operational decisions Especially automated operational decisions
These decision management systems support our existing platforms Providing decision making services to help these systems, and the users of these systems, make better decisions Using both explicit business rules and predictive analytics to make sure these decisions are accurate, compliant and analytically precise
And this is where the decision analysis piece comes in as we can collect all the operational data from those systems, reflecting how well our decision-making worked out for us, and feed it back into our big data infrastructure Where, combined with new external big data sources, it drives improved rules and better predictive analytics, closing the loop for continuous improvement.
So, what’s the roadmap to this new way of doing business – three steps turn out to be key DecisionsFirst Thinking –think first about decision design to drive practical innovation Mix and Match Technology – success will require to apply a mix of technology and allow to deliver business-led (not technology-led) automation Learn and Improve –integrate learning and feedback to drive continuous improvement in the decisions. Each step has a couple of keys and we’ll highlight them as we go through.
Decision first thinking instead of process Mix and match tech across under decision umbrella CI ok
Decision-centric design thinking Adopt decisioning technologies as a set Focus on continuous improvement not big bangs
Digital Decisioning: Using Decision Management to Deliver Business Value from AI “I’ve worked as a C-level executive in multiple insurance companies and engaged countless strategy consultants, IT consultants and technology vendors over the past two decades. This book describes the only approach that has actually allowed me to operationalize predictive models and deliver real ROI!” Digital Decisioning ensures your systems act intelligently on your behalf, making precise, consistent, real-time decisions at every touch point. It operationalizes machine learning and artificial intelligence, moving you beyond pilots and into production so you can make the best possible decision, every time. It uses business rules to guarantee the agility, transparency and compliance that established companies and regulated industries demand. Focusing only on decision-making, it supports continuous learning and improvement. Digital Decisioning is the most effective way to put machine learning and artificial intelligence to work. Digital Decisioning improves the customer experience, reduces fraud, manages risk, targets the right offers and actions to each individual customer, increases agility and drives business growth. Digital Decisioning applies machine learning and artificial intelligence at scale to automate the decisions essential for more profitable, more customer-centric and more digital business operations. “Essential reading for COOs looking to rigorously improve automation through AI.” Based on dozens of successful projects around the word, this book lays out the basic elements of the approach in a practical how-to guide. Aimed at managers, not technical teams, this book will focus your efforts to apply machine learning, artificial intelligence and predictive analytics. It emphasizes practical “do this next” advice delivered in non-technical terms, describing. the business value and impact of critical technologies without diving into technical detail. Stories of real implementations, real companies, show what can be done. A completely updated version of an established and popular book on Decision Management, this second edition has forewords by leading analytic experts, Tom Davenport and Eric Siegel. “A wealth of practical knowledge and advice for beginners and experts alike!” -- Written by James Taylor, CEO of Decision Management Solutions and the world’s foremost thinker, writer and consultant on using the decision management approach to deliver Digital Decisioning. “James has been at the forefront of decision management techniques for years. Anyone trying to automate and embed analytics to support decisions should read this book.” —Bill Franks, Chief Analytics Officer, International Institute For Analytics, speaker, and author “An absolute masterclass in analytics from one of the great masters himself. Nothing but solid knowledge, sage advice, and great examples without an ounce of hyperbole or fluff.” —Doug Laney, Principal Data Strategist with Caserta, and best-selling author of 'Infonomics'. This phrase trips me up. In my mind, digital decisioning isn’t what delivers systems. It’s an approach or a methodology that companies follow to deliver systems… I feel like we should say something brief about one of the biggest challenges for companies is operationalizing analytics (“last mile”) and that is specifically what digital decisioning is designed to address. Need to be consistent throughout on small or capital “D” for decisioning.