The Executive's Guide to AI and Analytics
The Foundations of Execution and Success in the New World
- 122 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
The Executive's Guide to AI and Analytics
The Foundations of Execution and Success in the New World
About This Book
The Problem? Companies are failing to deliver on AI and analytics with over half stating they are "not yet treating data as a business asset". Over half admit that they are not competing on data and analytics. Seven out of 10 companies in a 2020 MIT study reported minimal or no impact from AI so far. Among the 90% of companies that have made some investment in AI, fewer than 2 out of 5 (40%) report business gains from AI in the past three years. And only about 25% of organizations have actually forged this data-driven culture.
Is investment lacking? No. Companies now are spending more than ever in data, analytics, and AI technologies.
Is it a lack of technology? No. There are fascinating breakthroughs occurring on all fronts with image, voice, and streaming pattern recognition on the forefront.
Is it a lack of technical talent? Not really. While some studies cite that we need to train more data scientists, developers, and related professionals, the curve of demand by supply is dampening.
Is it a lack of creating an executable strategic plan? Yes. While there has been a lot of strategic wishing, organizations lack meaningful strategic plans. Specifically, the development of executable strategies and the leadership to see these strategies brought to fruition. This is the problem.
Lack of execution and lack of incorporating key components that align and enable execution of the business strategy to delivery is killing AI and analytics programs. Scott Burk and Gary D. Miner have written this book for executives at all levels who are charged with executing on analytics that need to address this issue. The book provides unique insights into repairing the gaps that programs need to fill to provide value from analytics programs. It complements their three-part series, It's All Analytics! by focusing on leadership decisions that augment data literacy, organizational architecture, and AI case studies.
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Chapter 1
Introduction: Sources of Failure
In any given moment we have two options: to step forward into growth or to step back into safety.What one can be, one must be!â Abraham Maslow
IBMâs Watson has been plagued with failure and lawsuits. In 2017 AlphaGo was retired.
- The majority of companies are failing with over three-quarters of their big data and AI initiatives remaining a challenge.
- Over half of organizations state that they are ânot yet treating data as a business assetâ.
- Over half of organizations admit that they are not competing on data and analytics.
- Seven out of ten companies (70%) in a 2020 MIT study reported minimal or no impact from AI so far. Among the 90% of companies that have made some investment in AI, fewer than two out of five (only 40%) report business gains from AI in the past three years.
- According to New Vantage Partners Big Data and AI Executive Survey 2021, only about 25% of organizations have actually forged this data-driven culture: This yearâs findings exhibit that challenge to an even greater degree. All questions relating to the long-term progress of corporate data initiatives exhibited declines from 2019 and 2020 levels, a disappointing development.
Eisenhower said, âLeadership is the art of getting people to want to do, what must be doneâ.
A Modern-Day Story and the Need to AdaptShe addressed the board of directors. She had been preparing for days. In fact, her brightest minds had been preparing for days. This was not going to be a typical meeting. That was evident from the last board meeting coupled with several one-on-one conversations with several board members. She had to successfully defend her position or she was out of a job.She was Tricia Garcia and she was the CEO of South State Medical, a medium-size nonprofit medical system with seven hospitals, various facilities, and over two million outpatient visits per year. She followed a ten-year rise and had been at the top for the last three years. She was a shining star, but now she might be a falling one.The problem? The board had brought her on with a limited set of business initiatives (expand the network, cost containment, and the like). But, most importantly, they wanted her to launch an AI and analytics program. Board members were well read and some had experience with the power of data-driven initiatives. They could not understand her failures.Susan was not alone. Despite all of the hype around analytics at conferences, from consultants, from software companies, and from marketing engines, analytics are not living up to expectations in the majority of organizations. AI and analytics have been far from successful. In fact, over three-quarters of AI analytics projects never run successfully in operations. Investment in infrastructure is markedly up, staffing is markedly up, and dozens of new masterâs degree programs are available at top universities. Mainstream news channels have been advocating the allure, the excitement of it all, for some time. We have heard âwe need to be data-drivenâ for years. Why arenât we more successful?Susanâs board is not uncommon. A majority of organizations (57%) believe that AI technology will substantially transform their company within the next three years with the window for competitive differentiation with AI quickly closing. * With leaders increasingly seeing AI as helping to drive the next great economic expansion, a fear of missing out is spreading around the industry.* https://www2.deloitte.com/us/en/insights/focus/cognitive-technologies/ai-investment-by-country.html There will be many more presentations like Susanâs in the coming years as boards push on CEOs to start new programs or turn around their AI and analytics lackluster results.
âSeventy percent of strategic failures are due to poor execution of leadership, not for lack of smarts or visionâ. â Ram Charan
Table of contents
- Cover
- Half-Title
- Title
- Copyright
- Dedication
- Contents
- Authors
- 1 Introduction: Sources of Failure
- 2 AI and Analytics Failure and How to Overcome It
- 3 The Six Foundations for AI and Analytics Success
- Index