I have known Tom for many years and enjoyed his books. He recently sent me a copy of his latest one – The AI Advantage: How to Put the Artificial Intelligence Revolution to Work (Management on the Cutting Edge).
Tom does his usual excellent job of introducing a technical topic – AI and machine learning – and focusing on what business leaders need to know about it. While he has a chapter on the various approaches to adopting AI technology, the book’s key message is that a technology-first approach to AI is a bad idea. Instead of technology-led “moonshots”, enterprises should use AI to solve practical, immediate, operational problems.
The book begins with a discussion of the role of AI in the enterprise and surveys what companies are doing today – both successes and failures. It was particularly refreshing to see failures discussed and Tom does a nice job of using some of these failures to illustrate how best to approach AI. He believes AI is going to transform companies, albeit perhaps more slowly than some believe, and encourages companies to identify a coherent AI strategy. He provides some good material on the elements of an AI strategy and outlines how different companies might take different approaches.
The biggest takeaway from the book is that success will not come from “moonshots” but from a more practical approach. As Tom says:
There are relatively few examples of radical transformation with cognitive technologies actually succeeding, and many examples of “low hanging fruit” being successfully picked
In our experience this is critical. Taking a big “we’ll just use AI” approach rarely works. Developing a comprehensive approach to a decision that mixes and matches AI with other technologies like descriptive analytics, predictive analytics and business rules works much better. Tom recommends that companies develop a series of less ambitious applications in the same area that in combination have a substantial impact. Each is less risky than a moonshot and you will have time to adapt to each piece. But, when combined, the overall impact is high.
We like to do this by building a decision model to break down a specific collection of closely related business decisions into their component sub-decisions. Some of these sub-decisions will be best done by people, some can be codified as business rules and some will need ML or AI. This lets you identify a set of smaller, less ambitious AI decisions and shows you how they will contribute to an overall more effective decision.
As Tom says:
Given all the media and vendor hype in the cognitive technology space, companies often feel pressure …to take on a cognitive project. It’s much better for a company to try and see beyond marketing blandishments about AI and to create the best fit with the organization’s strategy, business model and capabilities.
That requires that you really understand how you make decisions and how (and where) AI can help. Our experience is that a design thinking approach to decisions – DecisionsFirst Design Thinking as we call it – let’s you redesign the decision-making and take advantage of AI. Too many companies have used AI to “pave the cow path” by automating existing work process (particularly true with RPA technology). Really taking advantage of AI will require structured and controlled re-thinking of your decision-making.
Usefully for business executives, he provides a very accessible survey of the capabilities of AI:
- Create highly granular predictive and classification models
- Perform structured digital tasks (RPA)
- Manipulate information (OCR and data integration)
- Understand human speech and text
- Plan and optimize operations
- Perceive and recognize images
- Move purposefully and autonomously around the world
- Assess human emotions
For each of these he provides a concrete discussion of how they might reasonably be used, not in the future but now – discussing in passing how work is likely to need to be redesigned to take full advantage of these technologies.
He also discusses how, while ML and AI represent an extension of the world of advanced analytics, they also differ from traditional data mining and predictive analytic approaches in 3 ways:
- They are usually more data intensive and detailed, limiting when they can be applied to scenarios where there is a lot of data
- They need therefore to be trained on a subset of the data because there is so much available.
- They can learn continuously as data is fed through the resulting algorithm, rather than waiting for the next formal update.
AI and ML can be used in many of the same circumstances that predictive analytics and data mining can be used. Think of them as both an extension of these techniques and as something distinctly different.
In later chapters he talks about jobs and skills in a world where AI is increasingly pervasive and about some of the social and ethical issues such as transparency and bias. As he says:
As cognitive technologies are developed, organizations should think through how work will be done with a given new application, focusing specifically on the division of labor
He has a good discussion of transparency and our experience has been that considering AI as one part of the solution, mixing more opaque AI with more transparent business rules for instance, really helps. In addition, new technologies such as LIME and AI Open Scale help explain AI model results in a way that can be combined with the explanations produced by these other more transparent technologies.
One of the themes in the book is the challenge of getting AI to really affect an organization’s core business operations. In a cognitive-aware executive survey conducted by Deloitte, for instance, 47% said it was “difficult to integrate cognitive projects with existing processes and systems”. As Tom points out, this integration is essential if you want to make a real impact. This matches a recent McKinsey survey that found analytic “leaders” investing much more heavily in this “last mile” integration than others. A Decision Management platform -a digital decisioning platform as some call it – is a key ingredient in tying advanced analytics, ML and AI into your day-to-day operations.
Finally, from a Decision Management perspective, Tom has some great illustrations of the value of a decision-centric approach and of how AI is integrated into an overall approach. Early in the book he quotes Jeff Bezos’ Letter to Amazon Shareholders from 2017 (my emphasis added):
But much of what we do with machine learning happens beneath the surface. Machine learning drives our algorithms for demand forecasting, product search ranking, product and deals recommendations, merchandising placements, fraud detection, translations, and much more. Though less visible, much of the impact of machine learning will be of this type – quietly but meaningfully improving core operations.
“Quietly but meaningfully improving core operations since 2002” could be a motto for Decision Management! That’s the focus of Decision Management and always has been. In that sense, machine learning (ML) and AI represent powerful new tools for doing what we have always done rather than something requiring a radically different approach. Tom even makes this point, identifying how rules-based systems have been addressing many of the scenarios for which AI is being considered. Like Tom, we see potential in combining these rules based and machine learning approaches to produce adaptive systems.
I will end with one of Tom’s quotes from early in the book:
The businesses and organizations that succeed with AI will be those that invest steadily, rise above the hype, make a good match between their business problems and the capabilities of AI, and take the long view.
This is not a book that is going to add to your technical knowledge about AI but it’s a great book for business executives and for those who want to think more deeply about how AI will change their business. You can buy it here The AI Advantage: How to Put the Artificial Intelligence Revolution to Work.