AI has been hitting the headlines recently, with generative AI, in particular, generating a great deal of interest. Two tools – the large language model chatbot ChatGPT and image generator Dall-E – have caused a big stir since launching as public betas in recent months.

These can be thought of as the current cutting-edge, public-facing applications of AI. However, as they are both free to use, their creator – AI research organization OpenAI – has been open about the fact that in order to be sustainable, they will have to start making money at some point.

When it comes to commercializing AI technology today, businesses are generally following one of two strategies. One is to take things slowly, perhaps initiating a small number of trials and pilots, while taking a “wait and see” approach to the organizational, ethical, moral, and societal questions that are coalescing around the technology.

At the other end of the spectrum are companies that are “all in.” Those adopting this more bullish approach are investing in building smart technology and automation into everything they do while also, crucially, taking the lead when it comes to answering the big questions.

These all-in companies are the subject of the latest book from two authors who are quickly building reputations as authoritative voices in the field of AI. Tom Davenport holds a long list of credentials, including President’s Distinguished Professor of IT and Management at Babson College, Visiting Professor at the Said Business School, Oxford University, Fellow of the MIT Initiative on the Digital Economy, and Senior Advisor to Deloitte’s AI Practice. Nitin Mittal, meanwhile, is head of the Analytics and AI Practice at Deloitte Consulting.

What does “all in” mean when it comes to AI?

The book begins by highlighting Alphabet (parent company to Google) as a prime example of a company that is “all in on AI,” with machine learning powering many of its popular services like search, Maps, Assistant, and Gmail. On the other hand, both authors stressed to me during a recent conversation that, to them, a more interesting area to focus on is legacy companies. These are companies – often giants of their own industries – that have adopted and adapted to the AI revolution rather than, as is the case with tech giants, being born from it.

Mittal told me, “There’s been a lot written around the science and technology of AI, and a lot of articles and news stories about how tech-native companies, whether its Microsoft, Google, Apple, Amazon, Meta, Nvidia … are using AI.

“Unfortunately, not a lot has been written about how traditional companies have adopted AI. What are they focusing on … if you take companies that have been around for longer than Silicon Valley, what are their challenges and motivations?”

Mittal and Davenport have chosen to cast their gaze on companies that are placing larger-than-modest bets on their ability to create change and value with AI. By their reckoning, this elite group makes up less than one percent of the world’s largest companies. Why is this?

Davenport tells me, “Well … a lot of investment is required – a lot of leadership. You can’t really go ‘all-in’ on AI without the CEO being supportive of it … you need a lot of people to do this well. These [all-in] companies hire data scientists, machine learning engineers, and so on.

And as we mentioned previously, having some answers ready to those big questions that someone is undoubtedly going to ask you at some point is essential!

If you’re going to focus your business on AI, you better be ethical about it – almost all of these companies have done some interesting work in the ethics space trying to create responsible and transparent AI and thinking very carefully about how it affects the business model and strategy.”

What companies are “all-in”?

Among many others, some of the businesses that have been singled out by Davenport and Mittal for their no-holds-barred approach to adoption include:

Ping Am – the Chinese conglomerate has rolled AI out across its multiple divisions, which encompass insurance, banking, transport, and smart cities, but its applications within its healthcare division are a particular focus.

DBS Bank – the largest bank in Singapore, the CEO of which has publicly identified that its most important competitors are not other banks and financial institutions but tech-first behemoths such as Google and Tencent.

CCC Intelligent Solutions – A Chicago-based insurer that has pioneered combining computer vision with Big Data analytics to create systems that allow customers to receive almost-instant payouts based on photographs of their cars taken after collisions.

Shell – Creating AI systems that allow them to use drones and computer vision to carry out analysis of pipelines, refineries, and infrastructure in weeks that previously would have taken years.

Airbus – This has created an AI-based ecosystem of platforms that allows itself and its partners, such as airlines, to optimize flight routes, fuel usage and conduct predictive maintenance on aircraft.

How do “all-in” companies operate?

While researching their book, Davenport and Mittal identified three “strategic archetypes” that, more often than not, have been pursued and adopted by companies that have driven real value from AI.

Firstly, there’s the pursuit of innovation. This means that the companies have used AI to do something new that hasn’t been done before by themselves or their competitors. Standout examples here, Davenport tells me, include Morgan Stanley, which has created automated investment tools, as well as Airbus, as mentioned above.

The second strategy is focused on operational transformation. This involves using AI to get better at doing what you do. This could mean anything from creating more efficient marketing pipelines to optimizing supply chains, making the most efficient use of physical space, developing smart pricing strategies, streamlining procurement processes, or becoming better at hiring the right people for the right jobs.

Thirdly, top players in the AI game understand how to use this powerful emerging technology to influence customer behavior. This includes methods of separating customers from their data, pioneered by social media companies and now practiced across many other industries, as well as credit scoring and strategies developed by health and motor insurance companies to encourage good behavior, involving wearable and black-box technology.

What can any business learn from “all-in” AI companies?

Perhaps one of the clearest takeaways from the book is that the transformative powers of AI are not by any means limited to the technology-native businesses of Silicon Valley.

The authors also make it clear that although many of the challenges that have to be overcome in order to do so are technological in nature, by no means are they all.

Mittal tells me, “While it’s critical to understand the technology and the impact of AI, what is even more consequential … is understanding the human side – being thoughtful around the strategy, understanding the underlying role of data and the fact that data fuels all of AI and the associated capabilities that organizations need.

“It’s all those aspects that are far more consequential in traditional organizations than just the implementation and experimentation around the technology.”

You can click here to watch my webinar conversation with Tom Davenport and Nitin Mittal, authors of All In On AI: How Smart Companies With Big With Artificial Intelligence.

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