AI has been making headlines lately, and generative AI in particular has generated a lot of interest. Two tools — ChatGPT, a large language model chatbot, and Dall-E, an image generator — have created a huge buzz since launching as public betas in recent months.
These can be considered the most cutting-edge, public-facing applications of AI today. However, since they are all free to use, their creators, the artificial intelligence research organization OpenAI, have publicly stated that in order to be sustainable, they will have to start making money at some point.
When commercializing AI technologies today, companies typically follow one of two strategies. One is to take things slowly, perhaps launching a small number of trials and pilots, while taking a “wait and see” approach to the organizational, ethical, moral and social issues surrounding the convergence of this technology.
On the other end of the spectrum are the “all in” companies. Those taking this more optimistic approach are investing in the incorporation of smart technology and automation into everything they do, while, crucially, taking the lead in answering the big questions.
These all-out companies are the subject of the latest book by the authors, who are quickly building a reputation as the authoritative voices in AI. Tom Davenport holds a long list of credentials, including Presidential Distinguished Professor of IT and Management at Babson College, Visiting Professor at Oxford University’s Said Business School, MIT Fellow of the Digital Economy Initiative at MIT and Senior Consultant of Deloitte’s Artificial Intelligence Practice. Meanwhile, Nitin Mittal is the leader of Deloitte’s Consulting Analytics and Artificial Intelligence practice.
What does it mean to be “all in” when it comes to AI?
The book begins by highlighting Alphabet (Google’s parent company) as a prime example of a company “all in on artificial intelligence,” with machine learning powering many of its popular services such as search, maps, Assistant, and Gmail. On the other hand, both authors emphasized to me in a recent conversation that, for them, a more interesting area is traditional companies. These companies—often giants in their own industries—have adopted and adapted to the AI revolution, rather than emerging from it like tech giants.
Mittal told me, “There is a lot written about the science and technology of AI, and there are a lot of articles and news stories about how tech-native companies (whether it’s Microsoft, Google, Apple, Amazon, Meta, Nvidia…) are using AI.
“Unfortunately, not a lot has been written about how traditional companies are adopting AI. What are they focusing on … If you take companies that have been around longer than Silicon Valley, what are their challenges and motivations?”
Mittal and Davenport chose to set their sights on companies that have made big bets on their ability to use AI to create change and value. By their estimates, this elite group makes up less than one percent of the world’s largest companies. why is it like this?
Davenport told me, “Well…it takes a lot of investment—a lot of leadership. You can’t really go all-in on AI without the CEO’s buy-in…you need a lot of people to do it well. These [all-in] Companies hire data scientists, machine learning engineers, and more.
As we mentioned before, having answers to those big questions that someone will undoubtedly ask you at some point is essential!
If you’re going to focus your business on AI, you’d better be ethical about it – almost all of these companies are doing some interesting work in the area of ethics, trying to create responsible and transparent AI, and think very carefully How it affects business models and strategies. “
Which companies are “all in”?
Among the many other businesses, some that Davenport and Mittal singled out for their unrestricted adoption approach include:
Ping An of China — The Chinese conglomerate has rolled out artificial intelligence into several of its divisions, including insurance, banking, transportation and smart cities, but its use in the healthcare sector has received particular attention.
The CEO of DBS Bank, the largest bank in Singapore, has publicly stated that its most important competitors are not other banks and financial institutions, but tech giants such as Google and Tencent.
CCC Intelligent Solutions, a Chicago-based insurance company, has pioneered the combination of computer vision and big data analytics to create a system that enables customers to receive nearly instant payouts based on photos of cars taken after a collision.
Shell – creating artificial intelligence systems that allow them to use drones and computer vision to conduct analyzes of pipelines, refineries and infrastructure in weeks that would previously have taken years.
Airbus – has created an ecosystem of AI-based platforms that allow itself and its partners such as airlines to optimize routes, fuel usage and perform predictive maintenance on aircraft.
How does an “all in” company work?
In researching their book, Davenport and Mittal identified three “strategic archetypes” that tend to be pursued and adopted by companies that derive real value from AI.
One is to pursue innovation. This means that these companies have used artificial intelligence to do new things that they or their competitors have not done before. Prominent examples here include Morgan Stanley, which has created automated investment vehicles, and Airbus, as mentioned above, Davenport told me.
The second strategy focuses on operational transformation. This involves using AI to do better at what you do. This could mean anything from creating more efficient marketing channels to optimizing supply chains, making the most efficient use of physical space, developing smart pricing strategies, streamlining procurement processes, or getting better at hiring the right people for the right jobs.
Third, the top players in the AI game understand how to use this powerful emerging technology to influence customer behavior. This includes approaches to decoupling customers from their data, pioneered by social media companies and now implemented in many other industries, as well as credit scoring and strategies developed by health and auto insurers to encourage good behaviour, involving wearable and black-box technologies .
What can any business learn from “full-faced” AI companies?
Perhaps one of the clearest takeaways from this book is that the transformative power of AI is by no means limited to Silicon Valley tech-native companies.
The authors also make clear that while many of the challenges that must be overcome to this end are technical in nature, by no means all of them.
Mittal told me, “While it’s critical to understand the impact of technology and AI, it’s even more important … to understand the human side — to think through strategy, to understand the underlying role of data and the fact that data drives everything. AI and related capabilities that organizations need.
“In a traditional organization, all of these aspects are more important than around the implementation and experimentation of technology.”
you can Click here Watch my webinar conversation with Tom Davenport and Nitin Mittal, authors of All In On AI: How Smart Companies Can Get Big Through Artificial Intelligence.
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