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Thursday, March 30, 2023

What Early Adopters Can Teach Us About AI

 Some good thoughts.  

What Early Adopters Can Teach Us About AI

Interview with Thomas H. Davenport   in APQC

ChatGPT burst upon the AI landscape in November 2022 with a media and market frenzy not seen since Steve Jobs introduced the iPhone in 2007. Over one million people immediately signed up to test if OpenAI’s large language learning algorithm could perform as well or better than a human at answering text-based research questions, writing a poem in the style of Shakespeare or songs like Bob Dylan, completing high school homework assignments (you can imagine the handwringing here), and much more. In many cases, the answer was yes.  

Only three months later, there have already been three major upgrades to the search and auto correct algorithm. ChatGPT4 gets the answers wrong and “hallucinates” (makes up stuff) less frequently. Competitors from Google and many others have joined the race to use AI to empower everything we do online and virtually every process we use inside organizations.

As we all know, the promise of AI won’t be actualized unless we develop a strategy, explore use cases, and operationalize the technology in our organizations. This takes organizational savvy and change management skills, which no algorithm can give you. 

Fortunately, we can learn from multifaceted experts like Tom Davenport and a decade of early adopters who have used AI to dramatically accelerate their businesses. In addition to dozens of books and articles on topics like knowledge management and analytics, Tom is co-author (with Nitin Mittal) of All in On AI: How Smart Companies Win Big with Artificial Intelligence. In this interview excerpt, Tom speaks with me about what it means to be “All in” on AI, shares some key lessons from early adopters, and provides insight into what AI will mean for the workplaces of the future.  

What does it mean to be “all in” on AI?

Organizations that are “all in” on AI are highly invested in a variety of different ways. One is that they have different types of AI spread throughout the company quite broadly. We’re talking about dozens of use cases at a minimum, but more commonly it’s hundreds or even thousands of use cases because AI is a narrow technology. It tends to support or automate tasks, not entire jobs—and certainly not entire processes. So for example, if you want to automate all customer service or all order management, you will need to assemble many different pieces of AI to have a high level of impact. 

Being all in means you’re not just using AI for optimization at the margins of your business but really changing something dramatically. In the book, we talk in terms of organizations that use AI to transform their strategy, business model, products, services, operations, or even customer behaviors—not just making small tweaks for operational improvement. 

You’re probably going to get into trouble unless you’re ethical about the ways in which you use AI. Organizations that are all-in also have a framework for ethical and trustworthy AI in place that includes guidelines, policy approaches, and governance structures. 

What led you to focus on early adopters of AI in your book?

I wrote a book called Competing on Analytics in 2007. My readers found it quite helpful to look at companies that were aggressive early adopters of analytics, and I thought that the same thinking probably applies with regard to AI. The people I was working with at Deloitte were starting to talk about the ways in which AI could help transform these big legacy companies, a lot of whom were their clients. I think the nice thing about those examples is that even if people can’t or don’t want to go all in, they respond to reading about what it’s like to be all in and what companies can accomplish if they’re really aggressive in their adoption of the technology.  ... ' 

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