I recently attended an AI conference in NYC. 90% of the focus was on the capabilities of the technology and how it works. What was largely missing was:

Testing for what customers want (design thinking)

While simultaneously…..

Testing the business model of the service AI is enabling (entrepreneurship)

However, a few folks at the conference – Chris Butler, Jim Guszcza, Shane Lewin, and Patrick Suen were making huge strides in using design thinking and entrepreneurial thinking for AI, and I learned a lot from them. And Michael Jordan confirms in this Medium article that this is what’s missing from the AI conversation at large:

“What’s missing from AI are fundamental principles of analysis & design, a discipline of engineering.”

This call to action to bring more discipline to AI is compelling, especially considering how engineering is closely tied to design thinking in terms of core principles.

So, how does Design Thinking & Entrepreneurship move us from AI possibilities to AI profitability?

In moving from possibility to profitability, design thinking helps us shift AI….

FROM: What should my company do with AI?

TO: What are the biggest challenges my customer faces?

This reframe helps us move the focus away from AI as the end to AI as the means. Often for those responsible for standing up technology, AI itself is the end goal, and success is “the technology works as it should.” Whereas, business defines success as “the technology makes us money by solving a problem” and sees AI as a means to an end. These different viewpoints are nothing new but rather an age-old challenge along the road to standing up any new technology. But the R&D mindset that leads with “does this work” rather than “does this work for our organization and how” seems perpetuated by the current, macro environment that has 1) unprecedented amount of cash in the VC community over the last few years and 2) a market dominated by technology firms – a huge shift from the past, where technology advancements largely grew out of government and academia.

Only a few folks told the story of AI as the enabler that it is – its role as a piece of the problem-solving, or helping a company to meet the unmet need of a customer. The reframe is powerful: a higher education consultancy goes from asking “what should we be doing with AI?” to a more customer-driven question: “how do I help a cash-strapped undergrad candidate ascertain their probability of getting into a college so that they don’t have to apply to 100 programs?” Maybe AI could be a part of solving that problem.

Chris Butler speaks to AI as an enabler well, when he says, “In this article, I try to avoid talking about particular technologies or techniques because people don’t care what you use as long as it helps solve a problem.”

In moving from possibility to profitability, entrepreneurship helps us shift AI….:

FROM: What can I do with AI that was near-impossible without it?

TO: That, plus, how do I make money from it?

Entrepreneurs have to think about getting to that first dollar in revenue – they test their product over and over to ensure that they have the right combination of features (technical and non-technical) that creates value for a customer and captures value for their fledgling business. Testing and iterating is the most natural de-risking tool they have.

At the AI conference, the conversation centered around “what is this AI product capable of?” “what can it enable me to do that I didn’t think was possible?” And yes, AI as a term encompasses a wide range of incredible, mind-blowing capabilities – from language processing and voice recognition to machine learning and neural interface technology (you have to see some of the things CTRL-labs are creating!). The possibilities are truly becoming endless, which shifts the hard question from “what is possible?” to “which AI capability brings value to my business?…. will justify the return on investment in AI?”

The notion of testing to de-risk standing up an AI-enabled capability inside a big organization isn’t as much of the AI conversation. This could be because testing off-tech is way less thrilling than highlighting what AI can DO. Or maybe because testing off-tech seems to signal you’re not tech-forward or not using the capabilities of the tech to inspire other possibilities. If there’s one principle in moving into the unknown that seems most well-known, it’s to not lead with a solution. But with AI, like most new technologies riding the high of their novelty, it’s really hard not to be enamored (seriously, go back and click on the link about CTRL-labs!)

I’d love to hear how you’re using Design Thinking + Entrepreneurship + AI. The next piece of learning for me is on tactics to use in shifting FROM —-> TO. Will share my learnings soon!

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