Rolling out AI requires a total reimagining of existing business models.
You should be looking for butterflies—not faster caterpillars. The definition of transformation is exactly that—caterpillars becoming butterflies. Otherwise, you are not recognizing AI’s potential. As with the internet, it’s a once-in-a-generation opportunity to reimagine how you do business.
When it comes to implementation, there will always be a certain amount of experimentation, but many experiments fail because people think of AI as one monolithic thing. You need to break it into two steps.
First, establish what infrastructure you need, starting with your data. Is your data connected? Is it organized? Is it in a format that can be leveraged? AI is nothing but garbage-in, garbage-out otherwise. Establish the foundation, then second, pick one or two projects that will add real value.
Choose an area of the business where you see a real opportunity. Then find something that is not so big that it takes you years to deliver value, but not so small that it isn’t applicable to the rest of the business.
Many CEOs have been focused on cost-based use cases of AI, because it’s easier to prove their tangible value. But we’re seeing far more meaningful opportunities in the context of driving growth and sales. When you get it right, it demonstrates how AI can make a difference in everything from selling cars to increasing the value of shopping baskets.
If your child has just started playing football, you may not know what to buy. AI can say: “Tell us a few things, and we’ll check you out with everything you need for an 8-year-old who’s into football. You’re searching for a ball, but you also need cleats and socks and shin guards.”
This makes a real difference in revenue, unlike use cases where “we can optimize call centers and boost productivity.” Those projects are specifically focused on AI as a cost opportunity.
I remember talking to airline CEOs about paying to pick your own seat on airplanes. The CEOs wanted faster check-in lines at the airport. Back then, staff would have to show each passenger where they were seated at check-in, or fliers would have to call customer support, which took time and added cost.
Nowadays, choosing a seat is the second-largest revenue-generator for an airline after buying a ticket. This means that what started off as a cost-saving initiative creates more revenue for airlines today than even excess baggage fees.
At the time, the conversation was framed almost entirely around operational efficiency— reducing friction at check-in, shortening queues, and lowering support costs. What wasn’t immediately obvious was how these small changes in customer experience could fundamentally reshape behavior. Over time, those same decisions opened entirely new revenue streams that few had initially anticipated.
These are the trends you miss if you only use AI to save money. Industries which don’t make this pivot—from using AI to cut costs to using it to find new opportunities for growth—are going to be caught flat-footed.
—As told to Francessca Cassidy.
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