Good morning. For companies figuring out how to use AI, the conversation is changing from improving existing work to rethinking what the business can do.
I spoke with John Felton, SVP and CFO of Amazon Web Services (AWS), Amazon’s cloud computing and AI platform, about that shift. Felton speaks regularly with AWS customers, including fellow CFOs, and said their AI adoption is still uneven. Many are still early in their broader cloud and IT modernization efforts, a prerequisite for deploying AI at scale.
“I think in general, I would say it feels early across the board,” Felton told me. “It’s unbelievable the pace of change, how fast things are moving. I do think every company is in a different stage of it. I think everyone is still early.” That puts him at an interesting vantage point as AI drives a significant new investment cycle.
Felton also pointed out that customer AI discussions about two years ago focused largely on productivity and cutting costs. That is changing.
“There really is a lot more focus on: how do I use AI for new revenue products, for new customer experiences?” he said.
That perspective carries particular weight given the scale of the business Felton oversees. AWS is now a $169 billion annualized business, and Felton has been with Amazon for more than 20 years. He previously led Amazon’s worldwide operations before becoming AWS finance chief in 2024, reporting to Amazon CFO Brian Olsavsky.
The bigger shift, Felton said, is from adding AI to an existing process to rethinking the process. What impresses him is seeing companies move toward an “AI first mindset,” using AI to rethink what the business should be in the first place.
“That is where a bunch of customers, including ourselves, really see a lot more value,” Felton said.
AI in finance
Inside AWS’s finance organization, Felton is encouraging employees to participate in that transformation rather than wait for someone else to tell them how to use AI.
His instruction is straightforward: “Use AI every day.”
Rather than dictate which tasks employees should tackle or which tools they should use, Felton wants them to identify opportunities themselves. They understand their work better than he does, he says. He tracks whether employees are following through, although he did not detail those metrics.
One example came from AWS’s pricing team. Employees had relied on a complicated Excel model spanning 18 tabs to evaluate customer deals. The team converted it into a chatbot interface that lets users explore scenarios through natural-language questions.
They can ask what happens if a price falls 20%, how different payment terms affect a deal, or where the break-even point lies. Employees who prefer the spreadsheet can still export the model to Excel. The point isn’t simply that AI made an existing tool easier to use. It changed how employees interact with the underlying analysis.
Another team built an AI agent to compare payment terms in customer contracts with those recorded in AWS’s payment system. Previously, employees checked a sample of contracts. The agent allows the team to check all of them.
“Let’s not just do a sample size, let’s do all of it,” Felton said, describing the team’s approach.
The two examples illustrate the broader shift he sees in companies’ AI strategies: using the technology not only to make existing work faster, but to expand what the organization can do.
Felton also uses Amazon Quick, an AI assistant for work, himself, including to query supporting materials prepared for board meetings and locate answers in the underlying files. He sees that kind of everyday experimentation as important for CFOs figuring out where AI fits.
Sheryl Estrada [email protected]
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