Experian’s chief innovation officer gleans AI gains with startup collab
For CIOs and innovation leaders, the real takeaway is not that Experian is โusing AI with startups,โ but that it is turning a fast-moving technology question into a governed business capability. Agentic commerce introduces a new accountability gap: if software acts on behalf of a customer, who owns authorisation, exception handling and dispute resolution? That makes identity, payment verification and auditability management decisions, not just technical features. The strategic implication is that AI adoption in transaction-heavy businesses must be tied to clear rules for acceptable agent behaviour, not simply experimentation velocity.
The operating-model lesson is equally important. Experianโs use of demo days, cross-functional follow-up and selective routing of startups shows a practical innovation funnel, but it also raises a portfolio question: which ideas should remain exploratory, which should move into core product roadmaps, and which belong in a watch list? Leaders need explicit criteria for progression, such as regulatory fit, customer impact, integration burden and the cost of ongoing oversight. Without that discipline, startup collaboration becomes theatre rather than investment management.
There is also a trade-off between openness and control. Standards and partner ecosystems can accelerate adoption, yet they can also create dependency on emerging methods whose market acceptance is not settled. That means governance teams should ask: what evidence is enough to trust an agent, how will exceptions be monitored, and who signs off when a standard changes? The management priority is to define controls that scale with usage, because agentic transactions will fail politically and commercially long before they fail technically if customers cannot explain or challenge what happened.
Verifying AI agents
Given that fraudulent transactions and billing errors are nothing new, there can be a lack of consumer and merchant trust in AI-driven transactions, she said. That is one of the reasons why retailers include security layers, such as firewalls, on their websites. Many of those measures are meant to protect against intrusion, such as bots, but the spread of AI has cast bots in a different light. “Before AI, bots equaled bad. If there’s bots coming to your site, you wanted to block them because it’s not going to be something good. Either it’s an attack, it might be scrapers, who knows what it is,” Peters said. Verifying the bot-like actions of AI agents is where Experian and its collaborators aim to come in. The framework Experian developed with Skyfire’s input is a way to “know your agent” as it acts on a customer’s behalf. “It’s like KYC [know your customer], which we are very familiar with, especially in AML [anti-money laundering] worlds,” Peters said. “We’re going to use the same approved methods that we do for traditional KYC today, especially in digital banking onboarding.” This can include requiring a credit card to verify a transaction.Experian explores strategic investments in startups
Skyfire is an AI startup out of San Francisco that authored an open standard called KYAPay, Peters said. With KYAPay, developers can verify the identity of their AI agents and track agent activity. Working with Skyfire is part of Experian’s overall efforts to explore strategic venture investment opportunities, including M&As, she said. ”ย We’re excited about what’s on the bleeding edge in terms of capability.” With the AI space growing, moving quickly and churning in recent years, Peters said Experian conducts in-house demo days to understand what startups are creating. “My team goes to the shows, does the desk research, meets with companies, goes to the incubators and curates a list [of startups to watch]. We do this about three or four times a year,” she said. The demo days are conducted virtually, with pitches heard by attendees from different functions across Experian. Then Peters’ team divides and conquers — following up themselves or directing another Experian business unit to pursue collaboration with the startup. “That’s how we found Skyfire,” Peters said. What stood out about Skyfire was its specialization in e-commerce and agentic commerce, along with the open standard for identity that they authored, she said. “We knew they’d be a valuable partner in helping us navigate the [agentic commerce] ecosystems. They’ve got an early read on where a lot of pain points and challenges are.” Being a data and analytics company, Experian has used machine learning and AI techniques for a long time. The company’s research lab worked on neural networks for more than a decade, Peters said. As generative AI came to the fore, natural curiosity bubbled up inside the company, she said. “We put a risk council in place, we put guardrails in place, enterprise tools and we started looking for what would be the best AI stack within our company.” Peters said Experian continues to look for technology developed by startups that could be applicable or adjacent to operations and services that the company wants to expand. “We may partner with them. We may make a strategic investment in them. We may put them on a watch list for acquisition,” she said.Enjoyed this article? Sign up for our newsletter to receive regular insights and stay connected.