AI-slop or Not?

Avoiding the topic of AI in any internal meeting, board discussion, or ‘26 roadmap conversation is about as futile as keeping politics off the Thanksgiving table. Whether it’s Time Magazine naming AI architects “Person (or people?) of the Year,” or the nonstop launch of AI-everything, it’s unavoidable.

The problem is that in an ecosystem where every vendor claims they’re AI-native and every new feature is positioned as a “must-have,” it’s become increasingly difficult to separate meaningful innovation from point solutions with strong marketing teams — or, famously for Builder.AI, a few hundred human developers masquerading as AI.

This isn’t a theoretical concern, it’s an evolving discussion. The CPO of Bluevine put it bluntly in his recent r/Fintech AMA:

“There’s definitely a wave of ‘AI-washing’ happening right now, where teams just slap ‘AI’ on a roadmap item to make it sound more strategic than it really is. But we shouldn’t be implementing AI for AI’s sake. It’s about augmenting human decision-making without overcomplicating things.”

That framing resonates because in the world of onboarding, many of us are either building AI products tailored for compliance and fraud teams, or are fraud and compliance professionals considering how to leverage the best tools to increase conversion, improve efficiency, and reduce fraud.

The companies best positioned to win with AI aren’t necessarily the loudest or newest — they’re the ones that leverage AI to emphasize their strengths and minimize their weaknesses. In other words, AI doesn’t change the game, it simply adds another variable that you can win (or lose) by.

That lens shaped how we approached AI at Footprint. When we launched our AI agents to automate manual review, we were deliberate about avoiding AI-washing. For every product we launch, we ask the following three questions:

1. Is Footprint uniquely suited to build this?
2. Does this meaningfully improve the core platform, or is it a one-off feature?
3. Does it measurably improve conversion while reducing fraud?

If the answer isn’t yes across the board, we don’t build it.

In the case of Percy, our suite of AI agents, this was especially true. Footprint is not suddenly an AI-first company, instead, we’re AI-second. AI-first companies sell AI as the product. AI-second companies use AI to make an existing product materially better. Ramp is the canonical example: their core promise — save time, save money — didn’t change with AI. AI simply made the platform better at delivering on it.

That’s how we think about AI at Footprint. We’re not selling AI to cash in on the hype, we’re integrating agents that make us better at what we’re already best in class at: decreasing fraud and improving conversion.

Five years from now, we won’t talk about AI-first companies at all. Just like cloud-native, AI adoption will be assumed. Teams that don’t adopt it will be penalized — but teams won’t win simply because they use it. They’ll win because they chose the right tools, and used them in the right use-cases.