Noe Ramos on Building AI Governance at Agiloft

Michael, I'm really honored to be here to discuss one of my favorite topics.

So thank you so much for having me.

I'd like to start with a stat that I read really recently, which is that 40% of enterprise applications will have AI agents embedded in them by the end of this year.

And that's Gartner's number.

A year ago, it was less than 5%.

So the conversation in most boardrooms has already shifted from should we do this to we are doing this.

And that's exactly where the hard part begins.

So as you mentioned, my name is Noe Ramos and I'm vice president of AI operations at Agiloft.

And my job every day is to make sure that the AI we deploy works the way it's supposed to, not just in a demo, but at scale with real accountability and real human beings behind it.

And that's what I want to talk about today.

Here's the shift that I want the audience to sit with.

Six months ago, the enterprise AI conversation was about getting to production.

Now the data tells us that some of us are already there very quickly.

And that's not entirely good news, but it's positive news.

Only 21% of organizations have a mature governance model for autonomous AI.

Think about what that means.

Most enterprises have shipped AI they can't fully see, can't fully control, and don't have clear ownership over when something goes wrong.

What that means is the risk has moved.

It's no longer about whether you can get AI into production.

It's about whether you can govern what you've already shipped as you continue to ship and continue to evolve.

And most organizations don't even know that they have that problem yet because failure modes are quiet.

I call this silent failure at scale.

An agent drifts, outputs degrade slowly, errors compound across workflows, and because nothing triggers a loud alert, no one notices until the damage is real.

Traditional SaaS fails loudly.

It's up or it's down.