Zafin’s CEO Wants to Build the Control Tower for Enterprise AI

Every enterprise is hard at work building a workforce made of software. Almost none of them know how to manage it. How do you govern, audit, measure and coordinate employees that happen to be artificial intelligence agents? Most companies don’t. The problem is too new.

So Karen Webster put the question to Charbel Safadi, CEO of Zafin, directly. Is generative AI rewriting the enterprise operating model, and is governance the bottleneck that decides who wins?

Safadi pushed past the premise. The launch of Zafin AIOS, which he announced in this PYMNTS exclusive, isn’t about agents at all.

“It’s not just agents,” he said. “It’s the way work is done.”

His point: Stop treating AI as a pile of tools bolted onto workflows built for humans. Those workflows were designed around human decisions and manual handoffs. Drop autonomous systems into them and you get chaos, not productivity. What you need is the layer that sits above all of it. Safadi reaches for airports as the right analogy.

“What AIOS is trying to solve is becoming the airport for the airplanes,” he said.

Airports don’t fly planes. They route, monitor and govern thousands of movements so nothing collides. Enterprises now need the same thing for the fleets of AI systems running across their departments at once. Zafin built AIOS on itself first, as Customer Zero, using it to speed up its own product development and research before selling it. The lesson that surfaced fast.

The hardest barriers to AI adoption are cultural, not technical.

The Next AI Problem Isn’t Intelligence. It’s Management.

Factories once dropped robots onto lines designed for human hands. The robots didn’t pay off until the line itself was redesigned. Enterprises are repeating the pattern with AI, and the lesson sticks. Without redesigning the operating system around the new worker, the productivity gains never show up.

“We have to reimagine the way work needs to be done, which requires a cultural transformation with AI,” Safadi said. “Many organizations think the solution is adding tools to the human capacity, but not reinventing the way actual work is done across an enterprise.”

Bolt AI on in enough places and you get the opposite of efficiency. Teams spend their time managing a patchwork of apps, agents and workflows instead of running one system. That’s why so many banks have spent heavily on AI and moved cautiously anyway. They’ve bought tools and run pilots. Almost none have redesigned an end-to-end operating model around autonomous systems.

“What we’re trying to drive is a new operating fabric for organizations,” Safadi said.

On the other side of that fabric is what he calls “the company of yes.” An organization that can test an idea, prototype a product and answer a customer without hitting the usual resource wall. Because once autonomous systems can research, design, write code, run analysis and execute real workflows, the old scaffolding starts to creak. Approval chains, budget cycles, functional silos and reporting lines stop being safeguards and start being bottlenecks.

Clearing the Governance Bar Is the Competitive Unlock

If AI changes how work gets done, how products get built and how labor gets deployed, then this is a question of corporate design, not technology procurement. And corporate design runs straight into governance and compliance, especially in regulated industries like financial services. Banks have to satisfy regulators, auditors and risk managers who need to see how a decision was made and why a given outcome happened.

Safadi said the same demand surfaces in every conversation he has with bank boards, CEOs and CIOs.

“Every single one of them has said to me, we need a control plane,” he said. “We need an ability to organize structure, look at efficiencies, look at productivity, look at routing, look at cost, and make sure at the end of the day, we could still satisfy the regulatory requirements.”

That control plane is the center of AIOS. Zafin doesn’t assume anyone standardizes on a single AI provider. It assumes the opposite. A mix of cloud models, open-source models, bought agents and homegrown ones, all running at once. The job is to make that mix behave like one coherent system, with orchestration that runs end to end.

Which leads to the question Webster kept circling back to: when autonomous agents start making decisions and doing the work, who’s neck is on the line?

Proof of Work Becomes the Moat

Safadi’s answer flips governance from a cost into a feature. As AI sinks deeper into operations, transparency itself becomes the asset. Being able to show how the work was done starts to matter as much as the work.

Here’s the mechanism. “The humans put the compliance artifacts in AIOS, they put the knowledge artifacts in AIOS, they put the guardrails in AIOS, they put the policies and limits and standards,” Safadi said. The agents then operate inside those constraints, while separate compliance agents check their work against the rules in real time.

He calls the output “proof of work.” Every agent leaves a record of its actions, decisions, validations and compliance checks across a workflow, and every output ties back to an auditable chain of activity.

“My argument here is that this is even safer than the way we operate today,” Safadi said. “Most humans don’t document why they thought about doing something a specific way.”

Then the line that frames the stakes for every bank watching: “The banks that can move with speed will compete. The banks that can’t will effectively become a utility.”

That’s the real shift. AI isn’t just spinning out new products. It’s building a new operating model, one where the edge goes to whoever can redesign, govern and execute the work fastest.

Or, as Safadi put it: a new operating fabric for organizations.