Enterprises Embed Real-Time AI Controls To Protect Customers And Drive ROI

AI has moved into the operating core, and the way it’s governed is changing just as fast. Static, after-the-fact monitoring can’t keep up with generative systems that produce different outputs with every prompt and interaction. Leading enterprises are embedding oversight directly into live workflows, building real-time control layers that intervene in milliseconds, reduce risk, and tie AI performance to measurable business outcomes.

Marek Poliks, Head of AI at LaunchDarkly, a feature management and experimentation platform for enterprise software teams, has applied his award-winning research in human-AI interaction across commercial AI systems and academic curricula. Drawing on years of supervising AI agents in live environments, he emphasizes that observability must evolve from passive reporting to active intervention.

“The future of observability isn’t just watching what AI does. It’s intervening in real time to control outcomes before they reach the customer,” Poliks says. As generative models scale from pilots to production, companies must rethink how they manage AI performance and risk. Success comes from embedding oversight into live systems, giving teams the speed and control to operate safely at scale.

• Risk radar: Traditional monitoring surfaces problems only after they occur. “That old way of observability just gives you a list of bad experiences your customers have,” Poliks notes. Today, automated evaluators review outputs as they happen, measure them against business objectives and policy, and intervene within milliseconds. This creates a real-time control layer that reduces errors and protects customer experience.

• Instant insight: Continuous evaluation monitors and corrects every interaction immediately. “If you have the result of an evaluation in real time, you can handle problems before they hit customers. It’s code space, meaning that someone is talking, there’s an exchange happening, and you can look at the aggregate result and, if it doesn’t fit the bill, do something about it,” Poliks explains.

Accuracy alone no longer defines AI success. Poliks emphasizes that true evaluation measures whether AI achieves concrete business outcomes. Teams thrive when feedback loops link directly to measurable performance, allowing AI to continuously optimize for value.

• ROI on repeat: Real-time testing in live environments lets teams compare performance, measure results, and refine AI behavior against business metrics. “In the short term, that feedback loop has to be directly correlated to ROI in some real measurable way,” Poliks says.

• Outcome overseers: Rather than rely on a single generalized oversight system, enterprises deploy discrete evaluators, or “LLM judges,” focused on safety, compliance, tone, or task completion. “AI is really good at specific things. The mistake is building one giant system asked to handle general problems. Right now we’re living in a discrete, task-based AI world,” Poliks notes. Checkpoints allow automatic rewriting, escalation, or halting, increasing reliability and governance measurability.

As AI moves into production, authority and governance gain critical importance. Poliks recommends agile, hands-on governance boards and runtime control structures that empower teams to act quickly and responsibly.

• Hands on the wheel: Teams closest to the AI should retain authority to intervene. Feature management allows live adjustments without redeployment. “That kind of runtime intervention still belongs to the AI team. There is still a real skills differential between the AI engineer and the conventional engineer,” Poliks says.

• Policy in practice: Boards must combine legal, leadership, and hands-on technical expertise. Restrictive boards that only say “no” slow progress and limit AI performance. “You wind up in a situation where you are ultimately disempowered to make changes. That’s the easiest way to get a poorly behaving AI system,” Poliks warns. Enabling boards bridge policy and practice, supporting iterative improvement and responsive AI systems.

The gap is widening between organizations that actively build and refine AI and those still shaping strategy. Success comes from hands-on engagement, iterative governance, and embedding supervision directly into AI workflows. “Organizations that actively build, test, and refine AI are now setting the pace for tomorrow’s competitive advantage,” concludes Poliks.