Next AI advantage will be continuous improvement

This article is authored by Mukesh Bansal, founder and CEO, NuPlay AI.

For the past two years, much of the enterprise AI conversation has been about adoption--who is using GenAI, which companies have moved beyond pilots and how quickly AI agents can be put into production.

Artificial intelligence (Representative)

Those questions still matter. But as AI moves deeper into business operations, another question is becoming harder to ignore:

What happens after the AI goes live?

Consider a simple example. A retailer's customer support agent handles a return request and misclassifies a damaged-item claim as a simple changed my mind return, applying the wrong refund policy. A human agent catches it and corrects the case. Weeks later, a near-identical claim comes in from another customer, and the system makes the same misjudgment again.

The problem may not be the model. It may be what happened to the first mistake--whether the correction was captured, whether the failure became part of the next evaluation and whether anything actually changed as a result.

That is where the next enterprise AI challenge lies. Deploying an agent is one milestone. Building a system that can learn from its performance, improve against evidence and remain under control is another.

EY's India C-suite GenAI survey of more than 200 Indian organisations found that 47% have multiple GenAI use cases live in production, and 24% said their organisations were already deploying agentic AI.

The direction is clear: Enterprises are moving AI from experimentation into real business processes.

But getting an agent into production is only the first milestone. Once it is live, the harder work begins: dealing with failures, capturing feedback, identifying recurring patterns and improving the system without creating new problems.

The focus is shifting from how quickly an organisation can deploy AI to how effectively it can improve AI once it is deployed.

Most AI systems do not automatically turn every correction into a lesson.

A customer can challenge an answer. An employee can override a recommendation. A compliance team can reject a decision. A claims analyst can identify a false positive. That feedback has value, but unless there is a process for capturing and evaluating it, much of the learning remains with the people involved.

A more systematic approach creates a loop:

Experience → feedback → evaluation → diagnosis → change → testing → deployment → new experience.

The point is not that an AI agent should blindly learn from every interaction. That could create as many problems as it solves.

The point is to create a controlled mechanism through which relevant feedback can be evaluated, improvements can be tested and successful changes can be introduced.

Because changing a system is not the same as improving it.

Self-improving AI can describe very different capabilities.

An enterprise system can improve through a governed feedback loop, where performance is evaluated, problems are identified and potential changes are proposed, while people validate what ultimately gets deployed.

That is very different from an agent autonomously changing its own behaviour with limited human intervention.

The distinction matters because autonomy and improvement are not the same thing.

A system that changes itself is not necessarily a system that gets better. It could become faster but less accurate, reduce cost while increasing errors, or improve one metric while creating a problem elsewhere in the workflow.

For enterprises, the useful assessment, therefore, goes beyond whether a vendor calls an agent self-improving. It should examine what the system can actually change, what evidence it uses, who approves those changes, how they are tested and what happens if they make the system worse.

Those answers tell you far more than the label.

There is another problem with the word improvement: It needs a clear definition and a measurable outcome.

Depending on the use case, improvement could mean better accuracy, lower cost, faster response times, stronger compliance, fewer escalations or better customer outcomes.

The objective is not to make an agent change more frequently.

It is to make changes that can be tested and shown to produce better outcomes without introducing unacceptable risks.

This is where evaluation becomes as important as deployment. If enterprises cannot establish whether a change actually improved performance, they are not running a learning loop. They are simply changing the system and hoping for a better result.

This is also why human-in-the-loop oversight is not optional in this process--it is the control point that makes the loop trustworthy. No proposed change, however promising the evidence looks, should go live without a human validating it, particularly in high-stakes decisions like claims, underwriting or credit. HITL is not a bottleneck to improvement; it is what allows enterprises to improve with confidence rather than simply iterate faster.

As enterprises gain access to multiple foundation models and AI platforms, the model is becoming only one part of the overall system.

The infrastructure around it can matter just as much: Evaluation datasets, proprietary business data, workflow logic, feedback mechanisms, historical failure cases, domain-specific policies and monitoring systems.

These assets reflect how a particular organisation actually operates.

Two companies may have access to the same underlying model. Recreating the other company's accumulated evaluation data, failure patterns, workflows and validated improvements is a different challenge.

The competitive advantage may, therefore, increasingly sit not only in the model an enterprise uses, but in what the enterprise has learned about using it.

That learning is built through real-world interactions, exceptions, corrections and repeated evaluation. It is also much harder to reproduce than simply accessing the same underlying technology.

The more an AI system can change its behaviour, the more important it becomes to know what changed, why it changed, who approved it, what evidence supported the change and what happened after deployment.

For relevant high-risk AI systems, the EU AI Act includes requirements covering areas such as risk management, logging, documentation, human oversight, accuracy, robustness and cybersecurity. Its implementation is phased, with key high-risk obligations applying from 2027 and 2028. Article 50 transparency obligations apply from 2 August 2026.

For enterprises within scope, the direction is straightforward: As AI systems become more capable and more deeply integrated into business processes, monitoring, documentation, oversight and traceability become part of the operating model.

A system that can change also needs a way to explain, evaluate and, where necessary, reverse those changes.

Enterprise AI has largely been measured through adoption and deployment: how many use cases are live, how many employees are using AI and how quickly organisations can move from pilots to production.

Those measures will continue to matter.

But the next set of measures is different: how the system is evaluated after deployment, how feedback is captured, how quickly recurring failure patterns are identified, how proposed changes are tested, how regressions are detected and what controls exist when the system's behaviour changes.

Together, these measures shift the conversation from AI adoption to AI performance. The first enterprise AI race was about getting AI into the business. The next one is about getting better AI out of the business. The advantage may not belong to the company with the most AI agents.

It may belong to the company that has built the most effective system for making them better.

(The views expressed are personal)

This article is authored by Mukesh Bansal, founder and CEO, NuPlay AI.