Scored Thresholds And Runtime Rules Separate AI That Ships From AI That Stalls

Most enterprises don’t have a deployment problem. They can stand up a pilot, prove the concept works in a controlled setting, and show something impressive to the people funding it. The trouble starts when that pilot has to become a business process that runs reliably, every day, across the dozen systems and data sets a real workflow touches. That space between a demo that works and a system an organization can trust is where the majority of AI initiatives stall out and get written off. The sequence of decisions a team makes about data, evaluation, and control, before the first line of the project, is the key to closing that gap.

Krishnamurthy Rajesh is the Chief Information Officer of Digital Transformation and AI Strategy at Vinmegham, an XRMROI brand focused on enterprise AI governance and space-tech connectivity, where he leads enterprise AI governance, cybersecurity, and cloud modernization. Across more than nineteen years as a Group CIO and VP of Technology, he has built greenfield IT landscapes, rolled out SAP across FMCG manufacturing plants, and carried zero audit findings through SEBI, RBI, ISO 27001, and NIST-aligned environments spanning the US, India, and Europe. He is a Certified Chief AI Officer and an ISO 42001 Lead Auditor, and he approaches AI deployment the way an auditor approaches a control environment: one missing step invalidates everything downstream.

"AI is technical only up to the level where you are trying to analyze and get a compute faster," Rajesh said. "Behind that, there is a great deal one needs to put across, step by step. And if you miss one step, the whole thing goes out." His argument is that the pilot clears the technical bar easily, which is exactly what lulls teams into skipping the harder, non-technical work that determines whether the thing survives contact with production. The steps he has in mind are not model tuning. They are decisions about ownership, data, evaluation, and control that have to be made before the first line of the project, not discovered after go-live.

• Define what right means before autonomy: The older way of building software was sequential, Rajesh noted, one stage handing cleanly to the next. AI workflows don’t behave that way. "In today’s world there are a lot of moving parts in between," he said, with data flowing freely across systems, geographies, and regulatory regimes that each impose their own rules. He pointed out that a conversational system written for one market can read as offensive in another, because sentiment and language are not universal. The baseline a team measures against—the definition of an acceptable output—has to account for all of that from day one, and in practice the data around the model is what decides whether it pays off. For Rajesh the failure mode is predictable: the omission surfaces late, the data to fix it costs more than it would have upfront, and the skills to use it aren’t on the team.

• Put every function in the room: A business requirements document captures the business outcome, but Rajesh argued that AI needs a defined scope layered on top of it, and that scope can’t come from one team. "The major impact of AI is actually from ROI. It’s finance and operational efficiency. So the core team should consist of a stakeholder from each function. Because if one function fails to deliver what is supposed to be done from an AI part, the whole project will go for a toss." He wants the marketing input, the finance input, and the operational input identified as dependencies before build, with a steering structure that sequences them, so a gap in one doesn’t undermine the rest. Getting domain experts in at the start of a project, rather than calling them in once a prototype is already failing, is the same discipline applied earlier in the chain.

The through-line connecting those two points is that both belong to the scoping phase, and both are routinely deferred. Teams treat the baseline and the stakeholder map as things that can be sorted out once the model is working, when in fact the model working tells them almost nothing about whether the workflow around it will hold. By the time a project reaches the question of how to test it, the expensive mistakes have usually already been made upstream. That's where the second category of failure begins: not in defining the work, but in judging whether it's good enough to trust.

• Score the output, don’t just pass or fail it: Traditional user acceptance testing runs a script and marks each case pass or fail. Rajesh was clear that this breaks down with AI, especially anything predictive, where there’s no fixed answer to check against. “You may not get 100 percent, but you can say the probability of this is 90, the probability of that is 80,” he said. Each threshold needs a score, and someone has to decide what score is good enough to ship. He framed it as a deliberate judgment: whether the strong 70 percent of cases is enough to go live while the weaker 30 percent is managed by hand, or whether the 30 percent dominates and the project waits. A UAT tester can’t make that call alone. It’s a business verdict, not a technical one, and it belongs to a committee weighing the ROI against the shortfall. The same reasoning shows up in the use of decision records that let a company judge when an agent has earned the right to act on its own.

• Monitor for drift against a trusted baseline: AI doesn’t sit still after launch. “The beauty of AI is it not only works on given data, it also produces data,” Rajesh said, and that generated output becomes the input for the next round of predictions. If the underlying reality changes and the core isn’t updated, the model keeps compounding on a stale assumption. He walked through the risk in terms of baselines: a runtime value is temporary, but a certified, approved figure is sacrosanct, and the system has to know which is which. Promote the wrong number into the permanent baseline and every future output inherits the error. It’s the mechanism behind a lot of post-launch decay, and an approved system still drifts after launch without funded maintenance and sampling to catch it. Monitoring, in Rajesh’s framing, is the only thing standing between a working model and a confidently wrong one.

Evaluation and monitoring answer whether the system is doing what it should. A separate question is what happens when something, or someone, actively works against it, and that risk grows as the technology matures rather than shrinking. Rajesh is emphatic that security posture can't be set once and left. The attack surface moves, which means the controls have to move with it.

• Treat security as a moving target: “The moment the technology is maturing, the negative things are also coming up,” Rajesh said. He pointed to the risk of a model producing an output that looks benign, a PDF, say, with a prompt injection buried inside it that triggers a data leak when another system ingests it. Prompt injection sits at the top of the OWASP risk list for LLM applications precisely because the attack rides in through ordinary-looking inputs. His point is that an autonomous system needs the same discipline as any other production software: a posture that gets revisited and patched as new threats appear, not a one-time sign-off. The architectural answer is enforceable permissions that hold even when a malicious prompt gets through, which is what Rajesh is describing from the posture side.

• Build runtime guardrails around data handling: The sharpest operational point Rajesh made was about what the system does with data in the moment. Every runtime output needs an explicit designation: use and purge, use and store, or visualization only. Skip that and the system makes the choice for you, badly. "If I do not clearly identify it, AI will take it as a data set that it thinks needs to be stuck upon for the next level of query or transaction." In other words, a value that should have evaporated instead becomes training input for the next decision, widening the gap between what the model knows and what's actually true. He tied it back to a basic engineering habit, flushing a variable after use, and argued the same hygiene has to be designed into AI from the start: "Whenever we are doing something at runtime, those guardrails need to be built, to say what this particular runtime output is for."

What connects all of it, in Rajesh's telling, is that none of these are afterthoughts to a technical deployment. They are the deployment. The model is the part that works in the demo; the baseline, the cross-functional scope, the scored evaluation, the drift monitoring, the security posture, and the runtime rules are the part that lets the thing run in production without eroding trust or ROI. Treating AI as an isolated technical project is precisely what produces the pile of stalled pilots.

"Governance, control mechanisms, checks and balances, cost projections, and knowing when to pull the plug," he said. "All of that needs to be clearly defined before you start." The organizations that get AI into production are the ones that do the orchestration work first, mapping the whole business process across every system and team it touches, and only then letting the model loose inside it.