For years, enterprises chased the promise of artificial intelligence the way they pursue growth, with urgency and optimism. Each new model release, tool update, or benchmark score reinforced the sense that the next breakthrough was already within reach.
Much of that confidence came from experience. Early enterprise AI systems behaved predictably. They were trained, tested, deployed, and monitored against clear performance metrics. When something broke, it was visible. When accuracy dropped, alerts followed. Control felt tangible.
As AI adoption accelerated, that sense of control carried forward. Many leaders assumed the same rules would apply. That if something went wrong, they would know, and the failure would be obvious.
In reality, it rarely is. When AI systems begin to drift, the moment does not arrive as a crisis. It surfaces instead in routine review meetings, where outcomes begin to feel slightly off, even though nothing appears technically broken. The models are still live. Dashboards remain green. The system continues to produce answers. What changes first is not performance, but confidence. It erodes quietly and incrementally. Often, this is the earliest signal that AI has moved from a project to core infrastructure, while the operating model lags behind.
When AI keeps running, but decisions stop feeling right
In enterprise settings, failure is rarely binary. What weakens first is decision integrity. Outputs may still be produced on time, but certainty about their relevance begins to fade. Without clear ownership for how decisions are generated, validated, and reviewed over time, misalignment becomes easy to overlook and expensive to correct.
This shift matters because AI is no longer experimental. According to the EY–CII “AI Shift from Pilots to Performance” report, nearly 47% of Indian enterprises now have multiple AI or Generative AI use cases live in production. This marks a structural transition from experimentation to execution. As per the same EY–CII report (2025), 23% of enterprises are still in the pilot stage, which underlines how uneven “production readiness” still is across the market.
At this stage, even small deviations carry weight. Decisions influenced by AI increasingly shape credit risk, fraud detection, customer engagement, forecasting, and compliance outcomes. Once AI is embedded into everyday workflows, drift stops being a technical concern and becomes an operational and business risk.
Why most AI programmes lose control after the pilot
Most organisations did not redesign their AI operating models for this phase. AI systems continue to function, but accountability diffuses. Technology teams focus on keeping platforms stable. Business teams focus on outcomes. Risk and compliance teams step in when issues escalate. In between, no one is explicitly accountable for whether AI-driven decisions remain valid as business conditions change. Perfect data is a mirage. Most organisations discover this only when AI meets real workflows.
This operating reality explains why so many AI initiatives struggle to move beyond pilots. As per the recent report “The GenAI Divide: State of AI in Business 2025”, only roughly 5% of AI pilots achieve rapid revenue acceleration or clear business impact. The constraint is rarely model capability. It is the absence of a mature operational framework that allows AI systems to run reliably, efficiently, and responsibly at scale.
Generative AI amplifies this challenge. Unlike traditional machine learning systems with stable training cycles and deterministic outputs, GenAI is prompt-driven and context-dependent. Output quality is multidimensional, shaped by relevance, tone, clarity, and safety. Costs scale dynamically with usage and output length, often shifting with small changes in prompts or user behaviour.
Traditional AI operations, built around scheduled retraining and accuracy-based metrics, were not designed for this variability. When organisations force Generative AI into these frameworks, drift does not appear as a system failure. It appears as hesitation. More reviews. More overrides. Slower decision cycles. Conditional trust.
What changes when AI becomes part of daily operations
The enterprises adapting well are not those chasing more advanced models. They are those rethinking how AI is operated once it is live. They treat prompts as governed assets rather than ad hoc inputs. They validate outputs continuously, not just at launch. They actively monitor cost, quality, and compliance as operational metrics, not after-the-fact exceptions. Most importantly, they assign clear ownership for AI behaviour in production. They also introduce an operating rhythm that most AI programmes miss: regular review of drift signals, prompt changes, exception patterns, and cost spikes, with business, tech, and risk in the same room.
This reflects a broader shift in how AI maturity should be defined. AI maturity is not about deployment. It is about resilience and the ability to explain, audit, and correct AI-driven decisions as business conditions evolve. In regulated and reputation-sensitive environments, the winning capability is not just accuracy. It is auditability: being able to explain why the system produced a decision, and to prove that the decision remains defensible over time.
As AI becomes a permanent part of enterprise operations, the real differentiator will not be the speed of adoption. It will be clarity of ownership and discipline of execution.
(Ankor Rai is the Chief Executive Officer of Straive.)
Edited by Jyoti Narayan