Published
Many enterprises now have an AI strategy. Most have pilots, often several. Yet when technology leaders ask which AI systems and agents are operating reliably in production today, the answer is often less impressive than the roadmap suggests.
Most of that gap sits between pilots and how the business actually runs. We call this the last mile: the distance between a model that performs in a demo and one that works inside live, regulated operations. And it's where many AI initiatives struggle to move from pilot to production.
CIOs need to determine whether their organization has built the foundations required to let AI perform inside live operations. Genpact's study with HFS Research shows the scale of the challenge: roughly 13% of enterprise spend is already flowing into AI initiatives, yet 85% of executives say four enterprise debts – data, technology, process, and talent – are actively preventing them from realizing AI value.
The real barriers to scaling AI are often the surrounding elements of the model: process ownership, workflow integration, governance, escalation paths, and accountability. Process, data, and technology debts, in the form of fragmented workflows, inconsistent data, and aging systems, prevent AI from operating with enough context to act reliably.
Until CIOs address these operating-model constraints, promising pilots will remain stuck between demonstration and deployment.
The AI pilot trap
Scale in AI is not about doing more pilots. It's about whether the AI systems behave predictably under real-world conditions, in real workflows, under the oversight standards the enterprise already applies to other critical operations. This distinction is critical in regulated environments, but any business that depends on sound decisions, reliable core processes, and good customer outcomes – which is to say every business – faces the same test.
Pilots stall when AI is detached from three essentials:
Process ownership: Someone accountable for the decision the AI touches, not just the model behind it. Without it, no one owns the outcome when the AI is wrong
Platform integration: The AI running inside the systems and workflows where work actually happens, with access to live business context. Without it, the AI sits beside the work instead of doing it
Operating governance: Clear rules for oversight, exceptions, and escalation. Without it, every edge case becomes a new, unmanaged risk
All three converge in the last mile, where AI must operate inside the real workflow, absorb business context, and route exceptions without creating new risk. An organization can build an impressive assistant that summarizes documents, routes claims, reviews loan files, or drafts customer responses. But if no one owns the decision path, if the system sits outside the workflow where work gets done, or if there is no clear escalation path for edge cases, the organization has created a demonstration, not a durable capability.
Consider an insurance claims workflow in which AI handles first-pass intake: reading documents, classifying cases, identifying missing information, and routing straightforward claims and escalating exceptions to a human reviewer.
Ownership: When the AI clears or routes a claim, is a named adjuster or team accountable for that outcome, not just the model's accuracy?
Integration: Does it run inside the claims system where the work happens, with live policy and customer context, or beside it?
Governance: When a claim falls outside the rules, is there a defined escalation path and an audit trail strong enough to support a regulated review?
The question was never whether the model can read a claim file. It's whether the process around it can stand behind that decision in production.
This pattern shows up in different ways across the enterprise:
In regulated sectors, AI must be auditable and controllable before it can scale
In high-volume operations, speed only matters if accuracy and exception handling hold up under pressure
In customer-facing processes, inconsistency or risk immediately wipes out any productivity gains
AI is most likely to create enterprise value when it is embedded in core processes and platforms, not layered on as a parallel experience.
From copilots to agentic operations
Most of today's enterprise AI still operates as an assistant rather than a decision-maker. It recommends, summarizes, drafts, and highlights. The impact is valuable but doesn't represent operational transformation. The bigger shift comes when AI begins to handle bounded work inside a business process with human oversight, system controls, and clear accountability.
This is the real shift from copilots to agentic operations.
Before agentic operations can begin, a few elements must be in place:
Infrastructure that many organizations are still building
Process visibility that identifies where AI should act and, platform integration.
Governance that defines what the system can and cannot do
Monitoring that detects issues early
AI also needs to understand the context. Data must be clearly linked to systems and business rules so AI can operate with judgment, not just pattern recognition.
These are not secondary design choices. They determine whether AI can be trusted in production.
The AI production readiness test
For CIOs, the practical question is whether the enterprise can absorb and govern AI at scale so it delivers reliably in the last mile of execution. These five questions can help CIOs identify genuine enterprise readiness to move from pilots to production.
1. Who owns the business outcome when the AI is wrong?
Who owns the business outcome if AI makes the wrong recommendation, takes the wrong action, or creates an unintended customer, regulatory, or financial consequence? Someone must explicitly own and be accountable for these decision paths in the operating model. They must also define escalation rules for scenarios the system was not designed to handle.
2. Is the underlying process stable enough to automate?
Many AI initiatives fail not because of model performance but because they are deployed into undocumented, inconsistent workflows or live in a separate interface or side workflow, forcing employees to move between systems and manually transfer outputs.
AI in production works within the systems that carry the last mile of execution – the decisions, handoffs, approvals, and exceptions that run the business. These processes must be sufficiently standardized, governed, and measurable to support AI-driven execution.
3. Can you trust the data at the moment the decision is made?
Every CIO has experienced the problem of a successful pilot failing because production data was incomplete, delayed, duplicated, or inconsistent. Demos run on clean data, but production runs on incomplete records, conflicting signals, unusual edge cases, messy handoffs, and inconsistent formats. This variability defines last-mile execution. If performance depends on ideal conditions, the initiative may not yet be ready for production.
4. Can this operate inside the economics of the enterprise?
Most pilots work. Many fail when transaction volumes, exception volumes, infrastructure costs, or support costs increase. AI in production should operate without creating new cost, complexity, or technical debt.
5. Is it fully auditable after go-live?
Every AI system needs clearly defined rules of authority. What can it decide on its own? When should it escalate issues? Who is accountable for the review? You need to be able to reconstruct, end to end, why the system acted the way it did. In regulated settings, this is mandatory but is increasingly important in any enterprise environment where AI affects material decisions. If teams cannot trace inputs, logic, actions, approvals, and escalations, they do not have a production-ready system.
The real competitive divide
A key competitive differentiator in AI will not be defined by who has access to the most powerful model. It will be defined by the operating discipline to deploy AI safely, repeatably, and at scale and then make it work in the last mile to deliver business outcomes.
For CIOs, this means asking tough questions early in AI initiatives: Where should AI sit in the workflow? Who owns its decisions? How are exceptions managed? How will we know if the system is trustworthy in production? It also means confronting a harder reality: production AI succeeds or fails in the last mile of execution.
The organizations that move ahead will be those that treat AI as an operational capability to be designed, governed, and measured, rather than simply as a capability to be demonstrated. This is a clear shift from pilot intrigue to production value, and it's where the CIO agenda should be focused now.
