The Pilot-to-Production Chasm
By Bassam AlKharashi · Founder and CEO, Nuhaa AI · 9 min read
The most uncomfortable statistic in regional AI is one that almost nobody publishes: the proportion of enterprise AI pilots in the GCC that ever reach production. From the engagements I have led, observed, or audited over the past five years, the honest figure is somewhere between fifteen and twenty per cent. The other eighty per cent die in a state of polished suspended animation — demoed at conferences, written up in case studies, occasionally extended for another quarter, but never running against real business volumes, never serving real customers, never moving real numbers.
This is not because the pilots do not work. In a striking number of cases the technical artefact built during the pilot is genuinely impressive. The model performs. The interface is clean. The edge cases have been handled. By any narrow definition of "working," the pilot works.
The pilot is not where the failure happens. The failure happens in the chasm between a working pilot and an operating production system. That chasm is not technical. It is operational, organisational, and architectural. And it is the single most under-discussed problem in regional AI today.
Why the chasm exists
A pilot and a production system are different objects. They share vocabulary, they share some technical components, but they are governed by different physics. The pilot lives in a controlled environment with hand-curated data, generous timelines, a small expert team, and the implicit acceptance that imperfection is acceptable because the purpose of the exercise is learning. The production system lives in an uncontrolled environment with messy live data, real-time SLAs, a much broader operational surface, and an explicit demand for reliability because real consequences flow from each output.
A pilot proves that something can work. A production system proves that something will keep working. These are not adjacent achievements. They are different categories of engineering.
When a regional institution attempts to "scale a pilot to production," what it is usually doing is trying to elevate an artefact built under pilot physics into an environment governed by production physics, without rebuilding the artefact for that environment. The result is predictable: the artefact behaves erratically, the operations team that inherits it cannot maintain it, the original pilot team has moved on to the next initiative, and within six to twelve months the production deployment is quietly retired or relegated to internal-only use.
The four bridges that must be built
Closing the pilot-to-production chasm is, in my experience, less about technical heroism and more about deliberately building four specific bridges before the pilot ever begins. Institutions that build these bridges in advance reach production. Institutions that try to build them after the pilot succeeds usually fail.
The data bridge. Pilots run on snapshot data, often hand-curated, often de-identified by hand, often extracted from production systems by a one-off process. Production systems require continuous, governed, refreshed data flows that meet the institution's existing data governance standards. Building this bridge — the data engineering, lineage, governance approvals, and refresh infrastructure — typically takes longer than building the model itself. Institutions that begin the bridge work in parallel with the pilot reach production faster than institutions that wait until the pilot is "done."
The operations bridge. A production AI system requires a 24/7 operations posture: monitoring, alerting, incident response, on-call rotation, runbooks, fall-back procedures. Most pilots run on a 9-to-5 expert posture. The team that built the pilot is rarely the team that should operate it in production, and the operations team needs months of preparation to take responsibility for an AI system intelligently. This preparation cannot be improvised after the pilot ends.
The risk and assurance bridge. Pilots typically run under a permissive risk regime because they are explicitly experimental. Production systems must satisfy the institution's full risk and assurance apparatus — model risk management, internal audit, regulator notifications where applicable, business continuity, data protection impact assessments. Each of these processes has its own cycle time. Institutions that engage them only when the pilot succeeds discover, late and expensively, that the artefact built during the pilot does not satisfy the documentation, controls, or testing standards required for production.
The change-management bridge. A working AI system in production changes how work is done. Front-line staff must learn to use it, supervise it, override it when appropriate, and recognise its failure modes. Middle managers must redesign workflows around it. Customers or citizens, depending on the deployment, must be informed about it in ways that satisfy both regulator expectations and basic respect. None of this happens automatically. All of it requires preparation that should begin during the pilot, not after it.
A more honest framing of "scale"
Regional AI discourse uses the verb "scale" to describe the move from pilot to production. The verb is misleading. To scale something implies that the same artefact, in the same form, is being made larger. That is not what is happening. What is happening is that an artefact built for one set of conditions is being replaced by a different artefact, built for a different set of conditions, that does the same useful thing but is engineered for a fundamentally different operating environment.
A more honest framing is re-build for production. This framing has the virtue of being accurate. It also has the virtue of correctly setting expectations about cost, time, and team composition. The institutions I have seen succeed at production AI deployments understand that the pilot is the requirements document for the production system, not the production system itself.
A note on procurement
A great deal of the chasm is created at procurement. Many regional institutions sign vendor contracts that cover "delivery of a pilot" without explicit reference to what comes next. The vendor delivers the pilot, invoices for it, and exits. The institution is then left to either re-procure the next phase (often from a different vendor, since the original lacks production-operations capability) or attempt to take the pilot artefact in-house with insufficient documentation and no transfer plan.
The fix is mundane and important: write contracts that explicitly cover the four bridges above, with named deliverables, named owners, and named timelines for each. Pilot-only contracts are an anti-pattern. They produce a successful pilot and a failed programme, simultaneously.
What boards should ask
Boards that want to close the pilot-to-production chasm in their own institutions should add a single question to their AI programme reviews: for each pilot under way, show me the dated plan for each of the four bridges, with named owners and current status. If the executive cannot produce that artefact, the pilot is not yet on a path to production, regardless of how its demo looked. The pilot may still be a useful learning exercise. It is not yet a candidate for the kind of investment a production deployment requires.
Closing the chasm is not glamorous work. It is unromantic engineering, governance, and change management, performed in parallel with the more visible technical work, by people who rarely appear in conference panels. It is also the single highest-leverage thing a regional institution can do to convert AI investment into AI outcomes. The institutions that take it seriously will compound an advantage. The institutions that do not will continue to launch pilots, decade after decade, that never become anything more than pilots.
Bassam AlKharashi is the founder of Nuhaa AI. He has spent twenty years building and advising AI programmes inside Saudi Arabia's most regulated organisations — from sovereign banks to ministries.