Kuber Sharma

The graveyard is not full of failed pilots. It is full of pilots that worked.

That's the thing that took me longest to understand about enterprise AI. The initiatives that end up abandoned, the programs that get quietly defunded, the use cases that never make it to production -- most of them had a successful pilot. The demo worked. The accuracy was good. The business sponsors were impressed.

Then nothing happened for six months. Then someone got reorganized. Then the vendor contract came up for renewal and nobody could articulate the value. Then it died.

I've watched this pattern play out across three companies and twelve years in enterprise software -- at Microsoft Azure, at Salesforce, at UiPath. The shape of the failure is almost always the same. I call it the Pilot Trap.

It is not a technology problem. It is an infrastructure problem. And the infrastructure that fails isn't data or cloud. It's the boring stuff: who owns the thing, who signs off on the output, what happens on Monday when it's wrong. Nobody built any of that, because everyone assumed a good pilot result would carry its own momentum.

It doesn't. Here is the map.

The Five Stages

Enterprise AI initiatives move through five stages on the way from idea to production. Most of them stall somewhere in the middle.

Stage 1: Idea. Someone identifies a use case -- usually a digital transformation lead or the COO's office, and surprisingly often, the vendor. The criteria for selection are almost never "what is the most important problem we have?" They are "what can we show results on quickly?" and "what is the vendor's recommended starting point?" These are not the same question.

Stage 2: Pilot. A small team builds it. It runs on clean data with an IT person sitting two desks away, and the scope has been trimmed until it cannot fail. This is the part that usually works.

Stage 3: Validation. Results are measured. If the numbers look good, the pilot is declared a success. Stakeholders are briefed. A case study is drafted.

Stage 4: Transition. The initiative moves from the innovation team -- or the IT team, or the vendor delivery team -- to the business team that will actually run it. This is the stage that kills more initiatives than any other. Not because of technology. Because of accountability.

Stage 5: Production. The system runs at real scale, on real data, with real users, inside real workflows. This is the destination. Very few initiatives reach it.

The Six Gaps

Between and around these stages are six gaps. Each one is a place where an enterprise AI initiative can stop. Most initiatives fall into at least two.

Gap 0:  The Strategic Gap

This exists before Stage 1 begins. Most enterprise AI programs start without a clear answer to: what problem are we solving that we cannot solve another way?

The question sounds obvious. It almost never gets asked. Teams jump to use cases because the pressure to "do something with AI" is high and the time to answer strategic questions is short. The result is a portfolio of pilots that are technically interesting and strategically irrelevant.

Gap 1: The Selection Gap

The use cases that get piloted are the ones that are easy to demonstrate. Invoice processing. A chatbot for the IT help desk. Something that summarizes meetings. Nobody gets fired for picking these. They're cheap to scope and you can show one at the next offsite. They are also rarely the use cases that would move the business.

The cases that matter, the ones where AI changes how the company actually competes or decides things, are harder to pilot. Somebody's process has to change. Somebody has to admit they don't own the workflow they thought they owned. So those get deferred in favor of whatever can be demoed at the next leadership offsite.

Gap 2: The Measurement Gap

Here is a question I've started asking every enterprise AI team I work with: how will you know, twelve months from now, whether this pilot generated business value?

The answer is usually a pilot metric. Accuracy. Hours saved per week. Sometimes a completion rate. These are fine measures of whether the technology works. They are almost never connected to a business outcome that anyone in the C-suite cares about.

A pilot that saves 40 hours per week of analyst time sounds like a win. If those analysts are reassigned to work of equivalent value, it is a win. If the organization doesn't know what to do with the capacity, it is a math exercise that looks good in a slide deck and disappears from the budget the following year.

Gap 3: The Ownership Gap

Pilots are owned by innovation teams, IT teams, or vendor delivery teams. These are not the people who will run the system in production. When the pilot ends, someone has to take it. That someone, usually a business unit that was briefed once in month two, wasn't in the room when the use case was picked or the success criteria were written. They certainly never agreed to own it.

The ownership conversation almost never happens during the pilot. It happens after the pilot succeeds. By then everyone has scattered. The business team is back to its own quarter. The innovation team has a new deck for a new pilot, and the vendor's account exec is thinking about renewal, not rollout.

The Ownership Gap is the moment where a successful pilot becomes nobody's problem.

Gap 4: The Infrastructure Gap

Pilots run on clean data. Production runs on whatever data actually exists.

The pilot ran on a dataset somebody cleaned by hand and an integration somebody built just for it. Production means the ERP with eleven years of inconsistent vendor names, and a workflow that turns out to have four exceptions the pilot team never saw because nobody told them.

This is not a failure of planning. It is a consequence of the deliberate choice to pilot in a controlled environment -- a choice that was correct. The mistake is assuming the infrastructure built for the pilot scales to production without a second project roughly the size of the first.

Gap 5: The Trust Gap

The last gap exists in production. The system is running. The data is there. The workflow is connected. Users have been trained. Then the adoption metrics come in at 30% of projections.

The Trust Gap is the distance between a system that technically works and a system that people will actually act on. Enterprise users have been burned before. They've seen the dashboard that was wrong for a quarter before anyone noticed. They've cleaned up after automations that made more work than they removed. Their skepticism is not irrational. It is institutional memory.

Building trust in an AI system requires something most pilots don't build: a track record. Not a demo. Not a case study. Actual decisions, made by real users, with AI assistance, that turned out well. That takes time. It also takes a named person who owns the outputs and has to answer for the errors, and most enterprise AI programs never appoint one.

What the Pilot Trap Actually Tells You

I built this framework because enterprise AI teams keep solving the wrong problem. They tune the model. They redo the interface. They run the pilot again with a bigger sample. Meanwhile the initiative is dying in a gap that has nothing to do with any of those things.

The companies I've seen get through it didn't have better models. They had a business owner named before the pilot started, and a metric the CFO recognized.

The question worth asking is not how to improve the pilot. It's which gap you're standing in. Most teams can answer in under a minute once they see the list.

The full Pilot Trap diagnostic,  including the questions to run against each gap and the patterns I've seen determine outcomes, is at my website. All my frameworks are at kubersharma.com/frameworks.

Six months from now, when the renewal lands on someone's desk and they ask what this thing was worth, the pilot metrics will not answer the question. Decide now who will.

Kuber Sharma is Senior Director of Product Marketing at UiPath, where he leads GTM for the Agentic Business Orchestration portfolio. Previously at Microsoft Azure and Salesforce/Tableau. He writes about enterprise AI, product marketing, and category creation at kubersharma.com.

The Pilot That Never Graduates

There is a pattern playing out in enterprise AI right now that almost everyone in the industry recognizes but nobody has quite named. A company builds a compelling AI pilot. The results are impressive. Leadership is excited. Then nothing. The pilot does not scale. The budget does not expand. The vendor gets ghosted. The internal champion quietly moves to a different project.

This is the Pilot Trap.

The Pilot Trap is not a technology problem. The technology usually works. The pilot usually works. The problem is that a successful pilot and a deployable enterprise system are two completely different things, and most AI vendors conflate them.

Failure Mode 1: The Capability Trap

The most common form of the Pilot Trap is what I call the Capability Trap. The vendor designs the pilot to demonstrate what the AI can do. The demos are impressive. The benchmark results are strong. The use case is well-chosen. The pilot succeeds by every measure the vendor defined.

But the enterprise did not buy capability. The enterprise was buying confidence that the system could be trusted with real data, real workflows, and real consequences. The pilot proved the former and left the latter completely unaddressed.

When the procurement team, the CISO, the data privacy officer, and the head of operations all show up to evaluate the system for full deployment, they are not asking the same questions the pilot was designed to answer. They are asking questions the vendor never prepared for.

The vendor that escapes the Capability Trap builds the pilot to answer both sets of questions simultaneously. Capability is demonstrated. Trust is earned. The governance artifacts are ready before the evaluation team arrives.

Failure Mode 2: The Champion Trap

The second failure mode is subtler. The pilot succeeds because one person inside the enterprise made it succeed. This person had the organizational authority to clear obstacles, the technical credibility to evaluate the results, and the personal conviction to push the project forward.

When the pilot ends and the expansion conversation begins, this person hits a wall. They have to sell the same system to five other stakeholders who were not in the room for the pilot. The CFO wants a business case. The CISO wants a security review. The head of operations wants a change management plan. The legal team wants a data processing agreement. The board wants a risk assessment.

The champion has the conviction but not the artifacts. The vendor won the pilot but lost the champion to an impossible internal sales motion.

The vendor that escapes the Champion Trap treats the internal champion as the first customer, not the last one. They equip the champion to sell across the organization. They pre-build the risk assessments, the security documentation, the business case frameworks. The expansion conversation starts in the pilot phase, not after it.

Failure Mode 3: The Integration Trap

The third failure mode is the most technically specific. The pilot ran in a clean environment. The production data is messier. The existing systems are older. The IT team has requirements the vendor did not know about. The integration that looked straightforward in the demo is six months of engineering work in production.

This is not unusual in enterprise software. But in AI, it lands differently because the value proposition is usually time-to-value and operational efficiency. An AI system that takes eighteen months to integrate has already lost the business case that justified the investment.

The vendor that escapes the Integration Trap designs for the production environment from day one of the pilot. They ask about existing systems before they write a line of code. They document the integration requirements alongside the capability requirements. They have a realistic deployment timeline that the customer can defend to their board.

What Escaping the Pilot Trap Looks Like

The common thread across all three failure modes is that the vendor treated the pilot as the destination when the pilot is actually the beginning of a longer journey. The pilot is where you earn the right to the next conversation. It is not the transaction. It is the trust-building event that makes the transaction possible.

Vendors that escape the Pilot Trap do three things consistently. They design the pilot to answer the expansion team's questions, not just the champion's questions. They treat the internal champion as a sales partner who needs tools and artifacts, not just a point of contact. And they define success metrics for the pilot that map directly to the business case for full deployment.

The result is not just more pilots graduating to full deployment. It is shorter sales cycles, higher win rates on competitive evaluations, and stronger expansion revenue from existing customers. Escaping the Pilot Trap is not just good product design. It is the most important GTM motion in enterprise AI right now.

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Location: Virtual    Fees: 3000

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Location: Virtual    Fees: 2500

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