Every week I hear some variation of the same sentence. It can come from a bank CTO, a startup founder, or a technology director at a large retailer: "We ran an incredible pilot, the numbers were great, but the project never moved forward." Sometimes the tone is frustration. Sometimes, shame. Almost always, genuine confusion about what went wrong.
The reality is that this is not the exception — it is the rule. Studies from McKinsey and Gartner consistently show that between 70% and 80% of AI projects never reach production at scale. The pilot works. The use case is validated. The enthusiasm is there. And yet, the project dies in a PowerPoint presentation or lives forever as a "strategic initiative under evaluation."
After more than twenty years working in technology, passing through IBM and AWS and serving as an advisor to companies such as BTG, B3, XP, and Inter, I can tell you clearly: the problem is rarely technical. The pilot does not stall because the AI model was bad. It stalls because the organization was not prepared for what comes after the pilot.
The pilot was designed to impress, not to scale
The first mistake begins before the project even leaves the drawing board. Most AI pilots are built with an implicit goal: to convince stakeholders. You choose the most favorable use case, work with clean and manually prepared data, put your best engineers on the project, and deliver a demo that works perfectly under controlled conditions.
The problem is that this approach creates a trap. The pilot was optimized for the presentation, not for operations. When the time comes to move to production, you discover that real data is messy, that the volume is ten times larger, that the legacy system does not have the API you assumed it would, and that the team responsible for day-to-day operations was never involved in the project.
I have seen this happen at large financial institutions. The generative AI pilot for customer service had 92% satisfaction in testing. In real production, with thousands of interactions per hour, latency issues, integration with core banking systems, and insufficient training of the operations team, the numbers plummeted within weeks.
The right question is not "did this pilot work?". The right question is: "was this pilot designed to reveal what will fail in production?"
The absence of a real owner is fatal
AI projects have a governance problem that traditional technology projects rarely face with the same intensity: they live somewhere between the business unit, IT, and frequently a data or innovation team that exists in a kind of organizational limbo.
When the pilot ends and the time comes to scale, the question "who owns this?" has no clear answer. The business unit wants the outcome but does not want the technical responsibility. The IT team wants the project to follow governance standards but was not involved in development. The data team delivered the model but does not operate production systems.
In this vacuum, the project does not move forward. It circulates through meetings, accumulating new requirements, waiting for approvals that no one wants to give because no one wants to be held accountable if something goes wrong.
The solution is simple to state and difficult to execute: before starting any pilot, define who the product owner of the AI project in production will be. Not the executive sponsor. Not the technical lead. The operational owner — the person who will wake up at 3 a.m. when the model starts giving wrong answers and who has the authority to make decisions.
Infrastructure and MLOps: the foundation no one prepared
This is where the genuinely technical dimension of the problem comes in. Training a model or using a generative AI API is relatively accessible today. Operating it with reliability, traceability, and the ability to update it in production is a completely different discipline.
Most companies do not have what is called mature MLOps: CI/CD pipelines for models, drift monitoring (when model behavior changes because real-world data has changed), model versioning, fast rollback capability, and observability of latency and cost per inference.
Without this infrastructure, what happens in practice is that the model goes to production and no one knows when it starts to degrade. Or worse: it begins generating problematic outputs — incorrect, biased, or inappropriate responses — and the company only finds out when a customer complains or when it appears in a news report.
In the context of AWS, where I worked directly with hundreds of customers on AI adoption journeys, the projects that scale successfully are almost always those that invested in platform before investing in use cases. They built the MLOps foundation, defined security and compliance standards for models, created an internal data catalog, and only then accelerated the production of solutions.
It is counterintuitive for those under pressure to show quick results. But it is the only path that works.
The business model of the pilot is not the business model of production
There is a point that few leaders discuss openly: the cost of an AI pilot and the cost of operating that same solution in production can be completely different — and the latter frequently comes as a surprise.
A generative AI pilot processing a thousand documents per week for demonstration costs a few dollars. The same system in production processing one million documents per week, with availability SLAs, auditing, log storage, and redundancy, can cost tens or hundreds of thousands of dollars per month. If the use case was not validated with that real number in mind, the ROI that justified the project simply does not exist.
Beyond that: many companies underestimate the human cost of operating AI in production. You need ML engineers to maintain the models, data analysts to monitor quality, business specialists to periodically validate outputs, and depending on the use case and the regulated sector, a formal human review process for critical decisions.
The practical recommendation: the pilot's business case must include the total cost of ownership in production, with realistic volume scenarios. If the ROI only works in the pilot scenario, the project should not leave the pilot stage.
Organizational change: the elephant in the room
No AI project scales without changing the way people work. And changing the way people work is infinitely harder than training a model.
When a company implements generative AI to support credit analysts, for example, the decision-making process changes. What the analyst does changes. What they need to know changes. Their performance metrics change. If the HR department, direct managers, and the analysts themselves were not involved from the beginning, resistance is natural and legitimate.
In the projects I followed at financial institutions, the difference between those that scaled and those that stalled was almost always here. The companies that succeeded did not treat AI as a technology project with an impact on people. They treated it as a process transformation with technological support. The distinction seems subtle, but it radically changes the approach to change management, internal communication, training, and role redesign.
This requires the CTO or technology leader to step outside their comfort zone and work side by side with HR, legal, and business units long before the system goes into production. It is uncomfortable. It is slow. It is absolutely necessary.
How to break the cycle: a direct roadmap
If you recognize your project in any of the patterns described above, here is a clear path to breaking the cycle:
- Conduct an honest diagnosis of the current pilot. Explicitly map what was simplified or assumed in the pilot and what will need to be resolved in production. Data, integrations, volume, compliance, operations.
- Define the operational owner before any scaling decision. There is no point in approving a budget without having the person responsible for operating the system clearly identified and committed.
- Invest in MLOps before multiplying use cases. A solid platform with monitoring, versioning, and update pipelines is worth more than five simultaneous pilots.
- Build the business case with total cost of ownership. Include infrastructure at scale, operations team, human review when necessary, and model retraining cycles.
- Treat change management as a project deliverable, not a side activity. The people who will use and operate the system need to be co-designers of the solution, not just end users.
There are no shortcuts here. But there is clarity. And clarity, in this case, is worth far more than enthusiasm.
The time to act is now — seriously
The window of competitive advantage in AI is open, but it will not stay open forever. The companies that are building seriously — with real infrastructure, real governance, real organizational change — are creating a moat that will be hard to cross.
Those accumulating abandoned pilots are wasting capital, burning out teams, and worse, creating a culture of internal skepticism about AI that will make every future project harder.
The question I ask every executive who comes to me is not "do you want to adopt AI?". Everyone does. The question is: "are you willing to do what it takes to make AI truly work in your business?" That means investing in platform, defining clear responsibilities, working through organizational change, and having the patience to build the foundation before building the floors.
If the answer is yes, the path exists. And the results — in operational efficiency, cost reduction, and competitive advantage — justify every difficult step along the way.