The pilot trap: why we start with a production workflow

Our first implementation engagement is a first production workflow, not a pilot: one small, well-defined workflow, built from the start to run on real information, inside real security requirements, with a team that owns it. Our report Surviving the AI Hype Cycle looks at why so many AI projects stall between demo and daily use.

We've seen organizations put considerable time and effort into AI proofs of concept that show promising results but never make it into everyday operations.

The initial demonstration works. The model produces reasonable answers, the interface looks good, and the team agrees there's potential. Then someone asks how it will connect to existing systems, who will maintain it, or whether it can meet the organization's security requirements. The project slows down while those questions are worked through, sometimes months after development began.

We started RegainFlow with a different approach. Rather than treating deployment as something to figure out after a successful demonstration, we design for production from the beginning.

Where pilots tend to fall short

A pilot serves a legitimate purpose. It lets an organization test whether a technology can solve a problem before committing to a larger investment.

The trouble starts when the pilot is designed to prove the technology works without considering what it will take to operate it.

An AI assistant might perform well answering questions from a carefully prepared collection of documents. Moving that assistant into the organization's real environment brings complications that weren't visible during testing. The information may live across several systems, with different access controls and inconsistent formats. Some records are outdated or incomplete. The assistant may need to support far more users than the demonstration did.

There are operational questions too. Someone needs to monitor performance, investigate incorrect results, maintain integrations, and manage changes to the information the system relies on.

These requirements can be addressed after a pilot, but they can change the architecture, the cost, and whether the solution is feasible at all. We would rather find them early enough to shape the design. The budget side of this is in what AI really costs.

What we mean by a first production workflow

We select a specific workflow that can be improved within a manageable scope, then build the solution around the conditions it will actually operate in. That might mean helping an engineering team search historical records, automating part of a document review process, or connecting systems that currently require employees to move information by hand.

The scope is deliberately limited to one team, one process and one business outcome. Within that scope, we cover what dependable operation requires:

• Real information and integrations. The solution uses approved connections to the information and systems in the workflow. Where possible, we avoid depending on manually prepared datasets that nobody will maintain after deployment.
• Security and access controls. We establish how users authenticate, what information they're authorized to see, and how those restrictions are enforced.
• Operational ownership. A designated team takes responsibility for the system once it is in use. We agree who will monitor it, manage changes, and respond when something goes wrong.
• Testing and evaluation. We define acceptable performance, identify likely failure conditions, and decide how problems will be detected and addressed.
• Measurable outcomes. Before implementation, we agree on what the workflow looks like today and what improvement we're aiming for, so the results can be judged after deployment.

Not every first workflow needs enterprise-wide scale, extensive automation or a complex architecture. The requirements depend on the workflow and the risks involved. The test is whether the people it was built for can use it and support it.

How an engagement works

We organize our engagements into three stages.

1. 01

   Understand the problem

   An assessment of the existing workflow, the people involved and the systems they depend on: where work is delayed, repeated or more complicated than it needs to be, what has already been tried, and whether AI is the right tool. It ends with a proposed first workflow, its expected benefit, and the constraints that could affect it.

2. 02

   Build and deploy the first workflow

   Once scope and success measures are agreed, our engineers work directly with the client's team. The people who will use the system help us understand the process, review early versions and spot where it behaves differently than expected. Testing, security, documentation and support are built in during development, not left to the end. Deployment can start with a small group of authorized users before expanding to the full team.

3. 03

   Keep improving or transfer ownership

   Once the workflow is running, the client can keep working with us on maintenance and improvements, or take the system over with the documentation, runbooks and training to run it. Further workflows follow if the first one proves its value, shaped by what we learned deploying it.

This is part of the broader FLOW method, which guides how we find problems, work with stakeholders, implement solutions and evaluate results.

What we need from the client

Building for production needs more from the organization than approving the project.

We need access to the people who understand the workflow, including those responsible for the systems and information involved. Technical and security stakeholders need to help decide what can be connected and how the solution will operate within existing requirements.

We also ask the client to name someone who owns the workflow. That person doesn't need to manage every technical detail, but they should understand the intended outcome, coordinate decisions, and help judge whether the system is meeting expectations.

These conversations take more effort early in an engagement than a traditional proof of concept does. They also surface dependencies and constraints before much development has been done. If a team doesn't have the capacity to support the workflow after deployment, we would rather discuss that during planning than discover it once the system is built.

When a pilot still makes sense

A conventional pilot makes sense when a technology is unfamiliar or its feasibility is uncertain. For work meant to improve an existing business process, we design around real operational requirements from the start.

A narrowly scoped production workflow lets the organization evaluate the technology under realistic conditions, understand its operating costs, and see whether it improves the work. It also leaves the organization with something usable at the end of the engagement. Our engineers stay accountable for what they build, and clients get the knowledge and control to run it over time.

If you're considering an AI project, or have a pilot that hasn't made it into production, we can start with a free assessment to understand what you've built, what's blocking deployment, and whether there's a practical path forward.