The Hidden Cost of Pilot Purgatory in Portfolio Companies

The familiar pattern nobody likes to name

Across private equity portfolios, the same conversation repeats itself.

An AI pilot shows promise.
A proof of concept is funded.
Early results look encouraging.

Then time passes.

The pilot does not fail loudly. It does not crash systems or trigger board-level concern. It simply never reaches production. It lingers. It absorbs budget. It becomes politically awkward to shut down.

Eventually, a second pilot appears. Then a third.

This state has a name. It is pilot purgatory.

It is one of the most common reasons AI initiatives fail to translate into real operating improvement across mid-market portfolios.

What this article is about

This article explains why AI pilots so often stall inside portfolio companies, even when the underlying idea is sound. It explores organisational fear, system fragility, vendor incentives, and why pilots are often safer to start than to finish. It is written for operating partners, value creation teams, and portfolio leaders who are tired of paying for momentum that never compounds.

Why pilots feel safe and production feels dangerous

The psychology of risk inside portfolio companies

Pilots are attractive because they promise learning without commitment.

They allow teams to demonstrate progress without changing how work is actually done. They create the appearance of innovation while preserving operational stability. For CEOs operating under PE ownership, this is a rational response to pressure.

Production systems are different.

They touch real processes, real people, and real accountability. They expose data quality issues that were previously hidden. They force decisions about headcount, process ownership, and failure modes.

In mid-market companies with thin technical teams, these risks are amplified. Leaders are acutely aware that once a system goes live, they own it. When consultants leave, the consequences stay.

This is why pilots proliferate and production stalls.

How legacy systems quietly kill momentum

When demos meet operational reality

Most AI pilots are built in clean environments.

They use curated datasets. They assume consistent inputs. They ignore the informal workarounds that keep legacy systems running.

Production environments are messier.

Data is incomplete. Processes rely on human judgement and tribal knowledge. Errors are patched manually. Edge cases are the rule, not the exception.

When a pilot meets this reality, fragility is exposed. Performance degrades. Maintenance requirements grow. Confidence erodes.

At this point, the pilot becomes politically untouchable. Shutting it down feels like admitting failure. Fixing it feels expensive and risky.

So it stays in purgatory.

This dynamic is a recurring theme in mid-market execution pain, where impressive prototypes collapse under real-world constraints.

Vendor incentives that reinforce pilot behaviour

Why pilots are easier to sell than outcomes

Many AI vendors and consultancies are structurally rewarded for starting projects, not finishing them.

Pilots are short, well-defined, and easy to justify. They showcase capability without requiring long-term accountability. They reduce procurement friction and accelerate buying decisions.

Production systems are harder.

They require guarantees, support models, documentation, and handover. They expose whether a solution can survive without constant expert intervention.

For mid-market buyers who already distrust open-ended engagements, this misalignment is obvious. They have experienced the bait-and-switch too many times, a frustration repeatedly surfaced in customer truth research.

When incentives reward experimentation over durability, pilot purgatory becomes the default outcome.

Why multiple pilots make the problem worse

Fragmentation disguised as progress

As pilots accumulate, fragmentation increases.

Different vendors introduce different tools, assumptions, and architectures. Knowledge becomes siloed. Lessons learned in one pilot fail to transfer to another. Technical debt grows quietly.

From the portfolio perspective, visibility decreases rather than improves.

Operating partners may be told that innovation is underway, but cannot see which pilots matter, which are stalled, and which could scale across other companies. There is no shared language for success, only a growing inventory of unfinished experiments.

At this point, AI stops being a lever and starts becoming noise.

The time pressure pilots cannot survive

EBITDA does not wait for learning cycles

Private equity value creation operates on a clock.

Pilots that require prolonged iteration rarely survive contact with exit timelines. Even successful experiments struggle to justify further investment if their path to EBITDA impact is unclear.

Mid-market leaders understand this instinctively. If a pilot cannot demonstrate near-term operational leverage, it is deprioritised. Not because it lacks merit, but because it lacks urgency.

This is why so many pilots die quietly rather than failing visibly.

What breaks the pilot purgatory cycle

From experimentation to obligation

The difference between a pilot and a system is obligation.

Systems are designed with ownership, durability, and repeatability in mind from day one. They are scoped against financial outcomes rather than technical novelty. They assume imperfect data and constrained teams.

Most importantly, they are built to be absorbed by the organisation that inherits them.

This shift requires discipline at the portfolio level. It requires clarity on which problems are worth solving, which solutions must repeat, and which experiments should never begin.

Without that discipline, pilot purgatory is not an anomaly. It is an inevitability.

The question that changes everything

Before funding another pilot, there is a question worth asking.

Is this organisation actually ready to run what we are about to build?

That question is uncomfortable, but it is cheaper than discovering the answer six months later.

It is also why we created a readiness diagnostic designed to surface these constraints early.

If this article resonates, the AI Systems Readiness Check exists to help teams assess whether a portfolio company can absorb, sustain, and scale AI systems before another pilot is commissioned.

Use it to decide what should be built.
Use it to decide what should not.
Use it to keep pilots from becoming permanent.

Frequently asked questions

Are pilots always a bad idea?

No. Pilots are useful when they are explicitly time-boxed and tied to a production decision. They fail when they become substitutes for commitment.

Why do mid-market companies struggle more with pilots than enterprises?

Because they lack deep technical benches and redundancy. Fragility is felt faster and forgiven less.

Can pilot purgatory be solved with better project management?

No. This is not a delivery problem. It is a structural and incentive problem.

Should PE firms ban pilots altogether?

No. They should mandate clarity on what happens after them.

How can operating partners spot pilot purgatory early?

When pilots generate insight but do not change process, headcount, or cost structure, they are already drifting.