Escaping “Proof Of Concept” Purgatory: The Two Fixes Your Process Needs

In January 2026, Gartner reported that at least 50% of generative AI projects had been abandoned after the proof-of-concept stage by the end of 2025. S&P Global Market Intelligence found that organisations scrapped an average of 46% of their AI proofs of concept before production in 2025.

There are several reasons AI projects fail to make it into production. But in our work with clients, we see two recurring problems: teams discover too late that they don’t have the data needed to make the solution work, or they realise too late that adopting it requires changes to the way the business operates.

We have made it easier than ever to build an AI proof of concept. But proving that AI works is a long way from proving that a business can use it at scale. The answer isn’t necessarily to create a separate innovation process for AI. It’s to adapt the innovation lifecycle you already have, so these uncertainties are tested before teams invest too heavily in a POC.

Two checks your lifecycle needs

Most innovation frameworks already ask three questions: Should we build it? (Desirability validation) Does it make business sense? (Viability validation) Can we build it? (Feasibility validation)

These questions remain essential. But AI projects expose two uncertainties that existing lifecycles don’t always address early enough: whether the data makes the solution technically feasible, and whether the organisation can adopt it at scale. The solution isn’t to add another set of stages. It’s to make these checks explicit within the existing process.

1. Check the data before you build

An AI use case can solve a real problem and have a compelling business case, yet still be impractical because the required data is unavailable, inaccessible or unreliable. Teams often discover this after building a prototype, sometimes using a limited or specially prepared dataset that doesn’t reflect the real operating environment.

Before approving a POC, establish what data the use case requires, where it comes from, whether it can be accessed and what gaps need to be resolved.

The outcome should be a clear decision: proceed, run a targeted experiment to resolve a critical data uncertainty, or address a readiness gap first. Not every data problem needs to be solved upfront. But teams should understand the risks, the cost of resolving them and whether they change the business case. Data readiness is part of technical feasibility. Making it an explicit checkpoint helps teams identify problems before they become expensive surprises.

2. Validate the process before you build the solution

The second problem is less technical, and often easier to overlook. Even when the data is ready and the technology works, an AI solution may never move beyond the POC if the organisation cannot adopt it at scale.

Consider an AI solution designed to handle customer complaints. Which complaints can it resolve independently? When should an employee intervene? Who is accountable for mistakes? How will the new workflow fit existing systems and responsibilities?

These aren’t implementation details. They are fundamental assumptions about how the business will operate with the solution.

Before approving the POC, map the current process and outline the proposed one. Identify what will change, which tasks will be automated or augmented, where human judgement remains necessary and who will take on new responsibilities.

Then involve the people who will have to make it work: business owners, employees, IT, risk and compliance. They should understand the proposed changes and agree on the key assumptions before development begins.

Call this operational viability. It asks a simple question: Can we work with it?

The proposed process doesn’t need to be perfect. It’s a hypothesis that should evolve as evidence emerges. But the organisation shouldn’t discover the implications of the new workflow only after the technology has been built.

A POC tests whether the technical approach works. A pilot tests whether the solution and the redesigned process work in real operating conditions. Both matter, but they answer different questions.

Evolve the lifecycle, don’t create another one

AI doesn’t automatically require a new innovation lifecycle. It requires us to reconsider what we validate, and when. By making data readiness an explicit part of technical feasibility and introducing operational viability before the POC, organisations can adapt their existing process to address the uncertainties that often prevent AI projects from reaching production.

A POC should reduce uncertainty, not just demonstrate technical viability. The real test is whether the solution can work in the business, with the data available and the people and processes required to adopt it at scale.