Why GenAI Proof of Concepts Fail in Production and How to Fix It

As discussed in the latest episode of The Innovation Circuit with Sudeep Ghatak, Practice Lead in the Digital team, John van der Walt, Delivery Unit Lead for Dynamics 365, and Ivor Whibley, Principal Consultant in the D365 practice, the gap between a GenAI proof of concept (POC) and production isn’t just technical. It’s also organisational, cultural and operational.

POC, Pilot and Production

A key starting point is understanding that not all stages of AI adoption are created equal.

A proof of concept is about feasibility. It’s typically short, controlled, and often uses sanitised or dummy data. The goal is simple: can this work?

A pilot introduces reality. Real users, real data and real business context. The question shifts to: does this work for us?

Production, however, is an entirely different commitment. At this stage, organisations must consider ROI, governance, support, data quality and long-term sustainability.

As John van der Walt explained,

“The problem is that people, companies, fall in love with the POC and they're surprised that there's a big gap between the proof of concept that they wanted to see or thought that might work and what's actually happening in production.”

That gap is where many initiatives stall.

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Why POCs Create False Confidence

POCs are designed to succeed. They focus on a single, well-defined use case, run under controlled conditions, and showcase the best possible outcome.

But that’s precisely the problem.

Ivor Whibley points out,

“The POC is always built around a single well-defined use case… and the moment you try and scale this, bring in more users, more data sources, edge cases, these limitations are likely to show.”

Once real-world complexity is introduced, like messy data, inconsistent processes, and varied user behaviour, that’s when the cracks begin to appear.

This isn’t a failure of AI. It’s a reflection of the environment it’s being introduced into.

The Real Barrier: Readiness, Not Technology

As Ivor summarised,

“It’s not really an AI problem. It’s a readiness problem.”  

One of the most important insights from the podcast discussion is that most GenAI challenges are not technical, they’re about our readiness:

• Data quality is often lower than expected

• Processes are inconsistent or poorly defined

• Ownership and governance are unclear

• Change management is underestimated

A powerful example comes from sales environments. AI-generated summaries of customer interactions can look impressive in a demo. But in reality, inconsistent data entry across sales teams leads to inconsistent and often incorrect outputs.

Why GenAI Is Held to a Higher Standard

Unlike traditional systems, GenAI doesn’t fail in obvious ways. There are no clear error messages, just confident, articulate answers that may be wrong. This raises the stakes significantly. Incorrect outputs can quickly find their way to the customer.

John said,

“It's simply because you need to make sure that the data you're getting is accurate and it's reflective of what you are expecting. The difficulty is if you get it wrong, that wrong information will get into a customer report. It will get into an email to a customer.”

That’s why organisations must think differently about success. It’s not just about functionality; it’s about accuracy, trust, and impact.

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The Importance of Starting Small and Finding Your GenAI Advocate in Your Organisation

One consistent theme is the need to start small and learn fast. Rather than trying to transform the entire organisation, successful teams should:

• Identify a specific pain point

• Focus on a high-impact but contained use case

• Define clear success metrics upfront

• Iterate and refine continuously

Common starting points include:

• Knowledge management (e.g. querying company policies)

• Document processing (e.g. classifying or extracting information)

These areas are well-suited to AI and provide measurable value early on.

A common failure pattern is for organisations to become too focused on the technology itself rather than on the outcomes it delivers. Warning signs include:

• Conversations centred on features and roadmaps

• Measuring usage instead of business impact

• Tools being used primarily by IT, not the business

The solution is simple in principle: solve real problems for real people. When that happens, adoption follows naturally.

Ivor said,

“When you solve that problem for a real person, for a real business problem, they become the advocate… Stop trying to drive the adoption. Start trying to solve problems.”

One of the clearest signals of successful GenAI adoption is the emergence of the need for an internal advocate, someone in the business who actively champions the tool because it has genuinely improved their work.  

Rather than forcing adoption top-down, organisations see better results when they focus on solving a specific, frustrating problem for a real user. That individual becomes proof of value, helping to build trust organically across teams. In this model, advocacy replaces enforcement, and adoption grows naturally as the technology delivers meaningful, visible outcomes.

Data, Discipline, and Metrics

Even the best AI solution will fail without the right foundations. Success requires:

• Strong data discipline and defined metrics

• Clear processes and continuous improvement

• User training and support  

• Time for adoption and learning  

• Refine, refine and refine some more

Importantly, organisations must recognise that AI adoption is not instant. It requires sustained effort and cultural change.

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Human Oversight Still Matters

Despite the capabilities of AI, human accountability remains essential. AI can accelerate work, but it can also accelerate mistakes.

A practical approach is to treat AI as a collaborator and let AI handle 80% of the work, but ensure humans review the final 20%.

Ivor said,

‍“Speed of production is not the same as accuracy of output,”

so organisations must build in checks and balances accordingly.

Closing the Gap

The gap between a GenAI POC and production is significant—but it’s not insurmountable.

In summary, Sudeep says,

“The gap between a GenAI proof of concept and sustained production use is significant and it's really a technology problem... it's also a leadership problem in that sense that leaders need to be honest about expectations, clear about outcomes, and should be patient about the timeline between the investment and the return. So the organisations that are going to get this right aren't necessarily the ones with big AI budgets. They're the ones that start with real simple problems and stay close to the people actually doing the work, measure what actually matters, willing to say honestly when things aren't working, and that's what leads to success.”

Ultimately, this isn’t just about implementing AI; it’s about evolving how the business works.

Those who recognise that early will be the ones who move beyond impressive demos to real, lasting value.

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