Don’t Let Your AI Proof of Concept End With the Demo

In a September 24 letter to The Wall Street Journal, Sparq CEO Ingrid Curtis makes an important distinction between experimenting with AI and being ready to deploy it enterprise-wide. “A successful pilot proves little,” she writes. AI earns trust when it performs reliably “within the messy reality of a working business.”

One widely circulated 2025 statistic, based on IDC research for Lenovo (and reported by CIO), suggested that 88% of AI proofs of concept (PoCs) fail to result in widespread deployment. BCG reported in late 2024 that 74% of companies struggle to achieve and scale value from AI. But deployment may not be the only measure of a successful PoC. What did the organization learn from the experiment, and what happened to that knowledge?

Curtis argues that companies need to better prepare their AI applications, data, infrastructure, systems, and employees for that reality. I would add that the two most important contributors to this preparation are documentation and communication.

In my last two posts, I’ve focused primarily on external content—and the AI-mitigated audiences that interact with it. But demand for enterprise-ready AI is also creating demand for important internal content: the knowledge organizations generate as they experiment with the technology.

That knowledge shouldn’t disappear when the PoC ends.

An internal AI PoC is not complete when an organization determines whether the technology can perform as expected. It is complete when the organization documents, communicates, and applies what it learned to the decisions that come next.

Documentation creates organizational memory. Communication creates organizational knowledge. Both are essential if an AI PoC is going to become more than a successful experiment.

In this blog post, I answer these questions:

• What is an AI PoC?
• How can AI PoC documentation capture knowledge?
• How can communication about AI PoC findings serve future efforts?
• What kind of organizational knowledge can an AI PoC yield?

An AI Proof of Concept (PoC) Is a Learning Exercise

An internal PoC is a focused, controlled, small-scale experiment. A PoC tests an idea or system and provides evidence of whether it benefits the business and is technically feasible.

With AI, however, we can be tempted to focus on the visible result during a physical demonstration. Did the application produce the desired output? Did it save time? Did the demo impress the people in the room?

Those questions matter. They are also only part of what a PoC can tell us.

AWS’s Generative AI Lifecycle Operational Excellence (GLOE) framework describes a generative AI PoC as conducting strategically sound exploration with iterative learning. Ideally, an AI PoC should demonstrate potential business value and assess data readiness, technical feasibility, and risk mitigation. The goal is to provide evidence “not just enthusiasm” for the decision about what comes next.

Major consulting firms and other technology companies—including Microsoft, Google Cloud, and McKinsey—provide AI adoption pathways and maturity models. All recommend small-scale testing before broader adoption. Microsoft, in particular, advises organizations to “document lessons learned, technical challenges, and business value demonstrated” while capturing the results from an AI PoC.

The Australian Government’s guidance for moving AI from proof of concept to scale takes a similar approach. It distinguishes the PoC from the pilot and production stages. After a PoC is completed, the guidance calls for:

• A final value assessment, including benefits, buy-in, and barriers
• Stakeholder engagement, including workforce impact discussions
• Evaluation of technical feasibility, including technical debt and integration challenges

None of these post-PoC objectives can be accomplished without solid documentation and thoughtful communication.

AI PoC Documentation Captures Knowledge

PoC documentation can easily become a project afterthought. Many of us have seen the pattern. The team finishes the experiment, describes the results, celebrates the win—or quietly acknowledges the loss—and moves on. No detailed report, no next-steps plan, no difficult discussions.

Valuable knowledge can walk out the door with the project team.

Documentation Elements

Effective AI PoC documentation should capture enough of the experiment to help others understand what happened and make informed decisions later. Depending on the project, the documentation should first describe the AI PoC’s rationale and approach:

• The strategic alignment with business objectives
• The problem the PoC was intended to solve and the criteria used to define success
• The framework for ensuring data quality, governance, and risk management
• Assumptions and other background details that informed the test environment
• The data, tools, models, prompts, workflows, and human interventions involved

Next, the documentation should describe what happened and its potential implications:

• Where the AI performed well and where it struggled
• Any unexpected results, exceptions, risks, and workarounds
• The impact of assumptions—what proved correct or incorrect
• The human expertise and judgment required to achieve acceptable results
• Any unanswered questions that require further testing
• The data, system, infrastructure, security, governance, and other requirements that must be addressed before deployment

Safety and Ethical Considerations

When the team is documenting future requirements after an AI PoC, safety deserves particular attention. An AI PoC provides a controlled environment for discovering how an AI system behaves before it gains broader access to organizational data, systems, tools, or users. So, with safety in mind, organizations should also document:

• The boundaries of the experiment
• The safeguards used
• How well those safeguards worked
• What additional safety controls would be required before deployment

Governance and safety discussions should also address ethical boundaries, including uses of the AI system that the organization considers unacceptable. Recent decisions by AI developers to delay or halt models because of safety concerns reinforce a broader lesson: testing what an AI system can do should include testing what it should be allowed to do.

(Note that I address ethical frameworks for AI adoption and use in two previous blog posts: “Ethical Use of GenAI: 10 Principles for Technical Communicators” and “A New Code for Communicators: Ethics for an Automated Workplace.”)

A Documentation Model

The Australian Government’s AI readiness checklist provides a useful model for AI PoC documentation. It contains two sections: Before starting a PoC and After PoC completion. Its readiness questions address strategic alignment, product design, data quality and governance, system integration, privacy, security, and impact assessment.

The website also recommends establishing a decision register and lessons-learned log during the PoC itself. I would also recommend a risk register. I’ve advocated including AI-specific risks in project risk registers during recent talks to Project Management Institute chapters. (See my LinkedIn page for examples of my project management talks.) It’s a new twist on an established concept.

All this doesn’t mean every AI PoC requires a small library of documentation. The amount and form should fit the experiment, its risks, and the decisions the organization will make from it.

The objective is to preserve the knowledge that matters. Documentation creates organizational memory.

Communicating AI PoC Findings Serves the Future

Preserving the learnings is only half the job. Someone has to use it.

That requires communication.

A successful AI PoC naturally lends itself to a success story. Demonstrating an AI application or agent to a receptive audience can invite automation bias. People can see a slick performance, and suddenly a few enthusiastic comments become evidence that the organization is “ready for AI.” (For more about automation bias and other AI-related cognitive biases, see my blog post “Cognitive Bias in GenAI Use: From Groupthink to Human Mitigation.”)

The full findings may tell a more complicated and more useful story.

Perhaps the AI performed well only with carefully curated data. Perhaps subject matter experts spent considerable time reviewing the application’s output. Perhaps the application worked within the controlled PoC environment, while integration with existing systems remains unresolved. Perhaps governance requirements, security controls, monitoring, or employee training still remain to be developed.

Those findings belong in the story too.

Communication should also reflect the needs of different internal audiences:

• Executives may require information about business value, investment, and risk.
• Technical teams require details about systems, data, security, and integration.
• Human Resources must understand the level and type of training or retraining required.
• Managers need to understand changes to workflows and responsibilities.
• Employees need information about how their work may change, what skills they may require, and what decisions remain open.

The Australian readiness checklist makes this communication explicit. After a PoC, it asks whether results have been communicated to stakeholders “in business terms, not just technical metrics.” It also asks whether there are “clear steps” for further development, validation, integration, and testing.

That brings us back to Curtis. In her letter to the WSJ, she argues that companies must “rethink roles, retool work, and retrain employees as they deploy the technology.”

Doing those things well depends on understanding what the experiments have taught us and sharing that knowledge with the people who will put it into practice.

Communication creates organizational knowledge.

An AI PoC Must Yield Organizational Knowledge

A successful AI PoC is an important milestone. It can tell an organization whether an AI application can successfully (and ethically) perform a task and, in doing so, benefit the business.

But it is still only one stage in the much longer journey toward dependable and responsible AI deployment.

In fact, technology leaders warn against treating AI PoCs as technical demonstrations designed to impress. Through their guides, models, and frameworks, they position the AI PoC as a way to reduce investment risk by establishing evidence about business value, data readiness, technical feasibility, and risk mitigation.

That evidence has value beyond the project team.

An AI PoC creates organizational knowledge about the technology, the organization, and how the two interact. It may expose gaps in systems or governance. It may reveal where human expertise is indispensable. It may identify new skills employees will require. And it may tell leaders that the organization has more work to do before AI is ready for the “messy reality” Curtis describes.

Documentation preserves that knowledge. Communication puts it to work. Together, they help turn the lessons of an AI experiment into organizational knowledge that can inform the decisions that follow.

Before moving from AI experimentation toward deployment, organizations should ask: Have we documented what we learned, communicated it to the people who need to know, and used that knowledge to determine what comes next?

Disclosure: The author used AI tools to assist with research discovery, outlining, and an initial draft. The author conducted the research and wrote the final draft.