Still Stuck in POC Mode? How to Break Free from AI Experimentation Purgatory

Two and a half years after generative AI burst back into mainstream awareness, many organizations have made the leap from interest to investment. POCs are underway, tools like Microsoft 365 Copilot are being tested, and executive teams are asking how AI can unlock value across the enterprise.

And yet, momentum stalls.

It’s a pattern we see often at Lantern. A promising proof of concept (POC) is delivered. The technology works, but instead of scaling, the initiative lingers in limbo. There’s no formal rollout, no measured outcomes, and no return on investment.

If this sounds familiar, you’re not alone. In fact, Microsoft’s AI Strategy Roadmap highlights roughly 75% of organizations are still in the Exploring, Planning or Implementing phases of AI adoption. And the reasons for getting stuck aren’t just technical. They’re often strategic, organizational, and human.

In this blog post, we’ll explore why so many companies fall into “AI experimentation purgatory” and what it takes to move from pilot mode to scalable, enterprise-wide impact.

The Current State: Endless Experimentation

At first glance, launching a proof of concept seems like progress. It signals investment, curiosity, and a willingness to explore new technology. But the reality is more sobering: many AI POCs don’t go anywhere.

Microsoft’s five-stage AI Value Realization model indicates most organizations fall somewhere between Stage 2 (Planning) and Stage 3 (Implementing). That means they’re still defining strategy, building POCs, or testing tools, but haven’t yet scaled successful solutions across business units or functions.

And in many cases, that’s where the story ends. One or two use cases are trialed, maybe a chatbot or a Copilot integration in Office apps, but the POCs stall. There’s no next step, no enterprise adoption, and no clear path to scale.

This state of “endless experimentation” is what we call AI POC purgatory. It’s not due to a lack of effort or interest. Most of the time, the underlying issue is that the organization hasn’t resolved key barriers such as:

• Stakeholder alignment
• Business case clarity
• Organizational readiness

Root Causes of Why POCs Stall

When AI POCs don’t progress, it’s rarely because the technology failed. More often, the barriers are organizational. From misaligned expectations to missing change management, several common dynamics prevent progress.

1. No Shared Understanding of Value
Many POCs begin with a general sense that “AI could help,” but without a clearly defined, quantified value proposition. As a result, when it’s time to scale, stakeholders may not agree on why the POC matters or what success looks like. In some organizations, AI is still seen as a novelty—interesting but nonessential.

2. The Use Case Isn’t Compelling Enough
Even if a POC is technically successful, it won’t gain traction if it doesn’t address a meaningful pain point or strategic priority. This is especially true in resource-constrained environments, where only the most impactful initiatives are approved for scale.

3. Too Much Tech, Not Enough Context
Some POCs focus heavily on models, prompts, and integrations without connecting the effort back to business outcomes. These projects often struggle to gain support, especially from non-technical leaders.

4. Leadership Isn’t Bought In
Without executive sponsorship, AI projects can quickly lose steam. This is especially true when leaders lack fluency in how AI works and how it creates value. Without that understanding, initiatives may be quietly resisted, deprioritized, or underfunded.

5. Change Management is an Afterthought
Many teams assume that if a tool is rolled out, people will use it. But that’s rarely the case. Especially with tools like Microsoft 365 Copilot, success depends on proper enablement and support. A company that launches Copilot licenses without onboarding sees low adoption—not because the tool lacks value, but because people aren’t prepared to use it effectively.

Breaking the Cycle: How to Move from POC to Scale

Getting stuck in experimentation is often a sign that foundational elements for scale haven’t been addressed. Organizations that move forward do so by aligning around value, engaging the right people, and planning for what comes next.

1. Define and Measure Value Early
Every initiative should begin with a business problem. What are you trying to solve? What will success look like? How will it be measured?

POCs need more than technical milestones. They require measurable outcomes tied to operational priorities. Without this, momentum fades and teams lose interest.

2. Engage the Right Stakeholders
Scaling an AI solution involves more than the project team. Legal, compliance, IT, business leaders, and end users all need to be part of the conversation. Early engagement creates alignment and builds trust.

3. Assess Organizational Readiness Before Scaling
When teams try to expand a POC, hidden challenges often emerge—data gaps, undefined roles, or questions around cost and governance. These don’t have to stop progress, but they do need to be surfaced and addressed.

A structured AI readiness baseline provides a clear picture of where you are and what’s needed to move forward.

4. Prioritize Change Management
Even powerful tools will underdeliver if users aren’t equipped to adopt them. Change management is about more than training. It’s about communicating clearly, supporting adoption, and giving teams time and space to adjust.

5. Use a Phased Approach to Connect Vision and Action
Without a plan for what comes after the POC, teams risk losing momentum. A phased approach ensures progress by connecting vision to current capabilities. This typically involves:

• Envisioning: Define success and engage key stakeholders
• Current State: Understand your organization’s readiness and limitations
• Roadmap: Build an actionable plan that bridges gaps and enables scale

Real-World Insight: When AI Surfaced Data Gaps

AI POCs often reveal more than expected. In one engagement, a company tested a virtual assistant to support their customer service team. The goal was to reduce handling times by helping agents retrieve standard procedures more efficiently.

Initial results were strong. But as the assistant pulled from internal documentation, contradictions and outdated policies began to surface. These were materials teams had used for years—now exposed as unreliable under the scrutiny of AI.

The company paused rollout to address the issue. Not because the AI underperformed, but because it made visible what had previously gone unnoticed.

For many teams, POCs don’t just test the technology. They highlight foundational issues in content, process, and systems. Addressing these issues early builds a stronger foundation for scale.

Progress Beyond POCs

Delivering a successful proof of concept is an important milestone. But the real value emerges when organizations have the clarity and readiness to scale.

The ability to move forward depends on more than just tools and models. It requires alignment on value, leadership engagement, strong processes, and a culture ready to support change.

Leaders who assess readiness, engage teams, and address friction points early are in a stronger position to turn experimentation into enterprise impact.

For those navigating AI adoption, scaling starts when strategy, people, and technology begin to move together.