Why POCs Stall & the Real Causes of Slow Progress

Summary

Most data and AI POCs (Proof of Concept) don’t stall because of technical limitations — they stall because the organization isn’t ready to support them. The biggest drivers of POC slow progress include unclear success metrics, weak data foundations, lack of ownership, scattered pilots, and late-stage deployment planning. Addressing these root causes early creates a predictable, scalable path from experimentation to production and prevents prototypes from ending up in the POC graveyard.

Many Chief Data Officers (CDOs) know the feeling: great ideas, exciting pilots… and then? Nothing. Welcome to the “POC graveyard” – where promising data and AI prototypes go to stall out. 

It’s that all-too-familiar place where projects linger in limbo. Never fully scaling, budgets quietly disappearing, and the promised ROI staying just out of reach. 

McKinsey calls this the “Gen AI paradox”: nearly eight in ten companies experiment with AI, yet many still see no bottom-line impact.

The Wall Street Journal likewise reports that around 70% of Gen AI projects remain stuck in pilot. For CDOs, that means lost credibility, wasted spend, and weakened competitive advantage. 

Gartner analysts predict that at least 30% of generative AI projects will be abandoned at the POC stage by 2025, due to factors like poor data quality, inadequate risk controls, escalating costs, or unclear business value.  

So, despite booming investments in data initiatives, the majority of prototypes stall out before delivering business impact. 

Why data POCs (and pilots) get stuck

Most POCs don’t stall because of the model or technology — they stall because of the ecosystem around them: unclear goals, missing data foundations, shifting requirements, and weak ownership.

1. Lack of clear business value and success metrics

Many POCs never advance because they weren’t tied to a defined business problem or measurable outcome. Without a clear ROI hypothesis or executive sponsorship, the project slows down at the decision gate — there’s no basis to prioritize it for production. 

In fact, nearly one-third of CIOs said they didn’t know what success metrics their AI POCs were expected to meet. This ambiguity creates delays during review cycles because a pilot with no measurable outcome cannot be approved for the next stage. 

To avoid this, every POC should start with a “problem to solve” and one or two success criteria (e.g. reduce churn by 5% in six months).  

Tying the POC to a strategic goal and KPI creates accountability and gives stakeholders a clear, objective signal to promote the pilot into production without back-and-forth. 

2. Poor data readiness and infrastructure 

A major reason for POC’s slow progress on the path to production is poor-quality or siloed data.

Many prototypes are built using one-time extracts or manual workarounds that cannot be scaled or integrated, forcing teams to redo large parts of the work before anything can progress further. 

IDC’s analysis of failed AI pilots concluded that the low conversion rate “indicates the low level of organizational readiness in terms of data, processes, and IT infrastructure” needed to support production AI. 

To succeed, teams need to prepare the data environment early. That includes understanding what data is available, ensuring access, improving quality, and setting up even a basic pipeline. You don’t need perfection, but you do need enough to avoid the huge delays caused by rebuilding the data foundation later. 

This is where the Calibo platform helps: 

• With data crawlers and catalogs, teams can quickly discover and organize what they have. The platform also integrates with Snowflake, Databricks Unity Catalog, Amazon S3-based data lakes, and data warehouses. Data Fabric Studio makes it simple to build ingestion, transformation, and quality checks. There’s no blueprint required—it’s a drag-and-drop, quick-configure experience that saves time compared to writing SQL by hand. 

3. No ownership or stakeholder alignment 

Even well-designed pilots fail without the right champions. When POCs are led by technical teams alone, without business involvement or an accountable sponsor, they hit approval bottlenecks. No one feels responsible for the handoff into production, so timelines drag on. 

Find a sponsor who’s not just signing off on the POC, but really backing it through to adoption. Bring end users in early. Their input helps shape something that works and prevents late-stage resistance that slows rollout. 

And don’t treat the POC like just a tech demo — make it a team effort. It should feel like a true trial run for how the whole organization will use it, not just a cool project off to the side.  

Tools like Calibo’s Product Release Orchestration can help here by defining responsibilities, taking ownership, and giving teams clear visibility of progress through dashboards — reducing the delays that come from misalignment. 

4. Too many disconnected pilots 

The excitement around AI has led many companies to run dozens of scattered POCs. 

This “spray-and-pray” approach spreads resources too thin, and slows everything down. With no prioritization, teams keep context-switching, and promising pilots wait in the queue for engineering or infra support. 

Instead, prioritize POCs based on both business impact and feasibility. Use a simple scorecard to classify use cases into must-do, nice-to-have, and too-costly-to-scale. Focus your effort where there’s a clear payoff. 

Portfolio management features, like those in Calibo’s Product Release Orchestration, help ensure the right POCs move forward first — preventing delays caused by trying to do everything at once. 

5. No plan for the “last mile” to production 

A common reason pilots slow down is that deployment planning only begins after the POC is finished. Many POCs are never productionized because no one considers integrations, governance, support, or performance until it’s too late — resulting in months of re-engineering and compliance back-and-forth. 

Design with deployment in mind. Define ownership, plan system integration, and assess scalability and compliance early. Use MLOps and DevOps practices to make promotion smoother: containerize models, automate testing, and monitor performance post-launch.

It starts by tackling those underlying issues early, and having a clear, practical plan to move from experimentation to something real, scalable, and enterprise-ready. 

TOP TIP: Fast-tracking is all about eliminating post-POC surprises. The earlier you address production requirements, the smoother and faster the transition will be.