Fast-tracking data initiatives from prototype to production – Part 1

Summary

This blog will go through four core solutions for scalable innovation. Most data and AI pilots end up in the “POC graveyard,” never scaling to production or delivering ROI. The problem isn’t the technology—it’s the lack of strategy, data readiness, ownership, and focus. Common pitfalls include unclear success metrics, poor-quality data, scattered pilots, and no plan for the “last mile” to deployment.

To fast-track prototypes into production, leaders must tie POCs to business value, prepare the data foundation early, secure stakeholder alignment, and prioritize high-impact use cases. Above all, build with production in mind from day one. This shift transforms pilots into enterprise-ready solutions that drive real business impact.

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 just as many see no bottom‑line impact. 

The Wall Street Journal likewise reports that around 70 % of GenAI projects remain stuck in pilot. For CDOs, that means lost credibility, wasted spend, and slower 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 do so many data POCs fail to launch? 

It’s usually not the technology or the algorithm that’s the problem – it’s everything around it. The real challenge is often the environment and strategy that support (or don’t support) it. 

As an IDC survey found, organizations launched dozens of AI pilots “throwing spaghetti at the wall” yet saw only about 5 (and often fewer) POCs actually reach production – an alarming 90%+ attrition rate. The culprits cited include lack of success metrics, “bad data” and rushing in without proper preparation.  

Legacy systems, fuzzy ownership, or just plain resistance to change can end up being bigger roadblocks than the tech itself. 

Another common failure mode: ever-shifting requirements, nothing is truly frozen at the start, and as the POC progresses, new asks pile on, scope creeps, timelines slip, and the effort derails. 

The result? Teams keep spinning up pilots that never quite make it to production. It’s exhausting, and over time, it chips away at trust and momentum. 

So how do data leaders break out of this cycle? 

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. 

Why do data pilots get stuck?  

Avoiding the POC graveyard starts with understanding why prototypes tend to get stuck. 

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, there’s little motivation to invest in scaling. 

In fact, nearly one-third of CIOs admitted they didn’t even know what success metrics their AI POCs were supposed to meet. This lack of strategic goal-setting leads to pilots that, even if technically interesting, can’t justify further investment. 

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 keeps it focused on value, not just experimentation. 

2. Poor data readiness and infrastructure 

A big reason for stalled AI projects is poor-quality or siloed data.  

Many prototypes are built using one-time extracts or manual workarounds but fall apart when asked to scale or integrate. 

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 rework when it’s time to move to production. 

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, S3 data lakes and data warehouses. The 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. Too often, POCs are led by technical teams without involvement from the business or C-suite. If no one owns the outcome, the project will likely stall. 

Find a sponsor who’s not just signing off on the POC, but really backing it all the way through to adoption. Bring end users in early. Their input helps shape something that actually works for them, and builds trust along the way. 

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 providing a way to define and assign tasks, track ownership, and visualize progress through dashboards. That structure makes it easier to keep everyone aligned and accountable as the POC moves forward. 

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, leading to pilot fatigue and little follow-through. 

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. 

You can also use portfolio management features, like those in Calibo’s Product Release Orchestration, to define priorities and classify POCs in a more structured way. The point is to focus your effort where there’s a clear payoff. 

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

A frequent failure point is the lack of a transition plan. Many POCs are never productionized because no one considers integrations, governance, support, or performance until it’s too late. 

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. 

TOP TIP: The best way to avoid the graveyard is to build with production in mind from the start, align with business needs, prepare the data foundation, and keep your experiments focused and feasible. 

Watch this space for Part 2!