AI has rapidly transformed from a novelty into a necessity for enterprise organizations. Businesses leading the charge today have moved far beyond using generative AI to polish an email or organize data — they’re developing AI-driven products that can scale and deliver meaningful impact for their business.
For countless others though, successful AI development and implementation is still elusive. An IDC study found that 88 percent of AI pilots fail to reach production, and an MIT analysis suggests only about 5 percent of enterprise pilots deliver measurable financial returns, with the vast majority generating little to no ROI.
So, what separates the leaders from the laggards?
The Pilot Purgatory Problem
The frustrating gap between vision and scaled implementation is often called “pilot purgatory” — a place where once-promising projects stall before reaching full potential.
But failure to scale is usually not due to problems with the technology itself. It’s typically tied to a lack of governance, process maturity, and cross-functional alignment needed to move from pilot to organization-wide adoption.
With a background spanning business operation to technology development and implementation, I’ve come to rely on a few best practices that help my organization avoid pilot purgatory and bring AI-driven products to scale.
So, if your next great idea is at risk of getting stuck, read on.
Centralize Intake
Manual data entry, compiling lengthy reports and other tedious, repetitive tasks often pull top performers away from high-value work. That’s why the best ideas tend to come from your front-line team members.
While the pain points and resulting suggestions are valid, realistic leaders know they can’t be solved all at once. To avoid inundating multiple members of your tech team with time-consuming requests, create a review framework led by people with enough institutional knowledge to filter suggestions from across the business effectively.
When intake is centralized, common themes are more easily found. This enables decision-makers to identify opportunities for building solutions with broader organizational impact, rather than fixing isolated issues for one person or team that could create residual challenges over time.
Don’t Press the Easy Button
Of course, choosing the right projects to pursue is a challenge unto itself. A common mistake at this point is building pilots on top of broken processes. I call it pressing the easy button, and it only makes things harder in the long run.
Tempting as it may be, trying to automate your way out of a weak workflow rarely succeeds. Instead, conduct a critical process review and fix what you can without technology first, then automate — otherwise you’ll only scale inefficiency.
Define a North Star
Once you’re certain a process is running at peak efficiency without automation, it’s time to consider where AI can add value. That starts with defining your North Star — a clear objective that will keep project stakeholders focused on solving the same problem to achieve an agreed-upon outcome.
A strong North Star should push your team to ask the tough questions: What outcome and impact are we actually chasing? How will we drive adoption? How will we measure impact over time?
Without the answers to these and other key questions, you’ll build solutions that may create surface-level wins but don’t deliver results that move the needle.
Balance Experimentation and Exploration
Pilots shouldn’t just prove that technology works — they should explore different ways of achieving impact. Especially with data-driven projects, the system infrastructure may exist, but you still need to test configurations and logic to uncover the most effective way to deliver value.
That’s where the distinction between experimentation and exploration comes in. Experimentation is about trying different approaches during the pilot phase. Exploration is when you’ve learned enough to double down on what works and scale it for business impact, while always leveraging an experimental subset to ensure you’re continuously maximizing that impact.
Work in Phases
Fast-growing companies often see demand outpace operational capacity. In response, teams may race to streamline parts of the business with automation. But if pilot development lasts for months, needs may shift, and the solution could be outdated long before launch.
Phasing helps avoid that trap. Breaking development into smaller releases can deliver immediate relief while leaving room to revisit the ultimate goal as conditions evolve. That way, the pilot provides real value every step of the way while building toward the strongest overall solution.
The Pilot Kryptonite: Change Management
A key part of pilot planning is the change management strategy. Sequencing the rollout so people actually absorb it requires anticipating how much change each step will demand from the teams adopting it.
Low-impact changes can move quickly with minimal communication. High-impact changes — those that reshape core workflows or require broad collaboration — should be deployed over a longer period with increased communication and multi-tiered organizational support.
Structuring pilots around the level of change management required helps ensure strong adoption and maximizes the impact of new technology.
Monitor and Iterate
Of course, launching a pilot with strong adoption isn’t the finish line — it’s the beginning of a continuous monitoring and iteration process using the metrics you set at the start.
Conduct regular progress reviews from both a project and a metrics lens, then share reports with a trusted set of stakeholders familiar with the full project scope. The key here is balance — get enough credible voices to reveal issues, but not so many that noise clouds the picture.
Synthesizing report feedback will help leaders determine what to prioritize at various points to keep technology products moving forward.
Reevaluate Metrics
Iteration also means continuously questioning whether you’re still measuring the right metrics. That’s why pilots need checkpoints where stakeholders realign on the original goals. If a metric no longer reflects what matters, determine a new one that does.
Sometimes shifting metrics can significantly change the pilot’s end goal — and that’s a good thing because it ensures the work remains focused on the immediate impact, not outdated assumptions.
Know When to Stop
Some pilots fail outright. Others reach a minimum viable product that meets the objective but doesn’t warrant further development. In both cases, investing more time and resources rarely expands impact, so the discipline is knowing when to call it.
A “failed” pilot can be just as valuable as one that scales if your team learns through the process and applies those lessons to future efforts.
From Pilot to Impact
From healthcare and manufacturing to finance and retail, every sector struggles with pilot purgatory. Regardless of the specific problem you’re trying to solve, success depends on taking a holistic approach to pilot planning.
Define a clear North Star. Fix broken processes before layering on technology. Treat pilots as experiments, not showcases. Work in phases. Sequence change management. Monitor, iterate and reevaluate.
What looks easy on paper is often challenging in practice. Only disciplined execution leads to technology that delivers lasting value across the business — and that’s the ultimate measure of success.
