Our organisation ran four AI pilot projects this year. The internal excitement was substantial. Vendors promised transformative results. Business units were eager to see AI’s potential. We invested time, money, and political capital into these initiatives.

One pilot made it to production deployment. Three others are dead or in indefinite limbo. This failure rate is apparently typical for enterprise AI projects, but that doesn’t make it less frustrating. Here’s what went wrong and what we learned.

Pilot One: Customer Support Chatbot

The pitch was compelling. Use natural language processing to handle common customer support queries, reduce ticket volume for human agents, improve response times. The vendor demo was impressive.

We ran a three-month pilot with a subset of support queries. The AI performed well on simple, common questions. It struggled with anything remotely complex or ambiguous. Customers got frustrated when the bot couldn’t understand their actual problem and kept providing irrelevant scripted responses.

The real killer was accuracy requirements. Support leadership wanted ninety-five percent accuracy before they’d deploy to all customers. The AI achieved seventy percent in the pilot. Getting from seventy to ninety-five percent accuracy would require massive additional training data and tuning effort.

We also discovered the AI needed constant maintenance. Language evolves, products change, new issues emerge. Someone had to continuously update training data and tune the model. The vendor’s promise of “set it and forget it” automation was nonsense.

The project is paused indefinitely. The AI wasn’t good enough to deploy broadly, and the maintenance burden exceeded the cost savings from reduced support volume.

Pilot Two: Automated Invoice Processing

This one looked like a clear win. We process thousands of invoices monthly. Manual data entry is time-consuming and error-prone. Use AI to extract data from invoice PDFs and populate our financial system automatically.

The pilot worked beautifully with standardised invoices from major vendors. Clean PDFs with consistent formatting were processed accurately. The problem was that those invoices weren’t the ones causing pain. Humans could process them quickly already.

The invoices we needed help with were the messy ones. Handwritten additions. Non-standard formats. Damage that made text partially illegible. The AI failed on exactly the cases we needed it to handle.

We also hit integration challenges. Our financial system needed very specific data formats. The AI’s output required transformation and validation before it could be loaded. Building and maintaining these integration scripts took substantial development effort.

The cost-benefit analysis fell apart. The combination of AI licensing, integration development, and ongoing maintenance exceeded the cost of just having people process invoices. The pilot ended without moving to production.

Pilot Three: Predictive Maintenance for Facilities

Our facilities team manages HVAC systems, elevators, and other building infrastructure. They proposed using AI to predict equipment failures before they happen based on sensor data. Fix things proactively instead of waiting for breakdowns.

This sounded perfect for AI. Lots of sensor data. Clear patterns to learn. Measurable value from avoiding downtime.

The problem was data quality. Our sensor data was inconsistent and incomplete. Equipment logged different metrics at different intervals. Historical records were missing for many systems. The AI needed clean, comprehensive data to learn patterns, and we didn’t have it.

We could have fixed the data problem, but it would require significant investment in new sensors, data infrastructure, and time to accumulate quality historical data. The facilities team didn’t have budget for this infrastructure work.

The pilot ran for six months on the subset of systems with good data. Results were inconclusive. We couldn’t demonstrate clear value from the predictions because the sample size was too small. Without demonstrated value, we couldn’t justify the investment to expand data collection.

The pilot is in limbo. Not officially cancelled, but not actively pursued either.

Pilot Four: Sales Opportunity Scoring

This was our success story. Use AI to score sales opportunities based on likelihood to close, helping sales prioritise their efforts.

The project worked because we had the right conditions. Clean CRM data accumulated over years. Clear success metrics. Strong business sponsor in the sales leadership. Willingness to iterate and tune based on feedback.

The AI’s initial accuracy was modest but good enough to be useful. Sales reps tested the scoring and provided feedback. We tuned the model based on that feedback. After three iterations, the AI was recommending opportunities accurately enough that reps trusted it.

The integration was simpler than other pilots because everything stayed within our CRM system. No complex data pipelines or system integration required. Sales reps saw the scores directly in their existing workflow.

This pilot moved to production after six months. It’s not transformative, but it’s genuinely useful. That’s a win.

Why Most Pilots Fail

Looking across our pilots and comparing notes with other IT leaders, I see consistent failure patterns.

Unrealistic expectations are the biggest killer. Vendors demo AI using perfect test data. Reality is messy. The gap between demo performance and production performance is often unbridgeable.

Poor data quality undermines most AI projects. The AI is only as good as the data it learns from. If your data is incomplete, inconsistent, or biased, the AI will be too. Fixing data problems requires investment that often exceeds the AI project budget.

Integration complexity is underestimated. Getting AI systems to work with existing enterprise applications is hard. Data formats need transformation. Workflows need modification. Security and compliance requirements constrain architecture choices. The AI model is often the easy part. Integration is the hard part.

Lack of clear ownership kills projects slowly. AI pilots often sit between departments. IT builds it, business units use it, data teams maintain it. When nobody clearly owns the AI system’s success, it drifts without direction until it fails.

Finally, the business case often falls apart under scrutiny. The promised cost savings assume the AI works perfectly. When you factor in realistic accuracy, integration costs, and ongoing maintenance, many AI projects don’t deliver positive ROI.

What Actually Works

Our successful pilot succeeded because we had realistic expectations from the start. We knew the AI wouldn’t be perfect. We designed for gradual improvement through iteration.

We also had strong business ownership. Sales leadership championed the project and held their team accountable for testing and providing feedback. This wasn’t an IT project that business used. It was a business project that IT supported.

Clean data and simple integration were critical. We picked a use case where we already had quality data and didn’t need complex system integration.

Finally, we focused on augmenting human work rather than replacing it. The AI helps sales reps prioritise, but they still make the final decisions. This reduced the accuracy threshold needed for usefulness.

Advice for AI Pilots

Pick use cases where you have clean, comprehensive data already. Don’t start AI projects where fixing data quality is the prerequisite. That’s two projects, not one.

Set realistic accuracy expectations. AI won’t be perfect. Decide upfront what “good enough” means and whether that level of accuracy delivers business value.

Ensure strong business ownership with a sponsor who has budget authority and will hold their team accountable for engagement.

Start with augmentation rather than automation. AI that helps humans decide is more achievable than AI that replaces human judgment entirely.

Plan for integration and maintenance from day one. The AI model is maybe twenty percent of the work. Integration, deployment, and ongoing maintenance are the other eighty percent.

Be willing to kill pilots that aren’t working. Sunk cost fallacy keeps failed AI projects alive far longer than they should be. If the pilot isn’t demonstrating clear value, end it and reallocate resources to more promising work.

AI has genuine potential for enterprise applications. But most pilots fail because organisations underestimate the complexity and overestimate the technology’s current capabilities. Managing expectations and picking the right use cases makes the difference between pilots that go nowhere and AI that actually delivers value.