re You Wasting Your Time With AI Pilot Programs?
I talk to business leaders every week who are wrestling with the same challenge: they’re running AI pilots, but they can’t tell if any of it’s actually working.
One team has a chatbot. Another’s automating workflows. Someone uses Microsoft Copilot. And somewhere in the background, there’s a tool that nobody’s touched in months!
If that sounds like your business, this one’s for me. I’m Vaseem Ali, CEO of Tecvia, and I’ve spent the last year helping companies figure out which AI experiments are worth keeping and which ones are just burning budget.
Here’s What I’ve Learned.
Your company has a chatbot pilot running. Another team is automating workflows. Someone’s testing Microsoft Copilot. Somewhere else, there’s a tool nobody uses.
Sound Familiar?
You’re not alone. Most businesses are experimenting with AI right now. In fact, 59% of CEOs believe AI will be the most impactful thing for their industries. But here’s the thing: only a fraction of organisations actually manage to scale beyond just messing around with it.
Experiments without structure don’t scale. They just rack up costs.
The gap between trying AI and actually getting value from it? It’s wider than most people realise. And it comes down to one thing: you need a system to figure out what’s actually delivering results.
Pilots Without Purpose Don’t Pay
Running scattered AI experiments is like testing 20 different sales strategies with no way to track which one works. You might stumble onto something valuable. Or you might spend months and a chunk of your budget on absolutely nothing.
The problem isn’t AI itself. It’s treating adoption like you’re just exploring rather than making an investment.
Here’s what happens when you don’t have structure: You turn on features just because they’re there. Teams use tools inconsistently. You can’t tell if something’s actually saving time or just creating more work. Budget gets spent on abandoned projects while the genuinely useful stuff gets overlooked.
That’s not exploration. That’s just noise.
What Discipline Actually Looks Like
Start here: cap your AI use cases at 3 to 5 maximum. This isn’t about limiting yourself. It’s about forcing focus.
For each use case, assign a single owner. One person. Not a committee, not a shared responsibility situation. Ownership drives accountability.
Before you switch anything on, define your metrics. What does success actually look like for you? Hours saved per week? Fewer errors? Better sales forecasts? Revenue lift?
Then review monthly. Cut what’s not working. Double down on what is.
A Simple Framework That Works
- Identify 3 to 5 use cases you want to test
- Name an owner for each one
- Set measurable KPIs upfront
- Run for 30 days
- Review the data and decide
That’s it. No endless pilots. No tools gathering dust.
Some AI features will deliver. Others won’t. Your job is to filter ruthlessly and know which is which.
The Real Question to Ask
At Tecvia, we work with businesses using Microsoft Dynamics 365 Business Central. The AI capabilities are built in. Copilot for sales. Predictive analytics. Automated workflows.
But here’s the question we hear most: should we turn these on? Not do we have them, but should we use them?
That’s the right question.
We help clients evaluate which AI features actually move their specific metrics. Some save hours every week. Others sit disabled because they don’t fit the workflow. Both answers are useful. Both are data.
What Doesn’t Work: Adopting Everything, Measuring Nothing
You don’t need more tools. You need to know which tools are best suited for your business.
Before you add another AI solution, ask yourself three things:
- Does this use case have a dedicated owner? Is someone actually accountable for results?
- Do you have KPIs defined? Can you measure the impact?
- Are you reviewing monthly? Will you kill it if it’s not working?
If the answer to any of these is no, you’re not building payback. You’re just stacking pilots.
Stop Adding. Start Evaluating
AI theatre is cheap. Buy the latest tool, announce the initiative, move on.
Real results take discipline. Define what matters. Own it. Measure it. Keep what works. Cut what doesn’t. That’s how you move from experiments to returns.
If you’re running Microsoft Dynamics 365 Business Central and you’re not sure which AI features are worth your time, let’s talk!
