From Pilots to Agents: What Really Matters When Implementing AI in Healthcare

A lot of teams want to “do AI” right now. The problem is that most of them don’t know what that means. They either imagine AI as some kind of magic button that will reduce costs automatically, or they treat it like a buzzword to impress investors. That’s why most AI projects fail.

Why Projects Fail

According to Harvard Business Review, somewhere between 70–80% of AI projects never make it into production.¹ The reasons are usually the same:

• No one internally is responsible for outcomes.

• The goal isn’t clearly defined.

• The pilot is too big, or too vague.

• There’s no clear KPI to measure progress.

This is why we run workshops before a pilot. They force teams to define the real goal. Is it cost reduction? Is it patient experience? These are not the same thing. You can design an AI project for both, but you can’t design it for neither.

Someone Needs to Own It

AI projects die when no one inside the company takes ownership. Outsiders can help design, deploy, and optimize, but unless someone internally is responsible for watching results and moving fast, the system drifts. It doesn’t get updated, edge cases aren’t caught, and soon everyone says “AI didn’t work for us.”

During pilots, our team manages this risk by monitoring the agents closely, testing different scenarios and outreach timings, and sharing insights from proactive decisions to validate the strategy. Still, having a committed counterpart on the client side is key to ensure that things move quickly and effectively.

Pilots Should Be Small

Most people make the mistake of trying to implement AI everywhere at once. The better approach is to start with something small—a proof of concept or a lean pilot.

This is where our workshops play a key role. We begin by mapping out the biggest pain point in your current workflow and focusing only on that. The workshop isn’t just a discussion—it’s a working session where we map your patient journey, identify the single area with the highest impact, and align on a KPI to track. That way, the pilot is grounded in a clear problem and a measurable outcome.

For example: instead of automating your entire patient intake flow, we might start with one piece—appointment scheduling, or follow-up reminders. We validate it in the workshop, then iterate during the pilot until it works. Only then do we expand. That’s how you avoid burning time and budget.

Choosing a Partner

If you’re vetting partners, look beyond the sales pitch. Ask them:

• How do you measure success, and can you share examples of KPIs from similar clients?

• How do you handle edge cases and unexpected patient behaviors?

• What happens if the AI makes a mistake, and what safeguards are in place to protect patients?

• How do you support continuous improvement once the pilot is live?

The right partner won’t just hand you a product. They’ll help you design a process that includes monitoring, iteration, and feedback loops. They’ll also provide transparency, sharing insights from pilots, data on what worked and what didn’t, and a roadmap for scaling. That’s what separates a vendor from a true partner.

From Pilot to Production

Once you have an agent that runs correctly, implement it. Don’t wait for perfection. There will always be situations that the AI can’t handle. What matters is whether it solves a large enough part of the problem to create real value.

AI projects aren’t static. They need monitoring, updating, and expanding. But if you start with clear ownership, small pilots, and measurable goals, you’ll be among the minority—the 20–30% who actually make AI work in production.

Ready to Explore AI in Healthcare?

If you’re considering AI for your patient journey, the best place to start is with a focused workshop and a lean pilot. We’ll help you map the most pressing pain point, design the right AI agent, and iterate until it delivers real results.

👉 Book a free workshop with our team and see how we can help you move from pilot to production.