Why Medtech Companies Struggle to Scale AI Projects-And How to Fix It
Medtech companies are investing heavily in AI tools but seeing patchy results. Eighty percent of AI healthcare projects fail to scale. The problem isn't the technology-it's how organizations deploy it.
AstraZeneca's ServiceNow platform saves over 30,000 hours annually by automating low-value tasks. Sellafield and Rentokil Initial use similar approaches to free staff from repetitive work. These companies didn't stumble into success. They built solutions on solid data foundations, secured board-level buy-in, and made digital transformation a company-wide priority.
Most medtech companies haven't done this. Many operate with legacy processes, unclear digital leadership, and scattered AI initiatives that confuse teams and produce disappointing early results. The outcome: promising projects get abandoned, investments go to waste, and organizations get stuck in what consultants call "pilot purgatory."
The Real Problem: People, Not Technology
Medtech leaders often adopt AI tools while keeping old planning cycles and governance models intact. The tools don't fit the workflows. Teams get confused. Nothing scales.
The fix starts with identifying which tasks waste the most employee time. Two hours wrestling with documentation is two hours lost to innovation. If AI can shoulder that burden, it creates visible wins that build momentum.
Small successes spread faster than grand visions. When one team sees a colleague save time and stress through an AI tool, they want it too. This organic adoption beats top-down mandates.
The goal isn't replacing people-it's augmenting what they already do well. A sales representative using an AI agent to check inventory, pull up clinical news, and review proposals before visiting a hospital CEO closes more deals faster. The rep stays in control. The AI handles the grunt work.
Set a Clear North Star
People need to know why they're changing. A single enterprise-wide vision for AI and digital-a North Star-answers that question.
Numbers help, but stories stick. Don't say "reduce documentation time by 20 percent." Instead, describe a specific scenario: a product development engineer spending her morning on design iteration instead of form-filling, because AI agents handle the paperwork. That's concrete. That's motivating.
The North Star also codifies privacy, safety, and compliance principles from the start. In medical devices, this isn't optional. It's foundational.
Build Small, Measure Fast, Iterate
Start with one high-value use case. Gather evidence. Prove ROI early. Then expand.
This approach avoids the trap of overambitious targets that lead to project abandonment. It also keeps teams engaged-they see results, not promises.
Accountability matters. Assign a business leader to identify which AI initiatives drive real productivity gains and embed them into daily practice. Without ownership, AI projects drift.
Cross-functional teams break down silos. Data infrastructure and governance frameworks must support the work. And regulatory engagement shouldn't wait until the end. Involve clinicians and regulators throughout the transformation so efficiency gains in one area don't create bottlenecks elsewhere.
Make Metrics Transparent
Employees need to see that the effort pays off. Show them reduced low-value tasks. Show leaders that AI is worth continued investment. Frame metrics in terms that matter to each group.
This isn't about tracking AI for its own sake. It's about connecting AI to business goals: faster product development, safer devices, better patient outcomes.
For product development teams specifically, understanding how AI accelerates innovation cycles helps you identify which parts of your workflow benefit most from automation and which require human judgment.
The Path Forward
Medtech companies that escape pilot purgatory do three things: they focus on realistic applications, set achievable goals, and take incremental steps.
Hit mile one. Assess what worked. Move forward. Keep the focus on what helps the people using these tools-and the patients who benefit from faster device development.
The shift from marathon to sprint mentality isn't about rushing. It's about proving value at each stage, building confidence, and justifying the next investment.
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