Cracking the Code to AI Adoption in Healthcare
AI in clinical settings requires careful implementation, deep engagement with users, and a solid strategy to ensure it actually sticks.
AI in healthcare is exciting, transformative, and full of potential—but adoption isn’t automatic. Unlike consumer tech, where new tools can spread organically, AI in clinical settings requires careful implementation, deep engagement with users, and a solid strategy to ensure it actually sticks.
I work at an AI healthtech company as their growth manager during their 0 to 1 stage. I have been lucky to have had a front-row seat on AI hospital implementation, and it has been transformative in how I speak to prospects and think about our growth engine.
1. Implementation Starts with Deep Workflow Understanding
One of the most important parts of implementing AI in healthcare is getting inside the clinicians’ world. Watching them work, sitting in clinics, and truly understanding their day-to-day challenges is essential.
Every specialty has its own unique workflow, and AI adoption will only succeed if it fits seamlessly into their existing processes. Some key questions to ask before rolling out AI:
How do consultations take place? One-on-one in a clinic? On the phone? On ward rounds? At patients’ homes?
What are the biggest inefficiencies clinicians face? Is documentation slowing them down? Are they struggling with patient load? What are all the actions they have to do in a clinic?
Is AI actually a good fit for their setting? Some environments might benefit less from having AI within their workflows.
So that’s why in the last few months, I have been going to the field - shadowing clinicians, seeing how they interact with their EPR, with their staff, with their patients…. and it has become my best tip I could give you to come from a place of knowledge when speaking with similar prospect hospitals. It has helped me build trust by speaking the clinician’s language (which is a very hard when you’re not a doctor by background!)
2. AI Adoption Relies on Setting Clear Expectations
One of the biggest risks in AI adoption is misalignment between expectations and reality. Most specifically, expecting that AI will solve all your problems, and be perfectly tailored from day 1.
At TORTUS, we repeatedly reinforce what our AI scribe does—and just as importantly, what it doesn’t do. Over-promising on AI capabilities is a guaranteed way to erode trust and cause frustration when the tool doesn’t perform as expected.
A good AI adoption strategy means aligning from the start:
Can AI work in your clinical settings ? Some workflows are a great fit, others will struggle.
What’s the learning curve? AI isn’t magic—it requires an adjustment period.
How will feedback be integrated? AI needs iteration, and clinicians need to know their feedback will be heard and acted upon.
Setting the right expectations from day one makes clinicians more willing to engage, experiment, and improve the tool together rather than abandoning it when the first hurdle appears.
3. Find Your ‘Star Users’ – The Clinicians Who Will Make AI Work
Not every clinician is the right fit to lead an AI rollout. The best early adopters are:
✅ Excited about AI’s potential – They see the value and want to save time.
✅ Aware of AI’s limitations – They understand that AI isn’t perfect, but they’re willing to iterate and adapt.
✅ Collaborative and open-minded – They’re happy to provide feedback and co-develop improvements.
These ‘star users’ are essential to any AI adoption strategy because they help drive usage within their teams. If they see value in the tool, they’ll naturally influence their peers, creating organic momentum for wider adoption.
4. Pilots Need Clear Metrics and Boundaries
A well-structured pilot isn’t just about testing AI—it’s about proving its value in a measurable way. That means defining:
Key success metrics – What are we actually measuring? Time saved? Reduction in documentation burden?
Pilot scope – What’s in, and what’s out? Not every setting will be an ideal fit.
A vague pilot with unclear goals is a waste of time. A well-defined one creates a clear roadmap for adoption and expansion.
5. AI Adoption Isn’t Just About the Tech – It’s About Support
One of the biggest lessons I’ve learned is that successful AI adoption is just as much about onboarding and support as it is about the technology itself.
Clinicians need hands-on guidance to integrate AI into their workflow, and that means:
Providing in-depth onboarding – Not just a quick tutorial, but real-time support to help them use AI effectively.
Being available for questions and concerns – AI adoption doesn’t happen in a vacuum.
Iterating based on real-world feedback – If clinicians raise a pain point, they need to see changes happening.
Without strong onboarding and support, even the best AI tools won’t gain traction.
Final Thoughts: AI Adoption in Healthcare is a Process, Not a Switch
Rolling out AI in healthcare isn’t like rolling out a new SaaS tool. It takes time, trust, and continuous refinement to get it right.
The best strategies focus on:
🏆 Deep workflow understanding – Make AI fit into the clinician’s world, not the other way around.
🏆 Clear expectation setting – Avoid over-promising and ensure alignment from the start.
🏆 Finding the right early adopters – Work with clinicians who are excited but realistic.
🏆 Structured pilots with clear metrics – Define success before you begin.
🏆 Strong support and iteration – AI adoption is an ongoing process, not a one-time implementation.
By focusing on these elements, we increase the chances of AI adoption being successful, and impactful—rather than just another tech experiment that never truly sticks.
If AI is going to transform healthcare, it has to be done right. And that starts with getting adoption right. 🚀