According to reports, the AI in healthcare market is expected to grow at a CAGR of 38.6% between 2025 to 2030. By the end of the forecast period, it will be worth $110.61 billion.
This positive market sentiment has trickled down to the grassroots. Headlines promise faster diagnostics, smarter operations, and reduced costs. Patients are starting to expect AI-led experiences. Care givers are ready to be armed with the latest AI tech. Leaders in healthcare have started investing big in healthcare AI.
Everyone wants to grab a slice of the growing market pie. And everyone believes that adding AI will instantly transform productivity.
I’ve sat through more than 50+ AI implementation consultations with healthcare organizations. And not one of them missed asking this one question: When we’d start seeing the AI benefits in our accounts?
Well, the sad answer is: it will take a lot more time than you’d imagine or expect.
Historical data proves it. That’s exactly what we are seeing today. And in the short term, at least, AI implementation projects will drive no tangible outcomes.
Does that mean you shouldn’t bother jumping on the AI bandwagon? Absolutely not. The momentary loss in productivity (and maybe even profits!) is just the 1st step. If you plan and implement everything right, when the benefits kick in, they’d make it worth the pain.
The experience is consistent with “The productivity J-curve” theory by Brynjolfsson, Rock, and Syverson. The repetitive pattern is clear: with new technology, productivity gains (and by extension financial benefits!) lag behind expectations. In the long run, however, gains start to show up. Those who’ve made the investment reap its benefits, while those who didn’t end up feeling left out. The gains appear over time when organizations have made changes like:
- Reimagining healthcare workflows to be AI-first
- Shifting organizational culture from hands-on to automated
- Restructured the organization to match new AI roles and responsibilities.
But, this doesn’t help the fact that the AI J-curve in healthcare dampens leadership spirit. So what can or should you do during the J-curve downturn to prepare for the uptick?
Here’s what I can tell you based on my experience of helping more than 10 healthcare orgs implement enterprise-wise AI solutions.
1. Accept the Lag as Part of the Journey
There is no easy way to say this: You have to accept the lag. When I first started working with hospitals on AI deployments, I noticed a recurring pattern: even the most excited leaders ended up frustrated within weeks. Over time, I realized the most important advice I could give them was simply: expect the lag and accept it. Accepting that productivity gains take time changes the conversation from “Why isn’t this working?” to “What can we do differently to get there faster?”
Organizations that embrace the J-curve mindset are less likely to abandon projects prematurely. This makes them much more likely to reap benefits in the long run.
2. Focus on Culture, Not Just Code
AI in healthcare isn’t just about building accurate models. It’s about creating a culture that trusts and leverages AI insights. Early on, I’ve seen highly capable teams hesitate to use AI outputs because they feared making mistakes. One organization I worked with spent months integrating AI into workflows perfectly. Yet, they saw real outcomes only when they encouraged experimentation and stopped forcing everyone to follow the same workflow. My advice to leaders: invest in people and mindsets as much as you invest in technology. Without that, the J-curve will feel steeper than it really is.
3. Reimagine Workflows Around AI, Not the Other Way Around
Most healthcare organizations have archaic workflows. Stuff has been happening the same way since Day 1 and organizations think AI can just be added to the workflows. But that’s not how AI works. Not well, at least. You cannot slap on an AI layer to a workflow and call it a day. Instead, what you need to do is to design new flows around the insights that AI delivers. Of course, this will result in friction and resistance. Doctors, nurses, even patients, who are all used to the traditional ways of work will not be happy. But if planned properly, the new AI-centric workflows show great promise and productivity.
4. Invest in Cross-Functional Collaboration
AI projects stumble when teams work in silos. From my experience, the ones that succeed involve everyone—clinicians, operations, data scientists, and leadership—talking to each other early. The goal is simple: surface concerns, align incentives, and clarify who owns what. I often run workshops where these groups debate scenarios, interpret model outputs, and define success together. It can feel slow at first, but that alignment is what helps teams push through the tricky early phase of the J-curve.
5. Measure Early Signals, Not Just Outcomes
Waiting for hard ROI too soon is a trap. Real signals show up in quieter ways:
- Clinicians are adjusting how they work
- Faster, smarter decisions
- Better adherence to protocols
I once worked with a large health system where AI alerts seemed ignored. But engagement tracking revealed that teams were experimenting with ways to include the insights in daily care, just not out loud. By the time ROI appeared in patient outcomes months later, adoption was already baked into their culture. Small wins matter. They’re the sign you’re on the right path, even before the numbers catch up.
6. Prepare for Iteration, Not Perfection
No AI model is perfect out of the box. In healthcare, data is messy, inconsistent, and always changing. The key, however, is to embrace iteration and not aim for perfection. Keep refining models, test your assumptions, and adapt to shifting protocols or patient needs. Each iterative cycle makes predictions more accurate, often revealing operational insights you didn’t see before. Over time, these small improvements compound to deliver meaningful results.
7. Leadership Mindset Determines Success
At the end of the day, AI initiatives rise or fall on leadership. Technology alone won’t carry a project. Treat AI as a strategic capability, and the J-curve becomes manageable. Treat it as a quick cost-saving tool, and disappointment is almost guaranteed. Leaders should:
- Expect early setbacks
- Challenge entrenched habits
- Foster trust, learning, and accountability
The goal isn’t just to implement AI. The goal is to create the conditions where AI can deliver real, lasting impact on patient care, care giver productivity, and organizational bottom lines. Across more than ten healthcare organizations, these seven principles have consistently held true: the J-curve is real, but entirely navigable. AI in healthcare isn’t a sprint—it’s a marathon. And the organizations that run it thoughtfully, with patience and clarity, are the ones that unlock its real potential.
About Pratik Mistry
Pratik Mistry is the Executive Vice President of Technology Consulting at Radixweb. As a technologist and strategist, he helps businesses drive revenue growth through cutting-edge software development and value-based partnerships. Outside work, Pratik enjoys exploring new cuisines and catching the latest movies.
