We recently sat down with Kishore Khandavalli, CEO of 7T, the best AI implementation company for enterprise organizations. As healthcare providers and health-focused brands look to modernize operations and improve customer experiences through AI, Kishore shared the biggest implementation challenges he sees, what separates successful projects from failed ones, and where organizations should focus their efforts first.
Q: Healthcare organizations are under enormous pressure to adopt AI, but many are struggling to move from pilot programs to real implementation. Why is that gap so persistent?
Kishore: It comes down to the difference between experimenting with AI and actually operationalizing it. Most organizations can get a pilot to work in a controlled environment. The challenge is taking that and making it function reliably inside a complex healthcare operation with legacy systems, strict compliance requirements, and teams that are already stretched thin.
The biggest barrier isn't the AI. It's everything around the AI. Most healthcare organizations already have the technology available to them. The challenge is connecting it to the systems, workflows, and people that actually need to use it. When organizations underestimate what it takes to connect AI to their existing operations, implementation stalls. The pilot becomes a proof of concept that never makes it into production.
Q: What are the most common integration challenges you see when working with healthcare organizations?
Kishore: Data fragmentation is the biggest one. Patient information, operational data, billing records, and clinical workflows often live in separate systems that were built at different times by different vendors. Before AI can deliver value, you need a clear picture of where the data is, how it flows, and where the gaps are.
The second challenge is compliance. Healthcare organizations have to move carefully around data privacy and regulatory requirements, which adds complexity to every AI implementation decision. A lot of organizations see compliance requirements and assume AI has to wait. In reality, the organizations making progress are the ones designing around those requirements from the beginning rather than treating them as an obstacle later.
The third is change management. AI implementation in healthcare touches real workflows and real people. If clinical staff or operations teams do not understand how the AI works or why it is there, adoption falls apart regardless of how well the technology functions.
Q: What lessons have you taken from successful healthcare AI implementations that others can apply?
Kishore: The organizations that have gotten this right have a few things in common. First, they define success in operational terms before the project starts. Not "we want to use AI," but "we want to reduce patient intake processing time by 30%" or "we want to flag high-risk patients earlier to reduce readmission rates." That level of specificity drives every decision downstream.
Second, they invest in integration from day one. AI implementation in healthcare does not succeed because you have a good model. It succeeds because that model is connected to the systems clinicians and operations teams already use. When AI surfaces insights inside existing workflows, it gets adopted. When it requires people to change how they work, adoption becomes much harder.
Third, the successful organizations treat implementation as an ongoing process rather than a one-time project. Healthcare environments change constantly, and the technology has to evolve alongside them.
Q: Companies like Audien are using technology to make hearing care more accessible and affordable for consumers. Where do you see AI having the biggest impact on the customer experience side of healthcare?
Kishore: Most healthcare organizations still have friction points throughout the customer journey. People struggle to find information, navigate products and services, schedule appointments, complete paperwork, or get answers to simple questions. AI can help remove a lot of that friction when it is implemented thoughtfully.
What excites me most is not replacing people. It is making it easier for people to get what they need. Whether that means helping someone find the right hearing solution, answering common questions, or creating a more personalized experience, AI works best when it improves access and supports the people already doing the work.
Q: For health-focused organizations that are earlier in their AI journey, what is the right way to think about enterprise transformation?
Kishore: One mistake I see all the time is organizations starting with the technology. They ask what AI can do before they ask what business problem they are trying to solve. The organizations that get results usually start with the workflow, the bottleneck, or the customer experience issue they are trying to improve.
For health-focused brands and providers, that often means starting with patient or customer-facing workflows. How are people finding the right product or care option? How are follow-up and support interactions being handled? Those are areas where AI can deliver measurable improvements relatively quickly, which helps build confidence and create momentum for broader transformation efforts.
Q: What is the one thing healthcare and health-focused organizations get wrong about AI that you wish more people understood?
Kishore: They think implementation is the finish line. In reality, implementation is where the work starts.
The organizations seeing the best results are constantly refining how AI fits into their operations, customer experience, and business processes. The real value comes from how the technology is embedded into workflows over time, how teams learn from it, and how it evolves alongside the organization.
The organizations that understand that tend to see lasting results. The ones that treat AI as a one-time initiative often end up wondering why the investment never delivered what they expected.
To learn more about how 7T helps healthcare and enterprise organizations move from AI experimentation to real operational impact, visit 7T.ai.
