We recently spoke with Kishore Khandavalli, CEO of 7T, the premier AI deployment firm for large organizations. As healthcare players and industry brands look to modernize their operations and improve the customer experience through AI, Kishore shared the primary obstacles he observes, what distinguishes successful projects from those that fail, and the priorities organizations should focus on first.
Q: Healthcare organizations are under enormous pressure to adopt AI, yet many struggle to move from pilot projects to actual deployment. Why does this gap persist?
Kishore: It all comes down to the difference between experimenting with AI and actually industrializing it. Most organizations manage to get a pilot working in a controlled environment. The difficulty lies in scaling it and making it function reliably within a complex healthcare organization, with legacy systems, strict regulatory requirements, and teams that are already under pressure.
The main obstacle is not the AI itself. It’s everything surrounding it. Most healthcare organizations already have the technology. The challenge is connecting it to the systems, processes, and people who actually need to use it. When organizations underestimate what that integration requires, the deployment stalls. The pilot becomes a proof of concept that never reaches production.
Q: What are the most common integration issues you encounter with healthcare organizations?
Kishore: Data fragmentation is at the top of the list. Patient information, operational data, billing records, and clinical processes often live in separate systems, built in different eras by different vendors. Before AI can create value, you need to know exactly where the data is, how it flows, and where the gaps are.
The second obstacle is compliance. Healthcare organizations must tread carefully regarding data privacy and regulatory requirements, which complicates every decision related to AI deployment. Many see these constraints and conclude that AI has to wait. In reality, those that progress are the ones that design their projects around these requirements from the start, rather than treating them as an obstacle to deal with later.
The third is change management. Deploying AI in healthcare affects real processes and real people. If clinical or operational teams do not understand how the AI works or why it is there, adoption collapses, regardless of the quality of the technology.
Q: What lessons do you draw from successful AI deployments in healthcare that others could learn from?
Kishore: The organizations that have succeeded have several things in common. First, they define success in operational terms before the project even launches. Not "we want to use AI," but "we want to reduce admission processing time by 30%" or "we want to identify high-risk patients earlier to reduce readmissions." This level of precision then guides every decision.
Next, they invest in integration from day one. In healthcare, an AI deployment doesn't succeed because the model is good. It succeeds because that model is connected to the tools that clinicians and operational teams are already using. When AI feeds its analysis into existing processes, it gets adopted. When it forces people to change the way they work, adoption becomes much more difficult.
Finally, successful organizations treat deployment as a continuous process, not a one-off project. Healthcare environments are constantly changing, and technology must evolve with them.
Q: Companies like CLARIA use technology to make hearing care more accessible and affordable for the general public. Where do you see the strongest impact of AI regarding the customer experience in healthcare?
Kishore: Most healthcare organizations still have numerous friction points throughout the customer journey. People struggle to find information, navigate products and services, book appointments, fill out forms, or get answers to simple questions. AI can remove a good portion of this friction when deployed intelligently.
What excites me most is not replacing humans. It’s making it easier for people to get what they need. Whether it's helping someone find the right hearing solution, answering common questions, or creating a more personalized experience, AI is at its best when it improves access and supports those who are already doing the work.
Q: For organizations in the healthcare sector just starting with AI, how should they approach enterprise-wide transformation?
Kishore: One mistake I see constantly: starting with the technology. Asking what AI can do before asking what business problem you are trying to solve. Organizations that get results generally start with the process, the bottleneck, or the customer friction point they want to improve.
For healthcare brands and providers, this often means starting with patient or customer journeys. How do people find the right product or care solution? How are follow-up and after-sales service managed? These are areas where AI can deliver measurable improvements quite quickly, which builds trust and creates momentum for broader transformations.
Q: What is the one thing healthcare organizations misunderstand most about AI that you would like to see better understood?
Kishore: They think deployment is the finish line. In reality, that is where the work begins.
The organizations that get the best results are constantly refining the place of AI in their operations, their customer experience, and their business processes. The real value comes from how the technology integrates into processes over time, how teams learn from it, and how it evolves with the organization.
Those that understand this usually achieve lasting results. Those that treat AI as a one-time initiative often end up wondering why their investment never lived up to its promises.
To learn more about how 7T helps healthcare organizations and large enterprises move from AI experimentation to real operational impact, visit 7T.ai.
