Delivering Real AI Results and Why Enterprise Data Strategy Comes First

Summary

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Podcast Summary: Delivering Real AI Results and Why Enterprise Data Strategy Comes First

Podcast: Becker’s Healthcare Podcast
Host: Erica Spicer Mason, Becker's Healthcare
Guest: Ajay Capare, President and CEO of LK
Date: April 9, 2026

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Overview

This episode explores the real-world impact of AI in healthcare, with a particular focus on why a robust enterprise data strategy is the essential first step for any organization aiming to achieve meaningful AI outcomes. Ajay Capare, CEO of LK, shares candid insights about the state of data in healthcare, the difference-maker for scalable AI success, and practical advice for healthcare leaders looking to future-proof their AI initiatives.

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Key Discussion Points & Insights

1. Ajay Capare’s Background and LK’s Role in Healthcare IT

• Ajay’s Experience:
  • Over two decades in healthcare IT, including the evolution from the early days of EHR adoption to today’s AI wave.
  • Quote:

    “I joined right when we started with the wave of meaningful use... Now we all are in the AI wave.” (00:44, Ajay Capare)

• LK’s Solutions:
  • Positioned as a central player enabling interoperability and data quality.
  • Key platforms:
    • LK Opera: Orchestration of interoperability.
    • LK OSS: Long-term digitization and legacy data management.
    • LK Orbit: Expanding lab network possibilities.
• LK’s Value:
  • Provides the clean, clear data foundation critical for successful AI adoption.
  • Quote:

    “We are the foundation for that clean and clear data that drives these strategic initiatives to make AI enabler.” (01:55, Ajay Capare)

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2. Current State of AI in Healthcare: Results and Roadblocks

• Ubiquity of AI Initiatives:
  • 91% of healthcare leaders believe AI will be integral for growth, but strategic approaches are highly variable.
  • Quote:

    "If you actually do a survey with 20 different CIOs, chances are you might get actually 20 different answers because everyone's strategy is different." (03:18, Ajay Capare)

• Main Challenges:
  • Data silos, insufficient or poor-quality data, inconsistent formats, and weak governance.
  • LK’s Approach:
    • Lays the groundwork for AI/machine learning through data integrity and governance.
    • Quote:

      “You are always going to see that garbage in, you are going to see the garbage out. So we are the enablers of the data.” (03:57, Ajay Capare)

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3. What Enables Real AI Success in Health Systems?

• Distinguishing Factor:
  • The organizations seeing measurable AI results invested early and seriously in their data foundation.
  • Not just about funding or the latest tools, but about discipline in unification, interface cleanliness, and governance.
• Key Insight:
  • The quality and connectivity of your data at the foundational level determine the success of the whole AI stack.
  • Quote:

    “Anyone who works super hard on that foundation is going to absolutely come successful.” (05:51, Ajay Capare)
    “Integration, data aggregation, the quality and insight and then the reporting of that, it all plays cohesively with each other.” (06:11, Ajay Capare)

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4. Enterprise Data Management: What It Actually Means

• Beyond Buzzwords:
  • Ajay breaks down what an enterprise data management strategy looks like—moving from “data plumber” to trusted partner.
  • Core capabilities:
    • Interface and interoperability
    • Data translation and aggregation
    • Secure, accessible data
    • Speed to value and AI-enablement
  • Quote:

    “We are trying to make sure the competitive advantage, the models that you are putting, it all starts with the quality and connectivity of your data.” (08:15, Ajay Capare)

• Tangible Impact:
  • LK’s scale: two-thirds of U.S. lab orders/results processed; over 200M patient transactions via Commonwealth partnership.

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5. Advice to Healthcare Leaders: Where to Invest in 2026

• No Silver Bullet, But a Clear Priority:
  • In an environment of rapid AI innovation and budget constraints, focus on robust data foundations and interoperability.
  • Importance of a single enterprise data management strategy that unifies disparate systems.
  • Quote:

    “They're not going to be ones who are adopting any new technology first, but they are the ones who are going to build the infrastructure to sustain it for the longitude.” (10:53, Ajay Capare)

• Don’t Forget Compliance and Security:
  • Compliance, security, and adapting to regulatory change all hinge on strong data layers.

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Notable Quotes & Moments

• On Data as the Enabler of AI:

  “We are the enablers of the data. That's the big role that we are playing right now with our data and interoperability strategy.” (04:17, Ajay Capare)

• On Real Versus Hype in AI:

  “A lot of times with that excitement, actually a lot of dollars gets actually plugged in and then you see at the end of the day that there is no ROI.” (04:51, Ajay Capare)

• On the Challenge of ‘Enterprise Data Management’:

  “It’s easy to say than to actually put it in place.” (08:41, Ajay Capare)

• Final Advice to Leaders:

  “Your foundation, especially your data foundation, data governance has to be on the top. That is definitely going to be one thing that is going to differentiate the organization that win.” (10:53, Ajay Capare)

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Timestamps for Key Segments

• Ajay’s Background & LK Overview: 00:44–02:24
• AI in Healthcare: Real Results & Challenges: 03:03–04:28
• Foundation for AI Success: 04:28–06:32
• What ‘Enterprise Data Management’ Really Means: 07:09–09:30
• Where Leaders Should Invest Next: 10:02–11:55

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Closing Takeaways

• AI’s promise in healthcare is real—but only for organizations serious about their data strategy.
• The “winners” in AI won’t be the first to experiment, but those that invest in strong, integrated, future-proof data infrastructures.
• Enterprise data management is more than a buzzword: it’s the backbone of scalable innovation, compliance, and ROI.

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(End of summary)

Transcript

[A]

Hello everyone, this is Erica Spicer Mason with Becker's Healthcare. Thank you so much for tuning into the Becker's Healthcare podcast series. Today we're going to talk about delivering real AI results and why enterprise data strategy comes first. And joining me for this conversation we have with us Ajay Capare, the president and CEO of lk. Ajay, welcome to Becker's podcast. Thank you so much for being here today.

[B]

Thank you Erica for having me here.

[A]

Really happy to have you here. And I thought to get us started, it'd be great if you could share just a little bit about your background and also offer our listeners just a quick snapshot of what LK does. What are the types of data challenges that you're helping health systems navigate right now?

[B]

So Erica, you know, last two decades I worked in healthcare and healthcare IT and you know I have seen, I join right when we started with the wave of meaningful use and from meaningful use, from getting all the revolution done towards healthcare electronification of ems and every physician in the US trying to actually adopt to that change to population health, community health. And now we all are in the AI wave. Lk, to be honest, we play as a central role towards all the things going around us. LK as a company we are in five segment of healthcare it. We are an enterprise interop solution provider company where primarily the three solutions that we offer and the three problems that we solve. We are LK Opera which is a platform to orchestrate all the interoperability. Then we also take care of live and legacy data platform for a long term digitization strategy with LK OSS and then platform to expand the possibilities that things beyond connectivity with LK Orbit our lab network is a pretty big place out there. And as we look at LK the next generation where things are changing, we all are looking at what the power of AI and what AI can do. And I feel like we are the foundation for that clean and clear data that drive these strategic initiatives to make AI enabler. This is where we are all actually very excited about.

[A]

Great to learn more about you Ajay and LK as well. It's fantastic that you have this kind of central role as an organization. I think that's an important place to be especially as we're seeing so much enthusiasm and even hype around AI and healthcare and we hear from leaders across the industry that a lot of pilots struggle to scale. So I would love to know from what you're seeing across your own client base, where is AI actually delivering measurable results and conversely, where are organizations Hitting walls that maybe they didn't anticipate hitting.

[B]

I think if you look at it as we talk about fueling the AI innovation with access to quality data, that quality data access is where we play the big role. If you look at it today, 91% of healthcare leaders, they believe AI or machine learning will be an integral part of their organization's growth. But at the same time, if you actually do a survey with 20 different CIOs, chances are you might get actually 20 different answers because everyone's strategy is different. Strategy is still a foundation where LK plays or LK actually try to help them in the initiatives. What they are trying to achieve is data integrity, which also includes today. If you look at it, there is a lot of data silos, there is a lack of sufficient data, there is a poor data quality, there is inconsistent data formats, inadequate data governance. Where LK can come and really put a foundation is we can do the groundwork for AI and machine learning success by ensuring that there is a proper data integrity. We are actually ensuring that there is a quality of data which goes in. Otherwise you are always going to see that garbage in, you are going to see the garbage out. So we are the enablers of the data. That's the big role that we are playing right now with our data and interoperability strategy.

[A]

Fantastic. Thanks for giving us a kind of on the ground feel of what you're seeing, Ajay. And when you look at the organizations that are seeing real traction with AI versus those that have not yet, what would you say is the distinguishing factor? And how much of that comes down to the data foundation that they had in place before they even started to leverage an AI tool.

[B]

I think there is always going to be like, you know, there is always going to be that excitement looking at the use cases and looking at what you can actually achieve. A lot of times with that excitement, actually a lot of dollars gets actually plugged in and then you see at the end of the day that there is no roi. In today's time when things are difficult, I mean, you know, you open, you know, Beckers, the daily backers report and you will see that, you know, things are not that easy. And in these times you really have to start fixing your data strategy with Data foundation, the real organization who are seeing real traction. It's not just that, you know, here is the use case or here are the tools, I'm going to use it. And it's not even the budget, it's the discipline. How do you put together, you know, you Got to actually make sure that there are you, you are unifying the system, clean up your interfaces and you get serious about governance. The one struggling part that everyone is looking at it is how the top of the layer will perform in this complex environment if the bottom of the data which is inserted is not correct. So that foundation piece, the quality and connectivity of your data, one has to look at it and I personally feel anyone who works super hard on that foundation is going to absolutely come successful. At the end of the day, the integration, the data aggregation, the quality and insight and then the reporting of that, it all plays cohesively with each other. And that is the part I feel like we can really help a lot.

[A]

Yeah, Ajay, I know in a lot of these AI conversations that I've heard or participated in myself, the Data foundation is something that's stressed pretty universally among experts such as yourself. So I appreciate you sharing that and I'd love if you could just walk us through what an enterprise data management strategy actually looks like in practice. Maybe there's a lot of moving pieces. So if we could just give our listeners kind of a more granular sense of what that data management strategy looks like. And for a CIO who's heard that term frequently, you know, in vendor pitches, what does it mean to really get it right?

[B]

Yeah, and you know, we, we as a company we are moving towards from being enterprise, you know, interop solution provider company. And it's a journey for us. We started with being a data plumber. From being a data plumber, we started getting into becoming an enterprise interop solution provider company. And from there as we look at it, we are slowly and slowly becoming an enterprise data management as a company or we call ourselves very proudly, we are your enterprise healthcare data partner. But enterprise data management, it's a platform, is the foundation for a data driven healthcare strategic initiatives and it's the innovation across the care of continuum that, that we are trying to provide now. What comes in enterprise data management platform. It's your interface, it's your interoperability, it's the translation of data, is the aggregation of the data and it's the access of the data. And as you, as you look into it, there are multiple pieces to it. With our enterprise data management we are bringing in, we are trying to build a network strategy, maintain a legacy data experience, that speed to value that everyone is looking at it today. You want to accelerate your innovation, you want to put your AI LLMs on top of it. That innovation we are trying to accelerate, if you look at the Complete data visualization that is achieved in small words. I can actually just say that we are trying to make sure the competitive advantage, the models that you are putting it all starts with the quality and connectivity of your data. And we are actually trying to make sure we can do this. There are a lot of sales, a lot of noise around enterprise data management or data strategy. It's easy to say than to actually put it in place. But we have proved ourselves today. When you look at our lab network, two thirds of this country, they access their lab order and result through lk. If you look at our partnership with Commonwealth, there are more than 200 million patient transaction which happens through us. So we are making sure that the AI play is not just a pilot mode, but we are trying to build a future of 5 to 10 years strategy across the segment of healthcare.

[A]

I really appreciate you kind of translating enterprise data management strategy for us, Ajay. I feel like we have a much better picture of what that actually looks like. And you know, just to take this one step further, I guess and here to close us out, if you're sitting across the table from a health system leader today, especially someone who's in informatics or technology, what is the one infrastructure investment that you would tell them to prioritize in the next year to really set themselves up for meaningful improvements with

[B]

AI, I think, you know, it's not even fair to say if there was one, one advice I can give and that can solve all their problems. It's easy, right? But the fact is we are in this time frame right now where innovation and the changes of innovation with AI are changing every day. Lot of excitement around that. At the same time, you have so much budget constraints, you're trying to do more things in less money today. And these budget constraints, they're real and everyone is looking at it. What can I get the best out of a product with a great roi? And my simple simplification with everyone is that you got to actually look at your foundation, you have to look at your interoperability, you have to look at the number of systems that you're working and how you can actually bring all that data together with one single strategy. Enterprise data management definitely plays a big role here. AI is going to keep evolving, new models are going to keep coming, new vendors are going to keep coming, new partners are going to keep coming and you will end up doing more and more. But your foundation, and especially your data foundation, data governance has to be on the top. That is definitely going to be one thing that, that is going to differentiate the organization that win. They're not going to be one that who are adopting any new technology first, but they are the one who are going to build the infrastructure to sustain it. For the Longitude, remember, you also have to look at the compliance piece, you have to look at the security piece, you have to look at the changes happening in regulations. And all those things can happen only with the foundation of the data layer that you're putting together.

[A]

Ajay, it's been so insightful hearing what a sound data foundation can enable in terms of innovating in the first place, or just responding to the rapid changes that you're speaking to that are happening right now and will only continue as AI evolves. So I just want to thank you for sharing your time and your your thought leadership with our listeners today. It's been great learning from you.

[B]

Well, thank you for the opportunity. And you know I always love Beckers. I come to Beckers and I'm looking forward to attending in person and I hope more and more people can actually get advantage of the conference.

[A]

Oh, Ajay, thank you so much and we're excited to see you at our events this spring. And listeners, we'd like to thank you for joining us and of course would also like to thank our sponsor for today's podcast, lk. Be sure to tune into more podcasts from Becker's Healthcare by visiting our podcast [e-mail].