Overcoming Skepticism and Driving AI Adoption - with Umesh Rustogi of Microsoft

**Daniel Faggella** (0:17)
Welcome everyone to the AI in Business Podcast. Today's guest is Umesh Rustogi, General Manager of Dragon for Nursing at Microsoft Health & Life Sciences. Umesh joins Matthew DeMello, Editorial Director at Emerj, to explain why many healthcare AI initiatives fail to scale, and what leading organizations are doing differently to achieve real adoption, reducing nurse documentation burden, improving accuracy and compliance, and turning AI investments into measurable, operational, and patient care gains. But first, are you driving AI transformation at your organization, or maybe you're guiding critical decisions on AI investments, strategy, or deployment? If so, the AI in Business Podcast wants to hear from you. Each year, Emerj Artificial Intelligence Research features hundreds of executive thought leaders. Everyone from the CIO of Goldman Sachs to the head of AI at Raytheon, and AI pioneers like Yoshua Bengio. With nearly a million annual listeners, AI in Business is the go-to destination for enterprise leaders navigating real-world AI adoption. You don't need to be an engineer or a technical expert. If you're involved in AI implementation, decision-making or strategy within your company, this is your opportunity to share your insights with a global audience of your peers. If you believe you can help other leaders move the needle on AI ROI, visit emerj.com and fill out our Thought Leader submission form. That's emerj.com. Click on Be an Expert. You can also click the link in the description of today's show on your preferred podcast platform. That's emerj.com/expertone. Again, that's emerj.com/expertone.
Without further ado, here's our conversation with Matthew and Umesh. Listen to hear Umesh's proven approach to turning AI pilots into real adoption and measurable ROI in clinical workflows.

**Matthew DeMello** (2:42)
Umesh, welcome to the program, it's a great pleasure having you.

**Umesh Rustogi** (2:45)
Thank you, Matthew, really excited to be back again.

**Matthew DeMello** (2:48)
Absolutely, we're seeing AI tools move from pilot projects to everyday clinical use. Healthcare leaders face new questions about trust, interoperability, and culture change. We're seeing Microsoft's partnerships provide a model for scaling innovation that serves both clinicians and patients and a big reason why we wanted to have you on the show to talk about this with a much closer view of what's going on. What approaches are you seeing return results where health systems start weaving AI into everyday nursing workflows and how does partnering closely with clinicians help make that transition smoother?

**Umesh Rustogi** (3:23)
That's a great question, Matthew. Our early customers adopting Dragon Co-Pilot for Nurses actually reported outcomes that include reduction in documentation time, reduction in documentation latency, improvement in the quality and completeness of documentation, like for example, capturing of the invisible care, and reduction in incremental overtime, and most importantly, reduction in cognitive load for nurses. Here are some of the approaches that enable that wide adoption and all the outcomes that I just talked about. I would say, first and foremost, is the fact that we co-created Dragon Co-Pilot for Nurses with nurses.
We recognized very early on that the nursing workflows are unique. They are different than the physician workflows. It's a very mobile, fast-paced environment, structured documentation heavy. We had to build a solution which was purpose-built for nurses versus just repurposing the technology that was used for physicians as an example. From day one, we worked with the frontline nurses, the nurse informatics team, the nurse leaders to make sure that the functionality that we are building and deploying in Microsoft Dragon ProPilot for Nurses was actually suitable for nurses or fine-tuned for nurses. I would say the second approach that worked is our focus on usability and the direct integration into the existing EHR workflows, right? Which allowed nurses to document care simply by just speaking aloud about their patient interaction, and then AI then transforms these conversations into structured flow pushy documentation which nurses can quickly review and approve before it enters into the EHR, right? So we were laser focused on that usability and making sure that there was no friction in the workflow. Third, I would say is our little bit obsession about improving accuracy, right? So it was evident that the accuracy, if the accuracy of the solution was not good enough, the nurses will have to spend a lot more time editing or correcting the results versus just reviewing and accepting it, right? And we would not see the adoption if the accuracy was not good. So over a course of time, I mean during the early previews, we worked very hard on making sure that the accuracy improves and the nurses basically now have the output of the system, which basically works for them. A fourth thing which jumps out, I would say is change management, right? So organizations that see strong adoption, they treat, I mean, AI as a change management journey, not just a one-time thing.