What if a team of AI agents — each specialized in genomics, radiology, or pathology — could collaborate in real time to analyze multimodal patient data and propose treatment plans, all while keeping clinicians firmly in charge?

According to Christy O’Gaughan, Chief Data and Analytics Officer (CDAO) at GE Healthcare, it could potentially be a reality.

With operations spanning more than 100 countries and annual revenue exceeding $20 billion, GE HealthCare stands as a global leader in medical technology, pharmaceutical diagnostics, and digital solutions. Today, it is also at the forefront of exploring Agentic AI, driving forward its mission of improving lives in moments that matter.

At the helm of its data and AI strategy is O’Gaughan, who tells CDO Magazine that rather than chasing hype, the focus at GE Healthcare has been on building real, practical use cases—AI copilots that support clinicians, forecasting tools that strengthen finance and supply chains, and onboarding agents that personalize the employee experience. 

Edited Excerpts:

Q: With your deep experience in healthcare, how have you seen digital transformation reshape the industry over the years, and what shifts do you think have the most lasting impact? 

We are seeing the adoption of AI-enabled tools evolve from helping with administrative tasks like smart voice-to-text notes to now also helping with clinical tools that are helping care teams provide more personalized, data-driven care. Specifically, some big shifts have been in interoperability and data liquidity. They are making it possible for patient information to move freely and securely across systems. 

That flow of data enables a complete, longitudinal view of the patient — so care teams can make better, faster decisions together. It’s not just about connecting systems; it’s about connecting people to the insights they need, when they need them.

In each case, AI acts as a decision-support engine — surfacing insights, automating routine analysis, and enabling teams to focus on higher-value work. It’s not just about efficiency; it’s about empowering people with better information, faster.

Q: How has GE Healthcare’s data strategy evolved as AI moves from experimentation to playing a core role in clinical decision-making and innovation?

We’ve taken a proactive and principled approach to AI by establishing an Enterprise Data and AI Governance Council. This cross-functional body aligns AI initiatives with our strategic priorities and upholds high security and privacy standards.

We’ve also codified a set of Responsible AI (RAI) Principles that guide how we design, deploy, and manage AI to embed fairness, transparency, and human oversight from the start.

Equally important is our commitment to digital literacy and empowerment. We recognize that the true potential of AI is unlocked when employees feel confident using it. That’s why we’ve launched a robust enablement program that equips our teams with the knowledge and tools to innovate, solve problems, and make data-driven decisions — putting the power of AI directly at their fingertips.

Q: What’s one critical challenge data leaders face today that you feel is often overlooked? Could you share one or two current pain points in your data strategy and how your team is working to resolve them? 

Embracing AI at scale demands thoughtful, intentional change management. We understand that our workforce is global, spanning a wide range of roles, responsibilities, and levels of AI fluency. That’s why we’ve prioritized meeting employees where they are — through targeted enablement, clear and consistent communication, and a strong foundation of digital literacy.

We’ve built programs that not only demystify AI but also empower our teams to engage with it confidently and responsibly. This is essential to fostering a culture of innovation where AI becomes a tool for everyone — not just data scientists or engineers.

At the same time, we recognize that the potential applications of AI are vast, which can be both exciting and overwhelming. To bring clarity and focus, we’ve implemented a structured intake and prioritization process through our Enterprise Data and AI Governance Council.

This framework helps us evaluate use cases based on feasibility and business value, as well as alignment with our strategic goals and responsible AI principles. It helps us invest in the right opportunities — those that deliver meaningful impact while building trust and momentum across the organization. 

Equally important is the role of cross-functional collaboration. Legal, Compliance, and Human Resources are integral partners in this journey. Their involvement is critical to navigating regulatory requirements, managing risk, and scaling digital literacy in a way that’s both responsible and sustainable. 

Ultimately, our approach is about more than just adopting AI — it’s about building an ecosystem where AI can thrive, responsibly and inclusively, across every corner of the enterprise.

Q: Can you highlight some genAI use cases GE Healthcare is working on? As many organizations struggle with scaling beyond pilots, how do you evaluate the ROI and determine which projects to operationalize?

AI is not only transforming clinical workflows — it’s also reshaping how we operate internally. Across the enterprise, we’re using AI to make processes smarter, faster, and more adaptive: 

• In Finance, AI models are improving forecasting accuracy by analyzing historical trends, market signals, and operational data to enable more precise planning and resource allocation, reducing uncertainty and improving agility. 
• In Commercial Operations, AI is driving next-best-action recommendations by analyzing customer requests, preferences, and engagement history. This helps sales and service teams tailor their outreach, deepen relationships, and deliver more personalized experiences.
• In Supply Chain, AI is optimizing working capital by predicting demand patterns, identifying potential disruptions, and recommending inventory adjustments. This reduces waste, improves fulfillment, and helps get the right products to the right place at the right time.

To invest in the right opportunities, we use a structured value-realization framework to prioritize projects. This framework evaluates use cases based on business readiness, technical feasibility, potential for user adoption, and alignment to strategic goals.  

When it comes to measuring ROI, our focus is two-fold — financial impact and operational value, such as process efficiency and colleague engagement. We also emphasize responsible innovation. All AI solutions are designed to augment human decision-making, not replace it — especially in clinical and operational contexts where trust, transparency, and human oversight are essential.

Q: AI agents (or agentic AI) are generating a lot of buzz. What’s your perspective on their potential role in healthcare, and where do you see early opportunities for adoption? 

While much of the industry buzz focuses on clinical applications, we also see tremendous opportunity for agentic AI in internal enterprise functions such as:

• Supply Chain: Intelligent agents monitor inventory levels in real time, predict potential shortages based on demand signals and supplier trends, and automatically trigger procurement actions — helping teams stay ahead of disruptions and reduce excess stock.
• Commercial: AI copilots assist sales reps by surfacing next-best actions, delivering real-time customer insights, and streamlining quoting processes. This enables more personalized, timely engagement and improves conversion rates. 
• Finance: AI assistants automate forecasting by analyzing historical trends and real-time inputs, perform variance analysis, and generate reports — freeing up analysts to focus on strategic planning and scenario modeling.
• HR: Onboarding agents personalize the new hire experience by tailoring training journeys, answering policy and benefits questions, and guiding employees through systems and culture — accelerating time to productivity and improving engagement.

These agentic tools are helping us scale expertise by embedding domain knowledge into intelligent systems that support colleagues autonomously or collaboratively. They also streamline onboarding by reducing manual processes, personalizing training, and accelerating time to productivity – enhancing the overall colleague experience.

Additionally, our technology teams are actively exploring how agentic AI can enhance clinical decision-making by bringing the collective expertise of a multidisciplinary care team directly to the clinician’s fingertips. This approach uses multiple AI agents — each specialized in a domain such as genomics, radiology, or pathology — that work collaboratively to analyze multimodal data.

The vision is for these agents to continuously synthesize insights and proactively generate treatment plan recommendations, adapting in real time as new information becomes available. Importantly, these AI systems are designed to support — not replace — clinicians, ensuring that human expertise remains central to patient care while being amplified by intelligent, context-aware tools.

Q: What are the biggest hurdles to deploying AI models at scale in a regulated, high-stakes environment like healthcare? How do you balance innovation with risks and ethical considerations? 

GE HealthCare has a deep-rooted commitment to safety — it’s one of the reasons I joined the company — and we’re equally focused on delivering value to the healthcare system. Every AI use case we pursue goes through our Responsible AI framework, which evaluates risk, fairness, transparency, and alignment with our values.

We’re constantly having conversations about how to enable innovation while appropriately mitigating risk, and how to meet the organization where it is in terms of digital maturity. Of course, patient safety, regulatory compliance, data privacy, and governance are foundational to everything we do. These principles guide how we scale AI responsibly and sustainably across the enterprise.

Q: You work at the intersection of healthcare, data, and cutting-edge AI, a space that’s constantly evolving. What’s one recent innovation, tool, or idea that genuinely made you say, “Wow”?

We’ve seen a significant impact from implementing a data observability platform that helps us monitor data quality across our pipelines. The ability to proactively detect anomalies, broken data flows, or schema changes — before they affect downstream analytics — continues to reinforce our confidence in the reliability of our data and the decisions it drives.

On the GenAI front, large language models with advanced reasoning capabilities are pushing the boundaries of what’s possible. These tools go beyond content generation — they synthesize insights and help teams explore new ideas in ways that feel more like collaboration than automation. They’re becoming trusted partners in innovation, not just productivity boosters. 

Q: Is there a belief you once held about leadership, data, or even success that you’ve since had to unlearn?

I used to think success was about having the right answers. Today, it’s more about asking the right questions and empowering others to do the same.

As AI reshapes work, leadership means enabling people — through digital literacy, inclusive innovation, and responsible design. Our focus is on building a future where people and technology thrive together.