Medical device maker Abbott has been using AI to improve patient care for more than a decade, long before generative AI became an enterprise buzzword. CIO Sabina Ewing described how the company embeds algorithmic intelligence into glucose monitors, real-time imaging for surgeons, and now generative AI tools for dietary guidance. The approach, she said, rests on a foundation of trust, cross-functional partnerships, and a demand for measurable results from the IT organization itself.
AI in patient care for a decade
Abbott's FreeStyle Libre sensor, a continuous glucose monitor, has relied on algorithmic AI for years to deliver readings to diabetics. In some cases, it connects to insulin pump applications to automate insulin delivery. Late in 2025, the company launched Libre Assist, a generative AI feature that lets users photograph their meals and receive guidance on how the food might affect their glucose levels. The tool even advises on the sequence of eating, since the order in which a person consumes food changes how the body processes glucose.
In its medical devices business, Abbott's Ultreon software, launched in 2021, uses imaging AI to guide stent placement during cardiovascular procedures. The technology supports physicians' real-time decision-making, illustrating how the company matches AI capabilities to specific therapeutic problems. "Whether it's algorithmic, generative, or agentic, we're intentional about matching the capability to the specific therapeutic problem," Ewing said.
Governance built on trust and deliberate investment
"Trust is earned in drops and lost in buckets," Ewing said. To maintain that trust, Abbott's AI work follows principles of fairness, safety, quality, and transparency. The company established an executive steering committee on generative AI to ensure capital deployment is deliberate, not scattered. "We're not going out with a thousand flowers blooming," she said. Governance frameworks like the one Ewing described are a focus of AI for Executives & Strategy.
Standing financial measures apply to AI investments, and the company looks for high-impact opportunities where new technology can deliver results even as it matures. Those conversations happen in partnership with senior leaders in HR, finance, and the business units requesting specific capabilities. Ewing stressed that CIOs need strong relationships across the company to lead effectively. "I don't need to be in the spotlight, but you need those relationships in order to lead and effect change," she said.
The CIO's evolving mandate
For Ewing, the CIO role now demands "the strengths of conviction, credibility, and communication." Technical expertise is just the baseline. "If I tell the business it can use AI to drive outcomes, then I need to demonstrate it in IT," she said. She committed two commas of results-meaning millions-from new AI operational capabilities within IT, proving that the promise of AI translates to measurable impact inside the function before scaling to the enterprise. The skills Ewing highlights-credibility, business alignment, and technical depth-are central to the AI Learning Path for CIOs.
Ewing led a program to educate Abbott's top leaders on AI fundamentals, and her team embedded AI into talent processes. Continuous education runs through the ranks, both in person and virtual, to ensure employees are ready for new tools. The CIO's relationship-building across HR, finance, and business units is critical, she said, because sustainable change requires collaboration. "If you want to do something sustainable, you can't do it by yourself."
Ewing asks her team to adopt an AI-first mindset, but she's careful to distinguish augmentation from replacement. She pointed to the example of executive assistants: "The models that exist today can't be a great executive assistant. Models don't have the judgment, institutional knowledge, or nuanced reasoning required to prioritize work and navigate unspoken rules. That expertise is irreplaceable. Our question is how to augment it." This perspective keeps the focus on using AI to expand what people can do, not replace them.
Why this matters for executives and strategy
Abbott's experience shows that AI in regulated industries succeeds when it is tied to mission, not technology trends. The company's governance model-with clear principles, an executive steering committee, and strict financial discipline-prevents the wasteful "thousand flowers" approach. Ewing's insistence on demonstrating two commas of savings from IT's own AI use before scaling to the business underscores a critical point: executives must see measurable results from their own teams before they can credibly lead company-wide AI initiatives. The emphasis on cross-functional partnerships and continuous education also highlights that AI strategy is not a technology project-it is a business transformation requiring the full leadership team.
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