Beyond Connectivity: Inside BD’s Vision For AI In MedTech

From predictive patient monitoring to smarter clinical workflows, Becton Dickinson is putting AI to work across its medical portfolio. For newly appointed Chief Information and Digital Officer Arthur Hu, the opportunity lies in not just connecting data across the care continuum, but translating it into actionable insights that help clinicians improve care and reduce friction.

Last year, for example, the company introduced BD Incada, a cloud‑based, AI-powered healthcare data platform that unifies data from millions of devices into a single ecosystem. With natural language prompts, clinicians can verify medications are available for patients, among other use cases.

“In AI, there is a lot of co-discovery and co-creation, but many of the building blocks are already in place. The opportunity is to apply those capabilities to unlock doors that were previously closed, or that the business may not have known existed,” Art told CIOnews.

CIOnews talked to Art about how AI is reshaping healthcare delivery, why AI isn’t exempt from the rules of business, and how BD takes a bi-directional approach to innovation.

Tell me about your background: When did you first realize you wanted to be a technology leader? What are your career highlights so far?

In school, I studied computer science – and though my father was a computer science professor, I tried several different things before landing in CS. I became more interested in enterprise technology when I joined the workforce and chose a business and technology strategy consulting practice. What drew me in was the intersection of technology and business, particularly how technology can be applied in a specific environment to achieve impact and value. There wasn’t one defining moment. It was more of a progression as technology moved from the back office to the front office and became an increasingly important part of the business.

Most recently, I was at Lenovo Group, where I served as senior vice president, global CIO and chief technology and delivery officer for the Solutions and Services Group. My responsibilities included leading information services, digital technology and business transformation, driving Lenovo's services-led and AI transformation across customer, partner and employee-facing systems, and leading the customer services delivery teams to all of Lenovo’s customers worldwide. Prior to Lenovo, I spent nearly a decade at McKinsey & Company advising organizations on technology, operations, strategy, and organizational transformation.

What’s the significance of combining information and digital in one role?

The expanded title is by design. It’s not just about information anymore. It’s about taking all the value streams and helping the company make sense of it. Data and AI are key to how we operate now and well into the future. My role ensures we have the right tools, platforms, capabilities, and people in place, and then playing a critical role in shaping the strategy around digital, data and AI for the company. Specifically, I am tasked with helping the various initiatives add up to material impact in our business, and to drive performance and product and service differentiation for our patients through digital and AI technologies.

As executive vice president and chief information and digital officer, I’m responsible for leading all aspects of BD’s IT systems and processes, digital strategy, data, AI, cybersecurity, and the enterprise program management office. I report to the CEO and serve on the BD Executive Leadership Team.

What do you see as the biggest near-term opportunities for AI in the medical device industry? Are there any unique hurdles that BD and others face in delivering those?

I see a tremendous opportunity for BD in AI and MedTech by partnering with health systems to move these tools from concept to practical, real-world use and impact—ultimately enabling better care delivery and improved patient outcomes. What excites me most is how AI can help unify the data that exists across the care continuum. Many “connectivity” solutions simply move data from point A to point B, but the real value comes from transforming that data into clear, actionable insights that support clinicians at the point of care.

Across MedTech, we’re at an inflection point. AI is no longer just enhancing devices; it’s reshaping how entire health systems operate. Cloud‑native platforms like BD Incada™ are becoming essential for real‑time data access, remote device management, and scalable workflow automation. We also see AI accelerating innovations in robotics, automated documentation, predictive inventory management, and interdisciplinary care coordination. These technologies shift us from reactive support to anticipatory intelligence, helping clinicians act sooner, operate more efficiently, and spend more time on high‑value patient care.

What are you looking toward as the more “horizon” capabilities, and how are you working toward them now?

What excites me is applying technology and transformation, looking for parallels with what has worked elsewhere, and actively exploring new possibilities. In AI, there is a lot of co-discovery and co-creation, but many of the building blocks are already in place. The opportunity is to apply those capabilities to unlock doors that were previously closed, or that the business may not have known existed. For example, capabilities around AI could make it possible to compress four or five years of R&D into 18 or 24 months.

What do you think about closing the gap between adoption and value? Any strategies you personally use?

There is nothing inherently special about AI that exempts it from the rules of business. Ultimately, there has to be a return and real adoption that justifies the investment. It is important to set realistic expectations because organizations cannot move from no AI awareness or literacy to something transformative without going through the stages in between. Companies need to adopt, learn through action, and adapt based on what they learn. From there, they can set milestones around awareness and usage, identify the most insightful lessons, and scale the best applications through further investment.

What about at BD: How does innovation move from experimentation to production? What does that look like from a technology and organizational standpoint?

It requires both a top-down and bottom-up approach. From the top down, the company needs a constructive vision for how AI fits in, with a focus on augmenting human potential so people can do more and better with AI. From the bottom up, employees need to be empowered to combine their frontline knowledge with the ability to move quickly and create new capabilities. The company then needs guardrails and governance that promote innovation, identify the most promising ideas, and enable them to be copied, reused, and scaled from teams to functions, departments, and ultimately the enterprise.

Are you rethinking build versus buy? How does AI change the decision making here?

As writing code becomes more of a commodity and its marginal cost approaches zero, the focus shifts to how quickly organizations can explore ideas and move the right ones into production. AI can help enterprises assess ideas earlier and determine which are truly valuable. In some cases, the right answer may be neither build nor buy because the idea is not viable. When an idea is worth pursuing, the decision still depends on where the team should focus, where there is disproportionate value for the company, and whether there are sovereignty, business-control, or compliance requirements that point toward building or buying.