AI in Healthcare Construction: Leadership Priorities as Facilities Evolve
Former Mayo Clinic CIO on why AI, workflows, and facility design must evolve together
Contributors:
- Cris Ross, Former Chief Information Officer (CIO) at Mayo Clinic
- Brian Nahas, Director of Artificial Intelligence at Mortenson
Read time: 6 mins
Article Summary:
Artificial intelligence (AI) is advancing quickly in healthcare, but leaders are still deciding where to focus and how fast to move today. Strong decisions today create room to adapt and embrace what comes next.
As care models evolve faster than facilities, AI adds uncertainty to both workflow and capital planning. In this conversation, Brian Nahas and Cris Ross discuss why strategic alignment and process redesign matter most, and how those shifts should influence planning and design.
Key Takeaways
- Build trust with executives and clinicians through targeted, low-risk AI use cases (e.g., ambient documentation, operational analytics) that demonstrate measurable gains before scaling.
- Reimagine workflows and patient experience first—then apply AI to reduce friction, improve handoffs, and expand clinician capacity for patient care, rather than layering in tech onto inefficiencies.
- Design adaptable facilities around how care is delivered today—and how it may evolve, with flexible layouts and infrastructure that can evolve alongside shifting acuity, care settings, and AI-enabled workflows.
- Engage systems-minded partners who align technology, operations, and facility design to turn AI investments into measurable, real-world performance, long-term asset value and experience outcomes.
About Chris Ross
Cris Ross served as Chief Information Officer (CIO) at Mayo Clinic for more than 12 years, where he led the health system’s $1.5 billion enterprise EHR transformation and helped advance major digital and AI initiatives. A two-time cancer survivor and co-author of the 2024 book Diagnosed, Ross brings both executive leadership experience and a deeply personal perspective to conversations around healthcare innovation, patient experience, and the future of care delivery.
About Brian Nahas
Brian Nahas, Director of Artificial Intelligence at Mortenson, focuses on advancing enterprise AI literacy, responsible experimentation, and the integration of human teams with AI systems across construction, manufacturing, supply chain, and emerging design engineering capabilities. He brings a track record of delivering complex, high-profile projects nationwide, while leading innovation through initiatives like a DfMA incubator and the expansion of Virtual Design & Construction (VDC), including GIS capabilities.
What Makes AI Adoption Hard to Act On?
Cris Ross: Imagination runs ahead of reality with new technologies, and healthcare is no exception. We’re still in the hype cycle. At the same time, knowledge cycles are compressing. What once changed over years now evolves in months, creating urgency. Healthcare systems are both optimistic and risk averse, and while that balance is necessary, it slows adoption.
The tools themselves are not the expensive part. The real cost of AI is change management. Redesigning workflows, training teams, and clear communication drive the real risk and disruption. Progress may look slow, but it reflects an effort to move responsibly. We should go as fast as we can, but no faster than it is safe.
Knowledge cycles are compressing. What once changed over years now evolves in months.
Where are Healthcare Leaders Getting AI Wrong Today?
Cris Ross: The biggest mistake is treating AI as a technology “patch.” We’ve seen this before during earlier waves of digital transformation, including the productivity paradox of the 1980s and 1990s, when organizations invested heavily in computers and software without fundamentally redesigning workflows or operations. Productivity does not come from adding tools to inefficient or broken workflows. It comes from redesigning the process first and then applying technology to support it.
The systems that succeed are the ones that start with workflow redesign and then apply technology to support that new way of working. AI will follow the same pattern.
How Should Leaders Prioritize AI Investment?
Cris Ross: If I were advising a CEO, I would not swing for a grand slam first. I would look for meaningful, low-risk opportunities that can be implemented well and build momentum. Hit a few singles, learn from them, and build confidence.
Early failures can create long-lasting resistance, especially among clinicians. Quick, simple wins create interest and momentum that build over time. Ambient listening is a good example. If doctors have a few good experiences early, they will stick with it. If not, they may be reluctant to try again. The worst thing you can do is start with a massive, high-risk transformation as your first move.
Using AI to Redesign Workflows and Floor Plans
Brian Nahas: In my experience, technology is rarely the limiting factor. What matters more is how teams embrace change.
That’s where we’re spending time with several leading health systems today. Together, we’re using AI‑enabled analysis and advanced modeling tools to study how care teams move through a unit, sometimes down to the second. With our customers, we are looking at how long it takes to get from workstations to patient rooms, how often clinicians backtrack, and where time is being lost simply because of layout.
Those insights allow teams to test alternate floor plans early, while change is still easy. By adjusting adjacencies, standardizing room layouts, and relocating care team spaces closer to where care is delivered, systems are reducing unnecessary walking and time away from patients. In many cases, that work is also improving staff experience by lowering physical strain and making workflows more intuitive.
Our role isn’t to push technology for its own sake. It’s to help health systems redesign workflows first, validate them early, and then design environments that support how care is actually delivered.
BLUlabs™ is Mortenson’s 40,000‑square‑foot research and development facility and industrialization incubator, where teams test and validate ideas early, helping projects move forward with greater clarity and confidence before construction begins.
Artificial Intelligence will create many moments of choice. We can choose to either do the same thing we’ve always done — or rethink the process entirely to achieve better outcomes.
How Should Health Systems Think About AI and the Patient Experience?
Cris Ross: From both an executive and patient perspective, what matters most is a clinician’s attention. When I was being treated for cancer, I wanted to feel known when someone walked into the room, not watched while they searched for information.
That experience reinforces why care spaces should be designed to support conversation and shared understanding and AI can support that. Patients should be able to see images or data when it helps decision‑making, while clinicians still have access to private information when needed. Care spaces should also assume caregivers will be present. Even informed patients can be overwhelmed, and having another person in the room matters. All of this has implications for layout, screens, seating, and flow.
Improving patient experience also means designing healthcare around the full path a patient takes, rather than around departmental silos. Too often, systems say, “We would love to do that, but that’s not how our process works.” With today’s technology, that should no longer be acceptable.
Patient experience is shaped by the full journey: how people arrive, move, wait, interact, and transition between settings. When design decisions follow that pathway, the built environment becomes an enabler of better care rather than a constraint imposed by organizational structure.
Designing Healthcare Facilities for the Full Patient Journey
Brian Nahas: We see this focus on the patient journey clearly in how our health system partners are modernizing their care environments.
Across projects like Froedtert West Tower, Mercyhealth Beloit ED Expansion, and St. Anthony North Tower, systems are rethinking the front door of their facility to create the optimal patient experience from beginning to end. Arrival, intake, wayfinding, and circulation are being organized around how patients, caregivers, and care teams need to move through the space to support better care, rather than around departmental silos. And AI and other technologies support this.
Our role is helping systems carry that intent through the full lifecycle of a facility. That includes translating patient‑experience goals into layout and circulation decisions, and protecting that experience during construction on active campuses, where access, sequencing, and disruption directly affect care in real time.
When design and delivery are treated as part of the patient journey, the built environment supports attention, clarity, and continuity of care, not just on the first day of operations, but throughout the life of the facility.
Designed to simplify the patient journey, Hughes Tower, expands Providence Swedish’s First Hill Campus with an integrated, future-ready design that enhances access, connectivity, and care experiences for patients, families, and providers.
Where is AI and Technology Reshaping the Care Environment?
Cris Ross: Care delivery is shifting in ways that are both predictable and disruptive. Systems are moving toward more virtual care, higher acuity inpatient environments, and more care delivered beyond hospital walls.
At the same time, facilities are long‑lived assets. Buildings are expected to serve organizations for decades, while care models and clinical practices change much more quickly. That creates a central challenge for healthcare leaders: how to make capital decisions now that will not limit options later.
Rather than trying to predict a single future state, many organizations are prioritizing adaptability, with technologies like AI playing a critical component in that. The goal is not to design for a single moment, but to support a range of future targets.
Rather than trying to predict a single future state, many organizations are prioritizing adaptability. The goal is not to design for a single moment, but to support a range of future targets.
Technology Should Augment Expertise
Brian Nahas: AI works best when it expands capacity and supports expertise, not when it competes with it. For us, that means keeping experts as close to the work as possible.
We saw this on a recent surgical expansion for a large Pacific Northwest health system. Early in the design, our team partnered with clinicians and designers to develop full-scale, augmented reality mockups. The clinicians were able to walk their future space using Virtual Reality (VR). This allowed them to make decisions and get early buy-in on the final design of the operating rooms, pre-op, post-op recovery and other support spaces.
Surgeons, nurses, and technicians stood together in a shelled space wearing VR headsets, testing ergonomics, workflow, and equipment placement in real time. Key surgical equipment, including booms, could be repositioned instantly and evaluated on the spot.
By making decisions earlier, while the space was still in design, teams avoided late‑stage changes during construction and spaces that don’t truly support care. The technology didn’t replace judgment. It helped the people who know the work best shape the space around how care is actually delivered.
What This Means for Healthcare Leaders
AI is changing care faster than facilities can be replaced or upgraded. For healthcare leaders, the priority isn’t predicting the next technology. It’s designing environments flexible enough to absorb change.
When planning starts with care workflows and delivery is treated as part of the patient experience, facilities are better positioned to adapt over time. The result is a built environment that supports evolving models of care rather than holding them back.
The goal is not to implement AI for speed, but to design environments that advance your mission and support how care is delivered.