Is Your Health System Ready for AI at Scale?

As health system leaders look to expand their primary care footprint and accelerate the shift to value-based models, readiness to transform remains a hurdle. In particular, healthcare executives cite workforce shortages as one of their biggest worries when thinking about their organization’s 10-year outlook, according to a recent Bain survey.

AI, with its ability to augment stretched teams and unlock capacity, offers a path forward. Executives see the potential: Across use cases, they expect to sharply increase AI tool adoption and scaling in the next three years.

There will still be challenges ahead. Effectively scaling AI demands more than tech—it takes clear use cases, the right talent, and strong change management. To move from experimentation to organization-wide transformation, leaders can ask a few questions:

• What are our most pressing care delivery and operational challenges, and where can AI most meaningfully improve outcomes and efficiency?
• Do we have the clinical and operational data infrastructure—as well as the governance safeguards—to responsibly scale AI across our system?
• How will we engage clinicians and frontline staff to ensure AI reduces burdens and enhances patient care?
• Who is the dedicated sponsor on the leadership team, and are they empowered to lead organization-wide change?
• For our prioritized use cases—whether in population health, administrative automation, or clinical decision support—which AI capabilities will we build ourselves and which will we outsource to vendors?

Related

Why Health Systems Are Expanding Primary Care

Health systems are investing more in primary care—not just as the front door to care but also as a strategic move aligned with accelerating value-based models and boosting performance.

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