The AI Value Creation Gap in Healthcare
Closing the AI Gap to Margin and Multiple Expansion
AI interest in healthcare is real, and so is the hype. Yet too many firms are not seeing measurable value today. The reason: many firms are pursuing tactics that are unlikely to translate into sustainable improvements in provider capacity, utilization, reimbursement, clinical outcomes, or margins.
AI value creation requires a top-down approach. Like all forms of digital transformation, returns do not accrue from point solutions and tactical approaches. ROI emerges through aligned cross-functional execution of strategic priorities. This is particularly true given healthcare’s complex operating interdependencies.
Lack of clarity cannot cause inaction. When disruptive technologies enter a market, the immediate implications are often not clear. But firms that fail to embrace the moment typically under-perform. This trend was clearly demonstrated with EMR adoption, where earlier adopters gained valuable perspectives on how to overcome operating barriers like medical coding, integration, and workflow optimization.
Fragmented ownership is not viable. The technical complexity, rate of change, model performance, evolving costs, operating risks, and information protections mandate that AI not be undertaken as a part-time job. Dedicated AI leadership is needed to partner effectively with clinicians, business leaders, CIOs, CTOs, and CISOs to ensure workflow, governance, data, models, informatics, and HIPAA obligations are aligned.
Shadow AI wastes time and money while elevating risks. AI tools, lower-level AI resources, and “pilots” miss the common causes of failed AI investments. And those resources cannot mitigate well-established risks in strategy, quality, compliance, scalability, and ROI. Just as fragmented approaches to population health, quality, and care pathways fail to produce measurable benefits, AI requires comparably disciplined methods.
A Winning Formula
Most investors and boards should be mandating a more directed approach to AI adoption:
Accountable Leader: a senior leader should be accountable for AI strategy and delivery, with a remit covering internal capabilities, data, cross-functional orchestration, and solutions.
Measured Improvements: corporate strategies should include specific, measurable goals and financial performance targets (i.e., revenue, margins, care capacity) associated with AI adoption.
Operational & Change Management: AI leaders must have experience with, and design AI programs around, the business and technical dimensions of organizational development.
Repeatable Approaches: risks associated with AI development, deployment, and adoption are best addressed using consistent designs and models proven to accurately and reliably scale.
The business case is direct: organizations seeking future recapitalization will be valued in part on their AI maturity, competitive position, and business model sustainability. When executed in a disciplined manner, investments in AI can notably grow the expected multiple of growth-stage companies. Consider the evidence below.
The Case for Prioritizing AI Enablement
For most organizations, the question is not whether AI can have a meaningful contribution to revenue growth and profitability. Rather, the key questions revolve around how to ensure those investments actually produce returns.
Market research from numerous sources shows that AI investments produce measurable improvements in business performance when driven by expert leaders.
However, those results emerge only when mature, governed approaches to AI enablement and transformation are followed.
Mature programs and dedicated leadership also help offset risks.
From Industry Numbers to Impact
For investors and leaders concerned that statistics may not reflect real business gains, let me share a few examples from my own leadership experiences in following the winning formula above.
Accountable Leader: One organization moved from no capabilities to one of the top HIMSS-assessed providers. Critical to the success was having dedicated capacity and expertise partnering with existing leaders and organizational capabilities.
Measured Improvements: Targeted AI solutions conceived in 48 hours and aligned to corporate priorities produced $5M per year in savings.
Operational & Change Management: One AI solution reduced emergency department closure rates by more than 30%. The AI model could produce the insights, but workflow changes were the required value-creation step in seeing more patients.
Repeatable Approaches: None of those gains came from pilots; they all came from following proven, reusable methods applied to strategically aligned priorities.
One point worth emphasizing: AI model selection, available funding, and organizational size are not the critical factors for delivering this type of value creation. I’ve used the same winning formula and proven practices in multi-billion dollar enterprises like UNC Health and SAS, as well as PE-backed organizations grappling with cost, scalability, and growth opportunities.
The Bottom Line
The gap between AI ambition and AI returns is not a technology problem. It is an accountability problem.
Healthcare organizations that treat AI as a set of experiments or vendor-provided features distributed across functions will keep producing pilots. High performers do something different: they put a single accountable leader against measured business outcomes, and they redesign how the work is done rather than layering tools on top of it.
For investors, the implication is direct. AI maturity is becoming a factor in how healthcare businesses are valued, and the capability takes longer to build than a hold period forgives. Firms that start now will have something to show at the next transaction. Firms that wait may be explaining why they didn't.
AI belongs in the investment thesis. Someone has to own the outcome.
Sources: 1-McKinsey (State of AI; PE value creation; where AI creates value; AI in private equity), 2-IBM (chief AI officer; Cost of a Data Breach; AI-enabled breaches), 3-Grant Thornton (AI Impact Survey; private equity), 4-Gartner, 5-BCG, 6-PwC
