The use cases for artificial intelligence-enabled solutions (AI) in healthcare are rapidly expanding. For health system leaders charged with deploying resources and managing financial performance, AI raises understandable questions about how much investment will be required, how to consider AI uses alongside other solutions to business problems, when meaningful value may emerge, and how to weigh those commitments against other priorities.
We frequently hear a similar question from hospital leadership:
“What’s the right level of AI investment?”
This question comes with an important subtext: In many organizations, AI-enabled solutions will increase costs in the short term before efficiencies or transformation take shape. Training staff, integrating new tools, redesigning workflows, strengthening governance, and licensing fees all require resources.
At the same time, even as financial performance stabilizes for many providers, margins remain pressured, the environment remains uncertain, and the responsibility to steward capital carefully, particularly in mission-driven organizations, has not diminished. Despite accelerating AI adoption, many leaders still struggle to define a clear and credible financial return.
Importantly, AI-enabled solutions should be viewed as a means to an end—and not as an end in itself. From that standpoint, AI should be considered as a potential technology solution addressing a specific business issue. With that context in mind, this article is not intended to shed light on current and future use cases for AI, nor is it a commentary on what AI-enabled solutions health systems “should” pursue. Rather, AI is prompting health system leaders to revisit a familiar but foundational question:
How do we allocate capital?
Harvard Business School professors Joseph Bower and Clark Gilbert argued in a 2007 Harvard Business Review article that an organization’s priorities are revealed not just in plans, but in how resources are actually deployed.
“Strategy is crafted, step by step, as managers at all levels of a company commit resources to policies, programs, people, and facilities,” Bower and Gilbert wrote. “... Senior management might consider focusing more attention on the processes by which the company allocates resources. The leadership challenge is to give coherent direction to how resources are allocated and… that’s how you drive strategy in a big organization.”
Three practical realities
First, AI should be treated like any other strategic investment.
Rather than creating a protected “AI budget,” leading organizations are intentionally placing AI initiatives alongside all other major capital decisions—subject to the same prioritization, tradeoffs, discipline, and understanding of risk related to the success or failure of a given investment. Further, the balance between investing early in AI-enabled solutions and realizing long-term efficiencies and transformation varies widely depending on the types of AI solutions organizations pursue.
Second, that means the strength of the capital allocation process itself becomes essential.
Health systems benefit from a capital process that is structured, transparent, closely aligned with the strategic and financial plan, and capable of balancing near-term operational needs with longer-term initiatives whose value may develop over time.
Third, AI is prompting many leaders to revisit their capital allocation approach.
This action is not intended to limit AI innovation, but rather to ensure it is pursued responsibly, sustainably, and with a clear connection to strategy and financial capability.
Where capital discipline meets AI
In this environment and context, health system executives should consider the following capital allocation questions:
- How do we define capital capacity, and is the definition well understood by key stakeholders?
- Will our capital envelope, including routine investment and innovative investments, generate returns and value in excess of our cost of capital?
- How is our capital allocation process directly related to our strategic plan, financial plan, and budget?
- Do we have the right balance of centralized oversight and flexibility for operators to innovate?
- Is our process disciplined enough to maintain integrity, yet agile enough to respond when compelling opportunities arise?
- Are we sustaining or growing the capital capacity required to support innovation while meeting core organizational needs?
Considerations for AI enabled solutions within capital allocation
Start with a clear understanding of incremental capital capacity.
The level of AI ambition – like all capital investment – should be grounded in a realistic understanding of net capital available for investment, set by a standardized formula well understood by key stakeholders.
Anchor AI investments inside an integrated capital allocation framework.
AI investments should move through the same structured, enterprise-level capital allocation process as other major initiatives (e.g., investments above a certain dollar threshold), with consistent expectations for business cases and evaluation.
Reconsider whether the quantitative and qualitative criteria guiding resource allocation still meet organizational objectives.
Set by centralized oversight committee, qualifying capital should be evaluated against both quantitative and qualitative criteria. These criteria tangibly communicate organizational objectives (e.g., quality, patient experience, patient access, financial sustainability), with qualitative and quantitative results compared and each project ranked accordingly. Quantitative criteria should be rooted in corporate finance calculations (e.g., net present value, discounted cash flow) while qualitative criteria should be built to capture the comprehensive benefits of a project.
Maintain a portfolio mindset.
Rather than isolating AI funding, organizations should incorporate AI initiatives into a balanced portfolio that blends routine and strategic investment, including “bets” and innovative opportunities. The performance of the portfolio as a whole ultimately matters more than any single project.
Ensure strong governance and visibility.
Leading organizations are strengthening the governance of their capital allocation process—ensuring there is a clear, centralized body responsible for establishing capital capacity, setting thresholds, prioritizing major investments using consistent criteria, and monitoring outcomes against original expectations, while still thoughtfully delegating appropriate decision-making to operational leaders.
Build in post-investment review and learning.
Organizations should monitor the performance of approved initiatives against original expectations, determining what to scale, where to adjust, and when to stop. This reinforces accountability and improves decision-making over time.
This process should include establishing (at the outset) key performance indicators (KPIs) for analyzing the return on investment (ROI) or business impact and then tracking that diligently.
Support agility – but through structure, not exception.
AI will not always fit neatly into an annual cycle. Leading systems are creating structured ways to support out-of-cycle opportunities without abandoning the discipline of their process.
A note for boards
Many boards are asking thoughtful questions about AI as well. The most helpful board oversight often focuses less on “how much” organizations are investing in AI, and more on whether leadership is applying disciplined, corporate finance-based processes that protect sustainability while still enabling innovation.
Closing thoughts
AI-enabled solutions present both opportunity and uncertainty, but across the healthcare industry, one principle is increasingly clear:
The financial sustainability of AI adoption efforts will depend less on how much health systems spend, and more on whether those investments move through a disciplined, structured capital allocation process that aligns with strategy, supports long-term financial sustainability, continuously measures performance, and preserves an organization’s ability to invest in its future.
