How to employ intelligence stewardship in indirect healthcare spending
To move the needle on indirect cost management, it’s important to apply better judgment and data to AI-enabled source-to-contract decisions.
Healthcare leaders are rightly focused on clinical cost, labor pressures and reimbursement constraints. However, indirect spend is a significant portion of the income statement, and it rarely commands equal strategic attention.
Purchased services, IT, facilities management, logistics, professional services, marketing and outsourced operations account for approximately 20 percent to 22 percent of a typical health system’s operating budget. In large systems, that represents hundreds of millions of dollars in annual spending. Even modest structural improvements in how this segment is managed can generate tens of millions in annual savings.
Historically, indirect cost management has been episodic. Health systems commission benchmarking studies, launch consultant-led requests for proposals, negotiate savings initiatives and then move on. These efforts are not misguided; they are simply periodic. They produce snapshots in an environment that now demands continuous intelligence.
Artificial intelligence offers the potential to change that equation. But AI does not create discipline on its own. Layered onto fragmented data and imprecise thinking, it merely accelerates flawed assumptions.
The opportunity to manage indirect healthcare sourcing does not lie in implementing automation for its own sake. It is the construction of an intelligence architecture grounded in four foundations of judgment — curated data, disciplined inquiry, human values and professional expertise.
These foundations were introduced in A Return to Strategic Leadership: Judgment in the Age of AI, in which I argued that AI enhances performance only when embedded in disciplined decision structures. The same principle applies here.
Curated data
Most health systems operate source-to-contract processes within structural constraints that limit insight more than effort.
Contracts, pricing schedules and performance metrics reside in PDFs, spreadsheets and inboxes. Scope definitions vary across facilities. Pricing models differ — FTE-based, transactional, performance-linked — making clean comparison difficult. Negotiated terms and realized outcomes rarely form a closed feedback loop.
Compounding this is a cultural norm — healthcare typically benchmarks against healthcare. Facilities costs are compared with peer systems. IT contracts are assessed against industry averages. Purchased services are evaluated relative to similar institutions. However, the industry itself faces structural cost inflation. When inefficient systems benchmark against one another, inefficiency becomes institutionalized rather than corrected.
The architectural shift begins with curation. Unstructured contracts, supplier terms, pricing structures and historical outcomes must be normalized into a coherent, category-level intelligence fabric. This transforms sourcing from episodic analysis into institutional memory.
Other industries learned this lesson earlier. Retail, logistics and hospitality — long disciplined by thin margins — recognized that episodic benchmarking cannot protect performance. Institutional learning can.
For healthcare, curated data reframes the core question. Instead of asking, “What are other hospitals paying?” leaders can ask, “What does our longitudinal data reveal about cost structure, scope design and leverage?” Benchmarking evolves from static comparison to dynamic insight.
A robust intelligence layer also enables structured cross-industry transfer. Performance-based pricing models, bundling strategies and operational efficiencies developed under sustained margin pressure can be evaluated and adapted deliberately. Execution tools may commoditize. Institutional intelligence will not.
Disciplined inquiry
AI systems respond to the questions they are given. Poorly framed inquiry produces refined but shallow answers.
Within an AI-enabled source-to-contract environment, the shift is from workflow automation to decision augmentation. Leaders are no longer constrained by the analytical friction that once limited scenario modeling. They can evaluate alternative service bundles across facilities, simulate the implications of performance-based compensation, identify contract clauses correlated with measurable outcomes and surface scope definitions that conceal inefficiency.
But technology does not determine the quality of insight. Inquiry does.
Disciplined inquiry moves organizations beyond incremental savings toward structural redesign. It asks harder questions, such as the following.
AI amplifies clarity and superficiality. Organizations that cultivate rigorous questioning uncover opportunities previously too complex to model. Those that do not may simply arrive at marginal savings more quickly. The distinction is not computational. It is intellectual.
Human values
Indirect services are often labeled “non-clinical,” yet their impact is deeply clinical.
Facility decisions affect patient safety. Environmental services influence infection control. IT contracts shape cybersecurity and clinician workflow. Outsourced operations influence morale and patient experience. The boundary between indirect and clinical spending is porous.
AI can model cost curves. It cannot determine acceptable trade-offs between efficiency and mission integrity.
Healthcare institutions are mission-driven. Cost optimization that erodes quality, safety, workforce stability or community trust ultimately destroys value. An effective AI-enabled sourcing model therefore keeps human expertise firmly in the loop. Professionals shift upstream — toward stakeholder alignment, risk evaluation and complex negotiation — rather than being displaced by automation.
Human values are not obstacles to intelligence. They are its constraint and compass. Without them, optimization becomes abstraction. With them, it becomes stewardship.
Professional expertise
In an adaptive source-to-contract environment, each sourcing event strengthens the system. Negotiations inform future strategies. Outcomes refine predictive models. Contract structures are evaluated against realized performance. Over time, the intelligence layer compounds.
But compounding advantage requires interpretation.
AI-generated recommendations must be validated. Contextual nuance must be applied. Bias must be corrected. Risk must be governed. Professional expertise converts insight into consequence.
This represents an evolution of the procurement function. Rather than operating primarily as transactional cost managers, sourcing professionals become intelligence stewards, responsible not only for savings, but for the integrity of the decision architecture itself.
Indirect source-to-contract is a logical entry point for this transformation. It is analytically dense, establishes pricing and risk structures, and generates rich data for future learning cycles. Because indirect categories represent about a fifth of operating expense, structural improvements can produce material financial impact without compromising clinical care.
Healthcare will not bend its cost curve by tightening legacy processes or continuing to benchmark within its own constraints. What is required is a shift from episodic cost management to continuous intelligence stewardship.
In an era of abundant computational power, the differentiator is not access to algorithms. It is disciplined judgment, grounded in curated data, guided by precise inquiry, constrained by human values and executed through professional expertise.
Health systems that build this architecture will do more than reduce indirect costs. They will steward resources with greater clarity, strengthen operational resilience, and protect the integrity of their mission.
That is the leadership imperative in the age of AI.
Mark Van Sumeren is the general manager of healthcare practice at LogicSource; he is a supply chain expert with 45 years of experience in business strategy and healthcare supply chain leadership.