How Payers and Providers Can Control Costs Without Adding More Complexity

Healthcare cost pressure is no longer just a budgeting issue. It is a workflow, visibility, governance, and compliance problem. The organizations that improve margin without creating new operational risk will be the ones that redesign how decisions get made.

Farzin Espahani

Strategic Advisor, Insurance Growth, Medicare Distribution, and Compliance

Healthcare leaders do not need another abstract conversation about artificial intelligence. They need a clearer answer to a harder question: how do you control cost when utilization is rising, payment workflows are fragmented, staff time is constrained, and every improvement effort now carries operational and compliance consequences? McKinsey's June 5, 2024 article on payers is useful because it treats AI as an operating and cost problem, not just a technology trend.

McKinsey argues that payers are operating in a difficult economic environment: utilization of medical services has been higher than expected, providers are passing along higher costs through rate negotiations, and Medicare Advantage reimbursement rates have faced the strongest downward pressure payers have seen in years. That is why the article frames AI and automation as an untapped economic opportunity rather than a future-looking experiment.

Its headline numbers are material. McKinsey estimates that, using currently available technology, payers could see net savings of 13% to 25% in administrative costs, 5% to 11% in medical costs, and 3% to 12% higher revenue. It also points to especially meaningful opportunity in claims, network and contracting, utilization management, care management, and information technology.

That upside comes with a warning. McKinsey says incremental approaches will continue to yield only minor savings and that payers need to reimagine end-to-end processes if they want to capture full value. That distinction matters. Automating one step inside a broken workflow is not the same thing as redesigning the workflow itself.

Cost control is now an operating-model problem

In healthcare, cost pressure often builds in the spaces between teams rather than inside a single department. Prior authorization, claims, utilization management, payment integrity, appeals, care management, revenue cycle, and reimbursement workflows all depend on repeated handoffs, exception handling, documentation quality, and clear escalation. When those workflows are fragmented, organizations absorb extra labor, longer cycle times, avoidable rework, and weaker decision quality. McKinsey's payer advisory analysis points directly to those operating domains because that is where financial pressure and workflow complexity now intersect.

For providers, the friction shows up differently but often starts from the same structural problem: disconnected workflows, poor status visibility, inconsistent reporting, and delayed resolution across payer-facing processes. Deloitte's February 11, 2026 research says healthcare leaders are still under pressure to reduce operational costs, stabilize a strained workforce, and improve access and experience, while also reporting that more than 80% of health systems are prioritizing agentic AI for clinical operations, care delivery, and revenue cycle management.

That is why the payer-provider divide can be misleading. The incentives differ, but many of the expensive bottlenecks are shared. Deloitte says 70% of health plans are prioritizing agentic AI for utilization management, prior authorization, and claims management, while more than 80% of health systems are prioritizing it for clinical operations, care delivery, and revenue cycle management. The common thread is workflow burden, not just technology adoption.

The issue is not lack of ideas

One of the strongest points in the McKinsey article is that most payers are not blocked by a shortage of use cases. They are blocked by execution readiness. McKinsey says organizations need to close gaps across six areas: a business-led digital road map, talent, operating model, technology, data, and adoption and scaling. It also says this is a job for the CEO and the entire C-suite, not just the technology function.

Deloitte reinforces that point from a different direction. Its research says 61% of surveyed healthcare leaders are already building and implementing agentic AI initiatives or have secured budgets, and 85% expect to increase investment over the next two to three years. But the research also makes clear that the question has shifted from whether organizations will experiment to whether they can scale responsibly and redesign workflows beyond isolated point solutions.

That is the real lesson for both payers and providers. Most organizations do not fail because they chose the wrong concept. They fail because they try to layer new tools onto unclear ownership, inconsistent definitions, weak governance, and fragmented reporting. The result is often a promising pilot with very little durable effect on cost, throughput, or accountability. That is exactly the kind of gap McKinsey is warning against.

What the article is really recommending

McKinsey's recommendations are broader than "invest in AI." It says leaders should decide whether they want to lead or follow, identify the biggest pockets of value in their business, assess whether they have the right talent and leadership understanding, confirm that their technology infrastructure can support modeling and innovation without friction, and determine whether their data is clean, clear, and accessible. It also says leadership teams need a governance and risk framework.

Translated into practical operating language, that means cost control should start with a smaller set of questions:

• Where is the work most exception-heavy?
• Where are teams spending time on status checks, rework, escalation, or manual review?
• Which workflows generate the most denials, delays, leakage, or avoidable labor?
• Which decisions rely on inconsistent data or unclear ownership?
• Where does compliance review arrive too late to shape the workflow itself?

Those are not product-selection questions first. They are operating-model questions. That is why workflow redesign, visibility, adoption, and governance matter as much as the technology itself.

What payers should do next

For payers, the most practical reading of the McKinsey article is that the next move is not simply to "buy AI." It is to choose a few domains where financial and workflow pain are concentrated and redesign those processes end to end. McKinsey specifically points to claims, utilization management, network and contracting, care management, and information technology as strong opportunity areas.

A realistic payer sequence is straightforward:

That order matters. Visibility before automation. Governance before scale. Compliance inside the workflow, not bolted on after the fact. Those priorities are consistent with McKinsey's six-capability framework and Deloitte's emphasis on scaling, governance, traceability, and oversight.

What providers should do next

The McKinsey article is payer-specific, but its operating logic applies well to providers. If denials, prior authorization burden, reimbursement leakage, staffing strain, and repeated handoff failures are driving cost, the answer is rarely another isolated tool at the edge of the process. It is a better workflow, better status visibility, cleaner reporting, and stronger coordination across finance, operations, clinical-adjacent functions, and payer-facing teams. Deloitte's research supports that extension by showing that health systems are prioritizing agentic AI in revenue cycle and clinical operations while emphasizing the importance of enterprise scaling, oversight, and human validation.

For providers, cost control improves when the organization can see denial patterns earlier, route exceptions more consistently, reduce documentation friction, and clarify ownership across payer-provider workflows. The objective is not more digital noise. It is a more usable operating environment.

Why compliance has to stay in the center

Compliance should not be treated as a late-stage review step in cost-reduction programs. McKinsey says leadership teams need a governance and risk framework. Deloitte goes further by emphasizing guardrails, validation, human oversight, and management of financial and compliance risk when more autonomous workflows are introduced.

That matters because a faster process is not automatically a better process. A workflow that increases inappropriate denials, weakens documentation quality, obscures decision logic, or creates audit exposure is not an operational win. In healthcare, cost control has to remain compatible with traceability, accountability, and practical compliance discipline.

The VHealth view

The most useful takeaway from the McKinsey article is that healthcare cost control is becoming a design problem. Not just a budgeting problem. Not just a staffing problem. And not just a technology problem.

Payers and providers both need a clearer operating model for the work that drives cost: claims, utilization, prior authorization, payment workflows, care coordination, and the administrative layers around them. They need better visibility into where friction accumulates. They need stronger ownership over workflows that cross teams. And they need compliance-centered governance so that automation improves the system rather than making it harder to trust.

The path to lower cost is not more noise. It is fewer broken handoffs, better data discipline, stronger governance, and a more usable workflow from end to end. That is the operating issue healthcare leaders now need to solve.

Farzin Espahani

Principal Consultant, VHealth

Farzin Espahani is a Strategic Advisor focused on insurance growth, Medicare distribution, healthcare operations, and compliance-aware performance marketing.

He has led growth, marketplace operations, partner distribution, and Medicare marketing initiatives across regulated healthcare and insurance markets, with experience spanning QuinStreet, Health IQ, Solution21, and The Patient's Guide, which was acquired by IAC Publishing.

At QuinStreet, he worked across Medicare and health insurance distribution, performance marketing, partner economics, lead quality, compliance alignment, funnel performance, and AI-enabled consumer decision support, including the development of an AI-powered Medicare plan recommendation experience for Insure.com.

Farzin also serves in industry leadership and advisory roles with the Insurance Marketing Coalition and Lead Quality Alliance, where his work supports ethical marketing, lead quality, consumer transparency, and compliance-aware growth.

His academic background in biocultural anthropology, sociology, and social psychology informs how he evaluates consumer behavior, incentives, trust, decision-making, and organizational friction. His executive education includes programs at Massachusetts Institute of Technology (MIT) Sloan, Wharton, Cornell, and Northwestern Kellogg, with advanced study in artificial intelligence (AI), revenue analytics, pricing, leadership, change management, and communication.

For VHealth, Farzin brings a practical advisory lens on healthcare growth, workflow visibility, regulated distribution, compliance-aware interpretation, and responsible AI-enabled decision support.