Healthcare AI spending is growing despite SaaS disruption fears

Healthcare providers are investing in artificial intelligence partly because health insurers are automating their operations. As payers use AI to improve payment integrity, utilization management, and claims administration, providers worry that denied claims will become harder to manage. Their response includes investment in revenue cycle management (RCM) software, which helps organizations document care, submit claims, handle denials, and collect payment.

This creates technology demand driven by competitive pressure. Providers may need more automation to protect their financial performance as their counterparties become more efficient. The investment can be necessary even when its immediate financial benefit is harder to measure than a direct reduction in labor costs.

This distinction matters for healthcare software companies. Technology spending remains strong despite financial pressure on hospitals and insurers, while investors question whether generative AI will undermine established software-as-a-service (SaaS) businesses, which deliver applications through ongoing subscriptions. Yet continued spending tells us little about which vendors will capture the growth.

Three forces increasingly shape healthcare IT purchasing: the integration advantages protecting established platforms, the financial returns from automating specific workflows, and the pressure to keep pace with increasingly automated counterparties.

Healthcare organizations are buying AI to keep pace with counterparties

The provider response to payer automation shows why healthcare AI spending extends beyond conventional productivity improvements. Insurers are adopting systems that help them assess claims, review payment accuracy, and manage the use of healthcare services. Those activities directly affect the documentation providers must prepare and the work required to collect reimbursement.

As payer capabilities improve, providers expect greater demands on their claims processes. Automated clinical documentation can strengthen the information supporting a claim, coding tools can turn medical records into billing information, and denial management systems can help staff investigate rejected claims. Together, these capabilities help providers maintain revenue as the payment system becomes more automated.

The financial importance of RCM makes these purchases difficult to defer. A hospital can deliver care successfully yet face cash-flow problems when claims are delayed, rejected, or require extensive manual work. Software that protects collections can therefore receive funding even when the organization restricts spending elsewhere.

Payers face a related incentive from the opposite direction. They want to reduce administrative expenses, improve utilization management (UM), which evaluates whether healthcare services are medically appropriate, and control medical costs. As both sides automate, each organization’s technology decisions increasingly influence the other’s investment priorities.

For software vendors, this raises a question: will continued customer spending strengthen established platforms or create opportunities for specialized suppliers? The answer depends on where the software operates, how deeply it must connect to existing systems, and what financial result the buyer expects.

Strong technology demand does not guarantee strong software growth

Healthcare organizations continue to prioritize technology despite difficult financial conditions. Research by Bain & Company and KLAS Research found that approximately 95% of providers and payers rank software and digital technology among their five leading strategic priorities. Most respondents also expect technology spending to increase.

The findings include the Bain 2026 Provider and Payer Healthcare IT Survey of 303 US healthcare executives. Bain & Company is a management consulting firm with a commercial interest in helping healthcare organizations make technology and operating decisions. KLAS Research evaluates healthcare technology, giving it a business interest in the industry’s software purchasing and vendor-performance landscape.

The research examines investment priorities, barriers to adoption, and AI deployment across three groups: acute providers, ambulatory providers, and payers. The 2026 survey also examines independent physician groups as a separate population for the first time. This distinction matters because hospitals, physician practices, and insurers face different financial pressures and have different technology resources.

Financial pressure is sharpening purchasing decisions

The spending outlook is significant given the financial pressures healthcare organizations faced during the first half of 2026. Providers continued to struggle with staffing shortages and limited operating capacity, while median operating margins remained in the low single digits. Rural health systems faced particularly difficult conditions, making the financial case for new technology especially important.

Insurers faced a different combination of pressures. Premium-rate increases had slowed while healthcare utilization remained elevated, and specialty-drug expenses continued to rise. Although conditions varied across insurance businesses, they increased the need for administrative efficiency and medical expense management.

Technology can address both sets of problems because many underlying costs come from operating processes. Hospitals need to use scarce clinical capacity more effectively and reduce administrative demands on staff. Insurers need better information and faster processes for coordinating care, reviewing claims, and guiding members toward appropriate services.

Yet financial pressure also makes buyers more selective. Healthcare executives increasingly favor investments tied to specific operating problems, measurable financial benefits, and short implementation periods. A promising product can struggle to win approval when deployment requires extensive engineering, integration, training, or maintenance.

Internal capacity creates another constraint. Limited IT resources and competing organizational priorities remain major barriers to adopting software and realizing its expected benefits. Even when the potential return is attractive, organizations must consider whether they have the staff and technical resources to complete implementation.

Existing spending and new purchases follow different rules

These constraints create different purchasing conditions for established systems and new applications. A hospital may continue paying for its electronic health record (EHR) platform because replacing it would disrupt clinical operations and require substantial implementation work. The same hospital may reject a new application when its expected financial benefits fail to justify the additional integration burden.

Both decisions can sustain healthcare IT spending, but they produce different outcomes for vendors. Established suppliers benefit from recurring customer relationships. Vendors seeking new business must demonstrate that their products deliver enough additional value to justify the purchase.

Financial markets have begun reflecting this distinction. Private equity investment in healthcare IT companies and merger-and-acquisition activity have softened in recent months. Publicly traded healthcare IT companies have also faced valuation pressure as investors consider whether AI could disrupt traditional SaaS business models.

Generative AI (GenAI), which produces content or software from user instructions, contributes to those concerns by reducing some development costs. Smaller competitors may be able to build applications with fewer engineering resources and enter markets that previously required larger teams. But developing software is only one part of competing in healthcare technology.

Deployment, integration, regulatory requirements, and the cost of changing established workflows can remain substantial even when writing software becomes easier. These factors matter especially for systems that healthcare organizations depend on to manage clinical operations.

EHR integration protects established platforms

Electronic health record systems are among the most defensible categories of healthcare software because they are embedded in daily clinical work. An EHR manages patient information used in documentation, treatment, orders, scheduling, and other care processes. Once implemented, the platform becomes closely connected to the applications and procedures a health system uses every day.

Replacing it requires migrating patient information, rebuilding connections to other applications, training employees, and changing established clinical processes. These activities can be expensive and disruptive. That helps explain why providers generally have little interest in developing their own EHR platforms or replacing existing systems.

The integration advantage also affects purchases beyond the EHR itself. Providers frequently prefer applications supplied by their current EHR vendor, even when independent alternatives offer more extensive functionality.

Integration determines purchasing decisions near clinical operations

Bain and KLAS found that providers often prioritize how easily new applications connect to their existing EHR environment. Figure 2, titled “Providers often prefer EHR-native solutions with partial functionality over best-in-class solutions”, illustrates this purchasing preference.

The economics follow from investments providers have already made. Health systems spend substantial resources configuring EHR platforms, training staff, and connecting clinical and administrative applications. Buying another product from the same vendor can reduce the engineering work and operational changes required.

A more unified environment can also simplify daily work. Clinicians and administrative employees may be able to complete related tasks without repeatedly transferring information between applications or learning different interfaces. For organizations with limited IT capacity, these benefits can outweigh additional features offered by independent products.

Consider a provider selecting software for clinical workflow optimization. A specialized vendor may offer more advanced functions, but the buyer must evaluate how the application will access patient information, connect to existing procedures, and remain compatible with future EHR changes. An EHR-native alternative with fewer features may require less implementation and maintenance work, making it the more attractive purchase.

This preference is particularly strong for applications closely connected to clinical activity, including clinical workflow optimization and patient access. Epic customers show an especially pronounced preference for applications available through their existing EHR environment.

The strength of that preference depends partly on the breadth and quality of each EHR vendor’s additional products. Vendors with capable applications across related workflows can capture more spending from existing customers. Gaps in their offerings create opportunities for independent suppliers.

For specialized vendors, product functionality must justify the additional work required to connect and maintain the application. Integration becomes part of the buyer’s economic calculation rather than a separate technical concern.

Independent vendors retain opportunities outside the clinical core

The EHR advantage weakens as applications move farther from direct patient care. Enterprise resource planning (ERP) systems, which manage functions such as finance and procurement, offer one example. Governance, risk, and compliance (GRC) applications, which help organizations manage internal controls, regulatory obligations, and organizational risk, offer another.

EHR vendors have historically invested less heavily in these categories, while established enterprise software suppliers already serve many of the relevant business needs. Independent products can therefore compete more effectively through specialized functionality and existing enterprise relationships.

GRC also gives healthcare organizations a reason to maintain separate systems. They may prefer compliance and oversight functions that operate independently from the clinical platforms they monitor. In those circumstances, organizational separation becomes a purchasing advantage.

The value of EHR integration therefore varies by application category. Software closely connected to clinical records benefits from the platform’s existing connections and workflows. Enterprise administration and compliance applications place greater weight on specialized capabilities, independence, and long-term operating costs.

For buyers, the comparison includes functionality, implementation work, support requirements, and maintenance. For vendors, it determines how much of the established EHR provider’s advantage they must overcome.

Generative AI may reinforce EHR switching costs

These same integration requirements limit how quickly GenAI can change competition in core clinical software. AI tools can help developers produce applications with fewer resources, but an alternative EHR must still support complex clinical operations and connect to the systems surrounding it.

Provider expectations reflect this difference. Nearly 80% of acute provider organizations surveyed believe GenAI advances will either increase EHR switching costs or leave them unchanged. Across providers, a plurality expects switching costs to rise.

Figure 3 presents the finding under the title “A plurality of providers believe GenAI advances will increase EHR switching costs”.

These expectations suggest AI could deepen the connections between established platforms and surrounding applications. A health system might deploy automated processes that retrieve EHR information, update records, or initiate administrative tasks. Each connection would become another dependency to manage during a future platform replacement.

As organizations add these capabilities, switching could require more migration and integration work. AI may therefore strengthen established EHR vendors even as it reduces development costs for potential competitors.

That advantage supports customer retention, but growth depends on what vendors can sell to organizations whose core systems are already established.

Mature EHR adoption shifts growth toward adjacent services

EHR adoption and penetration of other core healthcare software modules are approaching saturation. Vendors consequently have fewer opportunities to grow by selling established products to organizations implementing them for the first time.

A stable installed base provides recurring revenue, but substantial growth increasingly requires additional applications, workflows, and services. One opportunity involves agentic AI: software that uses AI to complete sequences of tasks, interact with connected systems, and take actions within defined controls.

Healthcare applications could use these capabilities to retrieve information, coordinate administrative processes, and complete activities that currently require employees to move between systems. An established EHR vendor already has access to many of the clinical workflows and data connections needed to support such applications.

RCM presents another opportunity. Vendors with strong clinical integration and existing provider relationships may be able to expand into software-enabled billing and collections services, including activities traditionally performed through labor-intensive operations.

These markets already attract specialized competitors, however. Independent vendors can build customer relationships and operating capabilities while EHR companies develop their offerings. Incumbents must therefore move beyond retaining their core installations and create additional products that customers are willing to buy.

Payer platforms have weaker influence over adjacent purchases

The payer market operates differently. Health insurers use core administrative processing systems (CAPS) to manage essential claims and payment operations. These platforms can be expensive to replace, but they have considerably less influence over purchases in surrounding software categories than EHR systems do.

Only approximately 15% of payers describe themselves as “CAPS-first”, meaning they generally favor additional products from their existing core administrative platform vendor. Regional health plans are more willing than the broader payer population to purchase applications from CAPS suppliers, although specialized vendors remain competitive across the market.

Payers place substantial weight on total cost of ownership, expected financial returns, administrative savings, and automation potential. Their calculations also include technical debt, implementation resources, and the cost of maintaining existing technology.

A specialized application that produces faster or larger financial benefits can therefore win business even when the insurer already operates a major administrative platform. This purchasing behavior creates more opportunities for independent suppliers in payer software categories.

The comparison with EHR systems shows why a core platform’s importance depends partly on its influence over adjacent workflows. Both providers and payers rely on critical administrative systems, but those systems differ considerably in how much surrounding spending they control.

New AI spending follows measurable workflow economics

While established platforms benefit from existing integrations, new AI applications increasingly compete through the financial improvements they deliver. Healthcare organizations have spent several years exploring AI capabilities. Many are now evaluating which deployments justify continued investment.

The change is visible in organizational planning. The proportion of providers with an established AI strategy has almost doubled over the past two years. Approximately 80% of payers have developed or are developing an AI strategy, while roughly 75% of providers describe themselves as optimistic or highly optimistic about AI.

These findings show that AI has become part of healthcare technology planning. Deployment and financial performance still require separate assessment. An organization can have an established AI strategy while working to determine which applications produce sufficient returns.

The clearest opportunities involve workflows with identifiable operating costs and measurable outcomes. Clinical documentation and payer call centers illustrate how healthcare organizations are connecting AI capabilities to financial performance.

Ambient documentation connects physician productivity to revenue

Providers have concentrated many early AI investments on work that consumes substantial clinical staff time. Leading applications include ambient documentation, chart summarization, and clinical documentation improvement.

Ambient documentation captures information from a patient encounter and generates clinical notes for physician review. Chart summarization helps clinicians find relevant details in existing records, while clinical documentation improvement supports the completeness and accuracy of information used in care and administrative processes.

These applications address the time clinicians spend preparing and reviewing documentation. Reducing that burden can improve physician experience, particularly in organizations facing staffing shortages and limited clinical capacity.

The financial case becomes stronger when saved time allows clinicians to see additional patients. An unnamed community health system reported substantial changes after introducing ambient listening technology.

Its chief health analytics officer described the results:

“Documentation time for our top users of the ambient listening technology has decreased by 50% to 60% … We had an 18.4% increase in the number of patients seen about a year after we adopted this.”

The documentation-time reduction applies specifically to the technology’s top users. The reported increase in patient volume occurred approximately one year after adoption and provides a separate measure of the organization’s operational performance.

Together, these outcomes illustrate the potential connection between reduced administrative work and increased clinical capacity. When clinicians spend less time documenting encounters, organizations may be able to use the released capacity for additional appointments.

The financial benefit then depends on patient demand, reimbursement arrangements, and the cost of implementing and operating the technology. An ambulatory practice whose revenue depends heavily on visit volume may have a particularly direct path from additional appointments to higher revenue.

Hospitals can also benefit when documentation efficiency helps them address patient access constraints or make better use of available staff. In both settings, the commercial value of ambient documentation becomes clearer when organizations connect time savings to actual changes in clinical activity.

Payer call centers offer measurable returns

Payers are finding some of their strongest AI results in administrative workflows involving large volumes of repetitive interactions. Call centers are a leading example because insurers already measure handling time, staffing requirements, service performance, and the number of inquiries employees complete.

More than 60% of payers report piloting or fully deploying AI in call centers. Approximately 60% say these investments are meeting or exceeding their return-on-investment expectations.

The first measure describes deployment activity, while the second reflects organizations’ assessments of financial performance. Figure 5, “Payers are seeing the strongest AI traction in call centers”, highlights the category’s position in payer AI adoption.

AI can help call center employees retrieve information, respond to routine questions, and process administrative requests. Payers can then measure whether those capabilities reduce handling time, increase throughput, or change staffing requirements.

These operating measures provide a relatively direct way to calculate financial benefits. Automation may allow employees to concentrate on more complex cases while software handles repetitive inquiries.

Similar opportunities exist across member services, claims administration, and care management. Claims processing and payment integrity involve large volumes of information and repetitive review tasks, while utilization management requires staff to organize clinical evidence and assess whether services are appropriate.

Member navigation offers another application. Insurers can use AI-supported systems to help members identify suitable providers and care locations, potentially improving care decisions and avoiding unnecessary spending.

Care gap analysis is also emerging as a promising use case. These systems identify recommended healthcare services that members have yet to receive, helping insurers and care teams determine where follow-up may be needed. Adoption and demonstrated benefits remain less mature than those of call center automation.

Across these applications, the financial case is strongest when organizations can define the task being automated, establish its existing cost, and measure the change after deployment.

Scaling AI changes the cost calculation

The emphasis on measurable outcomes marks a change from earlier AI experimentation. During 2024 and 2025, healthcare executives and boards often encouraged pilots because they considered AI strategically important. Those projects helped organizations explore capabilities, gain experience, and identify potential applications.

As deployments expand, however, operating costs become more significant. One increasingly visible expense is token consumption. AI models process information in units called tokens, and usage-based charges rise as systems handle more requests or larger volumes of information.

A pilot serving a small employee group may incur modest model costs. Expanding the same application across thousands of users or millions of transactions can substantially change its financial requirements.

Healthcare organizations must therefore compare the value generated by additional AI activity with the cost of operating the system at scale. The calculation becomes more complicated when automated work requires human supervision.

Cost and accuracy remain the principal barriers to scaling AI among providers. Accuracy is particularly important when AI output affects clinical documentation, medical coding, or administrative decisions.

Incorrect output can require verification, correction, or additional processing. A system that completes tasks quickly may deliver smaller financial benefits once employees spend time checking its work and resolving errors.

The full cost of deployment includes implementation, integration, model usage, maintenance, and human review. Buyers increasingly need to account for these expenses before expanding AI across an organization.

Financial returns are becoming a purchasing requirement

Healthcare organizations commonly evaluate technology returns through three financial measures:

• Cost savings from reducing expenses or avoiding additional spending.
• Revenue increases from higher patient throughput, improved collections, or better use of operating capacity.
• Productivity improvements that allow employees to complete more work with existing resources.

These measures capture different ways technology can create value. An application may improve productivity while staffing expenses remain unchanged. Another may increase revenue collection without changing the number of healthcare services delivered.

Executives therefore need to identify the specific outcome a deployment should produce. They must also establish how to measure that outcome and when the financial benefit should appear.

Many healthcare organizations still lack formal thresholds for investment returns. Among those that use such thresholds, the most common requirement is a return of 3.0 to 3.9 times the original investment.

That expectation makes applications with clear operating benefits more attractive. Time to value also matters, especially for health systems with limited margins and insurers facing immediate medical cost pressure.

A technology that produces dependable improvements quickly may be more attractive than one promising larger benefits after several years. Buyers must also consider whether they have the IT staff and operational resources needed to implement the product.

These conditions favor applications built around identifiable workflows. Vendors that understand the costs, processes, and financial incentives of the activities they automate can make a clearer case for investment.

The same logic shapes customer relationships. Buyers increasingly need help translating technical performance into financial results, while vendors need to understand how their products affect revenue, expenses, and staff capacity.

Provider-payer automation creates demand beyond direct productivity gains

The economics of ambient documentation and call center automation explain why many organizations are investing in AI. A different purchasing incentive emerges when providers and payers respond to changes in one another’s operations.

Both groups face pressure to improve financial performance, but their priorities differ. Providers concentrate on protecting revenue and using clinical capacity effectively. Payers focus heavily on medical expenses, administrative costs, and the processes connecting members to care.

Those priorities determine which technologies receive funding and how automation on one side affects the other.

Providers prioritize revenue protection and clinical capacity

Revenue cycle management remains a major investment priority for acute and ambulatory providers. Figure 1, “RCM is a top investment priority for acute and ambulatory providers alike”, illustrates the shared emphasis in the Bain 2026 survey.

The survey did not ask ambulatory providers about clinical workflow optimization, which matters when comparing priorities across the two groups.

RCM connects clinical activity to payment through documentation, billing, claims submission, denial handling, and collections. Improvements can help providers protect revenue while reducing the administrative work required to secure reimbursement.

Clinical documentation improvement and denial management are particularly important because incomplete records and coding errors can lead to payment delays, rejected claims, and additional staff effort.

Beyond RCM, acute providers emphasize clinical workflow optimization. Their priorities include ambient documentation, clinical decision support, patient flow, and bed management.

Clinical decision support helps clinicians use patient information and established medical knowledge when making treatment decisions. Patient flow and bed management help hospitals allocate capacity and coordinate movement between care settings.

These investments reflect the demands of large healthcare organizations, where staffing constraints and patient movement through facilities influence financial performance.

Ambulatory providers place relatively greater emphasis on patient access and engagement. Scheduling, registration, and outreach affect whether patients can obtain appointments and whether practices use their available capacity effectively.

For independent physician groups, better access systems can support revenue by helping fill appointment slots. These organizations also place greater emphasis on IT infrastructure and services because their internal technology teams are often small.

External support and manageable implementation requirements can therefore influence purchasing decisions. A product’s financial value depends partly on whether the practice can deploy and maintain it with the resources available.

Payers are directing investment toward medical and administrative costs

Insurers approach technology investment from a different financial position. Higher healthcare utilization, increasing specialty-drug expenses, and slower premium-rate growth put pressure on operating margins.

Medicare Advantage plans also face changes associated with the Centers for Medicare & Medicaid Services (CMS) Hierarchical Condition Category model version 28, known as CMS-HCC V28.

The model affects risk adjustment, the process used to account for members’ health conditions when determining payments. Accurate clinical information and effective risk adjustment processes are therefore important to plan economics.

Exchange plans face additional pressure from adverse selection, which occurs when an insured population contains a higher concentration of people with costly healthcare needs.

Although these pressures differ across insurance businesses, they increase the incentive to reduce administrative expenses and manage medical spending more effectively.

Payer technology priorities reflect those objectives. Member care coordination, utilization management, and member and care navigation remain leading investment categories, while claims processing is attracting increasing attention.

Figure 4, “Member care coordination and utilization management remain top payer priorities, with claims processing rising”, compares findings from Bain surveys conducted in 2024, 2025, and 2026.

The surveys involved the following numbers of US healthcare executives:

These survey rounds provide historical context for changes in payer investment priorities.

Utilization management is particularly important because it concerns whether healthcare services are medically appropriate and how those services are authorized. Prior authorization is one component: providers must obtain approval for certain services before treatment or reimbursement.

Technology can help insurers review clinical evidence, coordinate with providers, and address authorization questions earlier. The intended benefits include lower administrative expenses and better control over medical spending.

More effective utilization management can also support a lower medical loss ratio, which measures the proportion of premium revenue spent on healthcare claims and related medical activities.

Member navigation addresses medical costs through a different process. By guiding members toward appropriate providers and care settings, insurers aim to improve care decisions and reduce unnecessary or unnecessarily expensive treatment.

Together, these priorities show why payer technology investment concentrates on the processes that determine where medical spending occurs and how efficiently insurers administer it.

Legacy systems limit implementation capacity

Payer ambitions must fit within existing technology environments. Many insurers maintain legacy platforms and separate systems across different lines of business. Supporting those environments requires maintenance work and can make it difficult to introduce applications that need information from multiple sources.

Technical debt adds to the challenge. Older systems often require additional engineering to connect with modern software, increasing implementation costs and extending deployment schedules.

Payers consequently emphasize total cost of ownership, administrative savings, and near-term financial returns when evaluating new products. These considerations become especially important when internal IT teams face competing projects.

Applications that automate labor-intensive processes can be attractive because buyers can identify the work involved and estimate potential savings. However, implementation must still fit the organization’s technical capacity.

This helps explain why administrative automation receives substantial investment even as broader systems modernization remains difficult. Insurers can sometimes improve individual workflows faster than they can replace the underlying platforms supporting them.

Defensive automation creates a distinct purchasing incentive

The relationship between payer and provider technology becomes particularly important in claims administration. As insurers improve payment integrity and utilization management through AI, providers expect claims processing to become more demanding.

Their concerns include denied claims that require additional documentation, corrected coding, or challenges to payment decisions. These activities consume administrative resources and can delay revenue collection.

RCM vendors are responding with automated documentation, coding, and denial management capabilities. Their products help providers meet the demands of interacting with increasingly automated payer systems.

The purchasing incentive differs from the direct productivity benefits seen in ambient documentation and call centers. A provider may invest in claims automation because it expects greater administrative difficulty in collecting revenue under changing payer processes.

That investment can still produce efficiency improvements. Better documentation and coding can reduce errors, while automated denial management can lower manual workload and improve collections.

But the motivation to purchase can arise before the provider has demonstrated those additional productivity gains. Protecting existing revenue becomes a financial objective in its own right.

This creates a feedback process. Payers invest in automation to reduce administrative costs and control payments, while providers invest in systems that help them respond to the resulting requirements.

The observed behavior concerns provider expectations and purchasing decisions. It does not establish that payer AI has already increased denial rates or raised total administrative costs across the healthcare system.

For software vendors, the perception of growing administrative demands can nevertheless support substantial demand. Providers may consider the technology necessary to maintain financial performance even when its incremental productivity return is difficult to separate from the revenue it protects.

The durability of that demand depends partly on how provider-payer interactions evolve. If both sides continue building systems to manage increasingly complex administrative exchanges, automation spending could remain strong.

But better coordination could change which capabilities buyers value.

Better coordination could redirect healthcare automation

The next development in provider-payer technology may involve resolving administrative problems earlier in the care and payment process. Clinical data exchange is improving, while healthcare organizations are making progress in eligibility verification and prior authorization.

These processes matter because many claims problems begin before a claim reaches final payment review. Eligibility questions arise when coverage or benefits are unclear. Clinical necessity disputes occur when providers and payers disagree about whether a service meets coverage requirements, while coding problems can prevent accurate claims processing.

Resolving these issues earlier could reduce the work required to correct, review, and resubmit claims. It could also change the financial incentives that currently encourage providers and payers to automate their separate administrative processes.

Regulation is accelerating information exchange

Regulatory requirements are adding pressure for improvement. The CMS Interoperability and Prior Authorization Final Rule, CMS-0057-F, establishes requirements with a January 2027 compliance deadline.

These requirements are prompting payers to invest in technology that supports information exchange and more efficient authorization workflows. Better access to relevant clinical and administrative information can help organizations address questions before they become downstream payment problems.

Health plans have also made commitments through AHIP, the health insurance industry association, to standardize submission requirements and reduce the range of claims requiring prior authorization.

Together, regulatory requirements and industry commitments could make interactions between providers and payers more consistent. Standardized information exchange may reduce repetitive requests, manual review, and administrative work caused by differences between organizations’ processes.

The potential benefit extends beyond faster authorization. Better coordination could reduce the volume of unresolved questions that later require denial management, claims correction, or payment disputes.

Shared workflows could change vendor opportunities

Improved coordination creates opportunities for technology vendors serving both providers and payers. A supplier with access to workflows on both sides may be able to support earlier information exchange, more consistent documentation, and better alignment of authorization requirements.

These capabilities could eventually extend into risk adjustment and utilization management. For example, systems that exchange clinical information more effectively could help payers assess relevant medical conditions while reducing the work providers spend responding to separate information requests.

Several vendors are already developing capabilities for customers on both sides of the market. Their opportunity is to connect activities that healthcare organizations currently manage through separate applications and administrative teams.

The commercial value depends on whether shared processes reduce the total work involved. If providers and payers can resolve eligibility, coding, and authorization questions earlier, they may need less software devoted to processing the resulting disputes.

That would change the economics of some established automation categories. Vendors whose products mainly help organizations manage administrative disagreements could face changing customer requirements as information exchange improves.

Meanwhile, vendors supporting shared data access, authorization coordination, and consistent documentation could gain opportunities to serve workflows spanning multiple organizations.

Business incentives remain the main obstacle

Software alone cannot deliver this shift. Providers and payers have different financial incentives, and their relationships have often involved disagreements over reimbursement, medical necessity, and responsibility for administrative work.

Greater cooperation would require changes in business models and stronger trust between organizations. Both sides would need confidence that sharing information and resolving questions earlier would produce benefits worth the investment.

Legacy technology creates another obstacle. Organizations operating separate systems across business lines may struggle to exchange the information required for coordinated workflows. Limited implementation resources could slow progress even when the financial case is attractive.

These constraints mean the transition toward shared processes remains an emerging possibility. Yet the direction matters because today’s automation investments are partly shaped by the cost of managing disagreements.

Providers currently purchase software to prepare claims, respond to denials, and protect collections. Payers invest in systems that review payments, evaluate medical necessity, and manage administrative costs.

Earlier coordination could alter that relationship. When eligibility, authorization, coding, and clinical information are resolved before payment disputes arise, the volume and nature of subsequent administrative work may change.

For vendors, this creates a product decision. Software built to process existing administrative complexity can address an immediate and measurable customer need. Software that reduces the creation of that complexity may serve a different, longer-term opportunity.

The two approaches require different capabilities. Processing disputes efficiently depends on automation within existing workflows, while preventing disputes requires information exchange and coordinated processes across organizations.

As healthcare providers and payers continue investing in AI, vendors serving these interactions will need to account for both possibilities. The financial value of automating a task ultimately depends on how often that task will still be needed as the organizations performing it change their processes.

Final thoughts

Healthcare IT investment remains strong, but continued spending does not guarantee growth for every software vendor. As providers and payers face financial pressure, purchasing decisions increasingly depend on integration costs, measurable returns, and the operational problems a product can solve.

Established EHR vendors benefit from their position within clinical workflows, where switching costs and integration requirements make replacement difficult. Independent suppliers have greater opportunities in specialized applications, particularly where they can demonstrate financial benefits that outweigh implementation and maintenance costs.

For healthcare executives, the challenge is distinguishing technology investments that improve financial performance from those driven primarily by competitive pressure. AI applications that reduce documentation time, improve patient throughput, or lower administrative expenses offer relatively clear ways to measure value. Automation intended to protect revenue against increasingly sophisticated payer systems may be equally important, but its financial contribution can be harder to isolate.

Software vendors face a related challenge. Lower development costs may make it easier to introduce AI applications, but successful deployment still requires access to existing systems, reliable performance, and an understanding of healthcare workflows. Products that demonstrate measurable results while minimizing implementation demands will be better positioned to compete.

The longer-term opportunity may also differ from today’s investment priorities. Providers and payers are spending heavily to automate administrative processes that often exist because information is incomplete, delayed, or disputed. Better data exchange and earlier coordination could reduce the need for some of that work.

For business leaders, the central question is not simply where AI can automate existing tasks, but which tasks will continue to create value as healthcare operations change. Investments that improve today’s financial performance while supporting more connected workflows will be better positioned to deliver lasting returns.