Predicts 2026: U.S. Healthcare Payers Bet Big on Agentic Workforce

15 December 2025 - ID G00839721 - 19 min read
By Faith Adams, Austynn Eubank,  and 1 more
Healthcare payers are normally cautious of, and slow to adopt, new and emerging technologies, but they have been quick to adopt agentic AI. These solutions are viewed as a strategic opportunity to overcome industry challenges, affect the bottom line, and gain a competitive advantage.

Overview

Key Findings

  • U.S. healthcare payers are becoming less risk-averse when investing in emerging technology to contend with continued cost pressures and regulatory uncertainty. As such, they continue to invest in AI — and particularly AI agents — but value realization remains challenging.
  • Healthcare data remains fragmented, with members’ clinical, claims, social and demographic, and eligibility/benefit information often residing in separate, incompatible systems. Additionally, manual processes — such as eligibility verification, claims adjudication and prior authorizations — are costly for U.S. healthcare payers and strain member experience and payer-provider relationships.
  • Agentic AI is introducing a novel approach when bringing fragmented data together for payers, as it unifies data from various sources into a single interface, streamlining and transforming clinical and administrative processes while creating efficiencies. For example, model context protocols (MCPs) and agent-to-agent systems (A2A) provide an easy way for datasets to connect workflows and for processing usage.

Recommendations

  • Payers must orchestrate technology solutions that address barriers to adoption, engagement and trust. As such, to fully realize the value of AI and agentic-system-driven service optimization, payer CIOs and involved business stakeholders should evaluate the end-to-end member, provider and purchaser experience when making AI agent investments.
  • Ensure governance by executing a master data management strategy aimed at eliminating data silos, ensuring data quality and security and building trust in autonomous systems’ outputs. This emphasis is necessary to achieve a responsible and successful agentic AI deployment.
  • Build your technology strategy with Gartner’s vision for healthcare and life sciences, intelligent health, in mind. Leverage capabilities that are AI-driven, ambient and deliver hyperpersonalized experiences to drive transformation. Start with a phased, targeted, composable approach focused on building organizational support and proving ROI and value.

Strategic Planning Assumptions

By 2028, AI agents and chatbots will field 80% of member, provider and purchaser inquiries to reduce costs. However, overall spend will rise due to fragmented experiences that further inhibit engagement and erode trust.
By 2027, 30% of payers will address critical interoperability challenges using AI and agentic AI, optimizing payer-provider clinical and administrative workflows and reducing manual workloads by 40%.
By 2028, 80% of all ambulatory claims will be processed through AI-enabled, real-time adjudication processes, resulting in a quantifiable reduction in manual processing costs.

Analysis

What You Need to Know

Agentic AI Offers Payers Transformation Potential, But ROI Hinges on Trust

To contend with industry challenges, maintain competitive differentiation and advance transformation, and innovation efforts, U.S. healthcare payer CIOs are rapidly turning to AI. According to the 2025 Gartner Business Outcomes of Technology Survey, the proportion of payer organizations investing in new technologies for business and IT transformation has increased dramatically, from 15% in 2024 to 52% in 2025 (see Business Outcomes of Technology Survey: Spending Strategy Drivers for Healthcare Payers).1
Agentic AI is a standout area of investment, with all payer respondents indicating they have already implemented or will deploy agentic AI by 2028. Agentic AI has long-term transformational potential due to its ability to autonomously execute tasks and workflows, particularly for repeatable processes that have access to AI-ready data (see Agentic AI — A Bet U.S. Healthcare Payers Are Willing to Take). The opportunity to reduce cost and effort associated with reducing human processes, such as claims processing and customer support, is tremendous. But extracting this ROI hinges on overcoming critical industry challenges and limitations, including:
  • Data privacy and security — Complex, often-changing regulations are a perennial concern that requirement payers must adhere to and address in their technology investments.
  • Fragmented data — Siloed applications and lagging data management capabilities can limit agents’ efficacy.
  • Integration with legacy systems — While payers continue to invest in modernization, integration with legacy systems is costly and complex.
  • Trust and AI literacy — Successful adoption of agentic AI depends on employees’ confidence that the solution will act reliably, safely and transparently — with capabilities and limitations they understand (see AI Ethics in Healthcare and Life Sciences: Part 2 — Building Trust).
To realize value from these investments, payers require foundational capabilities, such as data access, security and quality. Success also requires building trust with — and demonstrating value to — key business stakeholders, members, healthcare providers and purchasers.
Our 2026 predictions underscore the importance of payer CIOs being intentional as they seek to advance their agentic AI technology strategy (see Table 1).

U.S. Healthcare Payer Predicts, 2026

Strategic Planning Assumptions
Source: Gartner

Strategic Planning Assumptions

Strategic Planning Assumption: By 2028, AI agents and chatbots will field 80% of member, provider and purchaser inquiries to reduce costs. However, overall spend will rise due to fragmented experiences that further inhibit engagement and erode trust.
Analysis by: Faith Adams
Key Findings:
  • The 2025 Gartner Healthcare Payer Agentic AI Survey indicates that 21% of payers have already deployed agentic AI into production to create call center efficiencies, with an additional 32% in pilots (see Agentic AI Adoption Plans and Benchmarks for U.S. Healthcare Payers, 3Q25).2
  • AI agents and chatbots can optimize the service experience for members, providers and purchasers by providing on-demand answers to their key questions. They can be available 24/7 via multiple channels, including voice and chat, thereby reducing administrative burden with potential to improve service interaction satisfaction.
  • AI agents help minimize the reliance on human capital for certain interactions, reducing administrative costs (which is a priority for many payers).
  • AI agent and chatbot solutions like Hyro, Hippocratic AI, Infinitus, NiCE Cognigy, Notable Health and Talkdesk actively enable payers and healthcare organizations to automate interactions with the hopes of yielding a reduction in call volumes and costs. For example, a large payer leveraged Verint’s Intelligent Virtual Assistant and saw a 29% reduction in call volume in less than six months. The estimated impact was a $1.5 million savings due to call deflection.3
  • Consumer trust in the U.S. healthcare system — and especially payers — remains low. In addition, healthcare payer and provider relationships are strained, with providers also having low trust in payers. And while these investments improve the convenience of some interactions, they do not deliver holistic improvement to the industry fragmentation at the root of this trust challenge.
Market Implications:
  • While promising for certain interactions, AI agents are deployed only in discrete moments of a much larger, often complex, journey. Member, provider and purchaser loyalty and engagement are driven by the healthcare and life science (HCLS) total experience. Payers who fail to incorporate AI agents into holistic journeys will experience higher churn rates and total cost of care due to low levels of engagement (see HCLS Total Experience: Driving Engagement for Business Results in Healthcare and Life Science).
  • When given a choice, 30% of Generation Z and 22% of millennial consumers cited ease of doing business as a reason they switched from one payer to another (see Healthcare Payers: Understand Consumer Perceptions to Advance Your AI and IT Strategy).4 AI agents will help address the needs of younger, digitally native consumers, like Gen Z and millennials, who expect immediate answers.
  • AI agents will increasingly play a role in a variety of processes by learning from payer interactions. Agentic AI will help enhance intelligence and enable decision-making.
  • Implementation quality, and the resulting data quality and data integration, determines the accuracy of responses to members, providers and purchasers. Accurate responses reduce payer risk as well as maintain payer credibility and reputation — but inaccurate responses further erode trust and inhibit engagement.
  • AI agents will disrupt the role of service employees as routine tasks are automated, requiring change management and upskilling. Additionally, AI agent design and workflows will require human-in-the-loop oversight to ensure appropriate use cases, deployment and accuracy.
Recommendations:
  • Apply human-centered design to build end-to-end user experiences and empathetic AI agents that address the needs of members, providers and purchasers (see Operationalize Human-Centered Responsible AI in 5 Steps). For instance, while providers might appreciate the 24/7 access provided by these solutions, their overall perception and trust in payers is also shaped by experiences like prior authorization and payments.
  • Build an AI-ready data foundation. Payers have made improvements when it comes to data quality, but AI agents require high-quality and trusted data. Payers should continue to enhance their data quality and data governance practices (see 3 CIO Actions to Integrate AI-Ready Data into Governance Models).
  • Prioritize and ensure personalization. For example, by understanding a member’s preferences, health history and engagement patterns, the solutions can provide more personalized and empathetic responses aimed at improving health outcomes and member experience.
  • Address the risks and business value that AI agents can offer by embracing fusion teams — and bringing key business stakeholders together — to address workforce and regulatory challenges.
Related Research:
Healthcare Payers: Understand Consumer Perceptions to Advance Your AI and IT Strategy
AI and Digital Twins in Healthcare and Life Sciences: Eye on Innovation Case Examples
Hype Cycle for Intelligent Health, 2025
Agentic AI for U.S. Healthcare Payer CIOs
Emerging Tech: AI Vendor Race: Agentic AI Maturity Roadmap
Strategic Planning Assumption: By 2027, 30% of payers will address critical interoperability challenges using AI and agentic AI, optimizing payer-provider clinical and administrative workflows and reducing manual workloads by 40%.
Analysis by: Connie Salgy
Key Findings:
  • Agentic AI addresses critical data interoperability challenges and serves as a data bridge between legacy systems and modern, Fast Healthcare Interoperability Resources (FHIR)-compliant platforms. It interprets legacy formats and synchronizes with newer systems without needing a “rip and replace” strategy.
  • By creating a connection between front-end user interfaces and back-end processing systems, AI systems reduce administrative burdens and enhance decision accuracy, driving operational quality and efficiency improvements for payers and providers.
  • Through cross-system autonomous or semiautonomous automation, AI agents transform payer-provider processes. For example, they can pull clinical data from a provider’s electronic health record and validate member eligibility in a payer’s core administrative system for prior authorizations and care delivery purposes.
  • AI agents make payer-provider workflows intelligent and real-time by interacting across systems to solve multiple complex and multistep tasks. However, to accomplish this, an open-sourced platform, a single protocol or reimplementing agents within each ecosystem is needed.
  • Challenges remain — particularly in data quality, regulatory adherence and managing nondeterministic outcomes — but the strategic potential of agentic AI in revolutionizing payer-provider workflows is clear. Gartner has seen early reported outcomes in multiple payer-provider workflow use cases with vendors, such as Basys.ai, Cohere Health, Edifecs, Exponential AI, Five9 and Forum Systems.
Market Implications:
  • The White House and the U.S. Centers for Medicare & Medicaid Services (CMS) secured commitments from 60 healthcare technology organizations and multiple payer and provider organizations. The Health Tech Ecosystem commitment aims to improve CMS’ interoperability framework to create a “standards-based digital health environment.” This collaboration is an early indication of AI system usage in delivering multiple payer-provider clinical and administrative workflows.5
  • 2025 Gartner Business Outcomes of Technology Survey responses indicate that 45% of payer respondents have already deployed agentic AI systems, and the remainder plan to deploy them before 2028.1
  • The emergence of healthcare-specific language models, such as Google’s Medical Pathways Language Model 2. These models empower payers, providers and vendors to deploy agent systems within clinical workflows with increased accuracy and relevance.
  • Payer and provider data and technology are often siloed, which may prohibit agent systems within payer-provider workflows. One way to address these interoperability challenges is through the use of translation layers or mediators that convert messages and actions between different protocols or across an open-source platform. Vendors such as Cognizant, Epic, Oracle and Salesforce are addressing this challenge through AI-driven platforms that will allow AI agents to interact and solve for multiple payer-provider workflows.
  • Although payers are optimistic about using agentic AI to improve payer-payer provider workflows and experiences, conflicting state and federal mandates, undefined requirements under the CMS’ Interoperability and Prior Authorization Final Rule (CMS-0057-F), and newly formed coalitions may deter or delay agentic AI investments.
Recommendations:
  • Revisit CMS-0057-F and specific state mandates to avoid duplicative development and rework. This approach will help you understand all the mandated and agentic AI technology platform requirements before investing in payer-provider agentic AI workflow tools. Do this before procuring or building agentic AI tools and work internally and with your current or prospective vendors to vet any data or technology overlap within CMS-0057-F, such as payer-provider FHIR requirements.
  • Ensure AI data readiness before building or procuring agent systems and tools, and engage your chief information security officer early in the process to address governance and privacy concerns. This strategy will help build trust in agent systems, ensuring a successful and responsible technology deployment.
  • Pilot and deploy a phased agentic AI implementation. Start with small, localized applications to mitigate risks, prove ROI and build organizational buy-in before expanding the scope. For example, build or partner with vendors that fulfill specific payer-provider workflow solutions, such as payment integrity, service centers, provider contracting and prior authorizations (see AI Agents: Use-Case Examples for Health Insurers). Additionally, seek vendors that will partner to incrementally help build or implement for multiple agentic AI use cases.
Related Research:
U.S. Healthcare Payers: Interoperability Decision Framework for Agentic AI Data Sharing
Drive AI Impact and Value in Healthcare and Life Science by Establishing Strong Governance
Generative AI Use-Case Assessment to Prioritize U.S. Healthcare Payer Investments
Strategic Planning Assumption: By 2028, 80% of all ambulatory claims will be processed through AI-enabled real-time adjudication processes, resulting in a quantifiable reduction in manual processing costs.
Analysis by: Austynn Eubank
Key Findings:
  • Manual claims processes are expensive for healthcare organizations. Complex claims cost around $35 to $40 to adjudicate, and between $25 and $118 to be reworked by provider teams.6,7
  • Healthcare payers have invested heavily in AI-enabled fraud, waste and abuse solutions, while revenue cycle management (RCM) AI solutions have remained fairly immature. According to a survey from Experian, 67% of provider respondents felt that AI can improve the claims process, while only 14% are using AI to reduce denial.8 AI used upstream, such as ambient digital scribe, has increased documentation available for coding, which many payer organizations have interpreted as the use of AI in RCM, coding and billing activities.
  • Administrative processes like adjudicating claims are well-suited for agentic AI workflows, though data quality, inconsistent outcomes and lack of trust between payers and providers present barriers to adoption. Vendors like Codoxo, HealthEdge, Machinify, Sagility and Zelis have payment integrity products that use AI agents. Additionally, CarynHealth, Optum and Oracle offer real-time adjudication platforms that handle denials and adjudication for both providers and payers, reducing manual work for both parties.
  • Ambulatory care represents the largest portion of healthcare spending, according to a study reviewing personal healthcare spend between 2010 and 2019.9 Ambulatory claims are less complex than inpatient claims, making them a strong candidate for automation.
Market Implications:
  • In response to tightening reimbursements and advancements in payment integrity technology, provider organizations will invest heavily in automating appeals and resubmitted claims. This approach will increase the volumes for appeals and grievances (A&G) and core administration platforms, underscoring the importance of reducing burden in adjudication and review processes.
  • Clinical and claims data will become even more valuable for health plans as they will inform agentic AI decisions and workflows. Vendor products that link clinical and administrative data will continue to emerge, such as Cohere’s Match solution, which identifies prior authorizations and their corresponding claims or data management vendors, like Smile CDR and Intersystems, that align clinical and administrative data for provider contracting conversations.
  • Provider contracting teams will need to integrate data from payment integrity and A&G systems to track new provisions for clean claim rates from providers and to gather additional transparency and feedback from denials, supporting provider education.
Recommendations:
  • Measure the cost of manual claims processing on your organization by identifying the technology, employees and time spent to adjudicate claims. Use this data to predict a break-even point that signifies your organization’s tolerance for errors.
  • Invest in data management and process documentation solutions to kick-start your agentic AI journey. Focus on curating active metadata, which will optimize your agentic AI’s data selection and next best actions.
  • Work with your provider relations team to incentivize your provider partners, both in-network and out-of-network, to share clinical data with you. Consider financial incentives and using clear, plain language in claim denial explanations to ensure that clinical data is used to support member care rather than to inform medical policy creation.
Related Research:
AI Use Cases for Payment Integrity to Deliver ROI for U.S. Healthcare Payers
Innovation Insight: Autonomous Clinical Coding to Fill Resource Gaps
Total Cost of Care: Key Cost Improvement Strategies for U.S. Payer CIOs

A Look Back

In response to your requests, we are taking a look back at some key predictions from previous years. We have intentionally selected predictions from opposite ends of the scale — one where we were wholly or largely on target, as well as one we missed.
By 2026, 60% of prior authorizations (PA) will be processed electronically — up from 26% in 2021.
The number of PAs processed electronically continued to increase in 2025, and that number is expected to continue to rise in 2026. For example, more than 50 health plans have voluntarily committed to optimizing the PA process for members and providers. Payers aim to enable more than just 60% of electronic PA approvals, committing to achieving a minimum of 80% of real-time electronic PA approvals in 2027, keeping this prediction on track.
As several of the provisions of the CMS’ 2024 Interoperability and Prior Authorization Final Rule become a reality, with many rule deadlines occurring in 2026, payers are actively working toward improving the PA process to:
  • Enable faster decisions. By 1 January 2026, payers are required to issue decisions within seven calendar days for standard requests and within 72 hours for urgent ones.
  • Enhance transparency in key PA metrics. Payers must publicly report key PA metrics, such as approval and denial rates.
  • Increase automation and implement FHIR-based APIs. By 1 January 2027, payers must use an FHIR-based API to automate the PA process, creating a more seamless data exchange.
  • Improve continuity of care. Payers are actively committing to supporting member continuity of care by honoring a previous payer’s PA under certain provisions.
This focus on PAs is largely driven by government requirements, but payers also see value in these investments for their commercial line of business. Payers desire to streamline the process, reduce administrative burden and clinician burnout, increase transparency, and improve efficiency for all business lines.
Related Research:
Hype Cycle for U.S. Healthcare Payers, 2025

Evidence

1 2025 Gartner Business Outcomes of Technology Survey. This survey was conducted to understand how industries leverage technologies for various use cases. It assessed investment, deployment and implementation strategies for industry technologies. It also examined key areas intended to be impacted by technology investments, including challenges to realizing business outcomes and industry key performance indicators. The survey was conducted online from June through August 2025. The 648 respondents were from midsize, large and global enterprises from North America, EMEA and Asia/Pacific. The respondents were screened for senior IT and some business leadership roles with technology decision-making responsibilities. Disclaimer: The results of this survey do not represent global findings or the market as a whole, but reflect the sentiments of the respondents and companies surveyed.
2 2025 Gartner Healthcare Payer Agentic AI Survey. The main objective of this survey was to learn how U.S. healthcare payers are investing in agentic AI technology. This survey was conducted online from 10 September through 29 September 2025. In total, 33 executives at U.S.-based U.S. healthcare payer organizations participated. All 33 participants are members of Gartner’s U.S. Healthcare Payer Research Panel, a Gartner-managed panel. Respondents were all located in the U.S. (n = 33). Disclaimer: The results of this survey do not represent global findings or the market as a whole, but reflect the sentiments of the respondents and companies surveyed.
3 The Healthcare Payer’s Algorithm — V: AI in Member Experience and Customer Service, medium.
4 2024 Gartner Customer Experience Survey. The survey aimed to gain comprehensive customer insights for various industry providers, such as retail banks; wealth management advisors and insurance; healthcare; healthcare payers; power and utilities; communication services; and automotive. The survey assessed customer trust, the importance of personalization, understanding customers’ needs, the importance of value-added services, such as product bundling in insurance, and willingness to share data. By gathering insights from customers about their experiences, financial needs, and preferences, the survey intended to identify key factors influencing customer retention and customers’ overall experience with their industry provider. The survey also included time series analysis for retail banking and insurance customers to assess how customer behavior had changed with respect to channels of interaction. Additionally, the survey analyzed customer perceptions about using generative AI with different industry providers. The survey was conducted online from November through December 2024. In total, 4,251 customers participated in the survey. Qualified customers were 18 years of age and above and had interacted with a retail banking provider, insurance provider, healthcare provider, healthcare payer, power and utilities provider, communication service provider, or automaker in the past six months. The countries or regions covered were the U.S. (n = 1,745), the U.K. (n = 998), Canada (n = 591), Australia (n = 344), Singapore (n = 333) and the Republic of Ireland (n = 240). Disclaimer: The results of this survey do not represent global findings or the market as a whole, but reflect the sentiments of the customers surveyed.
5 White House and CMS to Launch Health Tech Ecosystem Initiative to Expand Use of Digital Health With a Focus on Consumers, Fierce Healthcare.
6 Claims Denials: A Step-by-Step Approach to Resolution, Journal of AHIMA.
7 Sahni, N.R., P. Gupta, M. Peterson and D.M. Cutler, “Active steps to reduce administrative spending associated with financial transactions in U.S. health care,” Health Affairs Scholar, 2023.
8 Healthcare claim denial statistics: State of Claims Report 2025, Experian.
9 Dieleman, J.L., M. Beauchamp, and S.W. Crosby et al. “Tracking U.S. Health Care Spending by Health Condition and County,” JAMA, 2025.