Shourya K Nautiyal, Sandra Clarke and Chris Dallas-Feeney coauthored this article.

Artificial intelligence (AI) is transforming how people search for, compare and choose their healthcare coverage. For years, payers invested in intuitive websites, better benefit explanations, digital campaigns and search engine optimization (SEO) strategies, expecting that improved digital experiences would reliably guide members’ plan decisions. That assumption no longer holds.

Members are beginning to integrate generative AI tools into their information-seeking behavior. A recent nationally representative survey by the Brookings Institution finds that 57% of U.S. adults report using generative AI for personal purposes, with 40% reporting increased use over the past year. Instead of studying benefit grids or using plan comparison tools, some members now pose natural-language questions to AI systems and receive synthesized answers in seconds.

At that moment, the algorithm’s interpretation of public data, documentation and signals of credibility—not the payer’s messaging—shape member decision-making and how the plan is represented.

This represents a meaningful shift from the digital marketing model that has dominated the past two decades. In many journeys, especially when members are actively comparing plan options, the way a plan is summarized and compared may now be shaped by AI systems rather than by the payer’s own digital experience. Organizations navigating this shift successfully treat AI visibility as a new strategic capability. Here are the five strategic shifts we believe are required for payers to compete in an AI-first world.

How is AI disrupting payer marketing?

Historically, payer marketing balanced long-term brand building with near-term enrollment activation through a predictable digital funnel: generate awareness, drive traffic, guide exploration and convert interest into enrollment. This model assumed that members would interact with payer-owned assets long enough for those channels to shape how plan information was presented and understood.

AI introduces 2 simultaneous shifts

Internally, it can materially reduce the cost and cycle time of producing high-quality, compliant content. Generative tools can accelerate drafting of benefit summaries, generate intent-aligned FAQs, identify inconsistencies across public sources and stress test language before regulatory review. For organizations operating under significant compliance and approval constraints, this efficiency gain is meaningful and increasingly table-stakes.

Externally, AI reshapes how plan information is assembled, synthesized and understood across the decision journey. Rather than relying solely on payer-owned digital experiences, members may encounter AI-generated summaries that draw from Centers for Medicare & Medicaid Services filings, Star Ratings and quality data, provider directories, public benefit summaries, regulatory disclosures, consumer-facing educational sites and transparency datasets.

The underlying member questions remain consistent: total cost including deductibles, access to specific doctors and local hospitals, prescription drug coverage and the perceived quality of available options. What changes is how those answers are assembled. AI outputs depend heavily on the clarity, structure and recency of a payer’s publicly available data.

Perceptions of quality are particularly sensitive. Members often equate lower cost with lower quality, and beyond formal ratings, such as Star Ratings or National Committee for Quality Assurance measures, reliable signals can be fragmented. Inconsistent or outdated information can distort how plans are represented.

The payer’s opportunity is not to change member intent, but to ensure AI can correctly interpret and represent the plan in response to these enduring needs.

This shift evolves marketing’s mandate in four meaningful ways:

• Influence now depends on upstream information quality, the clarity and consistency of data that AI systems ingest.
• Traditional digital metrics lose predictive value, as members may never reach the payer’s digital ecosystem.
• Narrative gives way to consistency of information, with AI elevating structured, neutral and current information.
• Visibility becomes algorithmic, not bought through media.

In this environment, marketing becomes responsible for stewarding public-facing information. AI does not inherently improve transparency or accuracy; it amplifies the signals it ingests. Ensuring the organization’s “digital truth” is coherent, consistent and authoritative is critical to reducing misrepresentation and misinformation risk.

Shift 1: From protecting past marketing success to building an experimentation engine

Payer marketing has historically prioritized consistency, supported by brand frameworks, formal review processes and regulatory guardrails. But AI changes rapidly, and small shifts in public data or model behavior can alter how plans surface in AI responses.

Leading organizations are building an experimentation engine, lightweight but continuous, that mirrors the pace of AI evolution.

Several practices emerge that translate effectively to payers:

• Testing multiple versions of plan descriptions to identify structures that reduce misinterpretation.
• Developing differentiated descriptions for distinct member types, such as older versus younger members, rural versus urban populations and members with differing health literacy levels as an initial step toward personalization.
• Running prompt-based discovery checks that simulate real member queries across AI engines to understand how plans surface for different segments.
• Evaluating cross-model variation, recognizing that different AI systems may present the same plan differently.
• Refreshing public content more frequently, given AI’s tendency to favor recent information.
• Conducting interpretability tests to identify which benefit elements are consistently simplified, overlooked or misunderstood across member scenarios.

This shift is less about standing up new frameworks and more about adopting new habits: rapid testing, rapid learning and rapid adjustment.

Organizations that treat experimentation as an ongoing discipline will be best positioned to maintain visibility and accuracy as AI models evolve.

Shift 2: From legacy metrics to understanding AI’s representation of the health plan

Clicks, impressions and traffic provide an incomplete view of influence as portions of the member journey increasingly occur within AI interfaces, where plan information may be summarized without requiring users to visit payer-owned digital properties. In these cases, engagement data is often owned by the platform, limiting direct visibility into how plans are evaluated or compared.

The fundamental question becomes: How does AI represent the plan when members ask about their options?

To answer this, leading payers are adopting a set of analytical practices that help them understand their representation across AI engines. These examples draw on analytics ZS uses to help organizations understand and improve plan visibility across AI engines:

1. Visibility and placement analysis evaluates how often plans appear, and where they are positioned, for common, unbranded member queries. Placement matters: AI engines often present short, ranked lists and the first few positions disproportionately influence perception. Visibility and placement analysis helps payers understand how consistently plans surface across engines, how they compare to competitors and which attributes are emphasized or omitted.
2. Sentiment and framing analysis assess how AI describes the plan—positively, neutrally, or unfavorably—and which qualities dominate the narrative (e.g., affordability, access, quality or behavioral health support). ZS’s work across healthcare shows that sentiment, even with accurate facts, strongly shapes decision-making. Emerging patterns in payer interactions suggest the same dynamic.
3. Accuracy and relevance assessment determines whether AI-generated descriptions reflect correct facts (premiums, deductibles, out-of-pocket limits, networks, drug tiers, quality scores) and highlight the right attributes in response to the member’s intent (e.g., surfacing insulin cost caps for diabetes queries or provider inclusion when access is the focus). Accuracy alone is insufficient. Relevance that matches member intent is equally essential.
4. Source influence mapping analyzes which external sources exert the greatest influence on AI responses, such as CMS plan benefit files, provider directories, quality datasets, state regulatory disclosures, major consumer-facing health sites and regional healthcare sources.

Across ZS analyses in life sciences, nongated, frequently updated, structured and plain-language sources have proven most influential in shaping AI-generated responses.

Together, these analyses provide a clearer view of how AI engines shape payer visibility, accuracy and narrative, not insights available through traditional key performance indicators. Payers can begin with lightweight diagnostic approaches, ranging from structured testing of real member problem statements to more comprehensive solutions such as ZS’s generative engine optimization capability to benchmark visibility, accuracy and sentiment across major AI models and monitor changes over time. Starting with these diagnostics provides a clear baseline of current AI representation, helping organizations prioritize where deeper investment and more advanced capabilities will have the greatest impact.

Shift 3: From keyword strategy to conversational positioning for payers

Generative AI interprets intent and context, not keyword density. Members are asking questions rooted in their circumstances:

• “Which insurance plans include my providers or PCPs?”
• “Which health insurance plans in NY are good for people with diabetes?”
• “Which health insurance plans have good mental health coverage?”
• “What’s the most affordable PPO in Chicago for a family of four?”

To support this shift, content must evolve from search-oriented formats to conversational, answer-aligned structures.

Leading organizations are adopting practices such as:

• Authoring benefit explanations as direct answers, not marketing brochures.
• Using plain, member-friendly language that reduces AI misinterpretation and aligns with how members speak.
• Providing clear comparisons to prevent AI from inferring its own distinctions.
• Embedding contextual scenarios, which help AI understand benefit relevance.
• Writing FAQs in natural-language formats, improving both readability and AI comprehension.

These practices improve how AI interprets plan information and increase the likelihood of accurate representation across engines.

Shift 4: From paid placement to ecosystem credibility to drive AI visibility

AI-driven visibility must be earned through clarity, consistency and credibility across the entire public data ecosystem.

Several well-established principles apply directly to payers in this context:

• Recency is influential—newer content often surfaces more readily.
• Nongated content is favored, making public benefit summaries more important.
• Local and context-specific sources matter, including state filings and regional provider data.
• Structured, factual content reduces interpretation errors.
• Consistent information across sources amplifies trust.

AI engines heavily weigh the reliability of CMS filings, provider directory data, quality measures, public benefit documentation, state regulatory sources and reputable consumer-facing health sites. If these sources conflict or contain outdated information, AI outputs will reflect the inconsistencies. If they align, AI-generated representations improve.

AI engines also surface signals from news coverage, legal actions and public sentiment. Negative press, such as lawsuits, regulatory actions or service disruptions can meaningfully shape AI-generated summaries. Managing reputation and crisis response becomes part of the ecosystem-quality equation.

This shift requires coordinated effort across marketing, operations, regulatory, network and communications teams to ensure that public-facing information remains accurate and coherent.

Shift 5: From traditional marketing roles to AI-enabled strategic teams

AI-driven plan discovery expands the scope of marketing from message creation to information governance. To meet these demands, leading organizations are augmenting their teams with new capabilities.

We see leading organizations introducing roles such as:

• A content intelligence lead to ensure clarity, structure and consistency across benefit descriptions.
• A discoverability strategist to analyze how plans surface across AI engines and identify visibility gaps.
• A structured content architect to produce machine-readable plan data and harmonize formats across sources.
• An AI accuracy and compliance lead to monitor AI-generated representations and mitigate regulatory risks.
• A digital ecosystem manager to maintain consistency across third-party and public-facing platforms.

These roles reflect a broader evolution: marketing now helps shape the organization’s digital truth— the version of the payer that members, employers and providers encounter through AI.

Strategic implications: Health plan marketing as enterprise infrastructure

As AI becomes the dominant interface for plan discovery, marketing becomes a cross-enterprise function shaping:

• Enrollment outcomes, influenced by algorithmic visibility and accuracy.
• Employer evaluations, where AI tools can supplement traditional decision support.
• Broker and benefit adviser assessments, informed by AI comparisons and summaries.
• Provider perceptions, shaped by AI descriptions of access and network strength.
• Regulatory expectations, tied to accuracy, fairness and transparency.
• Brand trust, influenced by consistency across public data ecosystems.

Payers who build the capabilities to influence AI-mediated discovery through clarity, consistency and data excellence will define the next era of competitive differentiation. Those who do not may find themselves absent from key decision moments—not due to lack of value, but due to lack of visibility.

AI has fundamentally reshaped payer marketing. Traditional channels remain relevant, but they no longer define the first impression. Algorithms now influence how plans are surfaced, understood and compared. To succeed, payers must embrace five shifts: experimentation, AI-focused measurement, conversational content, ecosystem credibility and AI-enabled marketing roles. The organizations that lead will recognize a simple truth: In the age of AI, marketing is increasingly shaped not only by what a payer says, but also by how algorithms interpret and represent it.