AI is reshaping member engagement for health insurers

For decades, health plans have invested in member engagement with a familiar logic: identify the right population, design the right campaign, send the right message, and hope enough members respond. The approach has improved over time with better segmentation and outreach channels, as well as the introduction of digital tools to reach members.

Yet the underlying model has remained largely unchanged. Engagement is still episodic, reactive, campaign-driven, and organized around internal plan priorities rather than the member's lived reality. A member receives a reminder to close a care gap, a letter about a benefit, a text about a wellness program, or a call from a care manager. The experience often feels disconnected, poorly timed, or insufficiently relevant to what the member is actually trying to solve.

At the same time, member expectations are changing. They are looking for highly personalized, timely, and responsive healthcare experiences that meet their needs. In fact, 53% of respondents said healthcare needs better-tailored products and services — the highest share of any sector — according to an Oliver Wyman Forum survey.

Consumers are increasingly turning to artificial intelligence (AI) to close this expectation gap. Sixty-five percent of people say they now use AI to assist with their healthcare decision-making, according to the Oliver Wyman Forum survey, which polled nearly 300,000 people globally over the past five years. More than half use AI for everyday health questions, such as diagnosing a minor condition, and more than a third use it for urgent health needs. In the US, roughly one in four of ChatGPT's weekly users now submit health-related prompts, with nearly two million messages per week focused on health insurance alone, according to OpenAI.

The implications for health plans are clear: AI is becoming the new front door to healthcare, giving members real-time, personalized guidance, often before they interact with their health plan or provider. Leaders need to rethink their member engagement strategy, embedding AI to deliver scalable solutions. That’s especially critical in a market where affordability pressures on payers are high. Delivering higher-quality, lower-cost care for members will be a competitive edge.

Three questions every payer leadership team should be asking

The traditional engagement model is outdated in a highly digital and connected environment, and insurers need to recognize that they have less control over the flow of information. Similar to the rest of their daily lives, members already access vast amounts of information on the internet. As AI gets layered on top of that, the interaction changes immeasurably.

To adjust their member engagement strategy for the AI era, leaders need to answer three important questions:

• What is our role in an AI-first member journey? Plans need to determine how they fit into an environment in which AI helps consumers make critical decisions about their care. Do they build their own AI-powered front door or make their data and services available to the assistant members already use? Both? The only wrong answer is to have no answer, ceding the navigation layer to platforms with no accountability for the member’s outcomes or the plan’s economics.
• Which plan-owned experiences will actually move quality and cost? Not every journey is worth orchestrating. The discipline lies in identifying the high-value journeys the plan is genuinely positioned to influence and that materially change outcomes — emergency department diversion for low-acuity needs, post-discharge transitions, chronic condition onboarding, high-cost imaging steerage, medication adherence for the conditions driving trend. Each has measurable endpoints, including impacting net promoter score, improvement on Stars measures, reduced administrative load, and avoided medical costs.
• Do we have the data and governance foundation to mobilize? AI-enabled engagement is only as good as the data beneath it and the guardrails around it. Acting in real time requires a unified view of the member — claims, clinical, benefits, engagement history and engagement preferences — exposed as reliable data products rather than trapped in departmental silos. It also requires the scaffolding to deploy AI responsibly: model risk management, clinical safety review, privacy controls, and clear escalation paths to humans. Plans that skip this step end up with impressive demos but unscalable operations.
• How do we move away from point solutions? Most plans are not starting from zero. Over the past several years, payers and their vendor partners have deployed a steady stream of point solutions, resulting in a portfolio of disconnected experiments, each defensible on their own, none adding up to a capability. Members experience the fragmentation directly — a portal that doesn’t know what the care manager said, an outreach call about a screening completed last month, a chatbot that can’t see a claim. 

Breaking out of this pilot purgatory requires an entirely different architecture for engagement.

Why plans need to adopt a precision engagement strategy

To ensure they not only meet members’ expectations but are positioned for what comes next as AI evolves, plans need to adopt a different architecture altogether. They should be moving toward precision engagement.

Under this strategy, plans develop a scaled and AI-enabled approach to deliver members highly personalized interactions when, where, and how they want them. Similar to precision medicine, it replaces population-level averages with individual-level intelligence: the right intervention for the right member at the right moment through the right channel. And done so continuously, not episodically.

Three attributes distinguish it from the engagement models most plans run today:

• It is personalized at the level of the individual, drawing on the plan’s full data asset — clinical history, benefits, claims, prior interactions, stated preferences — to tailor both the content and the delivery of every interaction.
• It is scaled across the full population, extending personalized guidance beyond high-cost members to the broader population, where earlier intervention can have the greatest long-term impact.
• It is orchestrated in real time, responding to member needs as they emerge and coordinating across channels, so interactions feel like one continuous conversation rather than a series of disconnected touchpoints. 

The AI capabilities behind precision member engagement

Precision engagement is not a product a plan can buy off the shelf. It is an enterprise capability assembled from a defined set of components, and the assembly matters more than any individual piece.

Building a precision engagement strategy starts with a longitudinal, real-time view of each member, delivered through governed data products that power every experience. A decisioning and orchestration engine sits on top of that to prioritize interventions against clinical and business value, determine the best outreach method, and sequence the next best action for every member, every day.

AI-powered interaction handles member intent, individualized content, and routine service and navigation tasks, operating within clinically reviewed boundaries that automatically escalate to humans for complexity, risk, or distress. Meanwhile, omnichannel delivery meets members based on their preferences, with full context carried across all channels.

Just as critical are the elements that make the system trustworthy and durable:

• A human-in-the-loop operating model, with redesigned roles so care managers, advocates, and clinicians supervise and augment the AI rather than work around it.
• Governance, risk, and model management that keep AI-driven engagement safe, compliant, equitable, and explainable — and allow the plan to move fast precisely because the guardrails can be trusted.
• A measurement and learning engine that ties every interaction to engagement, clinical, and financial outcomes so the capability compounds rather than plateaus.

Every plan will start from a different place. It’s important to have a detailed understanding of the organization’s maturity across each component and to develop a sequenced roadmap that builds shared infrastructure rather than another generation of disconnected pilots.

How precision engagement impacts the bottom line

The financial case for precision engagement spans both sides of the income statement. On the medical side, value comes from steering members to high-quality, lower-cost sites of care, diverting avoidable emergency department visits, improving adherence and chronic condition management, and smoothing transitions of care that otherwise end in readmissions.

On the administrative side, it deflects routine calls, reduces rework from confusion-driven contacts, and focuses care management capacity where human expertise changes outcomes, letting plans extend their reach without growing headcount in lockstep. And on the revenue side, the same capability lifts quality and retention: better gap closure and member experience flow through to star performance and the bonus revenue attached to it, while satisfied members are less likely to switch at renewal.

Members are not waiting for health plans to figure this out. They are already routing their health questions and, increasingly, their health decisions through AI tools. Every month of inaction widens the gap between where members make decisions and where plans can influence them.

Plans own the data needed for precision engagement — member data, clinical assets and trusted, if underused, relationships. What they need to do now is assemble them into a precision engagement capability with discipline.