Talha Bhatti cowrote this article

Medicare Advantage plan design has become a risk decision, not a product exercise. Rising utilization, tightening rate pressure and county level competitive variation mean small benefit changes now carry outsized enrollment and margin consequences. Plans that still rely on fragmented analyses and episodic reviews are making high stakes decisions without a clear view of demand, tradeoffs or downstream impact. AI-enabled decision systems change that by connecting insight, simulation and execution, so leaders can predict performance before they commit.

At the same time, we know that benefit design drives roughly 30% of annual enrollment shift in Medicare Advantage plans, putting product design teams on the hot seat to devise benefits packages that drive member growth and retention while also improving plan-level profitability. AI offers significant promise to help them do so more efficiently and, most importantly, more effectively.

Moving from AI pilots to using AI to make Medicare Advantage plans more competitive

While it’s true that Medicare Advantage providers are already using AI here and there to improve plan design, they’re currently missing the biggest opportunities to build competitive advantage through these new technologies. This is because health plans, like others across the healthcare ecosystem, have so far approached AI by greenlighting a hodgepodge of pilots that optimize isolated tasks but remain disconnected from one another, limiting their ability to translate insight into coordinated decisions and execution.

Creating true competitive advantage in Medicare Advantage product design with AI will require a more coordinated approach, one that combines agentic AI, gen AI, classical AI and traditional analytics into a connected set of decision flows rather than a collection of standalone pilots. Increasingly, that coordination is delivered through agentic AI, which sits on top of individual models to sequence analyses, trigger next best actions and move insights into execution. Instead of producing isolated outputs, this approach allows plans to link insight, simulation and decision-making across the full product design life cycle.

A decision flywheel turns these events into an always-on system. Signals are sensed continuously. Models update as conditions change. Scenarios are evaluated dynamically. Recommendations are generated with evidence. Actions are triggered through connected systems under guardrails. Outcomes are measured and fed back into the model so decision quality improves over time.

Transforming Medicare Advantage product design using gen AI and classical AI

Medicare Advantage plan design spans the full Medicare Advantage plan design life cycle—from market sensing and portfolio strategy to product design and field activation. The four categories below map to this end-to-end value stream, ensuring coverage of every major decision leaders must make across the journey. Reimagining plan design requires not only reengineering individual steps but also coordinating decisions across them. Advanced analytics, classical AI and gen AI strengthen decisions within specific stages, while agentic AI orchestrates the life cycle end-to-end—sequencing analyses, prioritizing tradeoffs and triggering next best actions as teams move from insight to execution.

Category 1: Product strategy - portfolio optimization

With local Medicare Advantage markets becoming increasingly heterogenous, health plans must start by deeply understanding customer segments in each market and then tailoring their portfolio of Medicare Advantage plans to these segments’ known preferences. Today, this process is more art than science. Traditional analytics and classical AI can make it more rigorous, while agentic AI can continuously monitor market shifts, flag portfolio gaps and trigger the next round of portfolio decisions when a county warrants action.

Align plans to key county-level segments. Medicare Advantage organizations have been busy adding new plans with incremental features, creating confusion among members and insurance agents alike, leading to slower sales and higher administrative expenses. Advanced analytics can identify counties with overlapping plans or plan gaps, alerting product design teams where to focus their time and resources.

• Simulate the effect of adding or dropping plans. For counties with coverage gaps or plans targeting the same customer segment, classical AI can remove uncertainty by simulating the portfolio-level impact on enrollment and retention by generating what-if scenarios based on adding or dropping plans.
• Use agentic AI to coordinate portfolio decisions. Agentic AI can continuously monitor county-level changes, surface emerging overlaps or white spaces, orchestrate scenario comparisons against business rules and route next-best portfolio recommendations to product teams for action.

Category 2: Product strategy - product strength mapping

To know where to focus attention, Medicare Advantage providers must understand how their current products compare with those of their competitors. Today, this process relies on a mix of classical AI, advanced analytics and a whole lot of manual work. Using gen AI to curate unstructured data can not only save time but also can significantly improve classical AI models that quantify relative product strength and automatically feed a decision flywheel that triggers follow-on product and bid actions and incorporates outcomes.

• Use classical AI to identify which benefits drive enrollment for specific demographics. Traditionally, when insurers seek inputs to drive product design, they rely on a mixture of subjective intelligence from insurance agents, sales teams and syndicated market reports to gauge member preferences. Today, they can use product and enrollment data published by the CMS, as well as county level SDOH data, to power models that pinpoint benefits and other variables that drive enrollment for specific member segments.
• Compare plans using gen AI curated datasets. Knowing which benefits drive enrollment and retention allows carriers to compare their plans against those of their competitors in each geography. To do this, they traditionally have used county level contract and enrollment data published by the CMS to find every plan in each county, download the summary of benefits and coverage for each and then manually compare them. Today, they can use gen AI to pull all the relevant benefits and coverage data from publicly available documents and then use this data to build a side by side comparison view. This can then be used to compare plans much more rigorously than was possible before.
• Use advanced modeling to see where your Medicare Advantage plan ranks in each geography. To aid decision making, executives want to see a visual representation of how their plans rank compared with their competitors’ plans in each geography where they are present. Using data on the relative impact of every coverage and benefit variable on enrollment, companies can then use classical AI to map company strength at the county level. This identifies those counties where a company is positioned to win and those that need attention, triggering the next set of product design and bid decisions in the flywheel.

Category 3: Product design - Plan design simulations

Once executives have a clear picture of relative Medicare Advantage plan strength across geographies, the challenge shifts from insight to action—deciding which benefits to change, where to invest and what trade offs to accept at the county level. Historically, these decisions have been fragmented and incremental, with limited linkage to enrollment and margin outcomes.

Using AI models allows product design teams to simulate a large number of Medicare Advantage plan design iterations to identify optimal designs in counties targeted for attention. By adjusting variables such as financials, plan type and supplemental benefits, AI can project expected enrollment lift across combinations of coverage benefits, helping teams make informed decisions for the following year’s CMS bids.

Agentic AI elevates product design from simulation to decision grade portfolio optimization. AI agents evaluate millions of plan design combinations, test them against financial, regulatory and competitive constraints and translate projected enrollment lift into clear county specific recommendations. Rather than presenting scenarios, the system identifies which benefits to add, remove or rebalance and where incremental investment will generate meaningful returns.

Importantly, agentic AI does not stop at recommendation. It prioritizes counties for intervention, aligns benefit structures to bid targets and produces decision-ready inputs for CMS submissions. The result is a deliberately optimized Medicare Advantage portfolio, reducing bid risk while concentrating resources where they drive measurable enrollment and profitability gains.

Category 4: Activation - co-pilots for information gathering and internal and external communication

With the proliferation of Medicare Advantage plans, members aren’t the only ones overwhelmed by the volume of plan information they need to sift through to make informed decisions about their coverage. Product designers, insurance brokers, inside sales and senior executives also need to be able to drill down to understand the specifics within a health plan’s portfolio. Gen AI can help.

• Chatbots for plan queries. Whether it’s a product designer informing their work or a broker sitting with a member shopping for a Medicare Advantage plan, insurers need a way to make complicated, detailed plan information available to anyone in the most frictionless way possible, especially in an era where members increasingly turn to answer engines to understand and compare benefits. Using the structured database of plan information already scraped from the CMS and rival plans’ benefits and coverage summaries, companies can build intuitive user interfaces on top of these inputs that deliver detailed plan information that can inform users as they make decisions.
• PowerPoint co-pilots. Product and analyst teams spend a lot of time producing decks to align internal stakeholders around a course of action. Co-pilots can take analysis graphs or commentary and import these directly into PowerPoint, saving teams from the time-consuming process of creating first drafts of PowerPoint slides and removing some friction from leaders’ decision-making process.
• Delivering personalized insights. With so many Medicare Advantage plans and plan providers in every geography, insurance agents and broker relationship managers can benefit from personalized insights highlighting specific motivators for brokers. Similar to next best action triggers that pharmaceutical companies send to field sales reps, these nudges can surface specific plan features most likely to appeal to a given broker based on their past preferences, the demographic of their customer base or both.
• Use agentic AI to orchestrate activation and next best actions. Beyond surfacing information, agentic AI can guide users through follow-on questions, tailor outreach and content to the audience and trigger the next step for brokers, sales teams or internal stakeholders based on context and prior interactions.

What it will take to reimagine Medicare Advantage plan design as an AI-enabled value stream

Reimagining Medicare Advantage plan design as an AI-enabled value stream requires more than deploying better models. It requires changing how decisions are made, owned and executed. First, plans must shift from episodic analysis to continuous decisioning, with systems that sense change, test tradeoffs and refresh recommendations as market conditions evolve. Second, AI must operate at the workflow level—so insight flows directly into pricing, bid and activation actions under clear guardrails rather than stopping at analysis. Third, agentic AI must coordinate across teams, sequencing analyses, prioritizing choices and routing decision-ready recommendations to the right owners.

The health plans that do this well will reduce bid risk, concentrate investment where it drives enrollment and build portfolios that adapt as markets change. Given the speed at which AI is advancing, models themselves are quickly becoming commodified. The value is in how payers choose to apply them to solve larger business problems and the degree to which payers can design decision systems that help them reimagine their existing workflows. The health plans that most effectively apply these lessons to the critical issue of Medicare Advantage benefit design will deliver key advantages now and in the future.