I recently had the opportunity to present alongside Sai Moturu and Kristie Stanton at a webinar focused on the essential steps to getting started with AI. We engaged with a group of leaders from health plans and large payers, eager to leverage AI for member personalization and engagement.

Throughout the session, we gained valuable insights via an online survey tool used to collect feedback from attendees. Our entire industry is changing as we speak—and you might find these data points as compelling as we did. The responses highlighted key challenges payers face in implementing AI and offered a roadmap for overcoming them.

The Readiness Gap: AI Ambition vs. Execution

A striking 78% of health plan attendees shared that their organizations have significant work to do before their AI initiatives can take off. This matches what we frequently hear—health plans recognize AI’s potential and are eager to get started, but the path to AI readiness is complex and resource-intensive.

Like every industry, healthcare leaders are excited about the potential of leveraging AI. From automating routine tasks to uncovering insights hidden in massive datasets, AI is driving efficiency and innovation at a scale we've never seen before. With rapid advancements in machine learning, natural language processing, and generative AI, organizations are racing to harness its power to better serve members and improve outcomes.

While the hype around AI paints a picture of near-magical capabilities, the reality is more complex. AI is undoubtedly powerful, but it still relies heavily on legacy data systems, thoughtful implementation, and expert human oversight. Many organizations find that integrating AI into real-world workflows is harder than expected. AI pilots are great—but if investment doesn’t impact a core business metric or an organizational OKR, then it’s just a shiny toy. Leaders are worried about wasted investment in AI technology without a clear use case or measurable ROI.

Jumping into AI without a broader strategy doesn't work well in real settings. We heard loud and clear that the investment is not just in the technology itself, but in the resources needed for training, systems integration, change management, and ongoing improvements. AI models in the real world rely on diverse data sources, and today these systems don’t communicate effectively—if at all.

On top of all this, privacy and security concerns—especially compliance with HIPAA, GDPR, and CMS regulations—add another layer of complexity. And there’s the equally important issue of ensuring the accuracy and clinical validity of probabilistic AI algorithms in healthcare use cases. Health plans need solutions that take on the guardrails for medical and legal compliance.

The Data Bottleneck: A Surprising Barrier

Here’s a learning that caught us by surprise: 74% of attendees reported feeling stuck in their data efforts or uncertain about their organization’s progress in data preparation.

Many payers struggle with fragmented, inconsistent, and unstructured member data, making adoption of AI a major hurdle. In the minds of many in our audience, they are held back by their inability to consolidate and enrich data to make it usable for AI-driven communications. Internally tackling this challenge requires significant investments in infrastructure and expert teams, along with the need to manage the long list of priority projects impacting timelines.

Without clear alignment on a data strategy, AI implementation stalls—or worse, takes way too long. This is an organizational problem as much as a technology issue. To drive success, organizations need a structured plan with defined goals and guidelines, along with leadership buy-in and cross-functional collaboration to break down silos.

Without these foundational elements, AI data efforts remain fragmented, with employees and stakeholders becoming frustrated—resulting in uncertainty about AI’s impact on their work.

The Prize: Personalized Member Engagement

Despite these challenges, our attendees overwhelmingly agreed that the primary goal of AI adoption is to empower members with personalized experiences tailored to their needs. They strongly believe that tailored communications will drive clearer understanding and meaningful action—whether it’s guiding members to wellness programs, chronic condition support, or more affordable care options. When members feel supported, they’re not only healthier—they’re more loyal and more likely to stay with their health plan.

To accelerate progress, health plans should consider partnering with AI experts that move them past the typical barriers. For example, RadiantGraph can significantly reduce the time to launch AI-based consumer engagement—turning what typically takes a year into a matter of weeks.

The right partner should be able to support payer clients throughout their AI journey—from understanding the gaps in their member experience today, to building powerful AI models, to content generation, voice AI, and beyond.

Final Thoughts

Payers recognize the promise of AI, but many remain stuck in the early stages of bringing it to life. The biggest unlock isn’t just technology. Success depends on structured planning, strong leadership alignment, and most critically, the ability to standardize, integrate, and extract insights from vast amounts of unstructured data scattered across legacy systems.

With the right strategic partnerships and a data-first mindset, payers can move beyond the hype and take real action toward AI-driven member engagement.