Health insurers have built real AI infrastructure. Claims data flows into unified analytics platforms. Predictive models score members for risk. Quality programs track hundreds of clinical measures with precision. And yet the care manager still doesn’t know about the gap before the audit closes. The member still doesn’t get the call in time. The insight sat in a dashboard nobody was watching.
This isn’t primarily a technology problem. It’s a distance problem — between what data knows and what care teams can act on. It’s one of the clearest themes emerging from conversations at this year’s Databricks Data + AI Summit.
The Industry Has Moved Past “AI Is Coming”
Databricks Data + AI Summit (DAIS) 2026 has been clarifying. The conversation has shifted. Fewer organizations are asking whether to adopt AI. More are asking why it isn’t producing outcomes yet.
The conversations this week show the reality — experimentation has given way to engineering: correctness, explainability, operational intelligence at the point of decision. The industry is entering its control era, where trust, context, and governance separate the organizations that scale from those that stall.
What’s striking is where many organizations actually are. Across the industry, organizations have made significant multi-year commitments to modern data platforms — but adoption is still unfolding in pockets, and the widening gap between investment and realized value is the real story. The commitment is real, but the outcomes haven’t followed.
Three Places the AI Gap Shows Up
Three realities keep surfacing in conversations with health insurer executives this week:
- Technical: Connecting clinical record systems to modern analytics environments remains one of the most persistent barriers to a complete picture of each member. Interoperability mandates have pushed the industry toward better data sharing for years — but while the vision is clear on paper, the pipelines often aren’t.
- Operational: The breakdown is most visible in quality performance programs. Metrics are tracked with precision, but that insight rarely reaches care teams early enough to change what happens next. The gap stays open not because the organization didn’t know about it, but because knowing something in a report is very different from having it surface for a care manager at the exact moment a decision needs to be made.
- Cultural: Getting clinical and operational teams to trust AI outputs, change their workflows, and act on new signals requires organizational transformation, not just new software. Technology readiness is often ahead of people readiness, and that mismatch quietly stalls initiatives.
“Most health plans don’t fail because of bad models — they fail because insights don’t reach care managers in time.” — Priyal Priyal, Associate Vice President
What Closing the Gap Requires
Two capabilities are needed here, and neither works well without the other.
A governed, unified data foundation
- Brings together claims, clinical, and social determinants of health data into a single trusted layer
- Enables AI models to move from development to real-world use without losing the guardrails regulated industries require
- Perficient’s Brickbuilder accelerators are designed to compress time-to-value on the Databricks platform, giving health insurers a faster path to that foundation without starting from scratch
Operational expertise to connect data to care
- Bridges the foundation to clinical workflow, care team behavior, and real change management
- Systems can be governed and models can be deployed, but member outcomes don’t improve unless care manager workflows actually change
- Healthcare expertise — built from years working with health insurer organizations on quality programs, care management operations, and clinical data integration — is what turns a deployment into a result rather than a proof of concept
Perficient’s work with health insurer organizations sits at that intersection. The platform is a starting point. Producing outcomes that reach patients is the goal.
Read more: Improve Healthcare Quality With Data &AI
The Forcing Functions Are Here
Federal interoperability requirements, prior authorization rules, and value-based care contracts are creating pressure that wasn’t there two years ago. For most health insurers, the question of whether to activate data and AI infrastructure is settled. The question is how fast, and with what.
From everything we’re seeing at DAIS this week, one thing is clear: Healthcare AI isn’t broken — but the way it connects to real decisions still is. And until that connection is solved, no investment, no model, no platform will be enough.
The organizations that close that gap won’t just move faster. They’ll be the ones that actually deliver on the promise of AI in healthcare. That’s what this week at DAIS keeps coming back to.
See where your organization is on the activation curve. Connect with Perficient’s Healthcare & Life Sciences team to explore our AI-first healthcare offerings and Brickbuilder accelerators on the Databricks platform or schedule a demo.
