Client need
A large U.S.-based health insurance organization operating at the center of a complex national ecosystem had already made a serious investment in enterprise AI. Leadership was not experimenting at the edges. They were leaning in.
Capability and commitment existed across the organization, but unevenly. Some teams were already pushing boundaries. Others hadn't yet found their footing. Most of the gains had come in personal productivity. Valuable, but the core work itself had not yet fundamentally changed. The opportunity was to go deeper, to move from AI-assisted individuals to AI-reinvented workflows.
Across the health insurance landscape, pressure was intensifying. Medicaid and government program contracts were becoming more competitive. Decision cycles were faster and more analytics-driven. Clinical evidence was evolving rapidly. Regulatory scrutiny was high. Security risks were constant. AI was no longer a future conversation. It was a present expectation.
Inside the organization, world-class experts were still constrained by manual processes.
Specialized teams were synthesizing large volumes of complex, fast-moving information, working to keep pace with an environment where the inputs never stopped changing. The work required deep expertise and judgment, and it also demanded repetitive processing that consumed days when it needed to take hours.
Other teams faced pressure where speed and precision directly influenced competitive outcomes. Manual approaches were creating lag at exactly the moments when faster insight mattered most.
Across functions, the pattern was consistent. Highly trained professionals were spending valuable time on low-leverage tasks, stitching together data, transforming files, and correlating inputs that AI could handle.
Leadership understood that AI licenses alone would not create advantage. To compete in an increasingly analytics-driven insurance environment, expertise had to scale. Insight had to move faster. Teams needed to reinvent how core work happened.
Solution
BTS partnered with the organization to move from AI access to AI application.
Through a series of focused design sprints, intact teams worked on their highest-value workflows using our GROUNDING → EXPERIMENT → BUILD → AMPLIFY methodology. The structure was simple and disciplined. Set context. Experiment quickly. Build against real work. Create a path to scale.
Participants brought their actual work into the room. Analytical frameworks. Competitive and operational documents. Risk and intelligence inputs. Data pipelines.
No generic demos. No abstract hypotheticals.
The turning point came when AI began working on their actual content.
Research syntheses that previously took days began structuring themselves in minutes. Competitive analysis that once required manual review surfaced patterns instantly. Data transformation workflows streamlined in real time.
Skepticism shifted to possibility.
We positioned AI as augmentation, not replacement. In a sector defined by professional expertise and accountability, that framing was critical. The goal was to elevate expert judgment, not automate it away.
Some teams left with working prototypes. Others left with detailed blueprints aligned to enterprise privacy and security requirements. Another team took away a re-prioritized set of additional tools to incorporate into a HIPAA-compliant environment. Every team left with a redesigned workflow.
Results
In five days, more than 100 leaders advanced 30 priority use cases tied directly to operational performance and competitive growth.
Early outcomes included:
- Significant reduction in manual research synthesis and data preparation
- Faster, more structured competitive intelligence to support high-stakes decisions
- Clear implementation pathways aligned to security and regulatory constraints
- A scalable model for continued AI-enabled workflow reinvention
Just as important was the mindset shift.
Participants stopped viewing AI as a tool sitting outside their work and began treating it as embedded infrastructure for how work gets done.
“This showed immediate relevance to our work.”
“Now I understand what’s actually possible for my team.”
“We just accomplished in two hours what used to take us two months.”
In a U.S. health insurance market where insight, speed, and precision directly influence who wins and who grows, the organization moved decisively from AI access to AI advantage.
