Building an AI-First SDLC: Lessons From Our Claude Pilot Program

Three months ago, I posed a question to our engineering leadership team: “What if we could use AI tools to eliminate the mundane and amplify human creativity in every phase of software development?”

Today, I’m excited to share the results of our Claude pilot program—a 21-day experiment that transformed how five of our core teams approach the software development lifecycle (SDLC). The numbers are impressive, but the real story is about human potential unleashed through intelligent automation.

The Challenge: Reimagining Software Development

At HealthEdge®, we’re no strangers to complex healthcare software challenges. Our teams regularly navigate intricate claims processing systems, regulatory requirements, and mission-critical integrations that directly impact millions of American lives. The traditional SDLC, while proven, often bogs down brilliant engineers in repetitive tasks that consume time better spent on innovation.

We set out to answer a fundamental question: Could we go all-in and create an AI-first SDLC that enhances human creativity rather than replacing it?

The Pilot: 21 Days of Rapid Discovery

We launched our Claude pilot with 53 contributors across five teams, giving them access to Claude for Enterprise with a simple mandate: “Experiment fearlessly, document everything, and share your discoveries.”

The results exceeded our wildest expectations.

Claude Enterprise By the Numbers

• 49 documented AI-enhanced use cases spanning the entire SDLC
• 680+ hours of estimated time savings in the pilot alone
• 53 active contributors across development, QA, architecture, and product teams
• A single tool eliminated $48,000 in direct business value identified in just hours

But metrics only tell part of the story. The real transformation happened in how our teams began to think about their work.

Powering Success Across Every SDLC Phase

Requirements & Planning

Our teams discovered Claude’s ability to transform scattered ideas into structured requirements. Carl Anderson, Director of Product Management, summarized it perfectly:

“I uploaded all the notes and thoughts I have had on the integration over the last couple of weeks and spent about an hour tweaking the output. Would have normally taken me a week to put together and countless formatting headaches.”

Key achievements:

• Product requirement document (PRD) generation reduced from 1 week to 1 hour
• User stories automatically generated from PRDs
• Requirements analysis and gap identification accelerated dramatically

Architecture & Design

Perhaps most surprisingly, Claude proved exceptional at architectural analysis and design work. Pratishtha Painuly, Software Engineering Manager, led groundbreaking work analyzing our complex SourceUiMetadata database:

“Claude analyzed our database design and provided simplified recommendations, reducing complexity by 90% while maintaining all functional requirements. This level of analysis would typically take weeks of architect time.”

Key achievements:

• Comprehensive system documentation generated from codebases
• Migration strategies for Angular to React conversions
• Database optimization recommendations with implementation roadmaps

Development & Code Generation

The development phase saw some of our most dramatic productivity gains. James Chang, Software Development Manager, pioneered an approach that fundamentally changed how we think about code generation for regulatory updates:

“We went from Visio flowcharts to complete C# implementations with unit tests in minutes. Claude didn’t just generate code—it understood our patterns, our testing standards, and even anticipated edge cases we might have missed.”

Key achievements:

• Complete edit classes generated from Visio diagrams
• Unit test suites with edge case coverage automatically created
• Legacy code analysis and modernization recommendations

Testing & Quality Assurance

Our QA teams found Claude particularly transformative for test creation and analysis. Usha Suryadevara VP of Integration Platforms, achieved remarkable results:

“I converted 490 manual test cases from a TestRail export into 31 user stories across 15 business domains. This type of analysis and conversion would typically take weeks—Claude did it in hours while maintaining accuracy.”

Key achievements:

• Manual test conversion to automated test stories
• Test case generation from decision tables
• Comprehensive test coverage analysis and gap identification

Operations & Maintenance

Even our operational teams found value in Claude’s analytical capabilities, from defect root cause analysis to system optimization recommendations.

Key achievements:

• Defect pattern analysis and resolution recommendations
• Performance optimization suggestions for SQL queries
• System configuration analysis and simplification

The Human Element: What the Numbers Don’t Show

While the productivity metrics are compelling, the most significant impact was cultural. We witnessed:

• Cross-functional collaboration as teams shared discoveries across disciplines
• Fearless experimentation as contributors pushed Claude’s boundaries
• Knowledge sharing that accelerated learning across the organization
• Creative problem-solving as teams found novel applications for AI assistance

Ryan Mooney, EVP and GM of HealthEdge Source™, captured this beautifully:

“The more we share, the more likely we’re able to learn at the pace of 53 people vs 1. So we’re getting the amplification of tech AND people. Perfection!!”

Five Tactics That Drove Exceptional Engagement

After analyzing our pilot’s success, I’ve identified five key tactics that other leaders can apply to their own AI initiatives:

1. Create Psychological Safety Through “Weaponizing” Early Experimental Prompts

Rather than leaving teams to figure out prompting on their own, we developed systematic approaches to turn experimental prompts into production-ready tools. This gave teams confidence to experiment knowing they could scale successful discoveries.

Implementation tip: Create a “prompt for prompts” system that helps teams transform their successful experiments into repeatable, reliable tools.

2. Gamify Discovery with Tangible Rewards

We introduced weekly contests with real prizes—Claude Code access, Amazon Q Developer licenses, and yes, even jars of spicy pickles from Brooklyn. The key was making rewards both practical and memorable.

Implementation tip: Mix high-value professional tools with quirky, memorable rewards. The combination creates both practical incentive and community humor.

3. Establish Daily Sharing Rituals

Our most successful engagement came from encouraging teams to share discoveries in real-time. We created a culture where every experiment, success, or failure was worth documenting and sharing.

Implementation tip: Make sharing the default behavior, not an exception. Create low-friction ways for teams to document and share their discoveries immediately.

4. Build on Each Other’s Success

We explicitly encouraged teams to build on others’ work. Some of our biggest breakthroughs came when one team’s discovery inspired another team’s innovation.

Implementation tip: Create clear pathways for teams to extend and build upon each other’s work. Make collaboration easier than starting from scratch.

5. Measure What Matters, Celebrate Everything

While we tracked serious metrics like time saved and business value created, we also celebrated every creative use case, unexpected discovery, and moment of breakthrough.

Implementation tip: Develop both quantitative metrics and qualitative celebration practices. Innovation needs both measurement and recognition.

Looking Forward: The AI-First SDLC

Our pilot proved that AI-first development isn’t about replacing human creativity—it’s about amplifying it. We’re now scaling these discoveries across our entire engineering organization with several key initiatives:

Immediate Actions

• Production tool deployment of our most successful pilot discoveries
• Training programs to help all teams adopt AI-first practices
• Infrastructure investments to support organization-wide AI integration

Strategic Investments

• Custom MCP servers for deep integration with our internal systems
• AI-powered development environments that understand our codebases and patterns
• Automated quality gates that leverage AI for comprehensive code review and testing

Cultural Evolution

• AI-first hiring practices that value AI collaboration skills
• Updated development standards that assume AI assistance
• New role definitions that blend traditional skills with AI amplification

The Bottom Line

In 21 days, we didn’t just run a successful pilot—we glimpsed the future of software development. A future where:

• Engineers focus on creative problem-solving while AI handles routine implementation
• Quality improves as AI provides comprehensive test coverage and edge case analysis
• Productivity multiplies as teams accomplish in hours what previously took weeks
• Innovation accelerates as teams can rapidly prototype and validate ideas

The healthcare technology landscape demands both reliability and innovation. Our Claude pilot proved we don’t have to choose between them.

As we continue scaling these practices across HealthEdge, I’m excited about what our teams will discover next. The journey from experimental pilot to AI-first organization is just beginning.

Want to learn more about how HealthEdge is thoughtfully integrating AI within our existing solutions? Read the data sheet.