About the Book

Stop Implementing AI: The No-BS Playbook for AI That Works

Most companies are stuck in "pilot purgatory"—endless AI experiments that never scale. Teams spend weeks sharing clever prompts and one-off scripts, but nothing makes it into production. Budgets evaporate, engineers' enthusiasm curdles into cynicism, and executives start asking "we spent how much on this?"

The problem isn't the AI tools—it's the approach. AI models are inherently non-deterministic (ask the same question twice, get different answers), while software engineering demands deterministic, reliable results. Without structure to bridge this gap, AI adoption fails spectacularly.

This book introduces PIT™ (Plan Implement Test), a proven methodology that transforms unpredictable AI outputs into reliable engineering workflows. Built from thousands of hours of real development work, PIT™ channels AI's capabilities through structured planning, phased implementation, and comprehensive testing—delivering the consistency that production code demands.

What You'll Learn:

- Why "just giving engineers AI" fails without structured guidance

- How to structure AI adoption for maximum business impact—not just productivity toys

- The complete PIT™ method: from loading context to delivering production-ready features

- Real-world case studies showing measurable results: 85% time reduction, 82% cost savings, 3-week projects completed in days

- Practical frameworks for governance, team rollout, and measuring ROI that executives understand

Who This Is For:

Engineering leaders, senior individual contributors, and product-minded managers who want to ship real outcomes—not slide decks. If you're accountable for budgets, release cadence, or cross-functional collaboration, you'll find direct guidance on capturing context, shaping prompts, governing adoption, and measuring ROI.

The Bottom Line:

Every month spent in pilot purgatory wastes time and resources while competitors ship faster. This book shows you how to lead that transformation—not through demos and experiments, but by putting AI on the critical path of work that actually matters. The organizations mastering AI-powered development aren't gambling on AI's capabilities—they're channeling those capabilities through proven engineering discipline that makes speed reliable and quality scalable.

That's what determinism looks like in practice: chaos transformed into structure, experimentation turned into process, and AI's potential finally realized through engineering rigor.

About the Author

Cliff Robbins

@deadbigreddog

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Cliff Robbins is a technology leader with more than 25 years of experience building and scaling enterprise systems across software, hardware, and the AWS cloud. He has led product and engineering organizations of every size, turning roadmaps into shipped outcomes by pairing disciplined execution with a deep understanding of how sales, marketing, technology, and finance intersect. Cliff has launched four companies from zero to multimillion-dollar businesses by applying that same blend of pragmatism, data-driven decision making, and customer focus.

He earned a Computer Science degree from Regis University and Data Science certificate, supplemented by electrical engineering coursework through MIT and data science studies at Stanford. Before tech, Cliff spent eight years in the United States Marine Corps—an experience that forged the ownership mindset, perspective, and growth orientation he brings to every team he leads.