Transcending Technobabble
An executive guide to real ROI from AI and advanced tech — what these tools can do, what they can’t, and where they actually belong.
The premise
Most leaders don’t need another AI hype cycle. They need clarity. This book is built to help executives and senior decision-makers cut through technical noise and make confident, practical choices about AI, data, cloud, and governance — with the business outcomes, costs, and risks clearly in view.
Who this is for
- Senior executives and business leaders who want clarity without jargon.
- Technology and product leaders seeking a shared decision language with the C-suite.
- Teams modernizing AI/data platforms while managing governance and cost realities.
What you’ll get
- A placement mindset for modern tools — when to use what, and why.
- Clear boundaries for GenAI, workflow AI, and traditional ML.
- Cost-aware thinking that avoids architecture-by-fashion.
- Governance that scales without slowing responsible progress.
- Real stories and patterns from enterprise environments.
Core themes
- AI is a toolbox, not a miracle.
- Cloud is a platform — powerful, but not automatically efficient.
- Context discipline matters more than clever prompts.
- Knowledge and relationships are the missing layer in many AI programs.
- Governance should be practical, measurable, and built into workflows.
What makes it different
This is not a technical manual and not a product pitch. It’s an executive-friendly guide that treats modern AI like an operating reality: useful, imperfect, and easy to misplace. The goal is to make good choices repeatable — across budgets, teams, and industries.
Architecture without the hype
The book explains the modern stack in practical terms: where classical ML, GenAI, vector search, knowledge graphs, and governance fit together — and where they don’t. You’ll see clear examples of model-plus-context approaches, and why relationship-aware knowledge is often the difference between a demo and a defensible enterprise system.
A note on frameworks
You may see brief references to governance concepts I’ve helped develop, including relationship- and consent-aware approaches. They are used as supporting examples of the broader argument: responsible AI depends on making rights, obligations, and provenance explicit.
Status
The manuscript is in active development. Agents and publishers can request the one-pager, sample packet, and updated chapter materials via the Media Kit.
No ads. No registration. Privacy-first by design.