Building Your Private AI

A Business Leader's Guide

De: Daniel Jacobs

Narrado por: Jon White

Lead with Clarity in the Age of AI Transformation

For CEOs, CIOs, CISOs, and innovation leaders—not coders.

As AI rapidly reshapes the enterprise landscape, the real challenge isn’t adopting it—it’s adopting it responsibly. Data privacy. Regulatory pressure. Ethical deployment. The stakes have never been higher.

Building Your Private AI is a strategic, non-technical guide tailored for business executives ready to scale AI while safeguarding trust, compliance, and performance. With real-world insights from UPS, Tesla, Mayo Clinic, and Siemens, author Daniel J. Jacobs offers a proven roadmap to secure, enterprise-ready AI transformation.

Actionable frameworks for privacy-first AI adoption

Executive-level explanations of federated learning, secure enclaves, and encryption-in-use

Clear ROI pathways from readiness to scaling

Governance strategies aligned with GDPR, HIPAA, and post-quantum risk

This is not a coding guide—it’s your C-suite AI playbook.

Private AI isn't just a technical decision—it's a leadership imperative. Make it wisely.

©2025 Daniel J. Jacobs (P)2025 Daniel J. Jacobs

A Leadership View of Private AI

I gave this 4 stars. The author is clear about who this is for, CEOs, CIOs, and CISOs, and it delivers well at that level. It focuses on high-level thinking around private AI, governance, and direction rather than hands-on implementation.

That said, there’s still value for entry-level analysts if they’re trying to understand how leadership thinks about AI and where things are generally headed. It’s less about doing the work and more about understanding the oversight and strategy behind it.

Build AI You Own, Control, and Trust

Building Your Private AI: A Business Leader's Guide is a clear, executive-focused playbook for leaders who want to go beyond generic, public AI tools and develop systems that are secure, strategic, and uniquely theirs. The book stresses practical decision-making over hype, guiding readers through data ownership, governance, risk management, and real business use cases. It effectively translates complex AI concepts into business language, helping leaders align AI projects with long-term goals, operational realities, and competitive advantages.