Forward Deployed Engineering for Enterprises: Become Self-Dependent for Scaled AI Rollout

Your AI pilots worked. So why is almost nothing in production?

"The role had a name and a reputation and almost no practical literature, and the people asking for it were being handed a term rather than a method."
— from the author's preface

Most enterprises have run dozens of AI proofs of concept. Very few have reached production at scale, and fewer still can point to measurable business impact. The gap is rarely the model. It is the last mile: turning a convincing demonstration into a system that understands your business, can be proven, is permitted to run, and costs what you said it would.

Forward deployed engineers close that gap. They embed with the business, sit with the people doing the work, and ship. The title is fashionable; the practical literature is almost non-existent. This book is the field manual.

Drawing on nearly three decades of enterprise technology delivery, Vasudevan Vijayaragavan argues that you should not rent this capability indefinitely, and you cannot simply hire it. You build it, largely from engineers you already employ, and you separate two decisions most organizations blur: whether to buy or build the platform, and whether to buy or build the people.

Inside, you will learn:

• What a forward deployed engineer actually does, and why enterprise AI pilots die in such numbers
• The dependency trap: what it really costs to rent the understanding of your own business
• How to build and scale an internal FDE team with a four-POD operating model: Foundation, Use Case, Control and Operations
• How to run a twelve-week first engagement that ships instead of drifting
• The four disciplines that separate a demo from a system you can run: the semantic layer, evaluation, control and tokenomics
• A six-level maturity ladder and a 90-day plan to prove you have become self-dependent

Three end-to-end engagements run through the book: a downtime problem on an automotive components production line, an agentic supply chain disruption system at a company that builds datacenters, and AI-driven field crew dispatch at a gas and electricity utility, where a wrong answer can hurt someone rather than cost money. Every chapter also tells you when not to do what it has just described, because in this field the most valuable advice is often a reason to stop.

Written for CIOs, CDAOs, heads of engineering, architects, risk and compliance leaders, and anyone who has just been handed the job of making AI work at scale. Executives can read the argument in an evening. Builders will keep it open for the first year.

About the author
Vasudevan Vijayaragavan has spent more than 29 years in enterprise technology across three continents, at Oracle, Cisco, Ramco Systems and HCLTech, moving with each wave from ERP and CRM to data, BI and now generative AI. He heads Data & AI Solutions and Offerings at HCLTech, where he works regularly with CIOs and CDOs of Fortune 500 companies, learning how they put AI to work and sharing what he sees across the industry. He writes on Medium about applied enterprise AI and is based in Dallas, Texas.