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Engineering AI Adoption

How Governance, Architecture and Agentic Systems Turn AI Use Into Accountable Value

Mark Jones

One operating design for leaders and builders turning AI activity into dependable organisational capability, from governance and architecture to secure agentic systems and accountable value.

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Edition
First, 2026
Length
276 pages
ISBN
978-1-0369-9882-0
Digital
EPUB + PDF
Access
Permanent portal access
DRM
None
Cover of Engineering AI Adoption by Mark Jones

Paperback · Kindle · DRM-free direct edition

The synthesis

AI adoption becomes dependable when the organisation engineers the capability around it.

The book draws Radical Geek’s research and field guides into one operating model. It shows how leaders can select work, authorise routes, protect data, qualify systems, observe operation and connect costs to accepted outcomes.

Capability system linking leadership, governance, architecture, platform engineering, secure agentic execution and accountable outcomes
The capability system. Adoption joins leadership, governance, architecture and engineering around accountable outcomes.
Matrix separating the consequence of work from the degree to which it can be delegated
Consequence and delegability. Separate the harm a wrong outcome could cause from the authority the system has earned.
Enterprise control plane connecting governance policy, approved routes, identity, evidence and operational feedback
The enterprise control plane. Turn policy into executable routes, evidence and operating decisions.

Who it is for

For the people responsible for making AI work across a real organisation.

01

CIOs, CTOs and AI leaders

Build a sanctioned path from experimentation to accountable organisational value.

02

Enterprise and solution architects

Design responsibility, data, evidence and authority boundaries across end-to-end journeys.

03

Platform and engineering leaders

Provide approved models, tools, controls and observability as a reusable internal capability.

04

Risk, security and governance leaders

Make governance operational, proportionate and capable of supporting faster sanctioned adoption.

Put it into operation

A lifecycle that joins adoption, engineering and value.

Each work class moves through a visible operating loop, with evidence passed forward and operating feedback used to improve, demote or retire the route.

  1. 01Select an accountable outcome and journey.
  2. 02Classify data, consequence and delegability.
  3. 03Assemble authoritative context and constraints.
  4. 04Route work through approved models, tools and workflows.
  5. 05Execute inside bounded and reversible authority.
  6. 06Validate independently and obtain the required decision.
  7. 07Record the work-item ledger and measure the full economics.
  8. 08Learn from operation and earn broader delegation.
Work-item ledger connecting intent, route, evidence, decisions, costs and outcomes
The work-item ledger joins tokens, tools, human effort, verification and accepted outcomes so leaders can calculate value from the complete system.

Contents

Four parts. One operating design.

Fifteen chapters move from organisational capability through system design and operation to evidence, economics and scale.

Part I

Build the capability behind adoption

  1. The capability behind AI adoption
  2. Start with accountable outcomes
  3. Governance that helps organisations move
  4. Consequence, delegability and Proof of Operation
Part II

Design the system of work

  1. Enterprise architecture and platform engineering
  2. Data, context and AI-ready work surfaces
  3. Secure routes and bounded authority
  4. People, roles and the operating model
Part III

Build, run and assure agentic systems

  1. Agentic workflow patterns
  2. Qualify models, workflows and routes
  3. Local, private and hosted inference
  4. Observe, validate and recover
Part IV

Prove value and scale

  1. Measure adoption, RAG and outcomes
  2. Full economics: tokens, human work, exceptions and tails
  3. Earn delegation and scale the capability
RAG impact chain from corpus fitness and retrieval quality through use, decision quality and accountable outcome

Measure the chain

Connect RAG, tokens and human work to value.

Retrieval metrics describe only part of a working system. The book follows the complete chain from corpus fitness and retrieval through use, decision quality and accountable outcomes, then includes model spend, verification effort, exceptions and tail behaviour in the economic calculation.

Choose your edition

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Buy the digital edition directly from Radical Geek for immediate, permanent access to both EPUB and PDF. Paperback and Kindle editions are supplied by Amazon.

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Print

Paperback

£12.99
Amazon listing in review

Ebook

Kindle

£7.99
Amazon listing in review

Continue with Radical Geek

Use the book with the field guides and training.

Free library

Nine practical field guides

Take individual parts of the operating design into governance, measurement and engineering work.

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Training

Becoming an Agentic Engineer

Learn the operating practices for specifying, building, validating and running agentic work.

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