CIO / 03IN DEVELOPMENT
Who Owns Enterprise AI?
An operating model for accountability, governance, funding, production ownership, and controlled improvement.
Enterprise AI guides
Practical guides for putting enterprise AI to work.
Decision frameworks for leaders moving from promising AI capability to governed workflow systems: measurable, operable, accountable, and able to improve from real work.
FIELDRUNTIME / EXECUTIVE GUIDES
BUILT FOR THE DECISION
These are working guides for CIOs, CFOs, operating executives, and workflow owners. Each one turns a broad AI question into concrete evidence, decision rights, acceptance gates, and measurable outcomes.
01
THE GUIDE LIBRARY
Begin with the decision in front of you.
From AI ambition to production evidence.
Use these frameworks to structure executive questions, align the operating team, and identify the smallest defensible next move.
CIO Guides
Make AI operable.
Readiness, deployment, operating ownership, security, data, and the controls required to run AI as a production capability.
GUIDE / 01
Enterprise AI Agent Deployment
Move one promising agent pilot into governed production through workflow economics, architecture, real-task evaluations, bounded launch, ownership, and learning operations.
GUIDE / 02AI Production Readiness
Evaluate one consequential workflow across seven operating dimensions—and require evidence before the organization commits capital, authority, or production responsibility.
CIO / 04IN DEVELOPMENT
Enterprise AI Agent Security
Identity, least privilege, credentials, action boundaries, evidence, containment, and recovery for agents that can act.
CIO / 05IN DEVELOPMENT
Enterprise AI Data Readiness
A framework for authoritative context, permissions, freshness, lineage, missing data, and safe write paths.
CIO / 06IN DEVELOPMENT
Operating AI Agents in Production
Observability, incident response, fallbacks, pause, replay, rollback, support ownership, and production learning.
CFO Guides
Make AI financially accountable.
ROI, total cost, funding gates, finance workflows, internal controls, and the evidence required to prove realized value.
CFO / 01IN DEVELOPMENT
Enterprise AI ROI
A CFO framework for baselines, value mechanisms, assumptions, evidence, measurement windows, and realized results.
CFO / 02IN DEVELOPMENT
The True Cost of Enterprise AI
A complete TCO model covering build, integration, inference, evaluation, human review, exceptions, controls, and operation.
CFO / 03IN DEVELOPMENT
How CFOs Should Fund Enterprise AI
Stage gates for deciding when an initiative should proceed, pause, expand, change scope, or stop.
CFO / 04IN DEVELOPMENT
AI Agents for Finance Operations
High-value finance workflows, appropriate authority, measurable outcomes, and the controls required around the work.
CFO / 05IN DEVELOPMENT
AI Agents, Internal Controls and Audit Evidence
Apply approvals, segregation of duties, traceability, exception management, and audit evidence to agentic work.
CFO / 06IN DEVELOPMENT
AI Value Realization
Move from modeled ROI to observed results with accountable owners, operating evidence, and finance-approved measurement.
Have one consequential workflow in mind?
Map the evidence required for production.
Bring the workflow, the pilot, or the unresolved operating problem. We will define its economics, system boundary, authority, acceptance gates, and smallest governed path forward.