AI ROI Calculator
Build a conservative business case from workflow volume, loaded labor cost, implementation cost, recurring cost, time reduction, and a confidence adjustment. The formula and sample calculation are visible.
Calculate AI ROI →
Research · tools · transparent methods
Enterprise AI research and decision tools.
A practical library for executives deciding where AI can create value, what evidence a project needs, how to govern risk, and when to stop. The collection connects original tools with source-graded statistics and named-company deployment evidence.
This enterprise AI research library contains a free ROI calculator, governance checklist, readiness scorecard, transformation roadmap, source-graded statistics, and documented company examples. Use the tools to test economics and controls; use the research to challenge assumptions. Each resource states its method, evidence limits, review date, and source links.
4reusable executive tools
9sector and role data collections
0lead gates on downloadable templates
Start with the decision
Tools for planning and prioritization.
These resources are designed to be used in an executive working session. Inputs stay in the browser; downloadable files are editable and require no email address.
Enterprise AI Readiness Assessment
Score seven dimensions: economics, process, data, technology, governance, leadership, and adoption. Every score requires a named evidence owner.
Use the scorecard →AI Governance Checklist
Thirty-four controls organized around Govern, Map, Measure, Manage, and EU readiness, with minimum evidence and accountable-owner fields.
Open the checklist →AI Transformation Roadmap
A four-phase roadmap with executive questions, required outputs, decision gates, and a separate use-case prioritization worksheet.
Build the roadmap →
Evidence and benchmarks
Source-graded enterprise AI reference pages.
Statistics are grouped by decision context rather than repeated across generic articles. Check the source date, population, geography, and denominator before using any number in an investment case.
Enterprise AI Agents Statistics 2026
Agent adoption, economics, governance, and failure-risk evidence with source notes and limitations.
Read the report →AI Consulting Statistics
Market size, enterprise demand, pricing context, adoption patterns, and source quality for consulting buyers.
Review the data →AI Governance Statistics
Governance adoption, board oversight, regulatory exposure, model risk, and operational-control evidence.
Review governance data →Fractional CAIO Statistics
Chief AI Officer demand, role design, executive ownership, and evidence limits for fractional leadership.
Review CAIO data →AI in Financial Services Statistics
Banking, fintech, insurance, fraud, operations, model risk, and named-source deployment evidence.
Review finance data →GEO Benchmarks 2026
A repeatable prompt-panel method for separating mentions, citations, referral traffic, and qualified pipeline.
Use the benchmark method →
Industry evidence
Sector-specific statistics and company examples.
Industry pages help teams establish a credible outside view. They do not substitute for a company-specific baseline, workflow map, control assessment, or investment decision.
AI in Healthcare Statistics
Clinical, administrative, operational, and adoption evidence with source boundaries.
Healthcare data →AI in Insurance Statistics
Claims, underwriting, service, fraud, governance, and workforce evidence.
Insurance data →AI in Manufacturing Statistics
Maintenance, quality, supply chain, scheduling, robotics, and implementation evidence.
Manufacturing data →AI in Private Equity Statistics
Diligence, portfolio operations, value creation, risk, and adoption evidence.
Private equity data →AI in Retail Statistics
Discovery, merchandising, service, conversion, forecasting, and named-company evidence.
Retail data →Companies Using AI
A directory of documented production deployments, plus a dedicated finance collection covering major institutions.
Browse companies →
Method
What makes a resource decision-useful.
A page is not useful merely because it contains many numbers. This library keeps the practical artifact, the external evidence, the commercial service, and the claim boundary separate.
| Test | What the resource should show | Failure to avoid |
|---|---|---|
| Source | Named publisher, direct link, publication date, and context | Citing a secondary list that removed the denominator |
| Method | Formula, scoring rule, weights, assumptions, and decision threshold | A proprietary score that cannot be reproduced |
| Ownership | A named business, finance, risk, data, or technology owner | Assigning “the AI team” to every decision |
| Evidence limit | What the number or tool can and cannot establish | Treating an industry survey as a forecast for one company |
| Refresh | Visible review date and corrections route | Evergreen language attached to stale regulatory facts |
About Paul Okhrem
Paul Okhrem is an AI Transformation Consultant and Fractional Chief AI Officer. He has built B2B and enterprise software since 2009 and publishes the assumptions, evidence limits, and decision rules behind these resources.