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 by Paul Okhrem contains a free ROI calculator, governance checklist, readiness scorecard, use-case scorecard, governance maturity model, transformation roadmap, banking implementation checklist, 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.
7reusable executive tools
9sector and role data collections
0lead gates on downloadable templates
Asset labels
Know what type of evidence you are using.
The label describes provenance, not quality. A calculator can be reproducible without proving an outcome. A company example can be documented without being independently audited.
| Asset type | What it contains | What it does not prove |
|---|---|---|
| Original, reproducible tool | Visible formula, scoring rule, assumptions, or editable template | That the tool caused a client result |
| Curated, source-graded evidence | External statistics with publisher, date, population, denominator, and limits | That an industry average applies to one company |
| Documented company example | A named organization, use case, and direct source | Independent validation of every provider claim |
| Disclosed commercial comparison | Buyer scenarios, provider sources, selection criteria, and publisher conflict | An independent analyst ranking or universal winner |
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 →AI Transformation Governance
Assign portfolio, workflow, production, risk, value, change, and stop decisions with a 12-decision board framework and editable RACI.
Use the governance framework →AI Use Case Prioritization Framework
Compare value, scale, data readiness, workflow fit, time to evidence, risk, dependencies, and adoption with explicit hard-stop rules.
Prioritize AI use cases →AI Governance Maturity Model
Assess six governance domains, record evidence for each level, and define the next control improvement without using an opaque score.
Assess governance maturity →AI Implementation in Banking
Move a bank AI workflow from mandate to controlled production with decisions, owners, model and vendor controls, evaluation, monitoring, and an editable CSV.
Use the banking guide →
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 →
Commercial buyer guides
Disclosed provider comparisons.
Paul Okhrem publishes these first-party comparisons and offers one of the services. They are buyer guides, not independent research, analyst awards, or objective rankings. Use the visible criteria, verify primary provider sources, change the weights for your mandate, and run your own procurement process.
Best Healthcare AI Consulting Firms
Compare independent, boutique, development, platform, and large-transformation options by buyer fit, with the method and publisher conflict visible.
Compare healthcare options →Best AI Strategy Consulting Firms
Compare seven strategy providers by mandate, strategy-to-production model, named senior accountability, governance, adoption, and scale.
Compare AI strategy firms →Best Agentic AI Consulting Companies
Compare named-principal, model-provider, global strategy, implementation, platform, and managed agentic AI delivery models.
Compare agentic AI providers →
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.