AI ROI calculator
Model workflow volume, labor cost, setup cost, recurring cost and uncertainty. Check the visible formula before using the result.
Enterprise AI research and decision tools.
Paul Okhrem's AI research library brings together data, comparisons, tools, templates and guides. Use the statistics to check outside evidence. Use the tools to test your own assumptions. Each company example states its source limits. Provider comparisons disclose Paul's commercial interest. Each resource explains its method and the decision it can inform.
Research · tools · transparent methods
Use this library to assess AI value, project evidence, controls and stop decisions. The collection connects working tools with source-graded statistics and named company examples.
Journalists citing this research can use the press and speaking resources for bios and attribution. Follow the research updates Atom feed for material page updates.
Toolseditable templates and local calculators
Evidencesector and role research with source limits
0lead gates on downloadable templates
Guides for AI leadership and buying decisions
These guides explain executive roles, provider selection and control ownership. They are working aids, not certificates or a substitute for legal advice.
- AI governance framework: connect system owners, controls and executive decisions.
- Agentic AI governance: define action permissions, approval gates and stop procedures.
- AI transformation governance framework: decide who owns each transformation decision, with a RACI template.
- Chief AI Officer role guide: define the executive mandate and its limits.
- AI leadership roles compared: compare the CAIO, Head of AI, CTO and other roles.
- Forward deployed AI engineer guide: distinguish engineering delivery from executive ownership.
- How to choose an AI consultant: compare proposals, evidence, conflicts and fees.
- AI consulting rates and pricing: compare scope and delivery models before comparing fees.
- AI in banking implementation guide: connect workflow selection, controls and production ownership.
- AI conferences in 2026: check organizer dates, locations and audience fit.
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 and templates for AI planning
Use these resources in an executive working session. Calculator inputs stay in the browser. Editable downloads require no email address.
Enterprise AI readiness assessment
Score economics, process, data, technology, governance, leadership and adoption. Name an evidence owner for each score.
AI governance checklist
Review controls across Govern, Map, Measure, Manage and EU readiness. Record minimum evidence and an accountable owner.
AI transformation roadmap
Plan executive questions, required outputs and decision gates across four phases.
AI maturity model
Assess five levels across six dimensions. Identify the evidence and steps needed to move up one level.
AI use-case prioritization framework
Compare value, readiness, risk and adoption. Use explicit stop rules before ranking the remaining options.
AI governance maturity model
Assess six governance domains, record evidence for each level and define the next control improvement.
AI policy template
Adapt staff rules for tools, data and human review. Download the template in Word or Markdown.
AI risk assessment
Document harm scenarios, controls and remaining risk before approval.
EU AI Act compliance checklist
Organize questions about roles, risk and evidence. Check the official timeline before relying on a date.
ISO/IEC 42001 readiness checklist
Plan management-system work before an independent assessment.
Chief AI Officer job description
Define the mandate, responsibilities and KPIs before hiring.
AI due diligence checklist
Request evidence about data, models, controls, costs and ownership before an investment.
Evidence and benchmarks
Datasets on AI adoption, outcomes and governance
These datasets group evidence by the decision it can support. Check the source date, population, geography and denominator before citing a figure.
Enterprise AI failure rate studies compared
Compare denominators, outcomes, samples, methods and measurement periods. Forecasts remain separate from observed results.
Enterprise AI agents statistics 2026
Agent adoption, economics, governance and failure risk, with source notes, charts and citation limits.
AI consulting statistics 2026
Sourced data on the AI consulting market, enterprise demand and pricing context.
AI governance statistics
Evidence on governance adoption, board oversight, model risk and operating controls.
Fractional CAIO statistics
Chief AI Officer demand, role design and evidence limits for fractional leadership.
AI in financial services statistics
Banking, fintech and insurance evidence, with source and deployment limits.
GEO benchmarks 2026
A repeatable method for separating AI mentions, citations, referral traffic and qualified pipeline.
Industry evidence
Industry datasets and company examples
Industry pages provide an outside view. Use a company baseline, workflow map and control assessment before making an investment decision.
AI in healthcare statistics
Clinical, administrative and adoption evidence with source boundaries.
AI in insurance statistics
Claims, underwriting, service, fraud, governance and workforce evidence.
AI in manufacturing statistics
Maintenance, quality, supply chain, scheduling, robotics and implementation evidence.
AI in private equity statistics
Diligence, portfolio operations, value, risk and adoption evidence.
AI in retail statistics
Discovery, merchandising, service, conversion, forecasting and named company evidence.
Companies using AI
A directory of documented company deployments, organized by industry.
Companies using AI in finance
Banks and finance firms with sourced deployment figures.
Insilico Medicine AI case study
Research milestones, clinical evidence and the limits of each source.
Klarna AI customer service case
Company-reported support changes and the evidence needed to judge them.
Zillow AI pricing case study
Model limits, inventory exposure and the decisions behind the iBuying exit.
Commercial buyer guides
Comparisons of AI providers and fractional leaders
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. Check the criteria and primary provider sources. Change the weights for your mandate and run your own procurement process.
Best healthcare AI consulting firms compared
Options for providers, payers and life sciences teams, with the method and publisher conflict visible.
Best AI strategy consulting firms compared
Strategy firms, boutiques and independent advisers grouped by buyer need.
Best agentic AI consulting companies compared
Providers grouped by delivery model, with a disclosed first-party method.
Best AI consultants for CEOs
Independent advisers and fractional CAIOs for CEO-level decisions.
Best fractional Chief AI Officers compared
Compare leadership scope, named ownership, fees and conflicts.
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 |
The consulting evidence register separates company delivery evidence from Paul's personal consulting work. Check the facts, roles and disclosures before attributing a result or credential.
Who maintains this research library?
Compiled by Paul Okhrem, AI transformation consultant and fractional Chief AI Officer. He is Co-Founder and CEO of Elogic Commerce. He has been Managing Partner at Uvik Software since June 2017.
Paul compiles this research as part of his fractional CAIO work. He advises CEOs and boards on AI and agentic AI decisions. These resources state their assumptions, evidence limits and decision rules.
Facts and disclosures · Press and speaking · Editorial standards
New and updated resources
Material editorial updates in the last 30 days.
- Best AI Strategy Consulting Firms (2026) · 11 October 2026
- The Proof Standard: AI Outcome Method · 7 October 2026
- How to Choose an AI Consultant (2026) · 7 October 2026
- GEO Benchmarks 2026: AI Search Visibility · 7 October 2026
- EU AI Act Compliance Checklist 2026 · 7 October 2026
- Companies Using AI in 2026: 30+ Examples by Industry · 7 October 2026
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