Collection
30 calculators, business cases, and frameworks for measuring AI value
University of Technology Sydney, Human Technology Institute
For: Boards, CFOs, CIOs, and enterprise AI investment committees.
Why explore: AI ROI should evolve with the maturity, scale, and strategic significance of the investment.
Brillian
For: Innovation teams, consultants, and executives evaluating AI opportunities.
Why explore: AI investment decisions improve when potential value and uncertainty are evaluated together.
Kiseki Labs
For: COOs, CFOs, transformation leaders, and operational teams.
Why explore: The economics of an AI workflow depend on the cost and quality of the entire human–machine process.
Associum
For: Consultants, project sponsors, and internal investment teams.
Why explore: A forecast return is only useful when it is connected to the capabilities, milestones, and responsibilities required to realize it.
Srivatsan, independent open-source contributor
For: Analysts and teams seeking an editable starting point.
Why explore: Transparent and adaptable assumptions are more useful than an impressive ROI figure whose calculation cannot be inspected.
AEGITz
For: Executives, operators, and AI project sponsors.
Why explore: Financial return requires a credible value-capture mechanism.
Numbers & Judgment
For: CFOs, financial analysts, and operational leaders.
Why explore: Operational capacity and realized cash benefits are different economic outcomes.
Kona
For: Managers evaluating individual AI-assisted workflows.
Why explore: AI productivity should be measured against the complete task, not the speed of its first output.
WayaNerd
For: Finance teams and organizations preparing formal investment cases.
Why explore: A credible ROI model should show what happens under unfavorable assumptions, not merely the most attractive forecast.
NextGen Code
For: Business owners and managers building their first AI ROI model.
Why explore: Simple financial models become valuable when their inputs are measurable, transparent, and regularly updated.
DORA
For: CTOs, engineering leaders, and software organizations.
Why explore: Engineering AI ROI depends on delivery outcomes and organizational adaptation, not simply coding speed.
DecodeSeries / Christine Aykac
For: Engineering executives and software delivery leaders.
Why explore: Software development ROI should connect engineering improvements to outcomes that matter economically.
Artificial Analysis
For: AI product leaders, engineering managers, and technology procurement teams.
Why explore: The relevant economic measure is often cost per successful task rather than cost per token.
TokenCanopy, open-source project
For: AI architects, analysts, and teams tracking infrastructure costs.
Why explore: AI cost assumptions should be monitored and revised as the provider landscape changes.
OpenAI
For: Product teams and developers using OpenAI services.
Why explore: Financial models require actual service-pricing inputs, updated as deployment conditions change.
Uravation, Japan
For: Teams wanting a structured recurring measurement system.
Why explore: AI ROI must be revisited as actual adoption, quality, costs, and realized benefits become visible.
NextStart.AI
For: Transformation leaders, HR executives, CIOs, and management controllers.
Why explore: Measurement begins before implementation, not after a positive result needs to be justified.
UK Government
For: Enterprise transformation teams and public-sector AI programs.
Why explore: AI value measurement should reflect the specific outcomes and risks of the use case.
UK Government / Magenta Book guidance
For: Evaluation specialists, policy teams, and enterprise AI program managers.
Why explore: Observing an improvement after implementation does not necessarily establish that AI caused it.
US National Institute of Standards and Technology
For: AI governance, engineering, assurance, and risk teams.
Why explore: AI value must account for reliability, monitoring, and the consequences of system failure.
Academic researchers / National Bureau of Economic Research
For: Leaders evaluating customer support and knowledge-worker augmentation.
Why explore: AI productivity gains can be heterogeneous, and the distribution of those gains affects the business case.
Harvard Business School researchers, in collaboration with BCG
For: Consultants, strategists, and leaders assessing knowledge-work applications.
Why explore: AI investment decisions should be made at the level of real tasks, not broad occupational categories.
Model Evaluation & Threat Research
For: Engineering leaders and executives evaluating developer AI tools.
Why explore: AI adoption should be evaluated using measured task outcomes, not only user enthusiasm or self-reported time savings.
Stanford Institute for Human-Centered Artificial Intelligence
For: Strategists, investors, CFOs, and executive planning teams.
Why explore: AI investment decisions should be informed by independent market and economic evidence.
Stanford Institute for Human-Centered Artificial Intelligence
For: Technology executives, strategists, and AI investment committees.
Why explore: Technical progress informs investment opportunities, but business value requires separate validation.
HM Treasury, United Kingdom
For: CFOs, investment committees, and enterprise strategy teams.
Why explore: A positive AI business case is not necessarily the best available use of organizational resources.
HM Treasury
For: Executives preparing major AI transformation proposals.
Why explore: Financial attractiveness, strategic relevance, affordability, and implementation feasibility must be evaluated together.
Australian Government Digital Transformation Agency
For: AI governance, procurement, risk, and implementation teams.
Why explore: Attractive projected financial returns do not remove the obligation to assess risk and organizational impact.
TAI Labs
For: HR leaders, transformation teams, and organizations investing in AI capability development.
Why explore: The value of AI technology depends partly on whether people possess the capabilities to apply it effectively.
US National Institute of Standards and Technology
For: AI governance committees, risk officers, and technology executives.
Why explore: AI investment decisions should be continuously revisited as new evidence becomes available.
