Three small tools that make the framework operational.

The curated, primary-source-linked corpus behind the framework. Each case has an organisation, period, industry, function, outcome, evidence statement, and source URL.

Strategic alignment: how well GenAI investments are tied to measurable business objectives with named executive sponsorship.

A five-element checklist that predicts deployment success: business objectives, performance tracking, change management, iterative deployment, and a named executive sponsor.

A specific cited case or academic citation that supports a claim in the framework. The Task Mapper labels every quantitative claim either anchored or illustrative.

On the team-comparison view (/compare/team): a dimension where every participant scored below 40%. Likely the cheapest place for the team to improve together.

Stable identifier (CASE-001 through CASE-045) for each entry in the 44-case dataset. Used in citations and cross-references throughout the site.

Width of the band around an industry benchmark, reflecting sample size. Industries with fewer than 10 cases (Healthcare, Manufacturing, Government) carry wider intervals.

Organisational adoption: change management, user training, and cross-functional collaboration that turn a deployment into a behaviour change.

DeLone & McLean (2003). Six interdependent dimensions of IS success: information quality, system quality, service quality, use, user satisfaction, and net benefits.

Rogers (2003). Innovations spread through social systems via adoption categories: innovators, early adopters, early majority, late majority, laggards.

Peffers et al. (2007). The development methodology behind RAPID: identify problem, define objectives, design artefact, demonstrate, evaluate, communicate.

Where a case sits on the maturity arc: Pilot/POC, Scaling, Production, Optimization, or Abandoned. The 44-case dataset is weighted toward Production and Optimization.

One of six categories where GenAI deployments tend to break down: data, technical, organisational, strategic, measurement, economic.

Measurement maturity: KPI definition, baseline establishment, and ROI review processes for GenAI initiatives.

Brynjolfsson & Hitt (1993). IT productivity gains require organisational change with a 3-5 year lag. AI productivity figures show the same pattern - per-task minute savings do not aggregate automatically into firm-level productivity.

Kotter (1996). Eight sequential steps for organisational change: urgency, coalition, vision, communication, empowerment, wins, consolidation, anchoring.

Buckets that summarise an overall RAPID score: Nascent (0-25%), Developing (26-50%), Established (51-75%), Advanced (76-100%).

Portfolio balance: diversification across risk levels and time horizons; the discipline to kill or scale initiatives based on results.

Where an organisation's score sits relative to peers in the dataset. The 60th percentile means 60% of peers scored at or below this point.

Share of GenAI proofs-of-concept that never reach production. Gartner forecasts 30% by end of 2025; BCG finds only 22% of organisations advance past POC at all.

A five-dimension maturity framework for GenAI investment - Readiness, Alignment, Portfolio, Impact, Diffusion - that scores an organisation against a 44-case enterprise dataset.

Data and technical readiness: data infrastructure, MLOps maturity, model-evaluation discipline, and AI/ML talent depth.

Industry-specific projected return on GenAI investment, derived from thesis Chapter 4. Tech 18-24%, Financial Services 15-22%, Healthcare 12-18% are the most-studied bands.

Share of measurable cases (success + failure + partial) in a cohort that achieved their stated objective. Excludes Too Early outcomes.

Davis (1989). Predicts technology adoption from perceived usefulness and perceived ease of use. Strongest predictor of whether a tool gets used at all.

Peer-reviewed journals, NBER working papers, government findings, and investigative-journalism cases with documented evidence trails.

Business press, corporate disclosures, and analyst reports with primary-source URLs but lower formal-vetting depth than Tier 1.

Typical payback window for GenAI investments by industry. Ranges from 12-18 months (tech, retail) to 24-36 months (government, regulated sectors).

Venkatesh et al. (2003). Unifies eight technology-adoption models. Performance expectancy, effort expectancy, social influence, and facilitating conditions explain ~70% of adoption variance.

One of six GenAI deployment archetypes: Automation, Generation, Analysis, Augmentation, Chatbots, Decision Support. Each has a verified success-rate band derived from the dataset.