Impact — Practices & Playbooks
Applicable methods that turn the framework into day-to-day work.
35 results
AR-IM01 · Strategic Value Intent Canvas
Translate an enterprise priority into a bounded AI value thesis before a solution, vendor or model becomes the organising idea. The canvas makes the intended advantage, beneficiary, time horizon and non-negotiable constraints explicit.
Impact
AR-IM02 · Business-to-AI Outcome Chain
Make the causal path from AI capability to enterprise value inspectable. The chain separates technical performance from human adoption, workflow change, intermediate outcomes and realised business effects.
Impact
AR-IM03 · Indicator Map
Create a balanced signal system that connects early indicators to lagging outcomes while protecting quality, safety and stakeholder value. The map clarifies which signals support learning and which justify value claims.
Impact
AR-IM04 · Outcome Ownership Charter
Establish who is accountable for benefit realisation and who controls the operational changes required to produce it. The charter prevents responsibility from collapsing into a generic project or technology role.
Impact
AR-IM05 · Multi-Dimensional Value Map
Evaluate an AI intervention across financial, operational, stakeholder, decision, risk/resilience and capability/learning value. The map makes trade-offs, disbenefits and value distribution visible before a single monetary number dominates the case.
Impact
AR-IM06 · Financial Baseline
Create a finance-verifiable starting point against which incremental AI value can be measured. The baseline makes eligible volume, unit economics, adoption, realisation and counterfactual movement explicit.
Impact
AR-IM07 · Flow Review
Measure value at the end-to-end workflow level rather than at the isolated AI task. The review exposes queues, hand-offs, review burden, rework and exceptions that determine whether local speed becomes system performance.
Impact
AR-IM08 · Decision Review
Assess whether a human–AI decision system improves decision quality, speed and consistency in the decisions that matter. The review relies on case outcomes, not confidence scores or user preference alone.
Impact
AR-IM09 · Risk Valuation
Translate risk reduction and resilience improvement into decision-relevant economic and operational value without presenting avoided loss as guaranteed cash benefit.
Impact
AR-IM10 · Capability Evidence
Recognise learning and reusable capability as evidence-backed option value rather than an automatic financial return. The practice tracks whether assets, skills and operating knowledge reduce future cost, lead time or uncertainty.
Impact
AR-IM11 · Time-to-First-Value
Measure how quickly an initiative moves from approved intent to credible evidence and then to realised value. The practice separates first technical output from first user value and first finance-recognised benefit.
Impact
AR-IM12 · Total AI Economics and Token Costing
Calculate the full economic cost of an AI-enabled workflow and expose the usage drivers that make cost move. Token costing is included as one component of lifecycle economics, not treated as the complete cost model.
Impact
AR-IM13 · Cost-to-Outcome and ROAI
Connect fully loaded AI cost to usable outcomes and attributable realised benefit. The practice turns return on AI investment into a decision model with explicit adoption, quality, realisation and confidence assumptions.
Impact
AR-IM14 · Value Curve and Sensitivity
Model how cost, value and risk change non-linearly with adoption, volume, performance and scope. The practice prevents pilot averages from being projected mechanically into enterprise-scale returns.
Impact
AR-IM15 · Compounding Register
Track the mechanisms through which repeated AI use may improve future value data feedback, reusable assets, workflow learning and option creation while distinguishing demonstrated compounding from aspiration.
Impact
AR-IM16 · Portfolio Allocation and ROAI Confidence Calibration
Allocate capital and capacity across AI initiatives using strategic intent, evidence maturity, economics, dependencies and confidence not a single incomparable ROI ranking.
Impact
AR-IM17 · Measurement Architecture
Design the evidence system that connects operational events, AI telemetry, human actions and business outcomes. The architecture makes calculations reproducible and preserves the context needed for attribution and decision-making.
Impact
AR-IM18 · Proxy Validation
Test whether an early or indirect signal remains a credible predictor of the outcome it represents. The practice protects decisions from proxies that are easy to improve but causally weak or vulnerable to gaming.
Impact
AR-IM19 · Performance and Learning Scorecard
Separate metrics used to run a stable service from metrics used to reduce uncertainty and learn. The scorecard changes as the initiative moves from exploration to operation, preventing premature performance theatre.
Impact
AR-IM20 · Human–AI Decision Review
Examine the combined human–AI system including roles, hand-offs, overrides and autonomy rather than evaluating the model in isolation. The review uses real cases to improve decision design and accountability.
Impact
AR-IM21 · Re-prioritisation Triggers
Pre-agree evidence thresholds that change scope, funding, autonomy or operating treatment. The practice turns measurement into action and limits escalation of commitment when the value thesis weakens.
Impact
AR-IM22 · Repeatability and Context Portability
Determine which parts of a proven intervention can travel across teams, markets or processes and which must adapt to local context. The practice converts one-site success into an evidence-based replication model.
Impact
AR-IM23 · Reusable Capability Value
Measure the enterprise value of shared AI capabilities through actual consumption, avoided duplication and improved delivery not through asset creation or platform spend alone.
Impact
AR-IM24 · Scale Readiness, Benefit Ownership and Dilution Review
Test whether the organisation can preserve value as an AI intervention expands. The review combines readiness, local benefit ownership, benefit-realisation actions and the sources of value dilution.
Impact