Impact

Introduction and source information

Impact is the discipline of converting AI activity into evidence-backed enterprise value. It connects approved strategic intent to outcomes, multi-dimensional value, AI economics, measurement feedback loops, scale and proof while making uncertainty, ownership and trade-offs explicit.

Impact

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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AR-IM25 · Value Proof, Attribution and Misdiagnosis Review

Challenge both positive and negative conclusions about AI value by testing evidence strength, attribution, measurement blind spots and decision bias. The review prevents premature scaling as well as premature ‘AI does not work’ conclusions.

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Operating Rhythms

Impact evidence becomes useful only when it changes a decision. These rhythms connect initiative telemetry, outcome evidence and enterprise allocation through existing decision conversations and formal gates. They are not mandatory standalone meetings or fixed-duration reporting ceremonies.

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AR-R27 · Weekly Impact Evidence Triage

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AR-R28 · Monthly AI Economics and Benefit Conversation

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AR-R29 · Initiative Evidence and Decision Gate

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AR-R30 · Quarterly Portfolio Value Conversation

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AR-R31 · Semi-Annual Scale and Sustainment Conversation

• Start with the decision due, not a tour of dashboards; pre-read evidence should be concise and reproducible.

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Minimum Consulting Templates

The following templates are the minimum evidence infrastructure for an Impact engagement. They may be implemented in the client’s existing tools, provided the fields, ownership and version history remain intact.

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Consultant Application Guidance

The playbook should increase the quality and reversibility of decisions, not reward document production. Consultants should calibrate the strength of a recommendation to the strength, relevance and reproducibility of the evidence.

Impact

Source headers, footers and notes

Impact