AI Business Value Framework

Your AI portfolio is spending.
Is it delivering?

Map every AI investment to the metrics that matter. Diagnose governance gaps. Hold up the mirror.

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Executive View

Board-ready dashboards. Role-based KPIs. See what your AI portfolio is really delivering.

CEO · CFO · CDO · CIO · CHRO · CRO

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Practitioner View

Full assessment toolkit. Build your portfolio, run the agent, diagnose governance gaps.

AI Leads · Transformation · Advisory

85% AI Pilot Failure Gartner 2025

$9T Bureaucracy Cost Gary Hamel / MLab

6× ROI with Change Mgmt Prosci 11th Ed.

How It Works →

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📊 McKinsey · BCG · Gartner benchmarks

🏛 EU AI Act & DORA aligned

⚡ Setup in under 5 minutes

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Quick Start Guide

How the BVF Works

The AI Business Value Framework maps your AI investments to the financial outcomes and governance standards your board expects. Here is how to get the most from it in under 10 minutes.

Set Your Company Profile

Enter your revenue, cost base, EBITDA and industry. This powers every benchmark, value range and drag cost calculation. The more accurate your numbers, the sharper the insight.

Define Strategic Priorities

Add 3 to 5 board-level priorities your organisation is measuring AI against. These drive the alignment scoring across your portfolio and the Boardroom Sentence on the Executive Dashboard.

Build Your Portfolio

Add each AI initiative with its budget, change management allocation, AI generation tier (Gen 1/2/3) and business function. The BVF calculates value ranges, governance gaps and pilot-trap risk automatically.

Read the Executive Dashboard

Switch between CEO, CFO, CDO, CIO, CHRO and CRO views. Each role sees the KPIs and language that matter to them, drawn from the same underlying data. Print the Executive Readout for board packs.

Explore the Alignment Map

See your full AI value potential mapped by business function, investment vs return, and strategic priority alignment. This is where the commercial story comes together.

What Each Tab Does

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Dashboard

Role-based C-suite views with live KPIs, strategic priorities and the Boardroom Sentence

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Portfolio

Add and manage AI initiatives. Budget, CM allocation, tier classification and value scoring

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Agent

AI-powered classification engine. Paste an initiative description and get instant BVF scoring

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Governance

EU AI Act, DORA, GDPR mapping. Regulatory exposure and compliance posture across your portfolio

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Alignment Map

Full-picture view mapping value potential by function, investment vs return and ROI ranges

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Benchmarks

Industry benchmarks from McKinsey, BCG, Gartner, Deloitte and Prosci powering every calculation

Ready to Start?

Choose your entry point and the BVF will guide you from there.

Strategic Priorities · Board-Level Objectives

Define 3-5 strategic priorities your board is measuring AI against.

e.g. "Cut operational costs by 35%", "Drive €50M new revenue via AI", "Achieve regulatory compliance by Q3"

Executive Intelligence · Select Your Role

CEO · Strategic Portfolio Alignment

Are my AI investments moving strategic priorities — or just consuming budget?

The BVF maps every AI initiative against your board-level priorities, quantifies the governance gap, and delivers a Decision Confidence Score that tells you which investments to accelerate, fix, or stop.

BVF Answer

Complete the Company Profile and build your Portfolio to see live strategic alignment metrics.

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AI Initiatives

Total portfolio tracked

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Total Investment

Combined AI budget

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Drag Cost

Hamel/Prosci model

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CM Health

vs. Prosci 10-15% benchmark

CFO · Financial Governance & ROI

What is the true ROI of our AI portfolio — and where is the financial drag?

Real ROI calculation inclusive of change management, training, and organisational drag costs that vendors never include in their projections. EBITDA impact per initiative.

BVF Answer

Complete the Company Profile and build your Portfolio to see financial governance metrics.

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EBITDA Impact

Projected AI contribution

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Drag Cost

Hamel/Prosci model

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ROI Range

Investment return potential

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CM vs Budget

Prosci compliance rate

CDO · Data Maturity & Scaling

Which AI initiatives are ready to scale — and which are governance liabilities?

Gen 1/2/3 AI maturity assessment. Agent Skills Directory. Regulatory mapping across the portfolio.

BVF Answer

Complete the Company Profile and build your Portfolio to see AI maturity and scaling metrics.

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Gen Maturity

Portfolio AI generation mix

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Initiatives

Total portfolio tracked

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Alignment Rate

Mapped to strategic priorities

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Agentic Ready

Gen 3 portfolio share

CIO · Technical Governance & Risk

How do we govern agentic AI deployment without becoming the bottleneck?

Cross-functional governance framework. Integration complexity scoring. Technical risk posture across the AI portfolio.

BVF Answer

Complete the Company Profile and build your Portfolio to see technical governance and deployment readiness.

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High Risk

Initiatives flagged high-risk

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Gen 3 Count

Agentic initiatives to govern

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Reg. Frameworks

Active compliance obligations

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Pilot Trap Risk

Initiatives failing readiness

CHRO · Change Management & Adoption

Is our workforce ready for AI — and are we measuring adoption or just deployment?

Change readiness assessment. Prosci benchmark alignment. Reskilling investment calculation. The people side of every AI initiative.

BVF Answer

Complete the Company Profile and build your Portfolio to see change management and workforce readiness metrics.

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CM Investment

vs. Prosci 10-15% benchmark

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CM Health

RAG status across portfolio

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Adoption Risk

Low CM initiatives

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CM Budget

Total people-side investment

CRO · Regulatory Risk & Compliance

What is our regulatory exposure across the AI portfolio — and can we prove compliance?

EU AI Act classification. DORA compliance posture. GDPR and AML exposure mapping across every initiative.

BVF Answer

Complete the Company Profile and build your Portfolio to see regulatory exposure and compliance posture.

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Active Frameworks

Regulatory obligations tracked

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Compliance Cost

Estimated regulatory investment

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High Risk Inits

Requiring governance attention

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Agentic Exposure

Gen 3 regulatory risk

Holding Up The Mirror

"What percentage of the ROI for this AI programme relies on people actually adopting and implementing the change?"

The silence that follows is diagnostic. — Craig Horton

AI Initiative Portfolio

A complete view of your AI investments — existing and planned

Why map your AI portfolio?

Most organisations already have AI initiatives in flight — some delivering value, others stuck in pilot. This isn't about starting over. It's about getting a complete picture of what you've invested, what's working, and where the gaps are. Add your existing programmes first, then layer in new opportunities. The BVF will show you how each initiative connects to board-level outcomes.

Start by mapping what you already have

The best AI strategies build on existing investments. Add your current programmes first — even if they're early-stage or underperforming. Then add planned initiatives to see the full picture.

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Add Existing Initiative

A programme already in flight — deployed, in pilot, or approved

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Add New Opportunity

A proposed programme to evaluate for investment and board alignment

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Use AI Agent

Paste a strategy doc and let AI extract and map initiatives automatically

AI Portfolio
Risk Intelligence

Paste your client's SaaS inventory. The BVF Agent classifies each tool against global analyst benchmarks, scores disruption risk, and recommends the optimal AI stack for replacement.

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Client name

SaaS & AI initiatives inventory

0 tools entered

⚙️ Agentic Control Panel

Add context about your client's environment. The BVF agent evaluates every Agent-Replaceable and Agent-Augmented tool and recommends the optimal LLM, deployment model, and implementation approach — scored against platform fit, process type, risk profile, and business case.

Industry / sector

Risk tolerance

Data sovereignty requirement

Technical maturity

Primary cloud platform

Annual AI budget range

Change Management Health Check

Prosci: a minimum of 10–15% of project budget must fund change management for AI value to land.

Add initiatives in the Portfolio tab to generate your CM Health Check.

Holding Up The Mirror

"What percentage of the ROI for this AI programme relies on people actually adopting and implementing the change?"

The answer is almost always: most of it. And the CM budget is almost always the first thing cut. — Craig Horton

Regulatory Readiness — EU AI Landscape 2025–2027

Select applicable regulations. Compliance costs calculated against your revenue profile.

Holding Up The Mirror

"What percentage of the ROI for this AI programme relies on people actually adopting and implementing the change?"

Regulatory readiness is not a legal question. It is a board-level risk and cost question. — Craig Horton

v2.0 Diagnostics

Three diagnostic layers that sit behind the Decision Confidence Score. Change Enablement measures your people-side readiness using the EY/Oxford Six Drivers. Pace Layer detects structural misalignment across transformation horizons and puts a financial cost on it. Healthcare Readiness applies sector-specific overlays when the industry warrants it. Together, they answer the question the DCS alone cannot: why an initiative will succeed or fail.

BCG research shows 70% of transformation value comes from people and process, not technology. This diagnostic uses the EY/Oxford Said Business School Six Drivers model to measure your organisation's capacity to absorb change, and feeds directly into the Decision Confidence Score.

What to do: Score each of the six drivers below from 1 (weak) to 5 (strong). The composite converts to a CE percentage that weights into the DCS. If CM budget is below 10% of initiative budget, CE is capped at 40%.

The Six Drivers

"Organizations that excel in all six drivers, Lead, Inspire, Care, Empower, Build, Collaborate, see 2.6x greater value realization from transformation programmes."

EY/Oxford Said Business School, Change Enablement Research 2022

Score Each Driver (1-5)

CE COMPOSITE (1-5)

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Average of all 6 drivers

CE PERCENTAGE (0-100%)

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((Composite - 1) / 4) x 100

CM BUDGET ADEQUACY CHECK

If CM Budget < 10% of Initiative Budget, CE is capped at 40%

AI Alignment Map — Board Summary

One-page board-ready view. Complete the Company Profile and build your Portfolio to activate all sections.

AI Alignment Map · Craig Horton Advisory · 2026

[Company Name]

Universal / Other

Holding Up The Mirror

"What percentage of the ROI for this AI programme relies on people actually adopting and implementing the change?"

This is the board view. Every number flows from your inputs. The gap between value potential and CM allocation is the conversation that needs to happen. — Craig Horton

Benchmark Methodology & Sources

Every number in the BVF is derived from published research, not opinion. This page explains what we use, who published it, when, and how we apply it.

✓

Benchmarks current as of Q1 2026

Next scheduled review: Q3 2026 · Sources are validated against latest publications annually

AI Value Potential by Function

Revenue uplift & cost reduction ranges per business function

CORE MODEL

PRIMARY SOURCES

McKinsey Global Institute — "The Economic Potential of Generative AI" (June 2023). Provides function-level productivity estimates across 63 use cases. Updated with "Superagency in the workplace" (Jan 2025).

BCG Henderson Institute — "Where's the Value in AI?" (2023) and "AI @ Scale" (2024). Maps AI value by industry and function with realised vs. projected returns.

Gartner IT Key Metrics — Industry-specific IT spending benchmarks and AI investment ratios (2024 update). Used for industry multipliers.

HOW WE APPLY IT

Base rates (e.g., Finance: 1.5–3.0% cost reduction) are derived from the median of reported ranges across all three sources.

Industry multipliers adjust the base rate — e.g., Financial Services 1.15× for Finance function, Manufacturing 1.2× for Supply Chain — based on sector adoption data from Gartner and McKinsey.

Rates represent achievable impact from mature deployments with adequate change management, not theoretical maximums.

Organisational Drag Cost Model

Adoption friction that erodes AI ROI based on organisational readiness

DRAG MODEL

PRIMARY SOURCES

Gary Hamel & Michele Zanini — "Humanocracy: Creating Organizations as Amazing as the People Inside Them" (HBR Press, 2020). Estimates $9 trillion in global bureaucracy costs. Provides the conceptual framework for drag rate tiers.

Prosci — "Best Practices in Change Management" 11th Edition (2023). Projects with excellent CM are 6× more likely to meet objectives. Provides the CM allocation adequacy thresholds (5%, 10%, 15% of budget).

McKinsey — "The State of AI in 2024" (Aug 2024). 74% of organisations struggle to scale AI beyond pilot. Informs risk amplifier rates.

BCG — "AI @ Scale" (2023). Only 11% of companies achieve significant financial benefit from AI. Validates the drag base rates at organisation level.

HOW WE APPLY IT

Four organisational readiness levels with base drag rates:

                Bureaucratic   40% drag

                Traditional    25% drag

                Mixed          15% drag

                Agile           5% drag

Drag amplifiers increase the base rate for high-risk initiatives (+20%) or low CM allocation (<5% adds +15%, 5–10% adds +5%).

Good CM allocation (≥15%) reduces drag by 5 percentage points; adequate CM (10–15%) reduces by 3 points. This reflects the Prosci finding that well-funded CM dramatically improves adoption.

Change Management Allocation Benchmarks

RAG thresholds for CM investment as percentage of programme budget

CM MODEL

PRIMARY SOURCES

Prosci — "Best Practices in Change Management" 11th Ed. (2023). 10–15% of total project budget is the recommended CM allocation for technology transformations.

Deloitte — "Tech Trends 2024: AI-Fuelled Transformation." Recommends 15%+ CM allocation for AI programmes specifically, given higher people-change requirements.

Forrester — "The Total Economic Impact of Change Management" (2023). Quantifies ROI differential between well-funded and under-funded CM programmes.

HOW WE APPLY IT

RAG (Red-Amber-Green) thresholds for CM as % of initiative budget:

RED     <5% — Significant adoption risk
AMBER  5–10% — Below recommended level
GREEN  ≥10% — Aligned with Prosci benchmark

These thresholds apply to each initiative individually. The BVF uses portfolio-level CM percentage to assess overall readiness for the CEO and CFO executive views.

Pilot Trap Diagnostic

5-question readiness check to assess whether an initiative can scale beyond pilot

DIAGNOSTIC

PRIMARY SOURCES

McKinsey — "The State of AI" (2024). 42% of companies abandoned most AI projects citing unclear business value. Informs the "clear KPI" and "executive sponsor" questions.

BCG — "From Pilot to Scale" (2024). Identifies 5 critical success factors for scaling AI. Maps to our 5 diagnostic questions.

Gartner — "Top Strategic Technology Trends" (2025). Emphasises the pilot-to-production gap as the primary barrier to AI value realisation.

HOW WE APPLY IT

Five binary questions (Yes/No), each worth 1 point:

1. Does this initiative have a named executive sponsor?
2. Is there a dedicated product owner?
3. Is there a plan to move beyond pilot?
4. Are KPIs defined and measurable?
5. Is CM specifically funded in the budget?

A score below 2/5 flags the initiative as at risk of the "pilot trap" — technically functional but unable to scale to deliver business value.

AI Generation Tier Classification

Gen 1 / Gen 2 / Gen 3 framework for categorising AI maturity

TAXONOMY

PRIMARY SOURCES

Gartner — Hype Cycle for Artificial Intelligence (2024). Positions Agentic AI in the "Innovation Trigger" phase. Informs Gen 3 classification.

McKinsey — "The State of AI in 2024." Distinguishes between traditional ML, generative AI, and autonomous agent systems in enterprise deployment.

Craig Horton Advisory — Practitioner experience across 50+ enterprise AI engagements. The three-tier naming convention (Gen 1/2/3) is our simplification for board-level communication.

HOW WE APPLY IT

Gen 1 — Automation
RPA, rules-based, ML classification, predictive models

Gen 2 — Generative AI
LLM-powered assistants, content generation, semantic search

Gen 3 — Agentic AI
Autonomous agents, multi-step reasoning, tool use, decision-making

Regulatory Compliance Frameworks

EU AI Act and DORA compliance mapping

REGULATION

EU AI Act — Regulation (EU) 2024/1689. Entered into force 1 August 2024. Prohibitions apply from Feb 2025; high-risk obligations from Aug 2026. The BVF maps initiative risk classifications against the Act's four-tier risk system (Unacceptable → High → Limited → Minimal).

DORA — Digital Operational Resilience Act (EU) 2022/2554. Effective 17 January 2025. Applies to financial entities. The BVF flags AI initiatives in financial services that require DORA compliance consideration for ICT risk management, incident reporting, and third-party oversight.

NIST AI RMF — AI Risk Management Framework 1.0 (Jan 2023). Used as supplementary guidance for risk categorisation methodology. Non-binding but adopted as best practice by multinational organisations.

EY/Oxford Six Drivers of Change Enablement

People-side capability assessment powering the CE dimension of the DCS

v2.0 DIAGNOSTIC

PRIMARY SOURCES

EY & Oxford Said Business School — "The Six Drivers of Change" (2022). Multi-year research across 935 transformations. Organisations strong on all six drivers achieve 2.6x greater value realisation.

BCG Henderson Institute — "AI @ Scale" (2024). Validates that 70% of transformation value derives from people and process, not technology. Supports the weighting of CE in the DCS formula.

Prosci — "Best Practices in Change Management" 11th Ed. (2023). Projects with excellent CM are 6x more likely to meet objectives. Underpins the CM budget adequacy cap.

HOW WE APPLY IT

Six drivers, each scored 1-5:

                Lead         Executive sponsorship & vision clarity

                Inspire      Purpose alignment & motivation

                Care         Wellbeing & psychological safety

                Empower      Autonomy & decision rights

                Build        Capability development & upskilling

                Collaborate  Cross-functional working & trust

CE Composite = average of all 6 scores. CE Percentage = ((Composite - 1) / 4) x 100. This feeds the Change Enablement weight (20%) in the DCS formula.

If CM budget is below 10% of initiative budget, CE is hard-capped at 40%, reflecting the Prosci finding that under-funded change programmes have a 6x higher failure rate regardless of organisational capability.

Healthcare Readiness Factor

Sector-specific multiplier for healthcare and MedTech AI initiatives

v2.0 VERTICAL

PRIMARY SOURCES

FDA — AI/ML-Based Software as a Medical Device (SaMD) Action Plan (2021, updated 2024). Defines regulatory pathway requirements that determine go-to-market timelines for clinical AI.

WHO — "Ethics and Governance of Artificial Intelligence for Health" (2021). Establishes six guiding principles for health AI deployment. Informs our clinical validation overlay scoring.

KLAS Research — "AI in Healthcare 2024." Maps EHR integration maturity and clinician adoption rates. Provides the interoperability overlay benchmarks.

AMA — "Augmented Intelligence in Health Care" policy (2023). Emphasises clinician time reclaimed as the primary value metric, not cost reduction. Informs our third overlay.

HOW WE APPLY IT

Four overlays, each scored 1-5:

                Regulatory Readiness     FDA/CE pathway status

                Clinical Validation      Evidence base & trial maturity

                Clinician Time Reclaimed  Workflow integration depth

                EHR Interoperability     Systems integration readiness

Healthcare Readiness Factor = Average of 4 scores / 5, with a floor of 0.5. This multiplies into the DCS calculation, ensuring healthcare-specific risks are quantified rather than qualitatively noted.

A factor below 0.7 flags material risk. This tab appears only when the company profile industry is set to Healthcare/MedTech, keeping the assessment focused for non-healthcare organisations.

Pace Layer Diagnostic & Organisational Drag Cost

Organisation-level misalignment detector across three transformation horizons

v2.0 DIAGNOSTIC

PRIMARY SOURCES

Stewart Brand — "The Clock of the Long Now" (1999). Introduces pace layering as a model for how civilisations and organisations operate at different speeds. We adapt this for AI portfolio alignment.

Jeroen Tas & Rob Theunissen — "Changing Fast and Slow." Applies pace layering directly to enterprise transformation, demonstrating how organisations that fail to synchronise fast-moving digital initiatives with slower-moving cultural and structural change create systemic drag. Core influence on the BVF Drag Coefficient model.

McKinsey — "Three Horizons of Growth" framework, updated for AI context in "Superagency in the Workplace" (Jan 2025). Maps Horizon 1 (defend), Horizon 2 (extend), Horizon 3 (create) to initiative classification.

MIT CISR — Digital Transformation research (2024). Identifies pace misalignment as a leading cause of portfolio-level failure, distinct from initiative-level failure.

S&P Global — "AI Implementation Survey" (2025). 42% of organisations abandoned most AI initiatives, with structural misalignment cited as a top-three cause. Validates the drag cost model.

HOW WE APPLY IT

Three pace layers with diagnostic questions:

                Operational   0-12 months   Defend core operations

                Strategic     12-36 months  Extend capabilities

                Existential   36+ months   Create new business models

Drag Coefficient = Fastest layer score / Slowest layer score (range 1.0 to 3.0). Failure rate is interpolated linearly: 27% at 1.0 rising to 90% at 3.0, derived from cross-referencing MIT CISR and S&P Global data.

Organisational Drag Cost = Total Portfolio Value x Failure Rate. This gives the board a single currency figure representing the financial exposure from structural misalignment, separate from initiative-level risk.

Update & Review Protocol

All benchmark data undergoes a structured review cycle. Major source publications (McKinsey State of AI, Gartner Hype Cycle, Prosci Best Practices) are released annually; the BVF model is updated within 30 days of each new edition. Industry multipliers are recalibrated when Gartner IT Key Metrics data refreshes (typically Q1). Regulatory compliance mappings are updated as enforcement deadlines pass or guidance is published. The "Benchmarks current as of" date at the top of this page always reflects the most recent review.

A Note on Methodology

"Every consultant can produce a number. The question is whether you can stand behind it in a boardroom. We use published research — not internal estimates — because when the CFO asks 'where does this come from?', you need an answer that doesn't start with 'we think'."

— Craig Horton, Craig Horton Advisory

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