Informed. Accountable. Decisive.
Clarity Before Capital.
56% say they have seen no significant financial benefit to date.
Source: PwC 29th Global CEO Survey, Davos, January 2026.
AI Decision Intelligence. A structured process that establishes the success criteria for organisations deciding on procurement or seeking to recover returns from existing AI initiatives.
Problem
The Generative AI Paradox.
AI adoption has reached a threshold across most sectors. Enterprise-level returns have not.
of CEOs report AI has produced neither increased revenue nor decreased costs over the past 12 months.
Source: PwC Global CEO Survey, Davos 2026. 4,454 CEOs across 95 countries.
of enterprises have scaled AI to deliver tangible value, despite two-thirds having active initiatives.
Source: McKinsey Global Survey on AI, 2026.
of enterprises report AI costs exceeded original projections. Token-based pricing is consumption-driven with no ceiling — enterprise AI budgets grew from $1.2M to $7M in two years.
Source: FinOps Foundation, State of FinOps, 2026.
Model Risk Assessment.
Most organisations select an AI model before assessing what that selection means for their data, their IP, or their cost exposure.
- All data and IP is transmitted to and processed by the model provider under their terms of service, which are subject to unilateral revision.
- Operational dependency on provider availability, API continuity, and pricing decisions is a structural risk that compounds at scale.
- Token-based pricing is consumption-driven and set by the provider. 73% of enterprises report AI costs exceeded original projections.
“AI inference is priced by consumption, not by seat, making costs unpredictable by default.” FinOps Foundation, State of FinOps, 2026.
- Data and IP remain within organisational control, with no external transmission to a model provider.
- Capability on most enterprise tasks is now within a narrow margin of frontier models.
- License terms require legal review before enterprise deployment: many include production caps and jurisdiction restrictions.
“Open-weight models trail frontier closed models by 4 to 7 months on capability evaluations, a gap that has narrowed significantly since 2025.” UK AI Security Institute, July 2026.
- Data sovereignty and regulatory control are highest in this deployment category.
- Significant in-house technical capability is required to deploy, red-team, and maintain safely at scale.
- Smaller active parameter counts can limit performance on complex reasoning tasks.
“The right control for the right risk. Proportionality of control to risk is the governing principle of responsible AI deployment.” NIST AI Risk Management Framework (AI 100-1), 2024.
- Most organisations have not made an explicit model selection decision before procurement begins.
- Organisations routing every workload to a frontier model paid up to 87% more per token than those operating a tiered deployment architecture.
- The engagement maps each identified use case to the appropriate deployment model before any vendor commitment is made.
“The decision we see most consistently producing this premium is not a technical mistake. It is a scoping one.” FinOps Foundation, State of FinOps, 2026.
Three patterns. One root cause.
- AI spend is not linked to defined performance metrics at most organisations.
- Activity is reported where results are expected.
- Enterprise-level EBIT impact from AI is reported by fewer than 4 in 10 organisations.
“88% of organisations use AI in at least one business function. Only 39% report enterprise-level EBIT impact.” McKinsey Global Survey on AI, November 2025.
- Multiple initiatives are active with limited evidence of scalable value.
- Without explicit stop/go governance, pilots persist beyond the point of demonstrated ROI.
- Nearly two-thirds of organisations have not yet begun scaling AI across the enterprise.
“Most organisations are still in the experimenting or piloting phase. Approximately one-third report that their companies have begun to scale AI.” McKinsey Global Survey on AI, November 2025.
- The workflow was not redesigned before the technology was selected.
- Existing failure points are reached faster when AI is applied to unredesigned processes.
- High performers are nearly three times as likely to fundamentally redesign workflows before deploying AI.
“Redesigning workflows is one of the strongest contributors to achieving meaningful business impact from AI deployment.” McKinsey Global Survey on AI, November 2025.
In most cases, the conditions required to support strategically aligned AI implementation are not established before commitment. This engagement establishes them.
Solution
How it works.
A structured six-stage process from problem definition to a board-ready capital commitment.
- Defines the decision environment before any resource is committed to analysis.
- Confirms that success criteria are measurable and approved by senior leadership.
- Surfaces conflicting assumptions before they become procurement risk.
- Validated Problem statement confirmed by leadership.
- Identifies friction and where current state performance diverges from target business outcomes.
- Classifies each zone as Replace, Human in Loop, or Augment based on risk, volume and value.
- Establishes data asset ownership and KPI alignment before any build begins.
High performers are nearly three times as likely to redesign workflows before selecting AI tools. McKinsey Global Survey on AI, November 2025
- Records each decision with the accountable role and the cost of continued deferral.
- Surfaces KPI misalignment and governance deficits before procurement begins.
- Provides direct input to board-level AI governance reporting.
- Board-defensible rationale is attached to each termination recommendation.
- Capital and leadership capacity released is quantified per initiative.
- Reduces drift from pilot to permanent programme.
- Days 1 to 30: highest-priority initiatives with sufficient clarity to proceed.
- Days 31 to 90: staged commitments behind explicit stop/go gates.
- Each initiative carries a named owner, defined KPI, and success threshold.
- Presents the problem, proposed solution, the pathway and aligned metrics for tracking.
- Frames the go/no-go criteria and metrics for leadership decisioning.
- Clarifies the highest priority areas for AI integration aligned to strategic objectives.
Common questions
Common questions.
What executives ask before scoping.
- Before an RFP is released, vendors are short-listed, or a statement of work is signed.
- The engagement locks problem definition, success criteria, and decision logic while leverage over scope and budget remains.
- Once procurement is under way, assumptions become expensive to unwind.
“Most organisations commit to delivery before the problem is sufficiently defined.” McKinsey Global Survey on AI, November 2025.
- Pilot purgatory is the gap between demonstrating technical feasibility and proving business value.
- AI pilots frequently proceed without a defined path to revenue impact, cost reduction, or risk mitigation.
- The engagement identifies the decision path that converts promising pilots into defensible investment cases.
“Nearly two-thirds of organisations have not yet begun scaling AI across the enterprise.” McKinsey Global Survey on AI, November 2025.
- The Map shows where value is created or lost across primary workflows.
- The Log captures the trade-offs leadership will face before procurement.
- The List releases budget and attention from initiatives without demonstrated value.
- The Roadmap sequences execution against explicit KPIs and stop/go gates.
- The Brief provides the one-page rationale for the next capital commitment.
Each artefact is structured for active decision-making at board and executive level.
- Large consultancy assessments are typically bundled with delivery intent.
- This engagement is structurally independent. There is no downstream delivery to protect and no incentive to extend scope.
- The output is decisions, not recommendations.
Independence from downstream delivery is the basis of the engagement’s value.
Start here
Request a scoping call.
If significant funding is under consideration, confirm the right problem is being solved first.