AI Value Creation Framework

This framework turns AI from a cost and a distraction into a measurable margin driver. It covers use case prioritization, a governance framework, back-office automation, FP&A acceleration, and a board-ready AI narrative. Each piece is tied to EBITDA.

The Business Problem

AI pilots run with no governance, no measurement, and no connection to EBITDA. The executive team cannot answer LP questions about AI strategy. Spend is increasing, and the board cannot see the return.

The engagement is designed to leave the company with measurable margin improvement, a governed AI program, and an account of AI results it can give LPs.

BCG's 10-20-70 rule holds that roughly 10% of AI value comes from algorithms and 20% from data. The remaining 70% comes from changes to the operating model and ways of working. BCG restated the rule, which it draws from its client experience, in its July 2026 report on how CEOs scale AI value.

The 6 AI Investment Categories

Every AI dollar maps to one of six investment categories. These categories describe where AI investment is deployed across the lifecycle. They are independent of accounting type: labor, infrastructure, and vendor spend can each appear within any category. CCA classifies each category as cost-reduction or new-cost and routes findings to EBITDA buckets:

#	Category	Financial Outcome
1	Foundational Infrastructure (cloud, compute, storage)	Cost-reduce or right-size
2	AI/ML Platform & Tooling	Consolidate or optimize
3	Data Preparation & Engineering	Productivity gain
4	Model Development & Fine-Tuning	Build vs. buy decision
5	Deployment & Integration	Speed to margin
6	Ongoing Operations & Governance	Risk containment

Findings route to EBITDA buckets: reduce cost / protect margin / expand revenue / compress multiple risk.

The Agent ROI Calculator works one level below the categories. It estimates what a single agent would recover from one recurring workflow in a year. An allocation has to pass that test before it goes on the prioritization map.

What We Deliver

• AI Use Case Prioritization Map: Initiatives ranked by EBITDA impact
• AI Governance Framework: policy, controls, model risk
• Back-Office Automation Implementation Plan: where to save the first dollar
• FP&A Acceleration Roadmap: faster close, better forecasting
• Engineering Productivity Improvement Plan: A plan based on the method behind the 30% productivity improvement described under Proof Point
• AI Investment Allocation Map: shows the board exactly where AI spend goes and why
• Board/LP AI Narrative: one page, buyer-defensible

When to Engage

• AI pilots running with no governance, no measurement, no EBITDA connection
• Executive team cannot answer LP questions about AI strategy
• Back-office costs consuming margin that AI automation could recapture
• Engineering team spending time on AI experiments with no commercial accountability
• Exit is 18–36 months out and the buyer will want a credible AI story

Engagement Format

90-day sprint for initial deployment; ongoing for governance. CCA role: use case leadership, governance design, implementation oversight, board narrative. Best paired with a Fractional CAIO retainer.

Proof Point

30% engineering productivity improvement through AI-enabled workflow modernization across globally distributed engineering teams. AI and ML models deployed in production including risk stratification and predictive analytics at scale.

Relationship to AI Value Attribution

This framework handles allocation. It decides where the next AI dollar goes across the 6 AI Investment Categories. The AI Value Attribution Framework handles verification. It checks an initiative against the five measurement levels (Cost, Adoption, Productivity, Business Outcomes, Enterprise Value) and records the highest level the evidence supports.

Run them as a loop. Allocation sets the thesis and names the outcome. Attribution tests whether the outcome was achieved, and the result informs the next allocation. The attribution framework's four pre-approval questions should be asked of every use case on the prioritization map, because a baseline cannot be reconstructed after deployment. Each of the three AI economics frameworks answers one question. Allocation belongs here, recovery to AI Cost Optimization, and verification to Value Attribution.

Relationship to CLEAR™

The AI Value Creation Framework deploys primarily in CLEAR™ Leverage (AI governance for EBITDA protection, productivity gains) and Accelerate phases (product capability, revenue growth). The Enterprise AI Control Plane is the governance architecture that makes AI deployment scalable and exit-defensible.