Cross-cutting Audit concern

AI across the Coppice Labs Audit

The AI Lens applies across the five pillars wherever AI materially changes customer value, product capability, economics, transferability or something else which is important.

This is a supporting lens across the Audit, not a numbered sixth pillar.

Purpose

The AI Lens is a cross-cutting extension to the Coppice Labs Audit. It is applied when AI is material to the product, its economics, its operation, or the investment/acquisition thesis.

It is not a sixth pillar and it does not replace the existing product principles. Instead, it ensures that AI-specific characteristics are examined within the pillar where they are most relevant.

The objective is to answer two questions:

  1. Does the use of AI materially change the assessment of the product, technology, commercial model, transferability or diligence readiness?
  2. Are there AI-specific strengths, dependencies or risks that are likely to matter during a raise or sale and that the company can address or evidence before diligence?

The lens should remain practical and evidence-led. It is not intended to become a standalone model audit, security review, legal review or regulatory compliance assessment. Issues requiring specialist expertise should be identified and referred.

When to Apply the AI Lens

The depth of the lens should depend on how material AI is to the product.

AI-native

AI is fundamental to the product's core value proposition or operation. Removing or materially weakening the AI capability would substantially change the product customers are buying.

Apply the AI Lens in depth across all relevant pillars.

AI-enabled

AI materially improves one or more important product capabilities, workflows or outcomes, but the product's fundamental proposition is not dependent on AI alone.

Apply the lens to the relevant focus areas, with particular attention to customer value, product evidence, technical dependency and economics.

AI-incidental

AI is used in peripheral features or internally but is not material to the product value proposition, product architecture or commercial model.

Use a light screening approach and pursue AI-specific questions only where a material issue emerges.

The classification is a guide to depth rather than a score or fixed label. It may change as the audit develops.

How AI changes each pillar

Pillar 1 of 5

Customer Value

Is AI creating meaningful customer value in practice, and do customers experience that value reliably enough for it to support adoption and retention?

Pillar 2 of 5

Future Growth

Does the company understand, measure and manage the behaviour of its AI capabilities well enough to develop them deliberately rather than relying on underlying model progress or anecdotal quality?

Pillar 3 of 5

Commercial Model

Does the commercial model capture the value created by AI while remaining economically sensible as usage, model costs and customer behaviour change?

Pillar 4 of 5

Transferability

Can another capable team understand, operate and continue developing the AI aspects of the product without unacceptable dependence on tacit knowledge, particular individuals or opaque experimentation?

Pillar 5 of 5

Foundational Readiness

Are there obvious AI-specific issues involving data, rights, security, privacy, third-party terms, product claims or applicable obligations that are likely to surface during diligence?

Cross-Pillar Boundary Guide

Some AI topics naturally affect several pillars. To avoid duplication, we assess each topic from the perspective relevant to that pillar.

TopicCovered by pillar
AI creates customer value Customer Value
Evaluation and learning discipline Future Growth
Model/provider operational dependency Future Growth
Inference and AI cost-to-serve Commercial Model
Defensibility of paid AI capability Customer Value and Commercial Model , from customer-value and commercial perspectives respectively
Tacit AI knowledge / key-person dependency Transferability
Data and model usage rights Foundational Readiness
Provider contractual restrictions Foundational Readiness
Security and privacy implications Foundational Readiness
Regulatory or specialist compliance questions Foundational Readiness , with referral where required

How the Lens Should Affect Audit Outputs

Applying the AI Lens does not produce a separate generic AI score. AI-related findings appear within the relevant focus area so that they remain connected to the underlying product or diligence question.

Where AI is material, findings should distinguish between:

Evidence of strength

AI contributes demonstrably to customer value, capability, economics or defensibility.

Unproven assumptions

An important AI claim is plausible but insufficiently evidenced.

Actionable gaps

Evidence, process, architecture, economics or documentation can realistically be improved before diligence.

Structural dependencies

The business has a meaningful reliance on a model, provider, dataset, person or workflow that an investor or acquirer should understand.

Specialist referrals

Legal, regulatory, security or privacy issues require expertise beyond the scope of the Coppice Labs product audit.

The purpose of the AI Lens is therefore not to judge whether a company is "good at AI". It is to make sure that, where AI is material, the audit asks the product, technical, commercial and diligence questions that a non-AI-specific framework could otherwise miss.

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