Most executives do not need another AI policy.

They need to know which assumptions their AI answers are carrying.

For CFOs, General Counsel, Chief Risk Officers, and AI Governance Leads who rely on AI-assisted analysis, reporting, disclosure, strategy, or decision support.

AI can produce answers that look accurate.

Well sourced.

Persuasive.

And still carry assumptions no one has examined.

The Executive AI Assumption-Risk Diagnostic helps you find where that may be creating business, disclosure, governance, or strategic risk before the next consequential decision.

There is a moment that is easy to miss.

An AI-generated answer enters the workstream.

It summarizes a source.

It compares options.

It drafts language for a board update, disclosure, risk memo, strategy document, sustainability claim, investment case, or executive recommendation.

Someone checks the facts.

Someone checks the sources.

The answer looks credible enough to use.

But the deeper question is still sitting underneath it:

What assumptions did that answer inherit from the material it was trained on, retrieved from, or prompted to treat as authoritative?

That is where the risk begins.

Most organizations are asking a narrow question.

They are asking:

Is the AI answer accurate?

That question matters.

But it is not enough.

The better question is:

What would have to be true for this answer to be reasonable, and has anyone examined those assumptions?

That is a different kind of review.

It looks beyond citation quality.

Beyond source reputation.

Beyond whether several credible sources agree.

It asks whether the agreement itself may rest on the same inherited frame.

Ken Alston’s proof domain is sustainability and circularity, where decades of serious work have often treated inherited definitions as settled fact. The same pattern now matters wherever AI-generated answers are used as support for consequential decisions.

When the corpus is treated like a data set, the assumptions behind that data need to be examined.

The Executive AI Assumption-Risk Diagnostic is a focused review of where AI-generated assumptions may be entering consequential business decisions.

This is not a technical model audit.

This is not legal advice.

This is not an assurance engagement.

This is not a broad sustainability workshop.

This is not a generic AI readiness assessment.

It is a founder-led diagnostic that identifies where unexamined assumptions may warrant executive review before they become business, disclosure, governance, or strategic risk.

Outcomes

01. You See Where AI Has Entered The Decision Chain

Ken helps identify where AI-generated or AI-assisted work is already influencing the organization.

Internal research.

Executive recommendations.

Sustainability or ESG language.

Regulatory or compliance material.

Investor communications.

Board materials.

Client recommendations.

The first outcome is visibility.

02. You See Which Assumptions Are Carrying Risk

The diagnostic separates factual checking from assumption checking.

It asks what makes the answer feel trustworthy.

It looks at whether the organization checks sources only, reasoning also, or assumptions explicitly.

It looks for shared-source risk.

It asks whether AI may be helping the organization do the wrong thing better.

The second outcome is an assumption-risk map.

03. You See What Deserves Executive Review

Not every AI-assisted answer deserves a deep review.

Some do.

The diagnostic identifies the workflows, claims, recommendations, measures, or decisions where the consequence is high enough to warrant attention.

The third outcome is a practical list of what should be reviewed, tested, or corrected next.

How It Works

Step 01 – Fit Discussion

You request a 20-minute conversation with Ken.

The purpose is not to solve the issue on the call.

The purpose is to confirm whether there is a real decision chain, a real executive owner, and enough material to make the diagnostic useful.

Ken may ask you to complete a short four-question pre-fit screen before the call.

Step 02 – Diagnostic Confirmation

If there is a fit, you receive the diagnostic scope and payment link.

The investment is $3,500.

Once confirmed, the diagnostic begins.

Step 03 – Assumption Check

After confirmation and payment, your team completes Ken’s reusable AI Assumption-Risk Check.

This is the once-built check.

It is not rebuilt for every client.

It is applied to your context so Ken can see where the highest-risk assumptions may sit.

Step 04 – Material Review

You provide selected materials connected to the relevant decision chain.

Examples may include board or executive excerpts, sustainability or ESG language, AI-generated summaries, claims, recommendations, governance materials, risk language, or strategy documents.

The review stays focused.

Step 05 – Diagnostic Delivery

Within 10 business days of receiving agreed materials, Ken delivers a concise diagnostic package:

• executive summary of the risk pattern
• map of the highest-risk assumptions
• applied assumption-check findings
• list of decisions, claims, workflows, or materials that deserve review
• recommended next steps

The risk is not that AI is always wrong. The risk is that confidence can outrun the assumptions beneath it.

Proof

Ken Alston has worked in and around sustainability and circularity for more than 40 years.

He helped start a major multinational company’s corporate sustainability function in 1992.

He spent 17 years running the firm of two well-known voices in the field.

He has written book-length work on why sustainability practice hit a ceiling and what comes next.

His current Perfectly Wrong thesis extends that experience into AI assumption-risk:

AI becomes risky when a corpus is treated like a data set while the assumptions behind that data remain unexamined.

That is the lens behind this diagnostic.

This is for you if:

• You own risk, disclosure, governance, finance, strategy, or AI oversight.
• You know AI-generated work is already influencing consequential decisions.
• You are less interested in reassurance than in finding the weak assumptions early.
• You have selected materials that can be reviewed.
• You want a focused diagnostic, not a broad transformation program.
• You are willing to examine whether accepted sources may share the same inherited assumptions.

This is not for you if:

• You want a technical model audit.
• You need legal advice or assurance work.
• You want a general AI policy workshop.
• You are looking for a public sustainability campaign.
• You only want validation that current practice is safe.
• You cannot identify a decision chain, executive owner, or material to review.

Download the AI Assumption-Risk Checklist and use it to pressure-test one AI-supported decision before it moves forward.

Investment

The Executive AI Assumption-Risk Diagnostic is $3,500.

It includes:

• a focused fit-confirmed scope
• Ken’s reusable assumption check applied to your context
• review of selected materials
• executive summary
• assumption-risk map
• recommended next reviews

This is private diagnostic work.

It is intentionally small in scope so the result can be delivered quickly and used by the executive owner.

Delivery target: within 10 business days of receiving agreed materials.

If AI-generated answers are not entering consequential workflows in your organization, this may not be the right diagnostic yet.

If they are, the question is whether the assumptions behind those answers have been examined before executives rely on them.

That question is easier to ask before the next board update, disclosure, strategy decision, claim, recommendation, or governance review.

If it feels relevant, request the fit discussion.

Ken will help determine whether there is a real diagnostic to run.

Request a 20-minute fit discussion
If approved, you will receive the private diagnostic scope and payment link. The full assumption check is completed after confirmation and payment.

Is this a legal review?

No.

This diagnostic may identify areas that warrant legal, compliance, governance, or assurance review, but it does not replace those functions.

Is this a technical AI audit?

No.

The diagnostic does not inspect model architecture, training runs, security, or system performance. It examines where AI-generated answers may be carrying unexamined assumptions into consequential decisions.

Do we complete the full assumption check before speaking with Ken?

No.

You may complete a short pre-fit screen before the fit discussion. The full AI Assumption-Risk Check is completed after diagnostic confirmation and payment.

What materials do we need?

Ken will confirm scope on the fit call. Useful materials may include AI-generated outputs, board or executive excerpts, sustainability or ESG language, risk or governance documents, strategic recommendations, product or customer claims, or disclosure-related materials.

Why sustainability and circularity?

That is Ken’s strongest proof domain.

The broader risk pattern applies wherever AI-generated answers rely on a corpus whose assumptions have not been examined.

What happens after the diagnostic?

You receive a concise risk map and recommended next reviews. If the findings indicate a larger advisory need, that can be scoped separately.