When an agent does the shortlisting, the questions that matter in diligence change. Revenue exposure, acquisition dependency, agent visibility and measurement integrity become as important as margins and growth.
This article gathers the practical side of the white paper: ten diligence questions for funds and acquirers, what to change if you own a business or run an agency, and the Selection Readiness Scorecard, a thinking tool you can fill in on this page. Opinions are labelled as Monarchy’s view.
01Implications for Investors
Ten diligence questions
- 01Revenue exposure. What share of revenue comes from selling production hours or deliverables that AI can produce at acceptable quality within two years?
- 02Acquisition dependency. What share of new customers depends on paid attention or organic search clicks, both of which are under pressure1?
- 03Agent visibility. When the major AI assistants are asked for a business like this one, is it recommended? Is the description accurate?
- 04Consistency. Do prices, specs, policies, hours and claims match across every channel where the business appears?
- 05Proof. How much third-party, verifiable proof exists (reviews, outcomes, partners), and is any of it at risk under the FTC’s rule on fake reviews and fake social indicators2?
- 06Owned relationships. How large and active are first-party channels: an email list, a community, a client base the company talks to directly?
- 07Proprietary know-how. Has the company captured what its best people know in a form AI can use? Or does it live in people’s heads and PDFs?
- 08Measurement integrity. Which KPIs in the board pack measure humans, which measure machines, and which are unknown?
- 09AI economics. For each AI initiative, what problem, what full cost (usage, supervision, errors, dependency) and what measured result? McKinsey’s data suggests most organizations cannot yet show EBIT impact3.
- 10Human accountability. Who, by name, approves what the company’s AI produces and says to customers4?
What good looks like
A business that scores well is not necessarily the most automated. It is one that owns its truth, its proof, its relationships and its know-how, and that uses AI to amplify them.
02Implications for Operators and Agencies
For business owners
- “AI can do it cheaper” is the fastest way to become indistinguishable. Invest in the people who know what makes you different, and in the structure that lets agents verify it.
- Look at your budget. How much buys attention, and how much builds preference? If almost everything is attention, you are investing in the part that is eroding.
- Advertising will still exist, but it will amplify preference, not replace it.
For marketing agencies and service firms
- Producing content is no longer the product. Differentiation, data structure and agent preference are. Agencies that sell volume will be replaced by the same tools they use.
- If your product is media buying and ad production, AI is your direct competitor. Move to strategy, brand and truth.
- Reports full of vanity metrics will not survive the next budget review. Clients will want proof of humans and proof of revenue.
- The job is shifting from teaching clients to adopt technology to helping them stand out once everyone has it.
- Consider moving from fees to ownership: equity or revenue share where your work drives results, or a business of your own in a vertical you understand.
For everyone
- Put a named human on the work. AI can draft, sort and suggest. Someone still has to say “this is right, and I stand behind it”.
03Self-assessment: The Selection Readiness Scorecard
Score each statement: 0 = not true, 1 = partly true, 2 = fully true. Be honest. The scorecard is a thinking tool built from the frameworks in this paper, not a benchmarked index.
A. Value and ownership
We have listed every service or product we sell and asked whether AI could produce it at acceptable quality within two years.
We have decided in advance where AI-driven savings in capacity and cash will be reinvested.
We own part of the value we create (equity, revenue share or our own operating business), not only fees.
B. Truth and consistency
Our offer, pricing logic, inclusions and guarantees are written clearly in one place, in a machine-readable form.
Our prices, specs, policies, hours and claims match everywhere we appear.
C. Proof and preference
We have verifiable third-party proof (reviews, case outcomes, partners) in places we do not control.
We have owned channels (an email list, a community, a client base) that would survive an algorithm change.
If we swapped logos with our closest competitor, clients would notice.
D. Measurement and judgment
Every KPI in our board pack is labeled human, machine or unknown, and we check regularly whether AI assistants recommend us.
A named person approves what our AI produces before it reaches a customer, and our best people’s know-how is captured so AI can work to our standard.
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Adapted from the Monarchy white paper “You are not talking to a human” (2026) by Andrea d’Agostini. Sources are listed below. Statements marked as views are opinions.
Sources
- SparkToro (Rand Fishkin) with Similarweb data, “In 2026, Less than One Third of Google Searches Still Send a Click,” 9 June 2026 (68.01% zero-click Jan-Apr 2026, US; 60.45% in 2024; AI Overviews on 20%+ of searches). https://sparktoro.com/blog/in-2026-less-than-one-third-of-google-searches-still-send-a-click/
- US Federal Trade Commission, “Federal Trade Commission Announces Final Rule Banning Fake Reviews and Testimonials,” 14 August 2024. https://www.ftc.gov/news-events/news/press-releases/2024/08/federal-trade-commission-announces-final-rule-banning-fake-reviews-testimonials
- McKinsey & Company, “The state of AI in 2026: On the road to ROI,” 25 August 2026 (survey of 1,719 participants, fielded 4 May-8 June 2026). https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
- Moffatt v. Air Canada, 2024 BCCRT 149 (Civil Resolution Tribunal of British Columbia). https://www.canlii.org/en/bc/bccrt/doc/2024/2024bccrt149/2024bccrt149.html
