Can AI Solve One of PR Agency M&A’s Biggest Valuation Problems?

Why Owner Dependency Could Become a Very Different M&A Risk in the AI Era

For decades, one of the most consequential questions a buyer could ask the owner of a PR agency has been deceptively simple:

What happens to this business if you leave?

For many agency owners, the answer can significantly affect what their company is worth.

Owner Dependency - the degree to which an agency's clients, revenue, strategic judgment, new business and day-to-day operations depend heavily upon its owner, CEO, president or other principal, has long been one of the major risk factors confronting strategic acquirers, private equity firms, PE-backed agency platforms, family offices and other buyers of PR firms.

And for good reason.

An agency can have impressive clients, healthy margins and attractive growth, but if its most important client relationships, institutional knowledge and decision-making capabilities reside disproportionately with one owner or senior principal, a buyer faces a fundamental problem: How much of what it is buying will still be there when that individual is gone?

That is why Owner Dependency can translate directly into valuation risk.

But artificial intelligence may be about to change that equation.

Not today. And certainly not completely.

But as AI becomes increasingly capable of capturing, organizing and operationalizing the knowledge that historically resided inside the heads of agency owners and senior executives, Owner Dependency could become one of the PR agency valuation variables most profoundly affected by AI.

The implications for agency M&A could be substantial.

Where Owner Dependency Ranks Today

Every transaction is different, but when The Stevens/Jachetti Group assesses a PR agency from a buyer's perspective, the principal valuation risks generally fall into a recognizable hierarchy.

For a typical established PR agency, a reasonable current ranking would look something like this:

1. Sustainable EBITDA and quality of earnings

2. Revenue stability, growth and predictability

3. Client concentration and retention risk

4. Owner Dependency

5. Management depth and employee retention

6. Recurring versus project-based revenue

7. Differentiation, intellectual property and competitive positioning

8. Scalability and operating leverage

The precise order changes from transaction to transaction. A 40-percent client concentration can instantly become the dominant issue. Weak earnings can overwhelm everything else.

But Owner Dependency consistently belongs near the top because it can contaminate several other valuation variables simultaneously.

If the owner controls the largest client relationships, client retention risk increases.

If the owner generates most new business, revenue growth becomes less transferable.

If the owner is the agency's chief strategist, management-depth concerns increase.

If important processes reside primarily in the owner's head, scalability becomes questionable.

Owner Dependency is therefore more than a single due-diligence issue. It is often a risk multiplier.

AI Could Begin Separating the Owner From the Owner's Knowledge

This is where the M&A implications of AI become particularly interesting.

One critical distinction is between the owner and what the owner knows.

Historically, separating the two has been extremely difficult.

Consider what an experienced agency owner carries around after 20 or 30 years: knowledge of why certain clients behave as they do; how client service should be priced; what constitutes acceptable work; when an account is in danger what is the owner’s/principal’s role in restoring client trust; and hundreds of decisions that collectively represent how the agency is perceived in the market. 

Much of that knowledge has traditionally disappeared when the owner walks out the door.

Increasingly sophisticated AI systems could change that.

An agency can begin capturing different forms of organizational memory—client and project histories, research and previous deliverables, operating procedures and workflows and records of significant decisions and their outcomes. 

As AI becomes more sophisticated, the ability to interrogate and apply that accumulated knowledge should become dramatically better.

The owner's judgment isn't literally transferred into a machine.

But a meaningful portion of the institutional intelligence surrounding that judgment can remain inside the company.

For buyers, that distinction could become enormously important.

The Future Buyer May Ask a Different Question

Today, buyers ask:

"How dependent is this agency on its owner?"

Tomorrow, the more sophisticated question may be:

"How much of what makes this owner valuable has been institutionalized inside the business?"

That is a very different due-diligence exercise.

Imagine two otherwise similar $10 million PR agencies.

Both have owner-CEOs who remain important to clients and strategy.

At Agency A, critical knowledge resides largely with the owner. Client history is fragmented across email and documents. Strategic decisions aren't systematically preserved. Employees routinely escalate problems to the owner.

At Agency B, years of client knowledge, strategic decisions, operating methodologies, pricing logic, successful and unsuccessful campaigns and proprietary processes have been captured in an AI-accessible institutional knowledge system. Workflows define when employees act independently and when senior leadership judgment is required.

Both agencies technically have Owner Dependency.

But they no longer have the same Owner Dependency risk.

That difference should eventually affect valuation.

What the Buyer-Risk Ranking Could Look Like

If AI develops as expected and agencies institutionalize their owner knowledge—not merely subscribe to AI applications—the hierarchy of buyer concerns could change.

A plausible future ranking might look more like:

1. Sustainable EBITDA and quality of earnings

2. Revenue stability, growth and predictability

3. Client concentration and retention risk

4. Management depth and employee retention

5. Scalability, proprietary systems and operating leverage

6. Recurring versus project-based revenue

7. Owner Dependency

8. Differentiation and traditional competitive positioning

Moving Owner Dependency from approximately No. 4 today to perhaps No. 7 in buyer risk ranking is not a prediction that buyers will stop caring about owners or senior leadership.

Quite the opposite.

Owner relationships and genuine human judgment will remain valuable. Negotiation, creativity, reputation, leadership and sensitive client relationships cannot simply be reduced to software. 

What could decline is the buyer's fear that the agency's accumulated intelligence leaves when the owner does.

That is a fundamentally different proposition.

The M&A Advisor's Job Will Change Too

This development could alter how agencies are prepared for sale and how acquisition targets are evaluated.

At TSJG, we increasingly think the M&A question will have to extend beyond conventional financial analysis.

When representing a seller, the job will increasingly involve identifying whether institutional knowledge, proprietary processes and AI-enabled operating capabilities constitute transferable assets that deserve recognition in valuation.

When advising a buyer, the reverse applies: Is the target genuinely becoming less dependent on its owner, or has it simply layered AI applications over an owner-dependent business?

That distinction will matter.

The Ultimate Test

There is an irony in all of this.

For years, agency owners have worried that AI might diminish the value of human expertise.

In M&A, the opposite may occur.

AI could make decades of human expertise more valuable because, for the first time, more of it can remain behind after the individual who accumulated it leaves.

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Joseph Franklin McElroy, Senior AI Advisor at The Stevens/Jachetti Group and Founder & CEO of Galileo Tech Media https://galileotechmedia.com, is a technology entrepreneur, AI strategist and digital marketing pioneer with decades of experience helping organizations use technology creatively, operationally and as a scalability tool. His career has ranged from developing Internet technologies and building entrepreneurial ventures to advising Fortune 500 companies well as startups and founder-led businesses.

Joseph graduated from Duke University with a B.S. in Computer Science and Economics. He attended Duke on a full mathematics scholarship won through a national competition.