How should private equity investors factor agentic commerce into due diligence and valuation?

By James Thomson, Managing Partner of Equity Value Advisors · Published October 12, 2026 · Last updated October 12, 2026

Short answer

Private equity investors should treat agentic commerce as a test of whether a target’s demand is durable, controlled and transferable, not as an upside story. Diligence should test category exposure, product data, channel control, customer data, contribution economics and platform concentration. Valuation should credit only proven, profitable capabilities and price the risk of new intermediaries.

Key points

A deal signed in 2026 will likely exit between 2030 and 2032, the period in which analysts expect agent-mediated buying to reach scale.
The main valuation risk is not missing the trend. It is paying for growth that depends on intermediaries the company does not control.
“AI-ready” claims in a management presentation deserve the same scrutiny as “Amazon-optimized” claims did a decade ago.
Most of the fixes are inexpensive and belong in the 100-day plan.

Why does agentic commerce belong in diligence now?

McKinsey estimates AI agents could orchestrate $900 billion to $1 trillion of US B2C retail revenue by 2030. That projection may prove high or low. What matters to an investor is timing: the next buyer will run diligence in a market where AI assistants shape a meaningful share of discovery. If the target’s product data is inconsistent, its channels leak to unauthorized sellers and its customer data sits with platforms, that buyer will discount the business.

Agentic commerce also amplifies risks that ecommerce diligence already covers. A target with high Amazon concentration, weak channel control or heavy paid media dependence carries more agentic risk, because agents make those weaknesses easier to exploit.

Which categories carry the most exposure?

Exposure depends on how shoppers buy, not on how advanced the brand’s technology is. The following view reflects EVA’s judgment, not a published benchmark:

Higher exposure: replenishment goods, specification-driven products (electronics, supplements, filters) and categories where shoppers compare on attributes an agent can read.
Moderate exposure: considered purchases where agents shortlist options but shoppers still decide, such as mattresses, furniture and appliances.
Lower near-term exposure: emotional, fashion-led or experience-driven purchases where brand affinity and visual judgment dominate.

Higher exposure is not automatically negative. A differentiated brand in an attribute-driven category can win more agent recommendations. A brand whose margin depends on comparison friction will face more price pressure.

What should diligence test?

Area	What to request	Red flag
Category exposure	Share of revenue in replenishment and spec-driven SKUs; AI referral traffic trend	Management cannot say how customers in the category use AI assistants
Product truth	System of record for product data; sample of listings across own site, Amazon, retailers	Material conflicts in specs, claims or images across channels
AI representation	Structured test of how major AI assistants describe top SKUs	Assistants cite outdated, reseller or competitor sources
Channel control	Authorized seller list, MAP enforcement, Brand Registry and account ownership	Unauthorized sellers win a significant share of marketplace orders
Customer data	Customer records, consent and repeat-purchase data by channel	The company cannot identify most of its repeat customers
Economics and concentration	Contribution margin by channel; dependence on top platforms	Growth concentrated in channels with rising fees or opaque terms

These tests fit inside the six workstreams in EVA’s ecommerce due diligence framework. Agentic commerce does not need its own workstream. It needs specific questions inside economics, concentration, channel control, demand quality and transferability.

How should findings change the valuation?

Findings should move valuation through three channels:

Revenue durability. Revenue that depends on one platform’s discovery or recommendation algorithm is less durable. Model scenarios in which an AI platform or marketplace agent introduces referral fees, promoted placement or a preferred-seller rule, the same way a buyer models a marketplace fee increase or Buy Box loss.
Margin. New intermediaries tend to capture part of the efficiency they create, through commissions, sponsored recommendations or integration costs. Do not underwrite a decline in customer acquisition cost from AI channels without evidence from the target’s own data.
Multiple. Buyers pay more for businesses they understand and can run. Clean product data, controlled distribution and owned customer relationships make the commerce system easier to transfer and support a stronger exit multiple.

Avoid the opposite error. Do not add value for “agentic readiness” unless the target can show agent-driven demand that is profitable and incremental.

What belongs in the 100-day plan?

Establish one authoritative source of product truth and reconcile the top SKUs across own site, Amazon and key retailers.
Run a baseline test of how major AI assistants describe and route the top products.
Confirm the company controls every marketplace account, brand registry, domain and data source that would matter to agents.
Tighten authorized seller policy where gray-market sellers capture demand.
Assign one executive with authority over agent connections, data sharing and commercial terms.
Add contribution by channel and AI referral trends to the board pack.

See which ecommerce issues belong in a private equity 100-day plan.

How will a buyer at exit assess agentic commerce?

A buyer in 2030 will likely ask what the company’s current diligence teams ask about Amazon today: how much demand depends on platforms the company does not control, what happens if their terms change, and whether the company owns the data and relationships that create value. Sponsors who address these questions during the hold can present agentic commerce as a controlled capability rather than an unexplained dependency. See what makes an ecommerce business diligence-ready for exit.

What good looks like

The investment committee memo states the target’s category exposure, quantifies channel and platform dependency, flags product data and customer data gaps, and assigns each gap an owner and cost in the 100-day plan. The model includes a downside case for intermediary fees and an upside case only where evidence supports it.

Frequently asked follow-up questions

Does agentic commerce increase or decrease enterprise value?

Agentic commerce increases enterprise value when it brings profitable incremental demand and strengthens the company’s commerce capabilities. It decreases value when it adds weakly profitable growth, platform dependency, loss of customer access or operating complexity a buyer cannot assess.

Should a sponsor walk away from a target that is not ready for agentic commerce?

Rarely. Most consumer brands are not ready, and most gaps are fixable in the first year. Readiness gaps become deal issues when they combine with high platform concentration, weak channel control or a thesis that depends on continued marketplace growth.

Who should run agentic commerce diligence?

The commercial or ecommerce diligence provider should cover it as part of the economics, concentration and channel control workstreams, supported by an advisor with marketplace and platform experience.

Sources

McKinsey & Company, The agentic commerce opportunity, October 17, 2025.

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