How The EdTech AI Divide Is Repricing M&A

Two EdTech businesses with similar revenue can now trade at radically different multiples, or in some cases, fail to trade at all. The gap is not new, but in our experience advising founder-led EdTech businesses on the sell side, it has widened to a point where buyer behavior is barely on a spectrum anymore. The market is currently less like a volume knob and more a light switch.

The Market Has Gone Binary

The bifurcation in EdTech has been visible since the SaaS correction of 2022, but in our experience, the threshold for what gets a deal done has moved sharply higher each year since. The threshold is high, but good deals for good companies are still getting done.

The line we see buyers drawing comes down to whether a product sits inside an institutional workflow or sits on top of one. The line we see buyers drawing is close to binary. A platform is either embedded in the institutional workflow or it is sitting on top of one, and that determines whether a deal gets done at all.

There are two ways to be on the right side of that line. The first is to be the system of record, the platform that owns the institutional data and becomes the source of truth an institution cannot easily walk away from. The second is to be an agentified system of action, the platform that executes the workflow itself rather than just informing it. Both are defensible. A system of record compounds value through data and switching costs. A system of action compounds it through the work the agent actually performs, working autonomously to close the loop and drive better outcomes.

A chatbot is a reasonable place to start, but it cannot be the whole strategy. Surfacing a problem or a data point is only the first step. What buyers are paying for is a platform that also solves it, which is what the system of action is really about: not just presenting the data, but acting on it. Products that sit on top of the workflow without owning the data or doing the work are being discounted or passed over.

The criteria we see applied most consistently are three questions:

• Is the platform a system of record or an agentic system of action?
• Is it hyperverticalized?
• How sticky is the customer base, measured most directly by gross retention?

Businesses checking two of the three can still find outcomes, though often not at the multiples they would have commanded two years ago. A business checking only one of the three may not see a deal close in the current market.

What Buyers Mean by "Applied AI"

12 to 18 months ago, applying AI in EdTech often meant a chat layer on top of a data set or dashboard, useful but easily replicated. The bar today is materially higher. To register with buyers as workflow-embedded, the AI needs to do more than surface an insight. It needs to act on it.

Applied AI in practice means the model is integrated into the institutional workflow at the point of execution: it is the engine driving how admissions teams communicate with prospects, how advisors identify at-risk students, or how learning and development platforms adapt content delivery in real time. The AI touches the data layer, learns from usage, and gets harder to replace the longer it runs.

The earlier generation of AI features could present an insight and prompt the user to take an action. The institution would then close the loop, and the data from that action would feed back into the system. The current expectation is that the loop closes itself. When the platform identifies the right 10 prospective students to re-engage, it also drafts the campaign, sequences the cadence, and executes the outreach once approved. Where the action is repeatable, the agent should perform it.

A genuine AI moat in EdTech tends to combine three elements: ownership of a system of record that captures first-party behavioral data, embedded execution across the workflow rather than at a single touchpoint, and switching costs that compound as the model improves with use. A product that accumulates proprietary behavioral data from hundreds of institutions and uses that data to improve its models creates a flywheel a competitor cannot replicate by plugging into the same foundational model. Acquirers underwrite the compounding, not the capability alone.

Element451 is an excellent example. The platform started as a CRM positioned inside higher-ed admissions and student success, then embedded AI across the full student lifecycle, from recruitment and enrollment through retention and ongoing communications. The AI was not a feature bolted on. It became integral to how institutions managed their workflows, the kind of deep operational dependency that makes switching genuinely costly.

The story buyers underwrote was system of record plus agentic execution, not a chat feature bolted onto a database. PSG Equity's $175 million growth investment in Element451 reflected that thesis.

How Buyers Are Diligencing AI Claims

Nearly every EdTech founder now claims some AI capability, and sophisticated buyers know it. The phrase that keeps surfacing in our buyer conversations is "AI washing," and the response has been a measurable expansion in diligence depth.

In practice, that means technical teams inspecting workflows, auditing data provenance, assessing vendor dependencies, and stress-testing whether the AI is genuinely proprietary or simply an API call to a third-party model with a thin wrapper on top.

Bain reported in late 2025 that 75 percent of strategic acquirers now assess the impact of AI on a target's business as a standard part of diligence, and roughly one in five have walked away from a deal specifically because of unfavorable AI exposure. In our experience, AI concerns have become the leading reason a deal dies in diligence.

The frameworks have followed. KPMG, for instance, has published a structured AI due diligence approach that evaluates targets across 10 dimensions, including data ownership, model performance, governance, and scalability. The diligence process is not necessarily more sophisticated than it was a year ago, but it is far more detailed.

For founders, the implication is straightforward. The AI story carried in the pitch deck has to survive the data room. Buyers will ask whether the AI is core architecture or a feature, whether the data is proprietary or rented, and whether the model gets better with scale or simply more expensive.

The Valuation Impact

The impact we are seeing on multiples has become binary rather than a sliding scale. The strong outcomes have not gone away, there are simply fewer companies positioned to capture them. The pool of haves has narrowed, but for the businesses on the right side of that line, the opportunity is very real.

For businesses on the right side of that line, the supply and demand imbalance is producing genuinely compelling outcomes. Platforms that combine system-of-record positioning with embedded AI have never been more sought after, and the buyer behavior reflects it. That is consistent with the broader 2025 dynamic Bain documented, in which 60 percent of all global deals greater than $1 billion last year were "scope deals," the highest rate on record, meaning buyers were paying for capabilities rather than scale.

What Founders Should Be Doing Now

Where a founder sits on the spectrum determines what they should be doing right now, and the answer is different depending on which side of the divide they are on.

For a founder who suspects the platform is more AI-exposed than AI-embedded, the realistic options come down to building toward the right side. What AI takes away, AI also gives back. The same model capabilities that are commoditizing content-led products have also collapsed the cost of building real workflow. Roadmaps that would have required 20 engineers across one year can now be executed by a small team in a fraction of the time. The focus should be on closing the loop, building toward agentic functionality that produces outcomes rather than insights the user has to act on themselves.

For founders already operating as systems of record with embedded AI, the work is not done. The priority is continuing to deepen the data moat, making switching costs greater, and using AI to compress the roadmap and deliver more to existing customers. The defensibility that attracted buyer interest is durable only if it keeps compounding.

In both cases, the buyer expectations on the financial side have moved in parallel, which is why the metrics that matter when running and selling an EdTech business increasingly need to be paired with a defensible AI narrative.

Know Where You Stand

The most important thing for an EdTech software founder right now is to know where the platform sits in the bifurcated market before a buyer or investor draws that conclusion first. Inbound interest, valuation curiosity, or an unsolicited offer can all be opportunities, but they can also be premature if the platform's defensibility has not been articulated in the language buyers use to underwrite it.

If you are evaluating inbound interest, exploring a capital raise, or simply trying to understand how buyers are likely to grade your business in today's market, we can help you assess where you stand.

This material and the opinions voiced are for general information only and are not intended to provide specific advice or recommendations for any individual or entity. All opinions and views constitute our judgments as of the date of writing and are subject to change at any time without notice. The material may contain "forward-looking" information that is not purely historical in nature. Such information may include, among other things, projections, forecasts, estimates of market returns and proposed or expected portfolio composition. Past performance is no guarantee of future results and there is no assurance this trend will continue.

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