A lot of AI education pitches begin with the tool.

The demo is smooth. The claim is familiar. This will save teachers time, personalize learning, improve outcomes, reduce workload, or make school more efficient.

But schools do not buy technology in a clean room. They buy it inside crowded schedules, tight budgets, complex approval chains, nervous parent communities, overworked IT teams, and classrooms where small implementation failures become real problems quickly.

The first useful question for AI edtech comes before the sales conversation: what problem does this actually solve, and what proof does the school have before it starts buying?

Rebekah Fant-Male, founder of Laine Education, works with education companies on positioning, evidence, and adoption. Her view is blunt: many founders underestimate almost everything about how schools make decisions.

“I often see early-stage edtech founders wait until they’ve got a functional MVP before they start ‘Sales and Marketing,’ misunderstanding that good edtech is built in collaboration, not isolation,” she said in comments shared with The Fast Now.

That sentence should make AI builders pause. Some products are overhyped, but the deeper problem is that too many products are built as if evidence comes later.

Evidence is not one thing

In the United States, the Every Student Succeeds Act, or ESSA, gives education companies a formal language for evidence. Fant-Male points to ESSA as a practical framework because it maps the journey from a research-based logic model through to more rigorous studies.

For companies seeking federal funds in the U.S., she notes that there is little point pitching without at least an ESSA Tier IV certified logic model.

That sounds technical, but the underlying idea is plain: before a school or agency trusts a product, the company should be able to explain how the product is supposed to create the claimed outcome.

Beyond the feature list and the founder’s hopes, the school needs a believable path from activity to outcome.

A logic model or theory of change forces a company to describe the problem, the intervention, the conditions required for it to work, and the evidence that would show whether it is working.

AI companies often prefer a faster story. The model is powerful. The workflow is smoother. The teacher gets time back. The student gets help. The district gets efficiency.

Schools need a slower story.

What problem is being solved? For whom? Under what conditions? What changes in the classroom? What new work appears somewhere else? What can go wrong? How will the school know?

“Saving teachers time” can mean reallocating workload

One of Fant-Male’s strongest points is that edtech companies often claim to save teachers time when the reality is more complicated.

“Edtech marketing often claims to save teachers time, but in reality, it’s often just reallocated,” she said.

That distinction matters.

A tool may reduce time spent on one task while creating new demands somewhere else: setup, review, correction, data entry, parent explanation, student support, compliance checks, or troubleshooting.

In many school systems, teachers already do planning and marking outside paid hours. That makes workload hard to measure and easy to misunderstand. A product can feel efficient in a demo while still failing to change the burden teachers actually carry.

This is especially important for AI tools.

AI can produce drafts, summaries, lesson ideas, quizzes, feedback, and administrative text at high speed. But someone still has to check the output, adapt it to the classroom, catch errors, protect student data, and decide when the machine’s answer is inappropriate.

The time may not disappear. It may move.

And if a company cannot say where the time goes, schools are right to be skeptical.

Early adopters are not the whole market

AI adoption often looks strong at the edge. A handful of enthusiastic teachers, principals, or districts try a new tool and report excitement. The product spreads through demos, webinars, conference chatter, and social media clips.

That is useful signal. It is not proof of broad adoption.

Fant-Male warns that founders can overestimate growth by extrapolating from early adopters’ interest and ease of implementation. Early adopters are often unusually motivated, unusually tolerant of friction, and more willing to work around gaps.

Most schools are not early adopters.

They have procurement rules, IT constraints, data protection requirements, budget cycles, staff training needs, parent questions, and competing priorities. A tool that works in a friendly pilot may struggle when it reaches a school with less capacity and more risk.

This is where many AI education companies confuse product interest with institutional readiness.

A teacher may like the tool. A department head may see the use case. A school leader may agree with the problem.

That still does not mean the school can adopt it safely, affordably, or consistently.

Schools need workflows that assume mistakes

The most sensitive part of AI in education is how it works, not simply whether it works.

Schools should ask what data enters the system, what the model does with it, who can see it, where it is stored, whether it trains future systems, how errors are handled, and what happens when students or teachers use the tool in unintended ways.

Fant-Male puts the responsibility where it belongs: founders and education leaders should not rely on teachers, or worse, pupils, to avoid entering information they should not enter. As far as possible, the risk should be engineered out of the workflow.

The safer standard is a workflow that makes risky behavior harder to do in the first place, instead of depending on reminders, policy language, or one-time staff training.

For schools, safety cannot depend entirely on perfect human restraint. The system has to assume mistakes will happen and reduce the chance that those mistakes become breaches.

The better AI education pitch

The better pitch for AI in education is specific.

It says: here is the exact problem we are solving, here is what that problem looks like now, here is how our tool changes the workflow, here is what evidence we have, here are the uncertainties that remain, here is how we protect student data, here is what implementation requires, and here is how we will learn with the school.

That is less glamorous than the usual AI story. It is also more credible.

The goal is to make sure AI in education has earned a claim before it becomes a pitch.

School skepticism is part of responsible adoption.

Founders should not wait until after the MVP, the deck, or the first big sales push to build evidence.

In education, the pitch is only worth hearing when the proof can survive the classroom.

What to watch

  • Can the company name the school problem in plain language before it demos the tool?
  • Does the evidence describe classroom implementation, or only product capability?
  • Where does the promised teacher time go after AI is introduced?
  • Can the workflow prevent sensitive data mistakes instead of only warning people not to make them?
  • Does early adopter interest translate into a realistic school-wide adoption plan?

Source note

Source comments from Rebekah Fant-Male of Laine Education, shared with The Fast Now. Public context includes Laine Education’s article on evidence journeys and RCT timing, EduEvidence, and ESSA evidence-framework references.