AI Literacy Is the Floor. Fluency Is the Differentiator. Almost No Institution Is Producing It Yet.
The gap senior leaders need to close in the next eighteen months is not whether their students can use AI. It is whether their institutions can produce graduates who apply it.
Three surveys landed in the same field this year and pointed at the same finding. Pearson and Amazon Web Services, working from 2,700 responses across six countries, found that only 14 percent of graduates report high proficiency applying AI in a professional workflow. Strada Institute, in a survey of 1,498 senior talent leaders, found that 42 percent of employers say AI has shifted entry-level work toward analytical and judgment-based responsibilities. The Graduate Management Admission Council, in its 2025 Corporate Recruiters Survey, named the same target on a five-year horizon: AI fluency paired with judgment as the top skill employers expect to need.
Three surveys, one finding. The full diagnosis is in my recent Forbes piece: AI literacy is the floor. Fluency — judgment applied through the tool — is the differentiator. And the institutions most at risk are not the ones that know they are behind.
The Missing Word Is Applied
The Pearson data names the institutional version of the problem with painful precision. Seventy-eight percent of higher-education leaders believe they are meeting employer expectations on AI readiness. Only 28 percent of employers agree. That fifty-point perception gap is the most useful number a provost will see this fiscal year. It tells you the institutional self-assessment is wrong, and it tells you in which direction.
Strada finds AI literacy ranks last in employer importance — not because employers do not need it, but because they assume graduates can already open an LLM and type a prompt. The bar moved without most institutions registering it. What employers cannot find is the elevation above that floor: graduates who can apply AI to domain expertise, pressure-test an analysis, catch a flawed recommendation, and defend why they overrode the tool’s first answer. That is fluency, and only 14 percent of graduates demonstrate it.
The volume of entry-level hiring is holding. Strada finds AI is increasing entry-level hiring at nearly three times the rate it is decreasing it. IBM announced in February it would triple US entry-level hiring in 2026, rewriting junior roles around judgment, customer engagement, and AI oversight. What has changed is the selection inside the volume. Stanford economists documented a 13 percent relative decline in employment for 22-to-25-year-olds in AI-exposed occupations — graduates who can elevate work using AI are getting hired; graduates whose preparation suits the old routine work are not.
That is the diagnosis. A graduating class of students who can use AI but cannot apply it is structurally underprepared for the entry-level labor market that already exists. And no curricular bolt-on solves it, because producing AI-fluent graduates is not a curriculum problem. It is a readiness problem.
Why This Is an Institutional Readiness Problem
If the fluency gap were solvable by adding an AI literacy module to first-year seminar, institutions would have closed it by now. They have not, because the work required to produce AI-fluent graduates touches every layer of the institution: who decides what counts as AI-appropriate assessment, whether faculty have the fluency to teach it, whether programs are aligned to the labor market it serves, whether operations support it at scale, and whether anyone is measuring whether it is working. That is five different organizational capabilities. Most institutions have one or two.
This is the gap I built the AI-Ready Institution Framework to make visible. Five pillars: Governance, Human Capacity, Workforce Alignment, Operational Redesign, Evidence and Accountability. The full framework is at edgenerative.com; the diagnostic version is the AI-Ready Institution Scorecard™ at edgenerative.com/scorecard. What follows is what each pillar looks like when an institution takes it seriously, with the two pillars most directly engaged by the fluency gap developed first.
The part nobody selling an “AI transformation” wants to say out loud
I have yet to see an institution that would score a clean 30 on the scorecard. Most land in Emerging or Developing. That is the starting line, not failure — and treating it as failure is how leaders end up chasing tools instead of building capacity.
Workforce Alignment: The Pillar the Fluency Gap Most Directly Engages
Workforce Alignment asks whether programs, credentials, advising, and employer partnerships are preparing learners for AI-shaped work — and how the institution would know. The diagnostic question is simple and most institutions cannot answer it: when did you last review your top programs by enrollment against current AI-impacted labor market data, and what changed as a result?
Georgetown’s Center for Security and Emerging Technology built an answer worth borrowing. Its PATHWISE project, launched in fall 2023 with the NobleReach Foundation, maps the supply and demand of the AI and cybersecurity workforce by region and names the institutions producing graduates into those fields. CSET’s own analysis put the AI-ready U.S. workforce at roughly 17 million in 2022, up from 14 million in 2018. The lesson for a provost is not to replicate a national research tool. It is that program-to-labor-market review can be evidence-driven rather than anecdotal, and the data to do it now exists.
The harder Workforce Alignment work, surfaced by Strada, is the math-intensive STEM shortage. Twenty-nine percent of Strada employers rank math-intensive STEM graduates as the hardest field to hire from. This does not respond to a fix inside existing STEM programs — it responds to gateway-course architecture. Most of the attrition happens between calculus, organic chemistry, and the second-year programming sequence. Wright State University’s EGR 1010, an NSF-funded first-year engineering math course taught by engineering faculty with embedded lab and recitation, has been the most-studied example: four-year graduation rates for students who took the course averaged 23 percent across the first three cohorts, compared with under 8 percent for those who did not. That is roughly thirty additional sophomores retained per year. Workforce Alignment lives in gateway courses as much as in program review.
Human Capacity: Fluency You Can Document, Not Assume
Human Capacity is the second pillar the fluency gap most directly engages, and it is the one institutions most often confuse with training. A webinar is not human capacity. A signed attendance roster is not human capacity. Human Capacity is whether faculty, staff, students, and leaders actually have the fluency and support to apply AI well — and whether the institution can show it.
The University of Manchester treated that as a workforce-development problem rather than an awareness campaign. In December 2024 it launched a Data and AI Academy with Multiverse, a 13-month, apprenticeship-levy-funded program enrolling an initial cohort of roughly 70 professional-services staff. Participants reported saving about four hours a week. The number matters less than the structure: role-specific training, funded through an existing mechanism, with outcomes someone is tracking.
For students, the Human Capacity question runs through course design. Strada found that 33 percent of employers report AI has reduced foundational, skill-building tasks for entry-level employees — a transfer of pedagogical responsibility back to the academy. New hires used to learn to write, analyze, and judge by doing those things badly under correction. If AI is doing that work in the workplace, students need to do it somewhere else. Minerva University built its entire undergraduate model around exactly this architecture: small seminars on its Forum platform delivering, in the institution’s own framing, frequent feedback and low-stakes evaluations through continuous session-level data capture. Other institutions now license the methodology, suggesting the design scales beyond a single niche school. The Human Capacity pillar asks whether your institution has rebuilt the formation environment AI removed from the workplace.
Governance, Operational Redesign, and Evidence: The Three Pillars That Make the Other Two Possible
Workforce Alignment and Human Capacity are where the fluency gap shows up. The other three pillars are what allow an institution to address it at scale rather than in one department.
Governance is the pillar that ends the Reactive pattern of vendor-led, crisis-driven AI adoption. Northwestern established a Provost’s advisory committee on generative AI in 2023; it began meeting that February. Purdue went further: its AI Use policy, published November 1, 2025, requires that any use case touching Purdue data or making decisions about people be reviewed and approved by a Data Ethics Committee, with the depth of review scaled to the risk level. Two different institutions, same underlying move — ownership, decision rights, and risk review in a standing body, not a single administrator’s inbox.
Operational Redesign asks whether you have rebuilt a core process around AI, integrated into the systems people already work in, rather than letting disconnected pilots accumulate. UC San Diego’s TritonGPT is the cleanest example I have seen at scale: a suite of in-house assistants now in the hands of roughly 38,000 faculty and staff, hosted on the university’s own infrastructure at the San Diego Supercomputer Center rather than a commercial cloud, doing concrete administrative work and beginning to extend access to neighboring systems. That is redesign, not a subscription.
Evidence and Accountability is the pillar institutions skip and the one boards and accreditors ask about first. The University of Hawaiʻi has started putting numbers on the board. In a report covering the first quarter of 2026, its student-support chatbots surpassed 100,000 student messages, with 51 percent of students engaging across the system’s ten campuses; UH reported the tools saved staff roughly 165 hours over the period and flagged 1,924 students for proactive follow-up. Engagement, time saved, early intervention — measured, reviewed, and reported. You can argue about whether those are the right metrics. You cannot argue that measuring beats not measuring.
The Pattern: One Pillar Caps the Whole Institution
Put the five pillars together and a pattern shows up almost every time. The constraint on producing AI-fluent graduates is not tooling. It is that one pillar — most often Governance or Human Capacity — is lagging far enough behind the others to cap the institution’s overall readiness. That is the single most useful thing a diagnostic surfaces. You do not close the fluency gap by buying more AI. You close it by strengthening the pillar currently holding the other four back.
Which is why a low score on the scorecard is not an indictment. It means AI activity is moving faster than institutional infrastructure — a common, solvable pattern, and the most honest place to start. The institutions that will look like leaders in three years are not the ones with the most pilots today. They are the ones treating readiness as something built deliberately, pillar by pillar, measured as it goes.
Take the Scorecard
If you want an honest read on where your institution stands across all five pillars, take the AI-Ready Institution Scorecard™. Ten questions, scored 0–30, organized around Governance, Human Capacity, Workforce Alignment, Operational Redesign, and Evidence and Accountability. Four tiers: Reactive, Emerging, Developing, Mature. Built for a leadership team to do together. Available at edgenerative.com/scorecard.
With gratitude,
Dr. Aviva Legatt
Founder, EdGenerative • Affiliated Faculty, University of Pennsylvania • Forbes Contributor
About Dr. Aviva Legatt
Dr. Aviva Legatt is the founder of EdGenerative, where she advises university boards, presidents, and systems on AI governance, adoption strategy, and microcredentials. She holds a doctorate from the University of Pennsylvania, where she serves as affiliated faculty teaching organizational dynamics. A Forbes Contributor (top 2% designation), she has covered education leadership and interviewed figures from Simone Biles to Adam Grant. She serves on the Montgomery County Advisory Council on Artificial Intelligence for Public Good and is the author of Get Real and Get In (St. Martin’s Press). Her AI Use Cases in Higher Education: A Community Handbook is the open-access resource behind this newsletter.