The Discerning Leader — An AI Integration Series
It is April 2026. The Class of 2027 has roughly a year before they hit the job market, and recruiters are already assessing them. The timeline is real and the stakes are familiar.
By graduation, AI fluency will be an entry-level expectation for at least 31 percent of corporate recruiters, up from 26 percent the year before. GMAC Corporate Recruiters Survey 2025 Most schools will not finish meaningful curriculum reform on that timeline, and that is not a failure of effort. Only 12 percent of business schools currently mandate faculty AI training. AACSB GenAI Adoption in Business Schools 2025 which means most institutions are working without the foundation they need.
Discerning leaders who see this clearly will recognize the readiness gap as a short-run threat to student placement, starting salaries, employer confidence, rankings, admitted-student yield, and the quiet conversations families have before signing a tuition check. None of these pressures are the dean’s making, but all of them land on the dean’s desk.
There is a workable path forward. The fastest, most practical way to close the readiness gap is co-curricular AI upskilling, paired with faculty development through a train-the-trainer model, such as what Better2Thirds offers.
Why this is urgent
In the short run, the readiness gap is an institutional issue, not a curriculum issue.
A senior associate dean confided to me recently that he had not considered any response to the readiness gap other than updating the curriculum. He is not unusual. Curriculum reform is the right long game because it is built for durability. However, the institutional consequences of the readiness gap will not wait for the next approval cycle.
When recruiters tell career services that they are screening for AI fluency, the consequences land everywhere on campus. Placement softens. Starting salary medians slip. Employer partners shift their funnel toward schools that moved earlier.
Lower rankings, yield, and applications are lagging indicators. By the time these numbers shift, the recruiting market has already chosen new schools. Reputational position in the AI-enabled hiring market is built over years and lost in just two recruiting seasons. The Class of 2027 is in the middle of that window now.
What it takes to build AI readiness into the Class of 2027
Employers are asking for graduates who can use AI tools well, judge their outputs, and apply them to real business problems. AI readiness requires a cluster of capabilities the current curriculum touches but does not fully develop, including technical capability with the tools, vision for where AI changes a function, and judgment about when to trust an output.
The capabilities required for AI readiness are not teachable on a three-year curriculum revision cycle, and not teachable through workshops that bypass faculty.
Leaders have two options to cultivate these capabilities within their faculty. The common approach involves importing an outside program, running it for students, and leaving faculty untouched. This rarely produces AI-ready graduates because the disconnect between workshops and classrooms becomes obvious in applied assessments.
The stronger strategy recognizes faculty as the long-term carriers of institutional capacity. A co-curricular paired with a train-the-trainer model means current faculty build fluency alongside, and slightly ahead of, the students they teach.
A co-curricular with faculty training is how a school addresses an immediate student need without renting a capability it should own.
What an effective co-curricular program looks like
An effective program is not a generic AI workshop, and it is not built once for the whole institution. The model is consistent across majors, but the content is tailored to each discipline because the AI skills demanded of a marketing student are different from those of a finance or supply chain student.
An effective program has three components:
1. Assessment
A per-major assessment of the gaps between the current curriculum and the skills the market is demanding. For example, marketing students may need exposure to AI-assisted campaign analytics or content sequencing tools, while management students may need AI-enabled workflow and coordination tools.
2. Prioritization
A structured decision on which gaps matter most within each major, based on employer expectations, student outcomes, and placement goals. Not every gap can or should be closed at once.
3. Tailored projects (two levels of upskilling)
Projects aligned to prioritized gaps, delivered through a model that first upskills faculty in the department, then students. This produces two outcomes: graduates with portfolio-ready AI-assisted work, and faculty capable of carrying that capability into the formal curriculum over time.
The Discerning Leader’s next move
You do not have to wait for the next curriculum cycle, and the Class of 2027 needs you not to.
The institutional consequences of delay will compound across recruiting cycles, rankings, and admissions. The leaders who act now will be the ones whose students, faculty, and programs remain competitive through the transition.
Save this post and share it with administrators, faculty leaders, and colleagues responsible for student outcomes. When you are ready to explore what a co-curricular and train-the-trainer model could look like at your institution, visit Better2Thirds to continue the conversation.