Your campus deployed AI. {Can you prove it's working?}
Most institutions are implementing AI. Very few can show a trustee, an accreditor, or a faculty senate what it's actually changing. Alex Goryachev builds AI readiness assessments and impact measurement frameworks grounded in the largest AI readiness dataset ever produced for a public university system.
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What separates an AI readiness assessment from a dashboard.
Most institutions are implementing AI. Very few can prove what it's changing. Here is the difference between evidence your board can act on and a usage report.
Alex Goryachev
Big-Firm Consulting
EdTech Vendor Dashboards
Concrete outputs, not dashboards nobody trusts.
Every engagement produces evidence your board, your accreditor, and your faculty senate can act on. The format varies; the standard does not.
AI Readiness Assessment
A structured baseline of where your institution stands — infrastructure, governance, faculty capability, student access — benchmarked against data from the largest readiness study in public higher education.
Impact Measurement Framework
The metrics that survive faculty scrutiny: learning outcomes, competency attainment, adoption rates, equity gaps, and institutional ROI — defined with your institutional research office, not imposed on it.
Accreditation-Aligned Reporting
Pre/post deployment evaluation structured so the same evidence serves your board, your accreditor, and your budget process — answers instead of anecdotes.
How an engagement works.
The same evidence-first process Alex runs inside the largest AI deployment in U.S. public higher education. Engagements range from a one-time readiness baseline to recurring board-ready reporting.
Scoping conversation
A direct conversation about what your leadership needs evidence for — a board, an accreditor, a budget request. That determines what gets measured. No intake decks, no pre-work forms.
Baseline and benchmark
Readiness data collected across faculty, staff, and students, then benchmarked against system-scale data. Alex does this work — not a survey vendor reporting up to a partner.
Findings working session
A working session with your leadership team that turns findings into decisions — plus the measurement framework your institutional research office keeps long after the engagement.
The playbook came from building inside universities.
As former Managing Director of Innovation Strategy and Head of Global Innovation Centers at Cisco, Alex built global innovation centers and programs inside universities and research institutions across 14 countries — a $1.1B portfolio that generated $400M+ in revenue, innovation tracks for three Olympic Games, and partnerships from Imperial College London to Keio, NUS, University of Toronto, and UNSW Sydney. That operating history is what his assessments measure against.
Countries
University-anchored innovation centers built with partners including Imperial College London, Keio, École Polytechnique, and UNSW Sydney.
Portfolio managed
Innovation strategy at Cisco run with the same discipline these assessments bring to campus: governance, measurement, ROI.
Olympic Games
Three Olympic Games — innovation programs delivered live on the world stage, with immovable deadlines, global partners, and zero tolerance for failure. That's the operating standard behind every campus engagement.
Select higher education engagements
In five years every institution will claim AI transformed it. Accreditors will ask {which ones can prove it}.
What would you measure first?
Accreditation evidence, board reporting, equity gaps, ROI — tell Alex what your leadership needs proof of.
Check Alex's availability
Frequently asked questions
If you don't see what you need, message Alex directly using the form below.
What is an AI readiness assessment for a university?
An AI readiness assessment for a university is a structured evaluation of whether the institution can actually absorb AI: governance, infrastructure, faculty capability, student access, and culture. Alex Goryachev's version adds the dimensions generic corporate frameworks skip, including shared governance, academic integrity, and equity of access across campuses. Findings are benchmarked against a 22-campus system serving 460,000 students. The output is a scored baseline the cabinet can compare against again next year.
How do you measure the impact of AI in higher education?
Measuring AI in higher education starts before the rollout, with agreed metrics for learning outcomes, competency attainment, faculty adoption, staff time saved, equity gaps, and cost per user. Deploying first and hunting for metrics later is the most common mistake, and it is the reason so many pilots cannot prove anything. Alex Goryachev builds the measurement framework with the institutional research office, so the evidence holds up in front of a faculty senate.
Where does AI actually pay off for a university?
AI pays off first in university operations, where the numbers are countable: enrollment and financial aid processing, advising load per counselor, retention of at-risk students, and administrative cost per transaction. Faculty time recovered from grading and course prep is the second line, and classroom learning gains take longest to show. Alex Goryachev helps institutions build the case in terms trustees and CFOs accept, using cost and adoption data from a $17M, 22-campus deployment.
How should universities prepare for accreditation questions about AI?
Accreditors ask for evidence about AI rather than policy statements, so universities should collect pre and post deployment evaluation data, documented learning-outcome effects, and equity monitoring by student population. Structuring that evidence once lets the same package serve the board, the accreditor, and the budget process. Alex Goryachev's measurement engagements produce that reporting framework as a standard deliverable, mapped to the review cycle the institution's regional accreditor already runs.
How much does an AI readiness assessment cost?
Pricing for an AI readiness assessment depends on scope: number of campuses, depth of stakeholder interviews, whether survey instruments go to faculty and students, and whether the institution wants benchmarking against peer systems. A single-campus baseline sits at the low end; system-wide benchmarking across dozens of campuses sits at the high end. Alex Goryachev returns an exact quote within one business day of a scoping call, and sells no assessment platform, so the findings have no product attached.
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