AI @ KSU

APLU CoR Fellows Visit

March 19, 2026

Dylan Goldblatt & Laila Harrison

Roadmap

  1. Situational Awareness
  2. Operations + Projects
  3. Infrastructure Layer
  4. Future Directions

1. Situational Awareness

Adoption

94% of higher ed professionals use AI for work ¹

86% of students globally use AI for academics ²

66% of institutions leveraging AI — up from 49% last year ³

¹ UPCEA/EDUCAUSE 2026 · ² The Conversation / Digital Education Council · ³ Ellucian 3rd Annual AI Survey

Intentions

92% of institutions have articulated an AI strategy ¹

13% evaluate ROI ¹

20% have formal AI policy ²

10% have governance to support effective use ³

¹ UPCEA/EDUCAUSE 2026 · ² Engageli · ³ AI and Education Studio / Medium

Jobs

66% of business leaders won't hire graduates who lack AI literacy ¹

70% of job skills will change by 2030 ¹

Workers with AI skills command a 56% wage premium ²

¹ AACSB/Microsoft via Open Access Government · ² Globe and Mail / KPMG

Growth

AI in education: $9.58B today → $136.79B by 2035 ¹

34.52% compound annual growth rate ¹

OECD education systems: $310–496B in potential savings from AI ²

¹ Yahoo Finance / Industry Analysis · ² Journal de Québec / TVA Nouvelles

ROI Reality

95% of AI pilots fail ¹

25% of AI initiatives hit expected ROI ¹

66% of orgs report productivity gains ²

29% of executives can measure AI ROI ¹

¹ IBM / MIT · ² Deloitte State of AI in the Enterprise

2. Operations and Projects

Our Approach

Seamless model work at any scale, edge to cloud

One unified, governed data lakehouse for machine intelligence

Self-service research compute and inference

A consistent open-source stack for open science

Chat Over Research Data

Office of Research Agent on Sharepoint

CITI Training Audit

Office of Research Chrome Extension on Cayuse

Pre-Award Proposal Screening

Expertise Portal

3. Infrastructure Layer

Infrastructure Footprint

vHPC cluster: 15 petaFLOPS

High-availability flash storage (Ceph)

400 Gbit/s networking (NSF CC*)

AI Garages: portable NVIDIA GPU, on-prem, on-demand

Inference APIs: guardrailed, secure, metered, BYOK

Serverless GPU on demand

Recommendation: decommission CPU clusters

AI Gateway for LLM Usage

portkey.ai

API Key Vending Machine

keys.theparley.org

Policy and Governance

KSU AI Policy

KSU AI Guidance Document

Public Goods

  • reconsider paid darlings
  • data commons (governed)
  • idle-capacity exchange

Buy vs. Build

Build People

certify leaders and teams

Buy Use Time

rent GPUs; only build on-prem with >70% average utilization

Build Software

pursue wins where ROI ≤ 36 months; AI changes the SaaS moat

Recommendation: Expertise Cohorts

  • Common tools: CRISPR, CAD, JAX/PyTorch
  • Common data: GIS, IoT, Kaggle
  • Common missions:
    • national defense
    • public education
    • life-saving

KPIs

tokens-per-second-per-watt

median user token burn per day

utilized high-bandwidth memory (HBM) and supercomputer power (PFLOPS)

4. Future Directions

Compound Intelligence

Research workflows that accumulate capability over time

Each refinement cycle compounds across the group

Readiness = Transfer × Velocity

groups.theparley.org

DOE Genesis Mission

  • Target: 100,000 AI-trained scientists and engineers within a decade
  • Adding AI electives to existing degrees will not reach that number
  • The target demands a change in what universities produce
  • Model-Output Sprints, core institutional IP
  • Genesis Problem Registry

Office of Research

Kennesaw State University

Dylan Goldblatt

ngoldbla@kennesaw.edu

Laila Harrison

lharr260@kennesaw.edu