How AI Is Helping Education Companies in Lincoln Cut Costs and Improve Efficiency

Lincoln matters for AI in education because the University of Nebraska system is turning research and policy into practical capacity: NU‑ITS is building an AI Resource Center and training pathways to help faculty and staff adopt AI responsibly (NU‑ITS AI Resource Center policy and resources), UNL's new partnership with Scott Data creates an AI makerspace with access to eight NVIDIA H100 GPUs to accelerate student research and industry collaboration (UNL–Scott Data AI makerspace announcement), and nearby pilots like UNO's INSIGHTS (backed by a $400,000 NSF grant) show how AI can measure and improve higher‑order thinking in classrooms; together these assets lower the barrier for Lincoln schools and edtech startups to pilot tools and train educators - a gap Nucamp's AI Essentials for Work bootcamp helps fill with a 15‑week hands‑on curriculum (AI Essentials for Work syllabus (Nucamp)).

“We want to integrate educational theory with AI to improve higher-order thinking skills.” - Tracie Reding

Global forecasts paint AI in education as one of the fastest‑growing edtech sectors, with estimates ranging from a multi‑billion market today to explosive growth over the next decade - for example, one analysis projects growth from USD 7.05 billion in 2025 to USD 112.30 billion by 2034 (Precedence Research AI in Education Market Projection (2025–2034)), while market reports highlight widespread adoption of NLP, intelligent tutoring systems and cloud deployments as core drivers (AI in Education Global Market Report - The Business Research Company).

Practically, those trends mean more vendor tools and funding flow into North America (the largest regional market), creating opportunity for Lincoln institutions to pilot adaptive learning, automated grading and virtual facilitators - proven cost and time savers (Gradescope has cut grading time by ~70% in tests), freeing instructor time for personalized support (AI in education statistics and use cases - AIPRM).

The takeaway for Lincoln: growing market momentum makes now the moment to convert university assets and local pilots into measurable efficiency gains for K‑12 and higher ed partners.

Lincoln and the wider NU system are moving from pilots to practical capacity: NU‑ITS is building an accessible guidance and training hub via the NU‑ITS AI Resource Center for faculty and staff to help faculty and staff adopt AI responsibly, a systemwide AI Task Force is coordinating cross‑campus priorities and an AI inventory survey to surface scalable projects, and UNL's UNL Open AI Impact Program (ChatGPT Enterprise pilots) is already provisioning ChatGPT Enterprise to up to 200 faculty and staff for classroom and operational pilots at no cost - a concrete shortcut that lets Lincoln educators and local edtech startups test generative AI in production settings without long vendor negotiations; complementary private support arrived when Google donated a $250,000 gift from Google to the NU Foundation for AI research and teaching to accelerate AI research and teaching across campuses.

“As AI rapidly transforms our world, it is crucial that the University of Nebraska positions itself as a leader in this new era.” - Jeffrey P. Gold

Nebraska companies are already converting AI and automation into measurable time and dollar savings: Carson Group's rapid deployment of Zocks captured meeting notes, synced work to Salesforce and saved advisors an average of 8 hours per week while delivering more than $2M in ROI in the first six months (Carson Group Zocks case study and ROI results), and Carson Wealth's switch to Workshop trimmed internal email work for a three‑person communications team by roughly five to seven hours weekly while improving targeting and analytics for 130+ partner offices (Carson Wealth Workshop case study and email automation improvements).

Even product work that unlocked data - like a client dashboard built with Aviture - translated into direct business wins, including a $68M account tied to the improved client experience.

The takeaway for Lincoln education companies: prioritize integration, analytics, and workflow automation first - those are the levers that turn weekly hours saved into faster pilots, higher adoption, and tangible ROI.

“It's incredibly easy… we have to use our time very critically and we have to be very responsible with it. Compared to the email sending platform that we were using prior to Workshop, it saves us five to seven hours every single week.” - Maddy Schwarz

Common AI use cases for Lincoln education companies cluster around three practical levers: early‑warning analytics that nudge struggling students, automated and AI‑assisted grading to scale timely feedback, and operational automation that frees instructor time for high‑value coaching.

UNL researchers showed one small predictive intervention - automated forecast messages at weeks 6, 9 and 12 - lifted pass rates in a computer‑science course from about 73% to 91%, a concrete “so what?” that makes predictive nudges worth piloting (UNL AI-based intervention study).

For grading and feedback, hybrid models that combine auto‑grading for objective items and AI‑assisted review for essays can trim instructor workload while preserving human oversight; institution reports highlight Gradescope‑style tools and AI‑assisted grading as efficiency boosters and urge transparency and audits to manage bias (AI and auto-grading capabilities and ethics review).

Operationally, NU‑ITS guidance and training make it easier for Lincoln partners to adopt these tools responsibly - pair pilots with clear policies, instructor training, and assessment redesign (e.g., proctored or in‑class checks) so efficiency gains don't erode academic integrity (UNL guidance on AI and academic integrity).

“AI‑Resistant” vs “AI‑Proof” is intentional.

Lincoln education companies should measure AI costs and returns by linking labor‑hour savings to student and operational outcomes, not just license fees: adopt a System Strategy ROI process to frame which programs to measure and why (System Strategy ROI guide from ERS: System Strategy ROI - ERS), then use a productivity‑first lens that compares labor cost versus output over a 12–24 month window to capture real gains from training and workflow automation (Productivity‑First ROI approach from Data Society: Measuring ROI of AI & Data Training - Data Society).

Start with small, instrumented pilots - track reduced grading or admin hours, time‑to‑decision, and downstream KPIs such as course pass rates or enrollment funnels (these are the levers that turn hours saved into dollars and faster product iterations) - and validate results with a practical ROI calculator or dashboard so finance and program leads can see break‑even timelines and reinvestment options (ROI calculator example from IntelliSee: ROI Calculator - IntelliSee).

The predictable payoff: when pilots measure both time saved and student impact, Lincoln teams can convert modest tool costs into scalable savings and clearer prioritization for campuswide adoption.

“The return on investment for data and AI training programs is ultimately measured via productivity. You typically need a full year of data to determine effectiveness, and the real ROI can be measured over 12 to 24 months.” - Dmitri Adler, Data Society

Begin with a tightly scoped, instrumented pilot: pick one measurable pain point (auto‑grading, discussion‑board analysis, or early‑warning nudges), run an 8‑class or single‑course pilot to collect baseline and post‑pilot metrics, and use that evidence to secure partners and funding; UNO's INSIGHTS pilot started in 8 classes and shows that a small, well‑measured rollout can demonstrate value before scaling (UNO INSIGHTS pilot overview - UNO AI classroom study).

Leverage shared infrastructure and training opportunities - apply to use NAIRR pilot resources for compute, models and user support (register for the NAIRR info session or contact Heather Borck at 402‑472‑4090) to avoid one‑off hardware costs (National AI Research Resource pilot presentation - NAIRR resources for education).

Tie pilots to the NU Taskforce playbook for governance, micro‑credentials and workforce development so pilots feed into campus centers and compliance plans rather than remain siloed (NU AI Taskforce recommendations for governance and workforce development); pair technical pilots with short instructor upskilling (no‑code workflow courses) so adoption doesn't stall at deployment.

Lincoln schools and edtech teams must treat AI as a governance problem as much as a technology one: UNL's guidance urges instructors to require students to read vendor privacy policies before mandating AI tools and to design assignments that reveal the learning process rather than just the finished product (University of Nebraska–Lincoln guidance on ethical AI use in the classroom); Concordia Nebraska highlights the twin risks of student privacy and embedded bias and recommends checking COPPA/FERPA compliance, vendor data‑use policies, and equity of access when selecting tools (Concordia University Nebraska recommendations for ethical AI use in curriculum and instruction).

Operationally, avoid overreliance on AI‑detectors (they produce false positives and can harm non‑native speakers), require data‑provenance reviews, and build short AI‑literacy sessions for instructors so pilots include clear consent, auditing, and remediation paths - a single, documented privacy review and an access plan can be the difference between a safe pilot and an exposed classroom.

For context on systemic risk and bias, see the broader ethical framing in Harvard's analysis of AI's societal harms (Harvard Gazette analysis of ethical concerns as AI takes bigger decision-making roles).

“If we're not thoughtful and careful, we're going to end up with redlining again.” - Karen Mills

Lincoln's AI ecosystem is converging around practical university–industry partnerships that make pilots faster and cheaper: the University of Nebraska–Lincoln's new AI makerspace, built in partnership with Omaha's Scott Data, gives students and local edtech teams access to eight NVIDIA H100 GPUs and a Tier‑III GPU‑as‑a‑Service node - a concrete resource that turns months of procurement into weeks of experimentation (UNL and Scott Data AI makerspace announcement with eight NVIDIA H100 GPUs); complementary private investment from Google (a $250,000 gift to the University of Nebraska System plus broader data‑center expansion in Lincoln and the region) creates funding and infrastructure pathways for longer‑running pilots and downstream hiring pipelines (Google $250K gift to University of Nebraska and regional data‑center investment).

For Lincoln education companies, the practical next step is clear: partner with campus makerspaces for shared compute, cobuild short, instrumented pilots with faculty, and use corporate grants or GPU‑as‑a‑service deals to avoid capital outlays - this combination shortens time‑to‑insight and helps convert small pilots into funded programs and hires.

“The intent with our partnership with the College of Engineering is to have a dedicated server of eight GPUs at Scott Data that will provide a powerful level of computing access and expertise.” - Ken Moreano

Conclusion - Lincoln education companies should move from curiosity to calibrated action: start with a tightly scoped, instrumented pilot (an approach proven by UNO's INSIGHTS pilot, which began in eight classes with NSF support) and pair that pilot with NU‑ITS policy and training so governance, privacy reviews and instructor upskilling happen up front (NU‑ITS Artificial Intelligence Resource Center); use shared campus programs and grants to avoid heavy capital - UNL's programs and NAIRR opportunities can supply compute, models, and user support - and measure success by linking hours saved to student outcomes (pass rates, time‑to‑grade) before scaling.

Upskill staff quickly to keep pilots productive: a focused course like Nucamp's Nucamp AI Essentials for Work bootcamp (15 weeks) arms operational teams with promptcraft and workflow automation skills so tools save time rather than create new work.

Treat the first pilot as a 12–24 month learning investment: small evidence-backed wins unlock grants, makerspace partnerships, and the hires that convert pilots into sustained efficiency gains (UNO INSIGHTS pilot overview - WOWT coverage).

“As AI rapidly transforms our world, it is crucial that the University of Nebraska positions itself as a leader in this new era.” - Jeffrey P. Gold