Grand Forks Embraces AI Revolution with Scribe's Milestone

At a downtown coffee shop off DeMers Avenue, startup founders compared notes on pilot projects as word spread that Scribe said it hit a $1.3 billion valuation in its latest funding round, positioning the company to prove where AI can deliver measurable returns. The company framed the milestone as validation for AI tools that automate routine work and surface process insights, according to a company statement shared with partners.

The timing matters locally. Grand Forks has spent the past decade building credibility in autonomy and data-driven operations around the University of North Dakota and the Northern Plains UAS Test Site. A company leaning into practical AI use cases signals opportunity for businesses here that want to move beyond demos to cost savings and new services.

Scribe’s pitch is straightforward: show where AI replaces paperwork, reduces errors, or accelerates training—and track the payback. That focus on utility over hype is the kind of framing regional firms say they need before spending on new tools, a point echoed in federal guidance such as the National Institute of Standards and Technology’s AI Risk Management Framework, which emphasizes testable outcomes and controls.

From Silicon Valley to the Red River

Scribe’s rise tracks a broader global sprint into AI, with private investment and model performance both accelerating, according to the 2024 Stanford AI Index. While many headlines focus on cutting-edge models, the fastest adoption is occurring in “applied AI” that wraps automation around existing workflows—exactly where regional economies can benefit, the report notes.

Founded to streamline documentation and reduce repetitive tasks, Scribe gained traction by embedding AI into process capture and training materials—areas that generate quick wins for enterprise customers. The company’s latest round, which it says pushed its valuation to $1.3 billion, indicates investor appetite for tools that turn AI promises into operating metrics.

Grand Forks is well positioned for that kind of adoption. UND’s long-standing focus on aviation and autonomy, anchored by UND Aerospace, has created a pipeline of talent versed in data collection, sensor fusion, and safety cases. The region’s role in unmanned systems testing and commercialization through the Northern Plains UAS Test Site has also normalized rigorous validation—an advantage when evaluating AI in high-stakes settings.

Impact on the Local Community

For residents, the near-term impact is less about headline-grabbing robots and more about faster service and fewer bottlenecks. Local clinics and service providers can use AI to generate summaries or prep forms, freeing staff for patient and customer interactions. City departments can pilot tools to triage constituent requests or draft repetitive documents, while following the guardrails laid out by NIST and state IT policies.

Small and mid-sized firms—manufacturers, logistics providers, and professional services—stand to benefit most from clear, bounded use cases: onboarding checklists that update themselves, quality checks that flag anomalies, and knowledge bases that keep institutional know-how from walking out the door. The Grand Forks Chamber of Commerce and the North Dakota Small Business Development Centers can help owners vet vendors and evaluate ROI before committing budget.

Workforce effects will be mixed but manageable with training. UND’s College of Engineering & Mines and Nistler College of Business offer coursework that can be tailored to AI literacy and data stewardship, and the university’s research updates via the UND Newsroom routinely flag industry partnerships and grant opportunities. For military families stationed at Grand Forks Air Force Base, short-format certificates in data analytics can translate into portable skills that complement on-base operations and post-service careers.

Expert Insights & Diverse Perspectives

Nationally, the conversation has shifted from “can AI do it?” to “should we, and how?” The NIST AI Risk Management Framework outlines practical steps—define intended use, test against bias and drift, monitor in production—that local implementers can adopt without heavy overhead. The Federal Trade Commission has also cautioned firms to substantiate performance claims for AI products, a reminder captured in its guidance to “keep your AI claims in check.”

At UND, researchers in autonomy and human-factors design have emphasized verifiable performance and safety cases—principles equally applicable to back-office AI. Their work in aviation and UAS operations, documented through UND Aerospace and the test site, provides a template for evaluating AI in other regulated settings like healthcare, finance, and public services.

Local economic development leaders point to Grand Forks’ collaborative posture: the City’s innovation agenda, the Chamber’s member education, and university-industry consortia. Residents can track pilot programs and procurement updates through the City of Grand Forks, which typically posts meeting agendas and vendor reviews, and through public affairs notices at Grand Forks AFB when missions entail new digital systems.

Looking Ahead: The AI Frontier

Expect the next six to twelve months to be less about splashy demos and more about procurement and proof-of-concept work. If Scribe and peers can document real savings—hours saved per employee, lower error rates, faster onboarding—local buyers will follow with contracts sized for pilots, then expand based on measured results. That stepwise approach aligns with how regional governments and base operations phase in new tech, with clear checkpoints for privacy, security, and accessibility.

Grand Forks could also emerge as a testbed for AI that supports field operations tied to aviation, logistics, and public works. Partnerships that combine UND research, city operational data, and private vendors—vetted via the Chamber and university tech transfer—would give local teams a way to validate AI in real conditions without overcommitting budget.

Quick steps for local organizations

• Start with a narrow workflow (intake forms, onboarding guides, or SOP updates) and define a target metric (time saved, error rate).

• Use NIST’s AI RMF categories (govern, map, measure, manage) to structure a 90-day pilot.

• Tap community resources: Chamber programming, UND capstone teams, and SBDC advising for contract language and vendor due diligence.

Resources

• City of Grand Forks — meeting agendas and procurement updates: https://www.grandforksgov.com

• University of North Dakota Newsroom — research and partnerships: https://und.edu/news

• UND Aerospace — autonomy and aviation training: https://aero.und.edu

• Grand Forks Air Force Base Public Affairs — mission and technology notices: https://www.grandforks.af.mil

• Grand Forks Chamber of Commerce — business education and networking: https://www.gochamber.org

• Northern Plains UAS Test Site — testing and commercialization: https://www.[npuasts](https://www.npuasts.com).com

• NIST AI Risk Management Framework — implementation guidance: https://www.nist.gov/itl/ai-risk-management-framework

• FTC guidance on AI marketing claims — compliance basics:

What to Watch

• Company signals: Any Scribe announcements about government, healthcare, or training partnerships—and whether North Dakota agencies or UND appear in pilot cohorts.

• Local adoption: City, Chamber, and UND program calendars for AI-focused workshops or procurement RFPs that cite measurable outcomes (time saved, error rates, compliance).

• Policy guardrails: Federal guidance from NIST and the FTC that could shape vendor claims, contract requirements, and ongoing monitoring for AI tools used by public agencies and base operations.