The Complete Guide to Using AI in the Government Industry in Fargo in 2025

AI matters for Fargo government because North Dakota's own Artificial Intelligence Guidelines make clear that secure, privacy-first adoption is required for state entities and that data entered into public AI services can be exposed, so agencies must balance innovation with risk management - see the North Dakota AI Guidelines for specifics on security, bias, and acceptable use (North Dakota AI Guidelines on secure and privacy-first AI adoption).

The state's generative AI push - partnering with gener8tor and Microsoft and completing a cohort of 67 participants who invested 938 learning hours - shows real momentum for citizen-facing services and cross-agency automation (North Dakota generative AI cohort and state initiatives).

For teams in Fargo, targeted upskilling such as the 15-week AI Essentials for Work bootcamp can translate policy into practice by teaching prompt design, tool selection, and risk-aware workflows (AI Essentials for Work bootcamp - practical AI skills for the workplace).

“We're exploring how to do that at the moment and Kim and her team are leading that effort.” - Kuldip Mohanty

North Dakota's Artificial Intelligence Guidelines set a clear compliance roadmap for Fargo agencies: the policy applies to all executive-branch entities (including the University Systems Office) and focuses on preventing misuse, protecting confidentiality and integrity of state data, and minimizing privacy and bias risks; crucially, the guidance warns that data entered into public AI/ML services is not secure and may be incorporated into provider models, so agencies should avoid using state‑issued email for public AI accounts and route any evaluated solution through official channels by submitting an Initiative Intake Request via the North Dakota Artificial Intelligence Guidelines - NDIT Self-Service Portal (North Dakota Artificial Intelligence Guidelines - NDIT Self-Service Portal).

The State pairs these operational rules with national best practices - consulting NIST frameworks - and directs agencies to verify AI outputs, exclude medium/high‑risk classified data, and work with their Technology Business Partner or Information Security Officer before piloting enterprise AI features (North Dakota Statewide IT Plan 2025–2027); the bottom line for Fargo teams: route pilots through NDIT, treat public tools as non‑secure by default, and require impact review to keep citizen data safe.

Security, privacy, and responsible AI for Fargo agencies start with treating AI as a socio-technical system: protect data and systems, and design governance that detects harms from computational, systemic, and human biases described in NIST Special Publication 1270 (NIST Special Publication 1270 - standard for identifying and managing bias in AI).

Practical core principles are straightforward and actionable - classify and exclude medium/high‑risk data from public tools, assign clear accountability for each model, embed continuous monitoring and documented audits, publish recourse channels so residents and staff can flag harmful outputs, and require cross‑functional review (legal, security, subject‑matter experts) before deployment - steps reinforced by regulators and industry guidance that call for AI that is secure, transparent, and accountable (NAIC model bulletin recommending NIST's approach to AI governance).

The so‑what: a named owner plus documented monitoring and feedback loops turns abstract fairness goals into operational controls that let Fargo teams find and fix biased or insecure behavior before it affects a single resident.

Practical AI use in Fargo city and county operations in 2025 centers on high-impact, low-risk pilots that free staff and improve resident access: citizen-facing chatbots for 24/7 permit renewals, tax and voter-registration FAQs, and 311-style service requests reduce hold times and routine workload; internal assistants can accelerate report submission, search policies, and give law enforcement quick access to guidelines during incidents while preserving audit trails and role-based security.

National examples show the scale and promise - IRS chat tools handled 13+ million inquiries and processed $151M in self-service payments, Georgia's “George” answered 2.5M user questions with high accuracy, and municipal bots like ATL311 run 24/7 for non-emergency services - models Fargo can adapt to local volumes and constraints (how AI and chatbots enhance public services and government websites).

For sensitive workflows, prioritize private deployments, RBAC, encryption, and integration with legacy systems so data stays under agency control; government-focused platforms and deployment guides explain secure setups, pilot steps, and monitoring needed to limit hallucinations and maintain trust (government chatbot deployment guide and best practices, Microsoft public-sector AI use cases and customer stories).

The so-what: a well-scoped Fargo pilot that routes permit lookups and simple service requests to an audited chatbot can cut routine case-handling time dramatically, letting small municipal teams focus on exceptions that actually require human judgment.

Data governance for Fargo agencies should follow North Dakota's Artificial Intelligence Guidelines as operational rules: classify data and exclude medium‑ and high‑risk categories under the State's Data Classification Policy, avoid inputting that classified information into public AI/ML services (which may incorporate inputs into provider models), and route any evaluated solution through NDIT by submitting an Initiative Intake Request via the Self‑Service Portal to secure Governance, Risk & Compliance (GRC) and Executive Leadership review (North Dakota NDIT Artificial Intelligence Guidelines - data handling, classification, and intake process).

Assign named Data Owners and Data Stewards for each dataset, require cross‑functional signoff (legal, TBP/ISO, security) before pilots, mandate periodic quality assurance and bias checks, and prefer enterprise, state‑managed accounts for production systems while using personally managed accounts only for low‑risk exploration; also, avoid using state‑issued email as a username on public AI tools to prevent account conflicts if the State later adopts an enterprise offering.

The so‑what: a documented intake, named owners, and exclusion of medium/high‑risk data turn abstract compliance into an auditable workflow that prevents state data from being exposed to or absorbed by third‑party models and makes pilots defensible as regulation and oversight evolve (North Dakota legislative attention on AI regulation - Inforum analysis).

“AI is not something that's going to go away.” - Rep. Josh Christy

Fargo agencies should adopt the GSA's three-part AI organization model - Integrated Product Teams (IPTs) embedded in mission units, an Integrated Agency Team (IAT) that provides legal/security/acquisition oversight, and a Central AI Technical Resource that supplies infrastructure and governance - because it preserves mission accountability while delivering shared tools; crucially, the central resource should enable hiring and tool access but not “loan out” data scientists, avoiding a detached center that dilutes ownership and slows outcomes (GSA AI Guide for Government - IPTs, IATs, and Central AI resources).

Start with small, high‑value Fargo pilots inside IPTs, route compliance and acquisitions through the IAT, and consolidate compute and code libraries later via the central resource so projects scale without reworking governance; practical examples from local Nucamp guidance on government use cases highlight how embedded teams accelerate secure deployment and reduce vendor dependence (Nucamp AI Essentials for Work - top AI use cases for Fargo government and ML-driven SOC automation).

Tooling and MLOps for Fargo implementations must pair secure, enterprise-grade platforms with North Dakota's intake and procurement controls so models run where data stays under state control: start any pilot with an Initiative Intake Request and a mandatory IT Review to align with NDIT's AI guidance and procurement templates, prefer enterprise or StateRAMP‑vetted vendors, and bake contract clauses that specify deliverables, ownership, monitoring, and incident response (North Dakota AI guidelines for secure, privacy-first AI use, NDIT IT procurement and IT Review process).

Architect MLOps like production software: versioned data pipelines, repeatable training, automated testing for bias and accuracy, role‑based access controls, and continuous monitoring tied to the State's risk assessment cadence; when NDIT partnered with a vendor to add AI/ML to its SOC, automation processed large volumes of phishing incidents and freed analysts for higher‑value forensics, demonstrating the tangible benefit of operationalized ML (reduced backlog, faster response) that Fargo teams should expect to replicate (North Dakota AI/ML cybersecurity case study by CSO Online).

The so‑what: require IT Review and procurement alignment up front, embed monitoring and contractual continuous‑monitoring obligations, and the city or county can convert pilots into safe, auditable production services without exposing sensitive data.

“As far as I know, NDIT is the first state agency in the nation to roll out AI/ML to enhance cybersecurity.” - Michael Gregg

Fargo teams should treat AI procurement as a phased checklist: begin every project by submitting an Initiative Intake Request and initiating an IT Review so NDIT can map existing solutions and confirm whether the agency needs a new acquisition (NDIT IT Procurement - initiate IT Review and intake); use a Statement of Objectives (SOO) for exploratory pilots under the Simplified Acquisition Threshold and switch to a Performance Work Statement (PWS) when KPIs and specs are clear, which lets vendors propose innovative approaches while meeting measurable standards (GSA AI Guide - use SOO for pilots, PWS for scale).

Make technical tests mandatory in RFPs so subject‑matter experts can validate approaches against local data and require deliverables that preserve government reuse (product backlogs, source code or sufficient usage rights, and documentation).

For any procurement over $500,000, assemble an Executive Steering Committee and plan for agency‑head and NDIT CIO sign‑off; embed contract clauses for monitoring, incident response, and periodic bias/accuracy testing so pilots can graduate to production without vendor lock‑in.

Quick, memorable rule: don't buy an AI system before proving it on a representative Fargo dataset and locking in rights to move the work in‑house or to another vendor.

Fargo's AI readiness depends as much on human capital as on policy - local training partners are already closing the gap: NDSU's Continued Learning and Distance & Continuing Education offer flexible non‑credit upskilling and certificate options for reskilling city and county staff, while the new NDSCS Career Innovation Center (CIC) in south Fargo - opening August 18, 2025 - will create hands‑on pipelines for allied health, trades, and computer science programs (including cybersecurity and AI) that let agencies recruit graduates familiar with practical, employer‑driven skills (NDSU Continued Learning non-credit upskilling and certificate programs, NDSCS Career Innovation Center (Fargo) CIC hands-on CTE and AI programs).

State grant support also makes workforce investments scalable: the State Board of Higher Education's Workforce Education Innovation Fund was appropriated $10 million for 2025–27 to expand curriculum, equipment, and instructor training in priority areas such as AI and cybersecurity - so what: Fargo governments can rapidly reduce hiring lag and training costs by tapping these local programs and grants to create on‑ramp apprenticeships, co‑op placements, and short, role‑specific AI courses that keep skills and sensitive work inside the region (SBHE Workforce Education Innovation Fund (WEIF) grant details).

“NDSCS is fully committed to helping solve the workforce needs of North Dakota. The CIC will allow us to shore up this commitment and continue to fill the workforce pipeline with the future leaders of industry and communities.” - Dr. Rod Flanigan

Takeaway and next steps for Fargo teams: convert strategy into defensible pilots by routing every idea through NDIT - submit an Initiative Intake Request and IT Review, assign a named Data Owner and Data Steward, and exclude medium/high‑risk data from public tools per the North Dakota AI Guidelines for artificial intelligence; for questions, use aiquestions@nd.gov to get alignment before procurement.

Start with a narrow, citizen‑facing pilot (for example, permit lookups or 311-style FAQs) on an enterprise or StateRAMP‑vetted platform, include technical tests and monitoring clauses in vendor contracts to preserve reuse and avoid lock‑in, and require periodic bias/accuracy checks so outcomes remain auditable.

Parallel to the pilot, invest in people: enroll operations and legal leads in role‑focused training - such as the 15‑week AI Essentials for Work bootcamp (AI at Work: Foundations, Writing AI Prompts, Job Based Practical AI Skills) - so staff can write risk‑aware prompts, validate outputs, and turn vendor demos into production workflows.

The simple formula for Fargo: intake + named owners + pilot + training = auditable, low‑risk value that frees staff for complex work while keeping citizen data secure.

“AI is not something that's going to go away.” - Rep. Josh Christy