The Complete Guide to Using AI in the Government Industry in Sioux Falls in 2025

Sioux Falls leaders face a clear 2025 choice: lean into AI infrastructure or risk missing jobs and tax growth as proposals for data centers - including one eyed between Sioux Falls and Brandon - push onto the table; U.S. Rep.

Dusty Johnson has framed those centers as critical to the U.S.–China AI race while warning against blanket opposition, and local boosters point to booming metro activity and even “world‑class research…in the middle of a cornfield” as reasons to plan for tech growth.

Read the South Dakota Searchlight coverage of Dusty Johnson's data center comments and the Sioux Falls Metro economic growth report for more local context. Balancing energy, water and tax fairness concerns will matter, and practical upskilling is part of the answer - Nucamp's AI Essentials for Work bootcamp offers a 15‑week path to teach staff how to use AI tools and write effective prompts so municipal projects translate into community benefits.

“Forget the NIMBYs, we've got BANANAs: ‘Build Absolutely Nothing Anywhere Near Anything.'” - U.S. Rep. Dusty Johnson

Generative AI is the class of models that Sioux Falls agencies can harness to turn large datasets into useful content - from drafting public notices and sifting bid documents to creating synthetic training data for municipal analytics - by learning patterns in text, images or code and then producing new, contextually relevant outputs; see Microsoft's guide to generative AI and how it works for a practical primer on training, tuning and token-based text generation.

Under the hood, these systems rely on neural networks and architectures like transformers, diffusion models, GANs and VAEs that identify structure in massive data sets and recreate it on demand, a process NVIDIA's overview of generative model architectures and tradeoffs breaks down well when comparing model types and tradeoffs for quality, speed and diversity.

For local governments, the “so what” is immediate: a tuned model can automate repetitive paperwork, generate visuals for planning meetings, or surface subcontracting leads and asset-management insights faster than manual review (explore Nucamp AI Essentials for Work: local government prompts and use cases).

Agencies must weigh benefits against real costs - compute, energy and the risk of hallucinations or bias - and start with small, well-scoped pilots and transparent data sources so the technology augments staff rather than replacing institutional knowledge.

“When it comes to the actual machinery underlying generative AI and other types of AI, the distinctions can be a little bit blurry.”

Sioux Falls leaders looking to build practical AI capacity without draining tight budgets should bookmark InnovateUS: the nonprofit's no‑cost, public‑sector training offers short, plain‑language courses and a steady stream of recorded workshops that translate directly to city hall tasks - from drafting plain‑language notices to designing pilot projects and procurement checklists; see the InnovateUS hub for an overview and the focused Using Generative AI at Work course (24 short videos, 1 hour 45 minutes) for a quick hands‑on primer.

InnovateUS pairs individual modules (Responsible AI for Public Professionals; Responsible AI for Public Organizations) with topical workshops - everything from “How to Write a Generative AI Policy for Your Jurisdiction” to AI for Public Safety - so teams can mix self‑paced learning with live sessions that answer local implementation questions.

With over 90,000 learners served and a library of practical worksheets, these resources let Sioux Falls staff pilot small, risk‑aware uses of GenAI and build the vendor‑savvy, procurement, and policy skills municipal leaders need to keep local projects accountable and community‑focused.

“I took this training and I was very impressed with it. It shows you how to effectively and securely use these tools to improve your efficiency. I also like that all of the use cases are from the public sector.” - Course participant

America's AI Action Plan, released in July 2025, matters for Sioux Falls because its three core pillars - accelerating innovation, building U.S. AI infrastructure, and leading on international AI diplomacy - translate into concrete local pressures and opportunities: expect federal pushes to speed permitting for large-scale data centers and chip-related projects (the Plan even targets “qualifying projects” that may require more than 100 megawatts), incentives for workforce training that could help upskill municipal staff, and a preference for open‑weight/open‑source models that changes how cities evaluate vendor contracts and IP risk; read the White House AI Action Plan summary for the official framing and a Ropes & Gray analysis of the AI Action Plan for practical implications and compliance flags.

For Sioux Falls leaders, the upshot is a mix of upside - federal programs, procurement toolkits, and training dollars aimed at accelerating local adoption - and risk: potential conditionality of federal funds tied to state regulatory stances, new export‑control-driven supply chain complexity, and pressure to rethink permitting, energy, and cybersecurity plans before proposals reach the council table.

The smartest path is pragmatic: map where local permitting, grid capacity, and workforce pipelines intersect with the Plan's priorities so pilots and procurement decisions capture federal support while protecting public interest.

“Winning the AI race is non-negotiable. America must continue to be the dominant force in artificial intelligence.”

Practical first steps for Sioux Falls start with a clear inventory: catalog every AI touchpoint - chatbots, OCR, translation, asset‑management models - laid out like tools on a long conference table so staff can see what exists, who uses it, and what data feeds it.

Use the federal playbook as a template - local teams can mirror the Consolidated 2024 Federal AI Use Case Inventory to capture planned and deployed systems and follow the DHS approach to flagging the following:

“safety‑and/or rights‑impacting” uses

under OMB guidance - this makes it easier to prioritize where minimum risk‑management practices are required before wider rollout (see the CIO consolidated inventory and the DHS AI Use Case Inventory for practical formats and categories).

Next, run a lightweight risk assessment on each item (data sensitivity, likelihood of hallucination, operational impact), then pick one or two low‑risk pilots - simple form extraction, FOIA search helpers, or automated meeting summaries - to build internal expertise without exposing the city to major liability.

Finally, document mitigations, a human‑in‑the‑loop review plan, and training pathways so clerical and IT staff can move from users to supervisors; the result should be measurable wins (faster permit reviews, clearer public notices) that make the case for scaling while keeping residents' rights and safety front and center.

When Sioux Falls buys AI or works with vendors that embed AI into services, procurement must shift from checkbox shopping to accountable partnerships: expect RFPs and RFIs to call out AI features, provenance, and governance, require vendors to disclose model training sources and human‑in‑the‑loop controls, and build auditability and FedRAMP‑style security into contracts so municipal leaders can prove decisions in FOIA or oversight reviews - a trend already visible in state and local bids and analysis like Deltek's Rise of AI in State and Local Procurement.

Practical steps include requiring explainability and bias‑testing evidence, accepting performance SLAs that tie payments to validated outcomes, and using AI‑enabled tools to streamline spend analysis and invoice matching so anomalies are flagged on a dashboard before payments clear, turning procurement from paperwork into proactive risk management (see Price Reporter's “Contracting in the Age of AI” for lifecycle examples).

Locally, Sioux Falls teams can also tap AI‑driven bid‑search services to find contracting opportunities and build competitive pipelines while insisting on clear licensing terms and post‑award monitoring to keep taxpayer interests front and center.

Drafting a generative AI policy for Sioux Falls starts with clear guardrails that turn abstract risks into everyday rules staff can follow: define purpose and scope so every department knows which tools are allowed and which are off‑limits for sensitive work, require data‑handling standards (anonymization, encryption, and no dumping of resident records into public LLMs), and codify human‑in‑the‑loop review and training expectations so AI augments rather than replaces judgment; practical templates - SHRM's Generative AI Usage Policy Template and WitnessAI's detailed AI Policy Template Guide - offer ready‑made language and a stakeholder workflow to adapt for municipal needs, while UMKC's sample syllabus statements show how tiered permissions (restrictive, supervised, permissive) can map to different city functions.

Include procurement language that forces vendors to disclose training data provenance and explainability, set an enforcement and review cadence so the policy evolves with law and tech, and make compliance as visible as a permit sticker on a crucial municipal file: clear, searchable, and tied to training records so auditors and residents alike can see how decisions involving AI were made.

Sioux Falls is building an AI-ready workforce by knitting together higher education, targeted upskilling programs, and employer partnerships so municipal leaders can hire, retrain, and retain local talent instead of chasing it; the University of South Dakota's downtown presence and Beacom School of Business programs (from Coyote Business Consulting to Executive Education) turn students into real-world consultants for local employers, USD's Discovery District promises incubation space for tech and biotech startups, and the USD–Sioux Falls campus keeps flexible, career-focused pathways close to home (USD Sioux Falls workforce and economic development overview).

At the same time UPSKILL Sioux Falls has already trained nearly 300 drivers and managers and offers up to 50% reimbursement for employer training - an example of how public–private funding can rapidly expand capacity for in-demand roles (UPSKILL Sioux Falls employer training and reimbursement program).

Southeast Tech's Campaign NEXT and new Healthcare Simulation Center are scaling certificates, scholarships, and industry‑aligned programs so technical skills keep pace with employer needs (Southeast Tech Campaign NEXT and Healthcare Simulation Center announcement), creating a pipeline that city hall can tap for AI supervision, data‑ops, and vendor‑management roles rather than hiring from outside the region.

“As a public university, USD has a specific mission to provide access to postsecondary opportunities, provide workforce and economic development.” - Sheila K. Gestring

Sioux Falls must treat synthetic media as a public‑safety and civic‑trust issue, not just a tech buzzword: increasingly realistic deepfakes - audio that mimics a familiar voice or a video that looks “live” - can undercut emergency messaging, enable vishing scams that authorize fraudulent transfers, and weaponize local politics unless cities act fast.

Start by folding detection and response into everyday practices: equip 911 and finance teams with deepfake screening workflows and liveness checks, require extra verification for any urgent executive request, and run public media‑literacy campaigns so residents know how to spot red flags (missing metadata, lip‑sync errors, or suspicious sources).

Convenings like the 2025 Sioux Falls Cybersecurity Conference are a practical place to build those skills and partnerships with regional experts, while the deepfake detection market is evolving tools - now pairing realtime synthetic‑media detection with intelligence analytics - that city IT and public‑information officers should evaluate before procurement.

Combine technical defenses (multifactor checks, automated detectors, and human review), legal readiness for contested evidence, and clear reporting channels so suspicious content is escalated to a trained analyst rather than reshared.

The goal: keep a single convincing fake from becoming the story that erodes trust in every other official message.

“the result is something unprecedented: real-time synthetic content detection embedded directly into intelligence analysis.”

Sioux Falls leaders can treat America's AI Action Plan as both an accelerant and a warning: federal priorities now favor rapid infrastructure buildout, open‑weight models, and workforce funding, but the Plan also signals that funding may flow most readily to jurisdictions that avoid “burdensome” local restrictions - a dynamic that makes local policy choices and procurement language strategically important (see the White & Case summary of the Plan for the federal framing).

Practically, the city should keep doing the basics already discussed here: complete a public AI use‑case inventory, run lightweight risk assessments, launch low‑risk pilots (permitting, FOIA search helpers, meeting summaries), and harden procurement contracts to require provenance, explainability, and human‑in‑the‑loop controls so residents' rights stay protected even as projects scale.

Workforce capacity is the linchpin: invest in targeted upskilling and partner with training programs so municipal staff supervise, not cede, AI decisions - one practical option is a focused course path like Nucamp's AI Essentials for Work to train staff in prompts, tool use, and governance.

Finally, monitor state law developments and federal guidance closely, map where data‑center and grid pressures intersect with community priorities, and aim for measurable, resident‑focused wins that turn abstract policy debates into tangible service improvements.

“The plan appears to suggest a reduced federal role in setting AI governance - with the possible exception of export control enforcement and ...”