How AI Is Helping Government Companies in Fort Wayne Cut Costs and Improve Efficiency

Indiana's public sector stands at a practical inflection point: as the state launches the $182 million Indiana Energy Saver Program with no‑cost home audits and a Fort Wayne pilot funded at nearly $60,000, lawmakers are simultaneously moving from curiosity to careful deployment of AI - from a statewide chatbot that speeds citizen access to services to an AI job‑matching pilot that produced top job recommendations paying nearly $4/hour more for applicants - showing clear, measurable upside for efficiency and household savings.

That combination - federal energy dollars focused on homes and state task‑force scrutiny of AI uses and privacy - makes Fort Wayne a testbed for cost‑cutting, faster service delivery, and workforce supports that local agencies can begin adopting today (see the Indiana Energy Saver Program and the Indiana AI task force for program details and meeting notes).

Bootcamp	AI Essentials for Work
Description	Practical AI skills for any workplace: tools, prompts, business applications
Length	15 Weeks
Cost	$3,582 early bird; $3,942 after
Syllabus / Register	AI Essentials for Work syllabus · AI Essentials for Work registration

“Cutting energy costs is at the very heart of what we are focused on. The Indiana Energy Saver Program prioritizes practical solutions to improve energy affordability and deliver quality products.” - Gov. Mike Braun

Administrative automation offers Fort Wayne a practical way to shave overhead while speeding service: pilots and products already show how routine work - ServiceNow ticket triage, PDF intake and classification, contract compliance checks, and identity verification - can be largely automated so staff focus on complex cases.

Federal examples include a ServiceNow generic ticket classifier and the G-REX AI document-classification pilot, which demonstrated measurable time savings and improved accuracy, and procurement tools that flag non‑compliant solicitations for human review (see the GSA AI Use Case Inventory for details).

Commercial case‑management platforms promise the same gains - automating approvals, document processing, and routing to shorten case lifecycles (see the Speridian CaseXellence article on AI in government case management).

Those efficiency gains come with real risks: Indiana's own Medicaid/SNAP modernization that shifted work to self‑serve channels correlated with a 50% rise in application denials, underscoring the need for human oversight, audit logs, and clear appeal paths before scaling automation across Fort Wayne agencies.

The practical payoff: faster routing and automated document capture can cut processing time by hours per case and redeploy limited staff to the toughest, highest‑value work.

Automation	Expected Benefit	Source
ServiceNow ticket classification	Faster routing; reduces manual reroutes	GSA AI Use Case Inventory
G-REX document classification	Time savings and improved accuracy on PDFs	GSA AI Use Case Inventory
AI case-management (automated approvals)	Shorter case lifecycles; automated routing/processing	Speridian CaseXellence article on AI in government case management
Identity verification (facial matching)	Faster remote account creation; fraud protection	GSA AI Use Case Inventory

“Failures in AI systems, such as wrongful benefit denials, aren't just inconveniences but can be life-and-death situations for people who rely upon government programs.”

Fort Wayne can sharpen decision-making by treating state and federal records as operational assets: weekly unemployment‑insurance claimant counts are published nearly immediately, while county‑level GDP can take almost a year to arrive, so pairing near‑real‑time administrative metrics with slower economic indicators yields both speed and context for policy choices (see the Purdue Fort Wayne government data guide).

Practical steps proven in federal research include building a central data warehouse, using probabilistic record linkage to match client records across programs, and establishing written confidentiality and MOUs to govern access and sanctions - measures that reduce duplicate records, improve longitudinal tracking, and protect privacy (see the ASPE HHS study on welfare populations and data linkage).

For citizen‑facing analytics and monitoring, apply privacy‑aware approaches such as metadata‑only video analytics and documented security policies so Fort Wayne gains faster insights without sacrificing trust or legal compliance (see Nucamp guidance on privacy‑aware video monitoring analytics).

Practice	Impact	Source
Use near‑real‑time admin data	Timely program adjustments using weekly UI counts	Purdue Fort Wayne government data guide
Probabilistic linkage & data warehouse	Cleaner longitudinal records across agencies	ASPE HHS study on welfare populations and data linkage
Written MOUs & security procedures	Enables data sharing while protecting privacy	ASPE HHS study on welfare populations and data linkage

Nucamp guidance on privacy-aware video monitoring analytics (Cybersecurity Fundamentals)

Citizen‑facing chatbots can make routine interactions faster and less costly for Fort Wayne residents by using the Executive Order 14058 “Life Experiences” approach to map the specific moments when people need government to work, design clear bot scripts for those repeatable tasks, and escalate to humans when issues are complex; the EO also shows a practical accountability model - 3‑, 6‑, 9‑month and annual progress updates - that Fort Wayne agencies can mirror to monitor chatbot performance and public trust.

To deploy responsibly, pair user‑centered flows with documented compliance checks and enterprise risk frameworks - aligning local chatbot builds to Indiana AI policy and NIST RMF guidance helps ensure privacy, auditability, and appropriate human oversight.

The payoff is concrete: simpler online transactions for residents, fewer routine calls for staff, and a measurable channel to publish service improvements to the public (Executive Order 14058 “Life Experiences” customer experience guidance; Nucamp AI Essentials for Work syllabus for AI policy and NIST RMF alignment).

Fort Wayne's workforce strategy is moving from pilots to pathways: Indiana's opt‑in AI job‑matching pilot at the Department of Workforce Development has already produced a tangible wage signal - Management and Performance Hub staff reported the top AI match paid nearly $4 an hour more than jobseekers found on their own - and local training providers are expanding supply-side options so residents can convert matches into credentials and hires.

Connecting that pilot to local upskilling makes the outcome actionable; for example, Indiana Tech secured nearly $1M in Workforce Ready Grant funding and added six new certificates, including an Artificial Intelligence option, giving Fort Wayne jobseekers credentialed routes to roles surfaced by AI. For city leaders, the clear next step is aligning opt‑in matching, transparent oversight, and accessible certificate pipelines so matches don't just surface better pay but lead to verified, employer‑recognized skills and faster placement into in‑demand work (Indiana AI job-matching pilot at the Department of Workforce Development; Indiana Tech Workforce Ready Grant and new AI certificate announcement).

Indiana Tech Workforce Ready additions (2025)
Advanced Accounting; Artificial Intelligence; Business Analytics; Financial Services; Health Science; Policing and Corrections (nearly $1M funding)

Management and Performance Hub Chief Data Officer Pete Miller said when people used the AI tool, the top job result would pay nearly $4 an hour more than if the person searched themselves.

Predictive analytics in Fort Wayne schools starts with practical, time‑sensitive signals - not more data - so educators can act before outcomes are final; early warning indicator systems (EWIS) rely on the ABCs - attendance, behavior, and course performance - to flag students at risk and trigger tiered supports, turning administrative feeds from SIS and LMS into actionable interventions rather than retrospective charts (Early Warning Indicator Systems (EWIS) in Education - Resultant).

The real payoff for Fort Wayne: when EWIS is aligned with MTSS-style interventions and monitored at the school level, struggling students can be identified and redirected long before end‑of‑year grades or test scores arrive, preserving graduation paths and reducing costly remediation.

Local practitioners and instructional leaders can review practical implementations and share lessons at the 28th Annual Fort Wayne Teaching and Learning Conference on Feb.

21, 2025 (Fort Wayne Teaching and Learning Conference - Feb 21, 2025), making the shift from insight to on‑the‑ground intervention faster and more measurable.

Early Warning Indicator	Role in EWIS
Attendance	Strong early predictor of off‑track outcomes
Behavior	Signals engagement and disciplinary risk
Course performance	Immediate academic progress vs. lagging final grades
Additional indicators	Chronic absenteeism, disproportionate discipline, mastery, SEL

Procurement and infrastructure planning in Fort Wayne can move from cost‑heavy guesswork to data‑driven precision by applying AI to siting, capacity modeling, and thermal orchestration: with Google already committing $2 billion to a Fort Wayne data center, AI tools that evaluate thousands of placement options in seconds can prevent “stranded capacity,” defer expensive buildouts, and align purchases with Indiana incentives and grid constraints (Data Center Frontier: Indiana data center investments).

AI‑powered thermal controls and workload orchestration have delivered concrete operational savings - Google/DeepMind examples cut cooling energy by up to 40% and improved PUE by ~15% - while predictive maintenance and capacity insights turn weeks of planning into minutes and expose underused assets that could fund other priorities (Nlyte: AI-powered data center optimization).

Practical next steps for city procurement teams include requiring DCIM/AI readiness in RFPs, scoring lifecycle energy savings alongside capital cost, and piloting capacity‑planning tools to uncover idle capacity (>50% of racks often run below 50% utilization) before buying new power or land (Hyperview: smarter data center capacity planning); the payoff: measurable delays to expansion and lower operating bills that make investments fiscally and environmentally smarter for Fort Wayne.

Metric	Source
Google investment in Fort Wayne: $2 billion	Data Center Frontier: Indiana data center investments
Cooling energy cut (DeepMind example): ~40%; PUE improvement ~15%	Nlyte: AI-powered data center optimization
Planning time reduction: weeks → minutes (AI placement)	Nlyte: AI-powered data center optimization
Typical underutilization: >50% of capacity runs below 50% utilization	Hyperview: smarter data center capacity planning

“Hyperscalers in 2024 are becoming increasingly selective about new data center locations, as new factors rise in importance,” Gartner analyst David Wright told Data Center Knowledge.

Fort Wayne agencies should treat AI rollouts like program launches: start small with human-centered pilots, require vendor transparency and robust contract terms, measure outcomes, then scale using a Plan‑Do‑Check‑Act loop - practical steps that Indiana's bipartisan AI task force and state tech leaders are already emphasizing.

Concretely, require baseline tool documentation (purpose, validation results, conflict‑of‑interest disclosures), run adversarial and benchmark tests before production, maintain audit logs and clear appeal paths for automated actions, and pair every citizen‑facing pilot with employee training and an escalation workflow so humans remain in the loop; the state's job‑matching pilot illustrates the upside of disciplined testing - when Hoosiers opted in, the top AI match paid nearly $4/hour more than their own searches.

Protect data and budgets by tightening vendor clauses on model access and data sharing (a priority raised by Indiana privacy and tech chiefs), record versioned evaluation reports, and publish simple performance metrics so councils and residents can see benefits and harms.

These steps align with national guidance and the World Privacy Forum's call for standardized documentation, continuous evaluation, and quality management for AI governance tools.

Best Practice	Action for Fort Wayne
Pilot + Human Oversight	Small trials with human escalation and staff training
Documentation & QA	Require tool metadata, validation reports, and adversarial tests
Vendor Controls	Tighten contracts on data use, model access, and change notifications
Public Metrics	Publish evaluation summaries and outcome metrics

metrics “can be oversimplified, gamed, lack critical nuance, become relied upon in unexpected ways, or fail to account for differences in affected groups and contexts.”

Further reading: Indiana AI task force findings and recommendations for state AI governance · World Privacy Forum report: Risky Analysis - assessing and improving AI governance tools · Coverage of Indiana tech and privacy chiefs on vendor contracts and data protections

AI promises efficiency for Fort Wayne, but tangible risks demand local governance: surveillance and sensor systems must use metadata‑only approaches and documented safeguards to protect civil liberties while delivering crowd‑safety insights (privacy-aware video monitoring analytics in Fort Wayne); procurement and vendor contracts should require model documentation, audit logs, and clear appeal paths so automated decisions stay transparent.

Equally urgent is a workforce plan: adaptation strategies for administrative staff - retraining, role redesign, and phased pilots - will determine who benefits from automation and who is displaced, making local upskilling a governance priority (administrative staff adaptation strategies for Fort Wayne government).

Anchor these actions to state standards by aligning city policy and procurement to Indiana AI policy and NIST RMF guidance, creating auditable controls that turn efficiency gains into accountable, equitable outcomes for Indiana residents (Indiana AI Policy and NIST RMF alignment for Fort Wayne).

Fort Wayne should measure a tight set of KPIs that link citizen outcomes, operational savings, and AI safety: customer experience (CSAT/NPS), cost‑per‑transaction and processing time (automation pilots can cut hours per case), digital adoption (daily/monthly active users and feature use), workforce outcomes (time‑to‑hire and the wage lift from AI job‑matching - the state pilot's top match paid nearly $4/hour more), and model health (accuracy, bias metrics, and drift rate with audit logs).

Make these metrics visible in an executive dashboard, set baselines and realistic targets, and run regular check cycles so teams can Act‑Do‑Check‑Plan on problems the data surfaces.

Use established KPI frameworks to pick only the measures tied to mission outcomes and avoid metric overload; see practical KPI lists for digital transformation at Kissflow digital transformation KPI lists and governance and monitoring guidance in the GSA AI Guide for Government on governance and monitoring to align measurement with responsible deployment.

Metric	Purpose
CSAT / NPS	Track citizen satisfaction with digital services
Cost per transaction / Processing time	Quantify operational savings from automation
Adoption rate / DAU‑MAU	Measure real use and digital shift
Time‑to‑hire & wage uplift	Assess workforce placement quality and economic impact
Model accuracy, bias, drift	Monitor AI reliability, fairness, and need for retraining
ROI / Financial KPIs	Link investments to short‑ and long‑term returns

Fort Wayne leaders should move from pilots to a short, visible action plan: catalog near‑term AI use cases (chatbots, job‑matching, document automation), require vendor documentation and audit logs in RFPs, and run 90‑day human‑in‑the‑loop pilots tied to clear KPIs so residents and councils can see outcomes.

Anchor procurement to state guidance and technical controls, pair every citizen‑facing pilot with appeal paths and staff retraining, and link opt‑in workforce tools to local certificate pipelines so matches turn into hires - Indiana's pilot showed the top AI job match paid nearly $4/hour more than jobseekers found on their own, a concrete economic payoff to replicate locally (see the Indiana task force coverage for meeting notes and results).

For policy and technical alignment, use state/NIST‑aligned checklists before scaling and convene a cross‑sector working group to publish simple quarterly metrics for transparency and course correction; practical templates and compliance advice are available in Nucamp's guide to Indiana AI policy and NIST RMF alignment.

Next Step	Resource
Join state AI conversations & review pilot results	Indiana Task Force on Artificial Intelligence - WBOI coverage
Align procurement & controls to state/NIST standards	Nucamp AI Essentials for Work - Indiana AI policy & NIST RMF alignment guide
Pilot opt‑in job matching + local certificate pathways	Indiana Tech Workforce Ready grant and certificate programs - 21Alive

“When people used the AI tool, the top job result would pay nearly $4 an hour more than if the person searched themselves.” - Pete Miller, Management and Performance Hub Chief Data Officer

Fort Wayne's path forward is clear: capture the tangible efficiency gains of AI while embedding the state's guardrails so residents don't pay the price for haste.

Indiana's bipartisan AI task force continues to scrutinize use cases and tradeoffs - and the State of Indiana already requires agencies to submit an AI Readiness Assessment and secure an AI Policy Exception (with annual or event‑triggered reviews and just‑in‑time notices) before tools go live, creating an auditable approval path that local pilots should mirror (State of Indiana AI Policy and Guidance).

That procedural discipline matters because pilots already show measurable upside: Indiana's opt‑in job‑matching tool produced top matches that paid nearly $4/hour more, a concrete wage signal Fort Wayne can replicate with transparent KPIs and human‑in‑the‑loop checks reported to councils and residents (WBAA coverage of Indiana AI task force findings).

To turn pilots into trustworthy savings, require vendor transparency, public metrics, appeal paths, and staff training - resources and practical curricula such as the Nucamp AI Essentials for Work syllabus can help Fort Wayne operationalize oversight while capturing real cost and service gains.

Policy or Result	Why it matters
AI Readiness Assessment & Policy Exception (Indiana)	Creates auditable approvals, annual reviews, and JIT notices to protect privacy and oversight (Indiana AI Policy and Guidance - State of Indiana)
Job‑matching pilot wage uplift	Top AI match paid nearly $4/hour more - demonstrates measurable economic benefit to replicate locally (WBAA reporting on Indiana job‑matching pilot)

“When people used the AI tool, the top job result would pay nearly $4 an hour more than if the person searched themselves.” - Pete Miller, Management and Performance Hub Chief Data Officer