The Complete Guide to Using AI in the Retail Industry in Lincoln in 2025

UNL Raikes School AI retail tools are already changing retail in Nebraska - UNL Raikes School students helped Buckle create an AI shopping assistant and used Claude Sonnet to generate 2 million tags for 50,000 inventory items - showing how better tagging and natural‑language search can shorten discovery cycles and lift conversions; national research echoes the upside if data is organized (Amperity 2025 State of AI in Retail report), yet adoption in the state faces headwinds because many Nebraskans remain uninformed or distrustful of AI (Nebraska Rural Poll on AI familiarity and trust).

The practical play for Lincoln stores in 2025 is narrow: partner with local tech teams for inventory tagging and pilot personalization, while running straightforward education for customers so AI delivers measurable sales instead of skepticism.

“Buckle's collaboration with the Raikes School team gives us access to some of today's brightest computer science and design minds who are looking to use technology to solve cutting edge problems.” - Brandon Hauff, Vice President of Information Technology, The Buckle

Think of AI in Lincoln stores as two practical toolsets: natural language processing (NLP) that understands customer words and powers product search, chatbots, sentiment analysis, touchscreen assistants, and smarter tagging, and machine learning (ML) that finds patterns across sales, inventory, and local demand to forecast stock, personalize offers, and tune prices; UNL's Raikes School example - 2 million tags for 50,000 items - shows how better tagging and NLP shrink discovery time and lift conversions, turning messy catalogs into searchable shelves.

For a small-to-mid‑size Lincoln retailer, that means a single FAQ handled by an NLP chatbot for retail customer support or semantic search can free staff while ML-driven forecasting and dynamic pricing (see top ML use cases and implementation steps) cut stockouts and optimize margins in real time, so pilot projects focus on clean POS data, one high‑value use case, and measurable KPIs rather than broad technology bets.

Machine learning in retail applications and key use cases complements NLP by turning behavior into operational wins - faster restock, fewer markdowns, and clearer ROI for the next upgrade.

“Machine learning in retail is about more than accessing big data. The quality and ‘purity' of this data are also crucial. Your software provider should be able to help clean the information and train the models to interpret the most probable causes of deviation.” - Jane Medwino, founder at LEAFIO AI Retail Solutions

Lincoln retailers can start with ten high-impact, practical AI use cases that map to real local needs: 1) AI-powered product recommendations to lift basket value (67–91% of shoppers expect relevant suggestions; see generative AI recommendation benefits), 2) behavioral-personalization at scale using customer event data to tailor offers, 3) in-store shopper‑behavior analytics to optimize displays and dwell time, 4) conversational AI agents and kiosks for faster service and guided discovery, 5) demand forecasting and automated restock to cut stockouts, 6) dynamic pricing engines to protect margins during local events, 7) loss‑prevention and fraud detection to reduce shrink, 8) smart‑shelf and inventory robotics for faster counts and restocking, 9) unified customer identity and audience activation to run targeted omnichannel campaigns, and 10) staff‑assist tools (autocomplete search, checkout helpers) that free employees for higher‑value service; combine evidence from national pilots - Happiest Minds reports cross‑sell lifts of 10–12% and Retail Aware documents a 250% growth case for a CPG client - to set measurable KPIs, keep pilots local and short (4–8 weeks), and partner with Lincoln vendors for agent development and deployment like the local MMC Global AI agent team in Lincoln, instrument stores with Retail Aware in-store analytics, and prioritize recommendation pilots shown effective in industry coverage (BizTech article on AI-powered product recommendations in retail).

“Retail Aware has proved an excellent strategic partner for us as we seek to better understand consumer engagement and dwell time within large retail environments nationwide. The service and support Tami Catron, EVP of Business Development, and her team provides is critical in delivering quality analytics that drive real insight into real-time consumer behavior. I would highly recommend Retail Aware; they have really gone the extra mile for us!” - Jake Leeman, Sr. Director, Experiential at MERGE

The defining AI breakthrough of 2025 will be agentic AI - autonomous, conversational shopping agents - paired with commoditized computer vision and energy‑efficient edge models, shifting AI from pilot projects to store‑level operating systems; Insider calls these “autonomous shopping agents” and shows how real‑time assistants and chat interactions can dramatically shorten discovery and reduce friction (Insider report on agentic AI and retail trends), while Neudesic warns 2025 is the year “AI takes the main stage,” with stores investing in upgraded networks, digital twins, and IoT to unlock that value (Neudesic 2025 retail AI insights).

For Lincoln retailers the implication is practical and immediate: deploy a focused pilot that pairs a conversational agent with visual search or smart‑shelf cameras to cut search time and free staff for service - evidence from national data shows chat-driven discovery can spike engagement (Adobe reported a 1,950% YoY jump in chat‑driven site traffic on Cyber Monday), and global computer‑vision capacity is expanding fast, creating affordable camera+AI solutions at the store edge (Fortune Business Insights computer vision market growth forecast).

So what? A two‑to‑eight week pilot that links agentic commerce to local inventory vision can convert curiosity into measurable sales without heavy upfront cloud costs, making AI a practical upgrade for Lincoln's small and mid‑sized stores.

“You cannot rest… your competitors are going to continue to raise the bar,” said Mary Dillion, president and CEO of Foot Locker.

Start small and concrete: write a short statement of work that names one high‑value use case, the measurable KPI (for example conversion lift or stockouts avoided), and a 4–8 week pilot timeline, then bring the right people - store manager, merch lead, IT/POS owner, and a local tech partner such as UNL's Raikes School - to a project kick‑off where the team reviews the 5 W's (who, what, where, when, why, how), scope, deliverables, risks, and communication cadence; Atlassian's kickoff playbook shows how pre-work and an interactive agenda keep the meeting from becoming a one‑way briefing, and Otter.ai recommends using meeting agents to capture notes and action items - saving teams roughly four hours a week - so align on tools up front (ticketing like Jira/Asana, a shared doc space such as Confluence or Google Docs, Slack for day‑to‑day, and Otter for transcriptions), assign clear owners for each deliverable, agree a short reporting cadence, and document next steps and mitigation plans so the pilot produces repeatable, store‑level playbooks that local teams can operate and scale.

“Buckle's collaboration with the Raikes School team gives us access to some of today's brightest computer science and design minds who are looking to use technology to solve cutting edge problems.” - Brandon Hauff, Vice President of Information Technology, The Buckle

Launch an AI business in Lincoln by sequencing three concrete moves: validate demand with local market intelligence (Nebraska's market research notes a 1.9 million statewide population and distinct regional buying patterns that affect product assortment), plug into the learning-and-partner network of meetups, legal forums and hands‑on experiments documented by Silicon Prairie News: How Nebraska's business ecosystem is learning and leveraging AI to recruit mentors and early customers, and secure development horsepower through university partnerships - UNL's new AI makerspace and the Scott Data collaboration (including access to eight NVIDIA H100 GPUs) give startups compute, student talent, and a low‑cost path to prototype models and demos that customers can test in weeks.

Start with a narrow MVP tied to a measurable KPI, use local meetups to find pilot sites and legal guidance, and treat the first 4–8 week pilot as a market experiment: if conversion, retention or operational savings move the needle, scale; if not, iterate with UNL researchers or local dev shops.

The so‑what: Lincoln's ecosystem supplies customers, talent, compute, and community - enough to prove revenue models without Silicon Valley burn.

“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, CEO, Scott Data

Lincoln retailers must navigate a fragmented 2025 U.S. regulatory landscape where federal agencies (FTC, EEOC, CFPB, DOJ) are already using existing consumer‑protection and civil‑rights authorities to police harmful AI outcomes and states are filling gaps with their own laws - 38 states adopted or advanced measures in 2025 alone - so compliance is local, fast‑moving, and enforceable; see the White & Case AI Watch US regulatory tracker for AI enforcement actions and agency guidance (White & Case AI Watch: US regulatory tracker) for agency actions and examples (FTC enforcement has included bans and data‑deletion remedies, e.g., the Rite Aid facial‑recognition settlement), and review the National Conference of State Legislatures summary of 2025 state AI legislation (NCSL 2025 state AI legislation summary) to map state rules - Colorado's risk‑based AI Act and Utah's GenAI disclosure rules (penalties up to $2,500 per violation) are concrete, near‑term examples.

Practical steps for Lincoln stores: inventory any AI touchpoints, insist vendors produce model documentation and bias‑testing evidence, align governance to NIST's AI RMF, and treat simple customer disclosures and human‑in‑the‑loop safeguards as low‑cost ways to avoid high‑visibility enforcement and preserve trust - so what? a single missed disclosure or unchecked hiring tool can trigger expensive enforcement or bans that stop a project cold, whereas a short compliance checklist turns AI from legal risk into operational upgrade.

Operational wins for Lincoln stores come from pairing smarter forecasting, price agility, and faster checkouts into a single, measurable flow: deploy AI-powered inventory forecasting to cut unknown out‑of‑stocks and automate replenishment, use models that set dynamic safety stock and suggest markdowns to protect margin during University events and local feedlot seasons, and add lightweight checkout assistants (kiosks or conversational agents) to shorten lines and free staff for upsell.

AI tools that fuse POS, supplier lead times, and external signals (weather, local events) deliver real‑time reorder triggers and scenario analysis - see practical implementation advice in Clarity's AI-powered inventory forecasting guide (Clarity guide to AI-powered inventory forecasting) - while Target's Inventory Ledger work shows ML can detect unknown out‑of‑stocks and trigger corrections that immediately restore availability (Target blog: solving product availability with AI).

The so‑what: C3 AI reports measurable forecasting improvements in about six weeks, and industry studies show AI pilots can reduce inventory and logistics costs substantially (StockIQ cites inventory reductions up to ~30% and logistics savings up to ~20%), meaning a focused 4–8 week pilot that ties forecasting to automated replenishment and a simple dynamic‑pricing rule can turn shelf availability into cash flow within one season.

Start with clean POS data, pick two KPIs (fill rate and days‑of‑inventory), and iterate rapidly.

Choose vendors and local partners by testing three realities: can they integrate cleanly with your POS/WMS and prove retail outcomes; do they operate or support teams in Nebraska so on‑site fixes and fast pilots are realistic; and can they supply model documentation, bias testing, and SLAs that satisfy compliance checks.

Start by short‑listing firms with retail case studies - Geneca for custom Lincoln software and systems integration (Geneca Lincoln custom software development), Hyland for content intelligence, agent builders and secure content governance (Hyland Content Innovation Cloud secure content management), and Spreetail for omnichannel listing, fulfillment, and proven logistics support from its Lincoln operations (Spreetail omnichannel fulfillment and logistics in Lincoln).

Require a 4–8 week pilot, clear KPIs (conversion, fill rate, shrink), and a data‑governance checklist (model cards, bias tests, human‑in‑the‑loop rules); the so‑what: a partner with Lincoln presence and real fulfillment metrics can turn a short pilot into same‑day shipped results - Spreetail reports 99.7% of orders shipped same day and next‑day coverage to 80% of the U.S., which shortens the path from model insights to customer impact.

Action steps for Lincoln retailers in 2025: pick one measurable use case (recommendations, forecasting, or a conversational agent), scope a 4–8 week pilot with clear KPIs, require vendor model documentation and a human‑in‑the‑loop rule, and automate checkout tax rates so local rates (Lincoln's combined 2025 rate is 7.25%) and nexus rules (economic nexus at $100,000/200 txns) are applied correctly at point of sale; use a tax automation partner to avoid manual errors and late‑filing penalties (Lincoln sales tax guide - Kintsugi sales tax automation).

Pair that pilot with immediate staff upskilling - a practical option is the 15‑week AI Essentials for Work program to teach promptcraft, tool selection, and governance so in‑store teams can operate and audit small models without outside dependency (AI Essentials for Work bootcamp registration).

Measure conversion lift, fill‑rate, and time‑saved per transaction; if the pilot hits targets, scale with a local systems integrator and a repeatable playbook that covers POS integration, sales‑tax automation, and minimal disclosure language to reduce regulatory risk.

The so‑what: a focused pilot plus tax automation and a trained team turns AI from experimental cost into measurable margin and fewer compliance headaches within one season.

“Buckle's collaboration with the Raikes School team gives us access to some of today's brightest computer science and design minds who are looking to use technology to solve cutting edge problems.” - Brandon Hauff, Vice President of Information Technology, The Buckle