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

Charlotte retailers competing in 2025 must treat AI as an operations and customer‑experience tool: market research shows the retail AI sector was worth $11.6B in 2024 with a ~23% CAGR and shoppers still rely heavily on stores, so local merchants can use AI for personalized recommendations, dynamic pricing tuned to Charlotte market signals, and real‑time inventory to keep shelves stocked and carts converting; agentic AI pilots report 25% higher average order values and dramatic stockout reductions (Walmart cut out‑of‑stocks ~30% in a pilot), making AI a practical lever to protect margins and win local share.

Learn practical, job‑ready skills with the AI Essentials for Work bootcamp to apply prompts, tools, and pilots in-store and online.

“You can't win on price alone anymore. You win by having the right product available when the customer wants it. Agentic AI gives us that edge.” - Doug McMillon

Charlotte retailers should stop force‑fitting customer behavior into a straight line and adopt an influence map that shows where streaming, scrolling, searching, and shopping actually move people toward action; BCG argues AI now makes it practical to plan media buys around

what truly drives influence

and to automate the right level of customization in real time (BCG report “Move Beyond the Linear Funnel” 2025 - AI for media planning and customization).

Influence maps focus decisions on which sources and moments matter for local shoppers - paid video, social creators, search, in‑store prompts - then test hypotheses with three concrete data inputs: a draft map from local knowledge, customer interviews and surveys, and passively collected social/web data to validate where attention actually lives (SparkToro blog: Influence Maps - the best marketing framework you've never heard of).

The payoff for Charlotte: fewer wasted impressions, faster pilots that improve store fulfillment and personalized offers, and actionable experiments that shift budget to the channels that drive measurable influence.

Centralizing first‑party data gives Charlotte retailers a single, privacy‑safe view of shoppers that powers personalization, local ads, and smarter inventory decisions: start by capturing consented inputs - loyalty programs, in‑store POS and beacons, mobile apps, email behavior, and CRM events - then consolidate them into a Customer Data Platform or unified CRM that feeds ad platforms and analytics in real time; research shows first‑party data lets marketers personalize without large privacy risks (New York Times article on first‑party data strategy: first‑party data strategy best practices) and, for retailers, can translate into major business impacts (a Google and BCG study cited by industry analysis found up to a 2.9x revenue lift and 1.5x cost reduction when firms leverage first‑party data for retail media).

Practical next steps for Charlotte teams: map touchpoints, enforce consent and data governance, enrich profiles with location and spatial analytics for neighborhood targeting, and connect the CDP to Google Ads and measurement tools so campaigns act on live signals (retail media strategies for first‑party data: first‑party data for retail media success; location intelligence solutions for retail: location intelligence and spatial analytics for retailers).

The payoff: fewer wasted impressions, offers that drive immediate foot traffic, and personalized promos that move Charlotte shoppers from browse to basket.

“We are confident the changes we have implemented will not only help improve productivity but will also deliver an increase in revenue for both Domino's and our franchisees” - Wayne McMahon, CIO, Domino's

Measurement and testing should make Charlotte marketing decisions defensible, not mysterious: start with segmented Marketing Mix Modeling to capture store‑group differences (the M‑Squared case study shows running separate MMMs by retail grouping clarifies media impact and produced an estimated incremental ROAS near $4 and clear Retail Media Network variance), then triangulate with geo‑lift and holdout experiments timed to local high‑traffic moments - for example, test around SouthPark and Ballantyne activations where Visit data and local reporting show outsized footfall and new-store momentum - and use small, rapid holdouts to surface true incrementality against noisy attribution signals (M‑Squared Marketing Mix Modeling case study; Sellforte Represent incremental measurement case study).

Local market context matters - use Charlotte market briefs to time tests around leasing and tenant changes that shift traffic patterns (JLL Charlotte retail Q1 2025 market report).

The payoff is specific: MMM plus validation testing can expose under‑ or over‑credited channels (RMNs ranged from ROAS 0.3 to 3.9 in published work) and, when validated, produce reallocation plans that drive meaningful incremental sales rather than optimistic last‑click numbers.

“Sellforte has brought new clarity to how we track and allocate our marketing budget. Being able to quantify the effects of our out-of-home advertising and events has been super valuable, especially with Sellforte's Long-Term model.” - Liam Shannon, Head of Marketing @ Represent Clothing

Charlotte retailers can run practical, local AI pilots today by deploying Google's Performance Max: consolidate your Merchant Center feed, add a local product feed to enable local inventory ads and store goals, and feed enhanced conversions and store‑visit values back into bidding so automation optimizes both online and in‑store sales; Performance Max runs across Search, YouTube, Display, Discover and Maps and - when guided with audience signals, rich creatives, and conversion values - has driven measurable uplifts (advertisers that moved from Standard Shopping to Performance Max saw a ~25% increase in conversion value on average, and including at least one video asset produced ~12% more conversions).

Start small: enrich product titles/images, create 1–3 focused asset groups by category or store cluster, run a one‑click A/B experiment to measure uplift, and give the campaign ~6 weeks to learn before major changes.

For Charlotte, the immediate win is clear: a local product feed plus store goals turns digital spend into measurable foot traffic and in‑store revenue instead of just clicks (Google Ads Performance Max retail best practices; AdNabu guide to Performance Max testing and timing).

de Bijenkorf used Web to App Connect: 2x conversion increase, 48% AOV increase when customers landed in-app vs mobile site, and 68% ROAS uplift for Performance Max campaigns. - Marc Mulder, Head of Search, de Bijenkorf

Scale authentic discovery in Charlotte by pairing local creators with AI-first production: AI video generators are “making waves in 2025” for marketers and content creators, enabling faster edits and variant testing across neighborhoods (Best AI video generation tools for marketers and content creators in 2025).

Recruit from a deep local pool - top creators like Tiffany Marie (113.8K), Brittney McDonald (91.6K) and Robin Tan (91.5K) reach distinct Charlotte niches - and brief micro‑creator shoots focused on inventory, seasonality, or neighborhood promos (Top 40 Charlotte Instagram influencers to recruit in 2025).

Combine those assets with generative marketing automation to cut creative turnaround and agency fees while delivering tailored social and video variants at scale (Generative marketing automation strategies for Charlotte retailers).

The payoff: targeted creator content that converts locally - measurable lift in foot traffic and online conversions when content aligns with store-level inventory and local promotion timing.

“Know better. Do better.” - Victor Ahdieh

Omnichannel operations in Charlotte hinge on tight inventory visibility, fast local fulfillment, and flawless in‑store execution: deploy a cloud POS that syncs online and in‑store counts as often as every five minutes so buy‑online‑pickup‑in‑store and ship‑from‑store work without oversells (see Realtime POS real‑time inventory and ecommerce integration Realtime POS real-time inventory and ecommerce integration), pair that system with a Charlotte 3PL or warehouse near CLT for expedited pick‑pack and real‑time tracking to turn clicks into same‑day or next‑day deliveries, and use dedicated merchandising teams to keep planograms accurate and speed new SKUs to shelf.

Local providers - warehouses offering white‑glove delivery, Charlotte fulfillment centers, and inventory services that run weekly cycle counts - make the difference between a lost cart and a happy repeat customer; integrating these layers reduces friction at the moment of purchase and protects margin by preventing costly stockouts.

For Charlotte retailers, the measurable win is operational: faster stock allocation and 5‑minute syncs mean online demand is routed to the nearest store or fulfillment node before a competitor captures that sale.

“They allowed our company to quickly and conveniently manage our inventory flow from any location... The support staff and customer service is fast and friendly.” - Jason, Golden Tyre West, CEO

Charlotte retailers that want frontline staff to use AI day one should build a layered training pathway that pairs short, affordable upskilling with deeper technical pipelines: encourage store managers and customer‑service teams to take Central Piedmont Community College's online BAS‑ITC8216 “Introduction to Artificial Intelligence” non‑credit course (asynchronous, $70) to learn chatbots, recommendation engines, and AI ethics, align store‑level hires into Central Piedmont's new Associate in Applied Science in Artificial Intelligence program for roles that require machine‑learning and generative‑AI skills, and tap campus convenings like the UNC Charlotte AI Summit for faculty‑grade workshops on human–AI partnerships and responsible deployment.

The local payoff is concrete: Central Piedmont's GAIT NSF award ($474,038) will expand curriculum and faculty training to grow Charlotte's AI talent pipeline - program graduates report starting salary ranges of $60k–$75k and local employers show strong hiring demand - so retailers that invest in staff training can turn employees into operational AI users (chatbots, inventory prediction, localized personalization) rather than replace them.

Start small: sponsor the $70 intro course for 10 employees, then create a six‑month apprenticeship or cohort tied to store KPIs so learning maps directly to traffic, fulfillment, and conversion improvements.

“This is not just a degree. It is a workforce solution and an investment in our region's long-term competitiveness.” - Dr. Heather Hill, Provost and Chief Academic Officer, Central Piedmont Community College

Protecting Charlotte customers and brands means treating privacy, governance, and security as product features: adopt North Carolina's Responsible Use of AI Framework guidance - embed Fair Information Practice Principles across development, testing, deployment and decommissioning, require privacy‑by‑default architectures, and run the NCDIT Office of Privacy and Data Protection's AI/GenAI questionnaire during every Privacy Threshold Analysis before a pilot touches customer data (North Carolina Responsible Use of AI Framework - NCDIT privacy guidance).

Make vendor due diligence non‑negotiable: contractually verify data handling, logging, and access controls, insist on provenance and quality checks for training data, and keep auditable documentation so deployers can answer regulators and customers quickly as state rules evolve.

The state has also begun institutionalizing governance - NCDIT added an AI governance leader to coordinate policy - so local retailers should mirror that structure with cross‑functional committees, continuous monitoring rather than periodic audits, and pragmatic policies that channel shadow AI into sanctioned tools.

Finally, plan for a fragmented regulatory landscape by tracking model disclosure and automated‑decision rules in neighboring states; a clear governance trail preserves customer trust and prevents a single incident from becoming a brand‑ending story (2025 state AI legislation summary - NCSL).

Privacy in AI governance is not just a compliance requirement; it is fundamental to maintaining public trust.

Charlotte retailers ready to turn insight into revenue should follow seven local next steps: 1) centralize consented customer signals into a privacy‑first CDP (start with the OneTrust first-party data checklist for marketers to map consent and governance: OneTrust first-party data checklist for marketers); 2) begin a 6–8 week retail audit cadence using AIMultiple's 10‑point retail audit checklist to fix planogram, inventory and promo leaks that cost local sales (set weekly cycle counts for high‑velocity stores: AIMultiple 10‑Point Retail Audit Checklist); 3) pilot a local Performance Max feed + store goals to convert digital spend into measurable foot traffic; 4) sync POS to an every‑5‑minute inventory update and enable ship‑from‑store/BOPIS routing to eliminate oversells; 5) recruit micro‑creators for neighborhood‑specific short videos and pair those assets with generative marketing automation to scale variants by store; 6) run segmented MMM plus small geo‑lift holdouts around SouthPark or Ballantyne to validate incrementality before you reallocate budgets; and 7) require vendor due diligence and run a Privacy Threshold Analysis (embed FIPPs and a cross‑functional AI governance committee) before any pilot touches customer data.

Start with one practical commitment - a 15‑week staff cohort (AI Essentials for Work) to teach store managers prompt design and operational AI so pilots scale without creating shadow AI - and sequence the audit + CDP + Performance Max pilot in a single 90‑day sprint so you measure real footfall impact rather than vanity metrics (this approach ties consented data to inventory and paid media and prevents wasted impressions).

These seven steps make AI actionable in Charlotte: better stock on shelf, fewer wasted ad dollars, and measurable lifts in in‑store conversion that protect margin and local brand trust; enroll teams in a practical bootcamp to move from experiment to repeatable capability: AI Essentials for Work bootcamp registration.

“We are confident the changes we have implemented will not only help improve productivity but will also deliver an increase in revenue for both Domino's and our franchisees” - Wayne McMahon, CIO, Domino's