Retail in the AI-Native Era The Connected Store Operating Model

Retail in the AI-Native Era

The Connected Store Operating Model

A Cisco Retail 2.0 Whitepaper

Edited v7.1 draft | May 2026

Retailers do not win the AI-native era by deploying more disconnected tools. They win by connecting intelligence to execution across the store estate.

Executive Summary

Retailers have not failed to invest in technology. They have modernized ecommerce, launched loyalty programs, deployed mobile apps, upgraded store networks, added self-checkout, expanded fulfillment options, tested AI, rolled out cameras and sensors, invested in retail media, and moved more systems to the cloud.

The problem is not a lack of technology. The problem is fragmentation.

Customer data is fragmented across channels. Store systems are fragmented across devices and workflows. Associate tools are fragmented across tasks. Loss-prevention signals are fragmented across video, POS, inventory, and case management. IT visibility is fragmented across store networks, cloud services, applications, APIs, and third-party providers. AI pilots are often fragmented from the operating systems they need to influence.

AI raises the cost of that fragmentation. AI cannot create trusted outcomes from disconnected systems. It needs reliable data, real-time context, secure identity, resilient connectivity, governed workflows, and operational telemetry. Without those foundations, AI remains a pilot, a dashboard, or a point solution. With them, AI becomes part of execution: sensing what is happening, correlating signals, triggering action, learning from outcomes, and helping the store adapt over time.

This is the shift at the center of Cisco Retail 2.0: the move from fragmented digital transformation to connected store execution.

The modern store is no longer just a place to transact. It is a fulfillment node, service hub, workforce environment, media surface, product discovery layer, data source, security perimeter, and distributed edge environment. It is where customer experience, employee experience, store operations, loss prevention, merchandising, retail media, cybersecurity, and IT resilience converge.

Cisco’s role is to help retailers build the trusted foundation for that operating model. Secure networking, Wi-Fi, switching, SD-WAN, PoE, smart cameras, sensors, electronic shelf label infrastructure, Webex Connect, Webex AI, Webex Contact Center, AppDynamics, ThousandEyes, Splunk, and Cisco security capabilities can help connect the physical and digital business. The value is not in any one technology alone. The value is in connecting the systems that shape retail execution in real time.

The economic implications are significant because connected-store capabilities influence multiple value pools at once. A more connected store can improve revenue enablement, service efficiency, associate capacity, shrink reduction, energy usage, support productivity, downtime protection, cybersecurity resilience, retail media monetization, and first-party customer understanding. The value story is strongest when those outcomes are treated as related but distinct: direct operating impact, strategic upside, protected value at risk, and future option value.

This paper uses selective benchmark signals to make the business case concrete without turning the narrative into a calculator. Industry shrink, return fraud, service-cost, retail-media, observability, and cyber-resilience benchmarks help frame why the opportunity matters. The companion TEV landing page should carry the detailed methodology, formulas, source registry, scenario inputs, deployment assumptions, and overlap controls.

The AI-native store is not a hype story. It is an execution story. Retailers that win in this next era will not simply deploy more AI. They will connect the store well enough for AI, automation, analytics, security, and human teams to act together.

1. Retail Has Entered the AI Execution Era

Retail’s first wave of AI was defined by experimentation. Retailers tested recommendation engines, demand forecasting, computer vision, workforce analytics, dynamic pricing, fraud models, chatbots, and generative AI assistants. Many efforts produced useful insights; some produced measurable value. But many remained disconnected from the operating core of the business.

A retailer can have an impressive AI pilot and still struggle to execute consistently across stores. A model can recommend the right action, but the value is limited if the data is stale, the workflow is manual, the associate never sees the recommendation, the customer message is not triggered, the application is slow, or the store network cannot support the connected experience.

Retail is now moving from the AI experimentation era to the AI execution era. Execution means AI is embedded into the daily flow of retail work: routing messages, detecting exceptions, adjusting content, recommending next actions, supporting associates, summarizing service interactions, improving investigations, prioritizing tasks, monitoring systems, and reducing friction.

This is why AI-native retail is not simply about AI models. It is about the operating environment that allows AI to act responsibly. AI execution requires trusted data, real-time context, secure identity, reliable connectivity, governed workflows, and systems that can coordinate action.

The retail technology paradox

Most enterprise retailers have more technology than ever, but many still experience more friction than they should. More devices can mean more support tickets. More customer channels can mean more inconsistent journeys. More data can mean more silos. More dashboards can mean less shared context. More AI pilots can mean more governance risk.

The issue is not whether a retailer has technology. The issue is whether the technology is connected enough to produce repeatable operating value.

In the AI execution era, isolated technology creates diminishing returns. Connected technology compounds.

From digitization to orchestration

Most retailers have already digitized many parts of the business. The work ahead is orchestration.

Digitization converts analog processes into digital ones. Orchestration connects digital processes so they operate together. A digitized store may have digital signs, mobile apps, ESLs, cameras, loyalty tools, and messaging. An orchestrated store connects those systems into customer journeys, associate workflows, operational alerts, business rules, and measured outcomes.

A digital sign displays content. An orchestrated sign adapts based on local inventory, daypart, traffic, promotion strategy, and campaign measurement.

A chatbot answers generic questions. An orchestrated AI assistant begins with product, store, loyalty, inventory, and service context.

A camera records video. An orchestrated camera estate supports loss prevention, queue detection, safety response, layout analytics, and investigation workflows.

A network keeps devices online. An orchestrated network provides visibility into which customer, associate, or operational workflows are affected when connectivity degrades.

Why the buying case changes

In the past, a retailer could evaluate store technologies one at a time: Wi-Fi modernization as infrastructure, digital signage as merchandising, cameras as security, contact center as service, ESLs as operations, and observability as IT. Each project had its own owner, budget, and ROI logic.

That model is becoming less useful because the value pools increasingly overlap. The same camera network can support loss prevention, queue visibility, safety, and operational intelligence. The same messaging platform can support pickup updates, service recovery, product-originated conversations, loyalty engagement, and contact deflection. The same observability layer can protect customer experience, associate productivity, and IT response.

Retailers need a buying case that reflects the connected nature of the work while still separating direct annual impact, strategic upside, protected value, and future option value.

2. The Store Is Not Going Away — Its Mission Is Changing

The physical store remains one of retail’s most important assets. It is where customers discover products, confirm choices, pick up orders, return items, compare alternatives, seek help, interact with associates, experience brands, and make immediate purchase decisions.

But the store’s mission has changed.

For decades, the store was primarily designed to present inventory, support service, and complete transactions. That role still matters, but it is no longer enough. Today’s store must also operate as a fulfillment node, service hub, workforce environment, media surface, data source, security perimeter, and distributed technology edge.

That expanded mission creates new pressure. A store now has to support physical shopping and digital fulfillment at the same time. It has to serve customers who may arrive with online research, app-based offers, loyalty status, pickup orders, return authorizations, product questions, and service histories already in motion. It has to give associates enough context to respond quickly. It has to protect margin in an environment where shrink, fraud, and safety are operational concerns. It has to keep devices, applications, cameras, sensors, signage, and networks working continuously. And it has to do all of this without making the experience feel complicated to the customer.

The store is not going away. It is becoming more demanding.

The store as a fulfillment node

Omnichannel retail has turned stores into active fulfillment infrastructure. Stores now support buy online, pick up in store; curbside pickup; ship-from-store; returns; exchanges; substitutions; local delivery; and inventory-aware customer journeys.

That shift changes the operating model. Store teams are no longer only serving shoppers who are physically present. They are also serving digital demand. A store associate may be helping a customer in aisle three while another customer is waiting in the parking lot for pickup, a third is asking about substitution options through messaging, and a fourth is returning an item purchased online.

For the customer, these are not separate channels. They are one relationship with the retailer. For the store, they are separate workflows unless the underlying systems are connected.

A connected store can make fulfillment feel coordinated. Pickup readiness can trigger a message. Customer arrival can trigger associate tasking. Substitution questions can route through a messaging flow. Inventory exceptions can be escalated before the customer arrives. Returns can be connected to order history. Store teams can see what needs action, not just what has already gone wrong.

The store as a service hub

As digital channels handle more routine transactions, the store increasingly becomes the place where customers seek confidence, assurance, and resolution.

A shopper may come to the store because they want to compare products physically. They may need help choosing a size, checking compatibility, understanding ingredients, resolving a return, asking about a warranty, or completing a high-consideration purchase. They may have already searched online and still need human reassurance.

This creates an opportunity and a burden. The store can strengthen loyalty when it resolves complexity well. But it can weaken loyalty when customers encounter inconsistent information, unavailable associates, disconnected service histories, or repetitive handoffs.

AI and connected workflows can help, but only if they are grounded in the right context. A product assistant is useful when it knows the exact item, local inventory, customer question, and escalation path. A contact center is more effective when it can see the store context. An associate is more helpful when they have access to product, order, loyalty, and service information in one place.

The store as a service hub depends on connected context.

The store as a workforce environment

The store is also where retail work happens.

Associates are being asked to support more channels, more devices, more fulfillment complexity, more safety concerns, more product questions, and more exceptions. They are expected to deliver human service while also executing tasks generated by digital systems.

When the operating model is fragmented, associates absorb the friction. They search across systems, repeat information, troubleshoot devices, manage customer frustration, walk the store to check conditions, manually update labels, handle pickup exceptions, and work around disconnected tools.

That friction is not just an employee experience issue. It is a business performance issue. Every minute spent navigating broken workflows is a minute not spent serving customers, fulfilling orders, preventing loss, or improving store execution.

The connected store should make work easier to execute. It should give associates better context, reduce repetitive questions, route exceptions clearly, automate routine tasks, and keep critical devices reliable.

The store as a media and merchandising surface

Retail media and digital merchandising are also changing the store’s mission.

The store is no longer only a place where merchandising is physically set and periodically changed. It is becoming a dynamic content environment. Digital signage, electronic shelf labels, mobile prompts, in-store audio, shelf-edge codes, and app experiences can help retailers adapt content by store, department, inventory, time of day, customer mission, supplier campaign, or local event.

That creates new monetization and engagement opportunities. But it also creates a risk: the store can become cluttered, irrelevant, or confusing if digital content is not connected to customer needs and local operating reality.

The best in-store media does not simply add more screens. It makes the store more useful. It can guide customers, highlight relevant offers, explain products, promote available inventory, support supplier-funded education, and connect physical engagement to measurable outcomes.

The store as a data source

Ecommerce has long had a data advantage. Retailers can see searches, clicks, carts, abandonments, conversions, and engagement paths. Physical retail has historically been less visible.

Connected stores narrow that gap.

Wi-Fi, location intelligence, cameras, sensors, POS, mobile apps, loyalty engagement, ESLs, digital signage, shelf-edge 2D codes, associate devices, and operational telemetry can help retailers understand what is happening before, during, and after the transaction.

That visibility can answer important questions:

Where do customers dwell but not convert?

Which departments need more support?

Which products generate questions?

Where do queues build?

Which promotions are active when inventory is unavailable?

Which store workflows are creating repeated service issues?

Which devices or applications are slowing associates down?

Which shrink patterns repeat across locations?

Which fulfillment steps create customer friction?

The point is not to collect data for its own sake. The point is to use store signals to improve execution.

The store as a security perimeter

The connected store also expands the security perimeter.

Every new device, application, vendor connection, customer channel, and data flow creates potential exposure. POS systems, payment devices, cameras, sensors, digital signage, associate handhelds, guest Wi-Fi, smart lighting, HVAC, refrigeration, ESLs, kiosks, and cloud applications all need appropriate access, segmentation, monitoring, and policy enforcement.

Physical security and cybersecurity are also becoming more connected. A compromised device can create operational risk. A disconnected camera network can limit safety response. A weak vendor access policy can expose store systems. A flat network can increase blast radius. A customer data flow without proper governance can create privacy and trust risk.

The store is now both a physical and digital risk environment.

The store as a distributed edge

Finally, the store has become a distributed edge environment.

It hosts devices, sensors, cameras, applications, local workflows, real-time data, and cloud dependencies. Some decisions need to happen locally. Some need cloud scale. Some require both. AI use cases such as computer vision, queue detection, product assistance, dynamic signage, smart lighting, and operational automation all depend on the ability to connect edge signals to enterprise systems securely and reliably.

This changes how retailers should think about infrastructure. Store infrastructure is not merely a cost center. It is the foundation that allows the store to fulfill its new mission.

A retailer cannot deliver AI-native customer experience, associate productivity, loss prevention, retail media, and IT resilience on disconnected or fragile infrastructure.

The modern store has become too important, too complex, and too connected to be managed as a set of isolated systems. It needs an operating model built for the role it now plays.

3. The Connected Store Operating Model

The connected store operating model is the bridge between retail’s AI ambition and retail’s daily execution. It is not a single product, platform, application, or dashboard. It is a way of organizing the store so that customer signals, associate workflows, operational events, technology telemetry, and business systems can work together in real time.

For many retailers, the challenge is that digital tools operate in parallel. The app knows one version of the customer. The loyalty platform knows another. The store team sees only part of the journey. Cameras may capture video but not connect to POS events, safety workflows, or operational analytics. IT tools may monitor systems but not show which customer or associate journeys are affected when performance degrades.

The connected store turns those isolated capabilities into a coordinated system with five jobs: sense what is happening, correlate signals into useful context, act through the right workflow, learn from outcomes, and evolve as conditions change.

Retailers enter the model at different points

Not every retailer starts from the same place. Some are integrating capabilities they have already deployed; for them, the work is orchestration. Others are establishing the foundation for the first time; for them, the work begins with reliable, secure, observable infrastructure on which connected experiences can be built.

Both groups need the same end-state: a store environment where connectivity, observability, security, engagement, and operational workflows compound over time. But the path is not a single-step transformation. Retailers that try to install AI-native experiences on top of unstable connectivity, ungoverned devices, unmanaged cameras, or flat networks often find that the experience layer cannot perform reliably enough to produce business value.

From systems of record to systems of action

Retailers have spent years building systems of record: customer records, transaction records, product records, inventory records, employee records, and incident records. Those systems remain essential. But AI-native retail requires systems that help the business act while the moment is still unfolding.

A pickup delay should not become visible only after the customer complains. A queue should not be addressed only after the line is obvious. A self-checkout exception should not require an unsafe or inconsistent associate response. A digital sign should not keep promoting unavailable inventory. A customer product question should not restart as a generic chatbot interaction when the exact SKU, store, and aisle are already known.

Connection must be technical, operational, and measurable

Technical connection: devices, applications, networks, APIs, cloud services, and data systems must communicate reliably and securely.

Operational connection: the business must define what happens when a signal appears, who receives the task, and when AI should act or escalate.

Measurement connection: outcomes must be captured so the retailer knows whether the action reduced friction, saved time, improved conversion, reduced loss, or protected uptime.

The purpose of the connected store is not to make the store feel more technical. It is to make it feel more coordinated: easier for customers, clearer for associates, more visible for store leaders, more manageable for IT, and more valuable for the business.

4. SCALE: Sense, Correlate, Act, Learn, Evolve

SCALE provides a simple operating language for the connected store: Sense, Correlate, Act, Learn, and Evolve. It explains how a store moves from fragmented signals to coordinated execution.

A retailer does not become AI-native because it has an AI model. It becomes AI-native when intelligence is connected to action.

A digitized store has connected devices. An AI-native connected store has a loop. That loop is what allows customer experience, employee experience, loss prevention, merchandising, facilities, security, and IT operations to work from the same foundation.

5. Why Cisco

Most retail friction is not created by the absence of technology. It is created by fragmentation between technologies. That is why Cisco has a distinctive role in the AI-native retail conversation.

Retailers do not need another disconnected point solution. They need the systems already shaping the store experience to work together: networks, devices, applications, cameras, sensors, messaging, contact center, security, observability, cloud services, associate tools, and operational workflows.

Cisco’s role is not to replace the retailer’s commerce platform, loyalty system, POS, order management system, product information system, workforce platform, or retail media stack. Cisco’s role is to help connect, secure, observe, and orchestrate the environment in which those systems operate.

The stronger Cisco argument is not: Cisco provides store networking. The stronger argument is: Cisco helps retailers move from fragmented technology deployments to connected execution.

Cisco as the connective tissue of the AI-native store

Store devices need reliable connectivity. Applications need performance assurance. Customer journeys need messaging orchestration. Associates need context and tasking. Cameras need network, power, policy, storage, analytics, and workflow integration. Electronic shelf labels, signage, and shelf-edge systems need connectivity and management. Security requires identity, segmentation, and telemetry. IT teams need visibility across local stores, SaaS, cloud, WAN, APIs, and third-party services. AI needs governed access to real-time context.

Cisco is structurally positioned across these domains. The network remains foundational, but the retail opportunity is broader: secure infrastructure, observability, customer engagement orchestration, contact center, AI-enabled workflows, cameras, sensors, identity, segmentation, and telemetry.

Why this matters to retail executives

Three priorities, one connected foundation

Cisco Retail 2.0 should organize the business case around three priorities: CX Growth and Loyalty; EX Workforce, Operations, and Margin Protection; and IT Resilience, Trust, and Protection.

These priorities should not be treated as separate technology tracks. They are three views of the same connected store. The same foundation that improves customer engagement can also improve associate workflows. The same camera estate that supports loss prevention can also support queue visibility and safety. The same observability layer that reduces IT resolution time can also protect digital customer journeys. The same secure network that supports POS can also support sensors, signage, ESLs, smart lighting, and AI workloads.

6. Priority 1 — CX Growth and Loyalty

Retail growth in the AI execution era will increasingly depend on how well retailers connect digital intelligence to physical moments.

Customers no longer experience store, app, service, loyalty, and fulfillment as separate channels. They experience them as one relationship with the brand. When that relationship feels fragmented — irrelevant offers, inconsistent communications, poor in-store guidance, unavailable product information, disconnected pickup updates, repetitive service interactions, or handoffs that lose context — loyalty weakens quickly. When it feels connected, customers make decisions more confidently, complete journeys more easily, and return more often.

This matters because stores remain central to retail economics even as customer expectations are increasingly shaped by digital convenience. Shoppers want the speed, relevance, and transparency of ecommerce with the immediacy, assurance, and human support of physical retail. They expect promotions to be timely, inventory to be accurate, product information to be accessible, service to be proactive, and transitions between web, mobile, messaging, contact center, and store to feel seamless.

For retailers, that creates a clear strategic opportunity: use data, location intelligence, AI, messaging, product information, and connected workflows to make the store journey more relevant, more helpful, and easier to complete. The goal is not to make every moment feel high-tech. It is to make every moment feel more intuitive.

Cisco’s role in this priority is to help connect the systems that shape customer experience in real time: secure wireless connectivity, location awareness, store analytics, messaging orchestration, application assurance, digital signage infrastructure, AI-enabled service flows, contact center escalation, and omnichannel coordination. Together, these capabilities can help retailers reduce friction, improve engagement, and turn disconnected touchpoints into a more continuous customer relationship.

Within this priority, five value drivers matter most:

Value Driver 1: Personalized In-Store Engagement

What challenge it solves

Many retailers know their customers well in digital channels but lose that intelligence when the customer enters the store.

A shopper may be recognized in the app, targeted through email, enrolled in loyalty, and actively browsing products online. But once that same shopper walks into a physical location, the experience can become generic. The store may not know what the customer has researched, which offers are relevant, what products are available locally, or where the shopper is likely to need help. The customer experiences the brand as disconnected: personalized online, anonymous in person.

That gap matters because physical shopping often involves moments where relevance and confidence influence the sale. A shopper may need to compare products, find an item quickly, validate a promotion, check inventory, understand a size or specification, or decide between a national brand and a private-label alternative. When the retailer can connect digital intent to physical context, the store becomes more useful. When it cannot, the customer is forced to do the work.

Personalized in-store engagement is not about making the store feel intrusive. It is about making the physical journey feel more aware, timely, and helpful.

Use cases Cisco enables

Arrival-aware engagement: When an opted-in customer or app user enters a store, the retailer can shift the experience into store mode with relevant offers, pickup instructions, or shopping-list context.

Location-based offers: Store zone, department, or dwell context can help the retailer present offers that match the customer’s current mission rather than generic promotions.

App-to-store continuity: Digital research, wishlists, loyalty preferences, and shopping lists can carry into the in-store experience so customers do not restart their journey.

In-store wayfinding: Customers can find products, departments, pickup areas, service counters, or promotional zones more easily.

Dwell and traffic tuning: Retailers can understand where customers spend time and where departments may need better content, staffing, or merchandising.

Known-customer recapture: If a known shopper engages but does not purchase, the retailer can follow up later through permissioned channels with relevant content or offers.

Personalized service triggers: High-value or high-friction moments can be routed to the right associate or support channel with customer and store context.

Technologies used

Cisco Spaces-style location intelligence helps retailers understand store presence, movement, dwell, and location-based context. It can support store-mode experiences, traffic analysis, wayfinding, and zone-level engagement.

Cisco Meraki and Catalyst wireless provide the connectivity layer required for guest, associate, and operational experiences. Reliable Wi-Fi is foundational for app engagement, associate devices, store analytics, and connected customer journeys.

Webex Connect can orchestrate opt-in customer messaging across SMS, RCS, WhatsApp, app chat, web chat, and other digital channels. It helps retailers turn context into timely engagement.

Webex AI can support intent classification, next-best-action logic, conversational assistance, and service automation.

Webex Contact Center can escalate higher-complexity needs with customer, store, and interaction context intact.

AppDynamics and ThousandEyes help assure the digital experiences that support app-to-store journeys, including application performance, API dependencies, and internet path visibility.

Splunk can correlate store, customer, application, network, and operational telemetry to help retailers understand what is happening across the journey.

Business impacts

Personalized in-store engagement creates measurable value across conversion, relevance, loyalty, and first-party customer understanding.

Up to 40% higher in-store conversion when shoppers use digital tools during the store journey.

Vendor-commissioned customer-data TEI studies have also reported conversion and messaging gains when customer profiles are unified and activated across channels.

Greater relevance at the moment of decision by connecting app, loyalty, Wi-Fi, location, and store context.

Stronger continuity between digital intent and physical behavior, including store-mode app experiences, wayfinding, offers, and post-visit follow-up.

More useful first-party context from physical shopping journeys, including known sessions, product interest, and consented engagement signals.

At an executive level, this value driver matters because it helps the retailer turn store traffic into measurable customer context. Instead of treating the physical visit as a blind spot, the business gains a way to connect customer presence with digital engagement, operational insight, and growth outcomes.

Illustrative example

A sporting goods customer researches running shoes online, saves two models to a wishlist, and later enters a store. The app shifts into store mode, showing aisle location, available sizes, and a loyalty offer on one of the models. Store traffic data indicates the footwear department is busy, so a floor associate receives a lightweight task prompt to check the area. The customer scans a shelf label to compare cushioning and size availability, then receives a Webex-powered prompt offering help. The experience feels personal without requiring the shopper to restart the journey.

Value Driver 2: Omnichannel Journey Orchestration

What challenge it solves

Omnichannel friction occurs when the customer’s journey moves faster than the retailer’s systems.

A customer buys online but pickup status is unclear. A substitution requires approval but the message never reaches the customer. A return begins in the app but has to be re-explained at the service counter. A loyalty offer appears in email but fails at checkout. A contact center agent resolves an issue but the store cannot see the context. Each breakdown may look like a small operational issue, but to the customer it feels like the retailer does not know them.

This is especially damaging because omnichannel customers often represent some of the most valuable relationships in retail. They engage across more touchpoints, generate more data, and expect the retailer to coordinate experiences across channels. When that coordination fails, the retailer risks losing not just one transaction, but trust in the broader relationship.

Omnichannel journey orchestration is the discipline of making those transitions feel connected.

Use cases Cisco enables

Pickup and curbside coordination: Customers can receive readiness alerts, arrival instructions, substitution prompts, pickup updates, and service recovery messages.

Returns orchestration: Return eligibility, instructions, receipts, and service counter context can move across app, messaging, and store workflows.

Service recovery: Fulfillment delays, failed offers, out-of-stock events, or pickup issues can trigger proactive communication before the customer complains.

Cross-channel escalation: A customer can move from messaging to contact center or store support without losing context.

Order-status automation: Routine order, delivery, pickup, and return questions can be deflected through AI-assisted messaging.

Post-visit follow-up: Service, loyalty, survey, replenishment, or support messages can be triggered based on completed journeys.

Associate-aware handoff: Store teams can receive customer, order, product, and issue context when a digital journey requires physical action.

Technologies used

Webex Connect orchestrates customer communications across channels and supports proactive messaging workflows for pickup, returns, substitutions, service recovery, and follow-up.

Webex AI helps classify intent, summarize interactions, automate routine journey steps, and support next-best-action logic.

Webex Contact Center provides live-agent escalation and preserves context across digital-to-human handoffs.

AppDynamics monitors customer-facing applications and APIs involved in order, pickup, loyalty, and service journeys.

ThousandEyes provides visibility into internet, SaaS, and network paths that can affect customer-facing digital experiences.

Cisco networking and SD-WAN support reliable store-to-cloud and store-to-enterprise connectivity.

Splunk can correlate operational events across orders, applications, messages, systems, and incidents.

Business impacts

Omnichannel journey orchestration creates value by reducing the friction customers feel when store, app, pickup, returns, service, and contact-center workflows do not move together.

A vendor-commissioned Forrester TEI study of unified customer data activation reported a 15% mobile conversion-rate lift, reinforcing the value of connected customer context across digital and physical journeys.

Fewer avoidable service contacts from proactive pickup, substitution, order-status, return-status, and service-recovery messaging.

Improved pickup and curbside experiences by connecting readiness, arrival, substitution, associate tasking, and customer communication.

Reduced failed handoffs when customers move from app to store, messaging to contact center, or digital service to physical action.

Higher journey completion and customer confidence when the retailer preserves context across web, mobile, messaging, store, and service channels.

At an executive level, this value driver matters because omnichannel customers do not judge the retailer by channel boundaries. They judge whether the experience works as one relationship. Cisco helps make that relationship more continuous by connecting messaging, service, application assurance, store connectivity, and operational context.

Illustrative example

A beauty customer orders a product for curbside pickup. Before arrival, one item becomes unavailable. Instead of discovering the issue at the store, the customer receives a Webex Connect message with approved substitutions. The customer selects an alternative, the order updates, and the associate handheld receives the revised pick task. If the customer asks a product question, Webex AI handles the first response and escalates to Webex Contact Center if needed. The customer experiences one coordinated retailer, not a set of disconnected systems.

Value Driver 3: AI-Powered Service and Outreach

What challenge it solves

Retail service teams and store associates spend significant time answering routine questions. Customers ask about order status, product availability, store hours, return policies, pickup timing, loyalty offers, warranties, product details, and service options. Many of these questions are repetitive, but they still consume human capacity because the answers depend on context.

Generic automation often fails because it lacks that context. A chatbot that does not know the customer’s order, store, product, loyalty status, or current issue can create more frustration than value. A message that is not connected to fulfillment status can confuse the customer. A contact center handoff that loses conversation history forces the customer to repeat themselves.

AI-powered service and outreach is most valuable when it is connected to real retail context and has clear escalation paths.

Use cases Cisco enables

Routine inquiry automation: Common questions can be answered through Webex-powered messaging or AI-assisted flows.

Product-originated conversations: A customer can begin a support interaction from a product, shelf-edge code, app experience, or digital sign.

Context-rich escalation: Complex issues can move to a live agent with order, product, store, and conversation context intact.

Proactive service alerts: Customers can receive timely updates about pickup delays, substitutions, returns, appointments, or service changes.

Associate-assist workflows: Store associates can receive AI-supported answers for product, policy, order, or inventory questions.

Post-interaction summaries: AI-generated summaries can reduce manual note-taking and improve continuity across channels.

Customer preference capture: Service moments can update permissions, preferences, and loyalty context when consent is appropriate.

Technologies used

Webex Connect routes service interactions across SMS, RCS, WhatsApp, app chat, web chat, and other channels. It supports proactive outreach and two-way service flows.

Webex AI supports conversational assistance, intent detection, summarization, next-best-action logic, and automation of routine steps.

Webex Contact Center handles human escalation, agent workflows, and service continuity when automation is not enough.

Cisco networking provides reliable in-store and cloud connectivity for service interactions.

AppDynamics and ThousandEyes help assure application and API performance during customer service journeys.

Splunk supports event correlation, service analytics, and operational visibility across service, messaging, application, and infrastructure data.

Business impacts

AI-powered service and outreach creates value by shifting routine, context-dependent interactions away from manual handling while preserving human escalation for higher-value moments.

Directional service benchmarks show a wide cost gap between self-service interactions and assisted support, with planning benchmarks often using roughly $1.84 for self-service versus $13.50 for assisted support.

Conversational-commerce TEI evidence has reported a 550% engagement lift and conversion-rate improvement from 3.3% to 12.2% in a WhatsApp-led commerce scenario.

Lower routine contact volume for order status, pickup, return policy, store hours, loyalty, and product-availability questions.

Faster response and improved consistency when AI has access to order, product, store, loyalty, and service context.

Improved escalation quality because complex issues move to agents with conversation, product, customer, and store context intact.

At an executive level, this value driver matters because service automation is only valuable when it reduces friction instead of adding another disconnected channel. Cisco helps connect AI, messaging, contact center, application performance, and store context so automation can improve both cost-to-serve and customer experience.

Illustrative example

A home improvement shopper scans a shelf tag for a power tool and asks whether it works with a specific battery system. Webex AI answers using governed compatibility data. The shopper then asks about warranty coverage, which depends on purchase history and service terms, so the interaction escalates to Webex Contact Center. The live agent receives the product, store, scan, and conversation context. What could have been a generic chatbot exchange becomes a product-specific service moment.

Value Driver 4: Dynamic Digital Merchandising and Retail Media

What challenge it solves

Physical stores have historically been harder to adapt and measure than digital channels. Promotions may not reflect local inventory. Signs can remain static even when traffic patterns change. Supplier-funded media often lives in ecommerce channels while the store, where many decisions still happen, remains under-measured. Retailers may know what was purchased, but not always what was considered, compared, scanned, or ignored.

At the same time, the store can become cluttered if digital content is added without context. More screens do not automatically create better engagement. More promotions do not automatically create better customer experience. Dynamic digital merchandising only works when the content is relevant, timely, measurable, and connected to the operating reality of the store.

The opportunity is to make the store more adaptive without making it feel noisy.

Use cases Cisco enables

Inventory-aware signage: Digital content can change when promoted products are low, out of stock, or substituted.

Daypart and mission-based messaging: Content can adapt for morning, evening, weekend, seasonal, event-driven, or mission-specific traffic.

In-store media activation: Supplier-funded content can appear on screens, endcaps, shelf-edge experiences, or mobile prompts.

Private-label promotion: Relevant alternatives can be promoted where appropriate and compliant.

Traffic-informed merchandising: Dwell, traffic, and zone analytics can inform placement, content, and campaign strategy.

Campaign attribution: Exposure, scans, loyalty, and transaction signals can be connected where governance permits.

Operational content: Signage can support wayfinding, service updates, pickup instructions, safety messages, or store announcements.

Technologies used

Cisco Meraki and Catalyst networking connect digital signage, ESL infrastructure, cameras, sensors, and store systems.

PoE switching can support connected displays, sensors, access points, cameras, and selected infrastructure deployments where appropriate.

Cisco Spaces-style location intelligence helps retailers understand traffic, dwell, and zone-level engagement.

Meraki cameras and sensors can support traffic, queue, and environmental visibility where appropriate.

Webex Connect enables scan-to-message, offer, service, and follow-up workflows tied to in-store engagement.

AppDynamics, ThousandEyes, and Splunk support assurance and performance visibility for signage, APIs, content systems, and campaign-related workflows.

Cisco security capabilities help protect connected signage, devices, data flows, and store networks.

Business impacts

Dynamic digital merchandising and retail media create value by making the physical store more adaptive, measurable, and monetizable without making the experience feel cluttered.

Market proof points show in-store retail media moving from experiment to scale: Grocery TV has reported more than 6,500 stores on its in-store media platform, while Albertsons has moved digital screens into more than one-third of its stores.

IAB in-store retail media standards are giving retailers a common language for impressions, audiences, measurement, and outcomes.

More relevant promotions when signage, shelf-edge content, and digital surfaces respond to local inventory, daypart, traffic, and shopper mission.

Reduced wasted impressions from out-of-stock or poorly timed promotions.

Stronger campaign measurement when exposure, scan, loyalty, traffic, and transaction signals can be connected appropriately.

At an executive level, this value driver matters because the store can become both a merchandising environment and a measurable media environment. Cisco helps connect the infrastructure, location intelligence, signage, security, Webex engagement, and observability layers that make that opportunity operational.

Illustrative example

A grocery store promotes a seasonal beverage on entrance screens, but local inventory drops below the campaign threshold. Instead of continuing to promote an unavailable item, the content shifts to an alternative product funded by the same supplier campaign. At the same time, dwell data shows strong traffic near prepared foods, so a dinner-bundle promotion is pushed to nearby signage. Campaign performance can later be reviewed against scan, loyalty, and transaction data.

Value Driver 5: Shelf-Edge Scan-to-Learn

What challenge it solves

Many purchase decisions stall because customers lack product-level confidence in the aisle. They may need allergen details, compatibility guidance, recipes, reviews, size help, accessory recommendations, or a coupon. They may want to know whether a product works with another item, whether an ingredient is safe for their household, whether there is a better alternative, or whether help is available.

Supplier-owned GS1 2D packaging adoption will expand over time, but retailers do not need to wait for every package to change. Shelf-edge 2D codes on ESLs, paper labels, endcaps, or digital signage can create an immediate product-level entry point controlled by the retailer.

This is an optional supporting CX use case, not a standalone pillar. Its importance is that it demonstrates how the connected store can turn physical intent into a digital, measurable, AI-assisted journey.

Use cases Cisco enables

Product education: Customers can launch product details, reviews, videos, instructions, or usage guidance.

Allergen or ingredient lookup: Sensitive questions can retrieve governed product data rather than rely on generic AI output.

Compatibility checking: Customers can compare electronics, accessories, tools, parts, or product bundles.

Recipe and solution building: Grocery, home, beauty, and specialty retailers can recommend complementary items or shopping lists.

Offer clipping: Shoppers can connect to loyalty offers, rebates, or supplier-funded coupons.

Associate assistance: Customers can request help with product, aisle, and store context attached.

Product-originated conversation: A Webex-powered interaction can begin from a specific SKU and physical location.

Technologies used

Webex Connect routes post-scan interactions across SMS, RCS, WhatsApp, app chat, web chat, and other channels.

Webex AI supports product-specific assistance, intent classification, guided next steps, and conversational automation.

Webex Contact Center escalates complex or sensitive questions to live support with product and store context.

Cisco networking provides secure store connectivity for scan, app, and messaging experiences.

ESL and shelf-edge infrastructure provide the physical entry point for SKU-level digital experiences.

AppDynamics, ThousandEyes, and Splunk help assure and observe the post-scan digital journey.

Cisco security capabilities protect identity, data, devices, and channel access.

Business impacts

Shelf-edge scan-to-learn creates value by turning product-level questions in the aisle into measurable, AI-assisted engagement.

GS1 Digital Link and the industry move toward 2D barcodes by 2027 show that product-level digital engagement is becoming a mainstream retail capability, not a niche experiment.

More confident purchase decisions when shoppers can access product details, allergen information, compatibility guidance, recipes, reviews, offers, or associate help from the shelf edge.

Reduced repetitive associate questions in categories where product information, availability, fit, compatibility, or policy guidance is frequently needed.

New product-intent signals that show which SKUs, categories, and departments generate questions before purchase.

Optional supplier-funded education and retail media opportunities when shelf-edge engagement is governed and measurable.

At an executive level, this value driver matters because it gives retailers a retailer-controlled way to make the aisle more useful before every package carries its own connected experience. The value depends on scan volume, product-data readiness, opt-in, and consent rules, but the strategic opportunity is clear: product interest becomes a measurable interaction instead of an invisible moment.

Illustrative example

An electronics shopper scans a shelf label for a camera and asks whether a specific memory card is compatible. The experience begins with the exact SKU and store context. Webex AI checks governed compatibility data and provides an answer. The shopper then asks about warranty coverage, which escalates to Webex Contact Center. The retailer captures product-level intent while helping the customer make a confident decision.

7. Priority 2 — EX Workforce, Operations, and Margin Protection

Customer experience depends on employee experience. Store teams are the execution layer of the retail promise. They are the people who help customers make decisions, fulfill digital orders, resolve exceptions, manage returns, restock shelves, monitor self-checkout, respond to safety concerns, and keep the store operating when systems do not work as expected.

In many retail environments, associates are also the hidden integration layer. When systems are fragmented, associates compensate. They search for product information across multiple tools. They walk the floor to verify inventory. They answer repetitive customer questions. They manually update price labels. They troubleshoot devices. They re-explain issues that should have carried over from the app or contact center. They respond to queues only after customers are already frustrated. They manage loss-prevention and safety concerns with incomplete context.

That is not sustainable.

The AI execution era should make store work easier to execute, not more complicated. The goal is not simply to add more associate tools. It is to reduce avoidable friction in the work itself: fewer disconnected tasks, fewer manual checks, fewer repetitive questions, fewer unclear escalations, fewer device failures, and fewer situations where associates are asked to solve problems that technology should have prevented or routed earlier.

This priority also includes margin protection. Shrink, fraud, and safety are no longer isolated loss-prevention issues. They are operating-model issues. A store cannot deliver strong employee experience if associates feel unsafe, if self-checkout exceptions are handled inconsistently, if inventory accuracy is poor, or if theft and fraud erode margin without connected visibility. In a connected store, loss prevention becomes part of operational intelligence.

Cisco’s role in this priority is to help connect the systems that shape store execution: wireless connectivity, associate devices, cameras, sensors, POS context, Webex workflows, task routing, observability, security, PoE infrastructure, smart lighting, electronic shelf labels, and analytics. Together, these capabilities can help retailers improve workforce productivity, protect associate capacity, strengthen safety, reduce shrink, and make store operations more visible.

Within this priority, six value drivers matter most:

Value Driver 1: Margin Protection and Connected Loss Prevention

What challenge it solves

Shrink, fraud, and safety have become executive-level retail issues because they affect more than the loss-prevention budget. They affect margin, inventory accuracy, associate experience, customer trust, store execution, and brand reputation.

Traditional loss prevention often operates after the fact. A suspicious transaction occurs, video is reviewed later, evidence is assembled manually, and patterns may only become visible after repeated losses. That reactive model is no longer enough. Retailers are dealing with external theft, organized retail crime, internal theft, sweethearting, self-checkout missed scans, barcode switching, return fraud, claims abuse, process failures, and safety incidents. Many of these events cut across POS systems, video systems, inventory systems, staffing patterns, and store workflows.

A connected store gives retailers a better model. Loss-prevention signals can become part of real-time operational visibility. Cameras can support both security and service flow. POS events can be correlated with video. Safety workflows can route alerts without encouraging unsafe associate confrontation. Investigation teams can search, review, and act with more context. Store leaders can understand where shrink, process breakdowns, and customer friction intersect.

Connected loss prevention should not be framed only as catching theft. It is about protecting margin, improving visibility, and making stores safer and easier to operate.

Use cases Cisco enables

POS/video correlation: Link transaction exceptions to video events so investigation teams can review context more quickly and consistently.

Self-checkout anomaly review: Identify missed scans, barcode switching, walkaways, or repeated exception patterns without relying only on manual observation.

Internal theft and sweethearting detection: Connect exception reporting, transaction data, and video context to investigate suspicious employee-assisted transactions.

Return fraud and claims review: Correlate returns, receipts, customer history, video, and case data where policy and governance permit.

Safety and de-escalation workflows: Route alerts to appropriate teams without pushing frontline associates into unsafe confrontation.

Investigation acceleration: Reduce time spent searching video, assembling evidence, and reconstructing events.

Dual-use camera analytics: Use the camera estate for queue visibility, safety monitoring, layout intelligence, and operational review in addition to security.

Inventory accuracy support: Connect shrink signals to phantom inventory, out-of-stock conditions, and fulfillment failures.

Cross-store pattern detection: Identify repeated events, high-risk zones, or recurring process failures across locations.

Technologies used

Meraki cameras and sensors provide cloud-managed video, event capture, and store visibility. They can support security, safety, queue visibility, and operational review use cases.

Cisco networking and PoE connect and power camera, sensor, and security infrastructure across distributed store environments.

Splunk can correlate POS, video, security, inventory, and operational events so teams can identify patterns and investigate faster.

Webex Connect can support alerting, workflow routing, and communications across store teams, LP teams, and operational support functions.

Webex Contact Center can support remote escalation, investigation support, customer communication, or associate support workflows where appropriate.

Cisco security and segmentation help protect camera networks, POS-related systems, operational devices, and sensitive investigation workflows.

AppDynamics and ThousandEyes help assure applications, APIs, and network paths connected to LP, POS, and store operations.

Business impacts

Connected loss prevention creates measurable value across shrink, fraud, investigation productivity, safety response, inventory accuracy, and operational visibility.

NRF/LPRC reported average retail shrink of 1.6% of sales, representing $112.1B in U.S. retail shrink in the 2023 National Retail Security Survey.

Appriss Retail / Deloitte research has framed return fraud and abuse as a $100B+ challenge, with return fraud and abuse representing a material share of total return value.

ECR Retail Loss Group research supports treating self-checkout as a distinct loss-exposure area when fixed SCO, Scan & Go, or BYOD checkout is in scope.

Faster investigations when POS exceptions, video, inventory signals, case data, and store context can be correlated.

Broader operating value from the same camera estate, including queue visibility, safety monitoring, layout intelligence, and cross-store pattern detection.

At an executive level, this value driver matters because shrink and fraud are no longer isolated LP problems. They affect margin, inventory accuracy, associate confidence, customer trust, and store execution. Cisco helps connect the video, network, workflow, security, and analytics layers needed to turn LP from a reactive review process into a more connected operating capability.

Illustrative example

At a self-checkout area, a camera and POS system identify a repeated mismatch between scanned items and items placed in the bagging area. The system does not prompt a floor associate to confront the customer. Instead, the event is routed to the appropriate team for review with the video clip, transaction data, and store context attached. Over time, the retailer identifies a pattern tied to specific lanes and times of day, adjusts staffing and signage, and improves both shrink control and customer flow.

Value Driver 2: Intelligent Labor and Service Flow

What challenge it solves

Store labor is often managed with limited visibility into what is happening in the moment. Managers may know the schedule, but not where service friction is forming. Associates may be busy with tasks while a queue builds at returns. A department may need coverage because customers are dwelling without help. A pickup customer may be waiting while the responsible associate is unaware they have arrived. Fitting rooms, service counters, checkout lanes, pickup areas, and high-consideration departments can all become friction points before leadership sees the problem.

This creates a reactive operating model. Store teams respond after customers are already waiting, after associates are already stretched, or after a service issue has already escalated.

Intelligent labor and service flow gives retailers a way to sense friction earlier and route work more clearly. The objective is not to monitor associates for its own sake. It is to protect scarce labor capacity and make store work easier to coordinate.

Use cases Cisco enables

Queue detection: Identify congestion at checkout, returns, service counters, pickup zones, pharmacy, or other high-traffic areas.

Dynamic associate tasking: Route work to associates based on live store conditions, department activity, and service needs.

Pickup readiness: Notify associates when pickup or curbside customers are arriving or when order staging requires action.

Department coverage: Detect high dwell or traffic in departments where customers may need assistance.

Returns flow management: Identify service desk congestion and trigger staffing or communication workflows.

Fitting room support: Notify associates when assistance, item retrieval, or room turnover is needed.

Dwell-based intervention: Identify high-consideration zones where customers may need help deciding.

Store manager visibility: Give leaders a clearer real-time view of where execution is breaking down.

Technologies used

Cisco Spaces-style location intelligence helps retailers understand traffic, dwell, and zone-level activity across the store.

Meraki cameras and sensors can support visibility into queues, occupancy, service areas, and traffic patterns.

Cisco wireless and switching provide the connectivity foundation for associate devices, operational systems, and store analytics.

Webex Connect can route operational alerts, task-related messages, and workflow notifications to the right teams.

Webex AI can support prioritization, summarization, and intent classification for service and tasking workflows.

Splunk can correlate operational signals across traffic, staffing, POS, fulfillment, and service systems.

AppDynamics and ThousandEyes help assure applications, APIs, and network paths that support tasking, pickup, and fulfillment flows.

Business impacts

Intelligent labor and service flow creates value by helping stores respond to friction before customers are already waiting and associates are already overloaded.

Zebra’s 17th Annual Global Shopper Study coverage found that roughly eight in 10 retail associates are concerned about the lack of technology deployed to spot safety threats or criminal activity, underscoring the need for better frontline visibility.

Lower abandonment risk when queues, service counters, pickup zones, fitting rooms, and high-intent departments are visible sooner.

Better associate utilization when traffic, dwell, staffing, fulfillment, and task signals can guide where work is needed most.

Improved pickup, returns, pharmacy, service desk, and fitting-room flow through earlier detection and clearer task routing.

Improved employee experience when associates receive clearer direction instead of compensating for disconnected systems.

At an executive level, this value driver matters because labor is one of retail’s scarcest resources. Cisco helps make labor more responsive by connecting location intelligence, cameras, sensors, messaging, tasking, and operational telemetry into a clearer view of store conditions.

Illustrative example

A general merchandise store sees a returns line building on Saturday afternoon. Instead of waiting for customer complaints, the system detects congestion, correlates it with staffing and transaction volume, and sends a task to a cross-trained associate. A second associate receives a prompt to handle pickup orders separately so the returns line does not block fulfillment. The store improves customer flow without adding labor.

Value Driver 3: AI-Assisted Workflow Protection

What challenge it solves

Store associates and contact center agents spend significant time answering questions that are repetitive but still context-dependent. Customers ask where their order is, whether an item is in stock, what the return policy is, whether a pickup is ready, where a product is located, whether a coupon applies, or whether one product works with another.

These questions may seem simple, but they interrupt store work constantly. Every routine question that reaches an associate can pull them away from selling, fulfillment, recovery, shelf work, service, or safety responsibilities. The same pattern appears in the contact center, where routine inquiries consume agent capacity and increase cost-to-serve.

AI-assisted workflow protection reduces that burden by helping customers and associates get routine answers faster while preserving escalation for more complex needs.

The goal is not to keep customers away from people. It is to protect human capacity for moments where human judgment, empathy, physical action, or expert guidance is actually needed.

Use cases Cisco enables

Routine customer question automation: Resolve common questions through Webex-powered messaging or AI-assisted flows.

Associate assist: Provide associates with quick answers from policy, inventory, product, order, or service systems.

Order status deflection: Automate pickup, delivery, and fulfillment updates before customers call or ask in store.

Return policy guidance: Provide consistent answers before a customer reaches the service counter.

Product Q&A support: Help with product details, compatibility, availability, or usage questions.

Escalation routing: Move complex issues to store teams or contact center agents with context attached.

Post-interaction summaries: Reduce manual notes and improve continuity across store, digital, and contact center interactions.

Service recovery triggers: Detect failed or delayed journeys and launch proactive outreach.

Technologies used

Webex Connect orchestrates messaging and workflow automation across SMS, RCS, WhatsApp, app chat, web chat, and other channels.

Webex AI supports conversational assistance, intent detection, summarization, guided next steps, and automation of routine service tasks.

Webex Contact Center handles human escalation and gives agents better context when automation is not enough.

Cisco networking supports reliable in-store and cloud connectivity for customer, associate, and service workflows.

AppDynamics and ThousandEyes help assure the digital systems that answer customer questions, retrieve order context, or support associate tools.

Splunk can correlate service events, application telemetry, messaging flows, and operational signals.

Business impacts

AI-assisted workflow protection creates value by reducing the routine questions and disconnected tasks that pull associates away from service, fulfillment, recovery, and selling.

Directional service benchmarks often use roughly $1.84 for self-service versus $13.50 for assisted support, showing why routine containment can be a meaningful capacity lever.

Zendesk / Forrester TEI evidence supports the broader impact of AI-assisted support on contact-rate reduction, automated resolution, handle time, and agent productivity.

Fewer associate interruptions for order status, item location, return policy, product availability, coupon, and product-comparison questions.

Improved service consistency when policy, inventory, order, loyalty, and product information can be surfaced through AI-assisted workflows.

More human capacity for moments that require judgment, empathy, physical action, or expert guidance.

At an executive level, this value driver matters because better EX is not only about adding tools. It is about protecting associate attention. Cisco helps connect AI, Webex workflows, contact center, networking, and observability so routine work can be handled more consistently and complex work can reach the right person with context.

Illustrative example

An apparel shopper asks whether an item is available in another size. Instead of searching manually across systems, the associate uses an AI-assisted workflow on a handheld. The system checks local inventory, nearby store availability, and ship-to-home options, then offers to reserve the item or send a purchase link. If the customer asks about returns, the interaction can move to a Webex-powered messaging flow. The associate stays focused on service rather than system navigation.

Value Driver 4: Task Automation and ESLs

What challenge it solves

Retail stores still rely on many manual execution tasks. Price changes, promotion updates, shelf checks, signage replacement, compliance audits, item lookups, and exception handling consume associate time every week. These tasks are repetitive, error-prone, and often compete with customer service and fulfillment work.

Pricing and promotion errors also affect customer trust. If the shelf price does not match the register price, the customer sees the retailer as inconsistent. If a promotion remains posted after inventory is gone, the store creates frustration. If associates spend hours replacing labels, they are not available for higher-value work.

Task automation and electronic shelf labels can help reduce that burden. The broader opportunity is not only to digitize price tags. It is to connect shelf execution to pricing, promotion, inventory, media, associate workflows, and customer information.

Use cases Cisco enables

Automated price updates: Reduce manual label changes and improve shelf-to-POS price consistency.

Promotion execution: Update shelf-edge content in sync with campaigns, offers, and store events.

Inventory-aware shelf messaging: Support local availability, substitutions, order options, or product alternatives.

Shelf-edge product education: Use ESLs or labels to launch product information, comparisons, recipes, reviews, or scan-to-learn flows.

Compliance support: Reduce mismatches between shelf price, advertised price, and POS price.

Associate tasking: Route exceptions when updates fail or shelves need attention.

Retail media activation: Support supplier-funded shelf-edge content where appropriate and measurable.

Operational visibility: Monitor update success, device health, and exception patterns across stores.

Technologies used

Cisco Meraki and Catalyst networking connect store systems, ESL gateways, associate devices, and supporting applications.

PoE switching can support connected infrastructure where relevant, including access points, gateways, sensors, cameras, and signage.

Meraki access point and ESL integration patterns can reduce separate gateway complexity where supported and approved.

Webex Connect can support customer or associate workflows triggered by shelf-edge interactions, product questions, or service needs.

Splunk can correlate ESL updates, pricing events, device status, and operational telemetry.

AppDynamics and ThousandEyes help assure supporting applications, APIs, and cloud services that update pricing or content.

Cisco security and segmentation help protect ESL infrastructure and related store systems.

Business impacts

Task automation and ESLs create value by reducing repetitive shelf-execution work and improving the accuracy of pricing, promotion, and product information in the store.

Walmart has announced a rollout of digital shelf labels across its U.S. stores by the end of 2026, highlighting how quickly shelf execution is becoming a digital operating layer.

Reduced manual label-change labor when price and promotion updates can be executed digitally instead of through paper-tag replacement.

Improved shelf-to-POS consistency, reducing customer disputes and pricing-error friction.

Faster promotion execution and greater flexibility for local inventory, daypart, campaign, or category changes.

A stronger foundation for shelf-edge product education, dynamic merchandising, and in-store retail media.

At an executive level, this value driver matters because store teams spend too much time on repetitive work that can be automated or exception-managed. Cisco helps connect ESL infrastructure, store networking, PoE, application assurance, security, and telemetry so shelf execution becomes more accurate, visible, and adaptable.

Illustrative example

A home improvement retailer needs to update prices across thousands of SKUs during a weekend promotion. Instead of associates spending hours replacing paper tags, ESLs update centrally. Exceptions are flagged only where a shelf update fails. Digital shelf content also supports product comparisons and accessory suggestions for high-consideration items, giving the retailer both labor efficiency and better customer information at the shelf.

Value Driver 5: Connected Facilities and Smart Lighting

What challenge it solves

Facilities systems are often managed separately from store operations. Lighting, HVAC, refrigeration, occupancy, energy management, and building controls may operate on independent systems with limited connection to customer traffic, associate workflows, safety needs, merchandising, or IT visibility.

That separation creates missed opportunities. Lighting may remain at full output in low-traffic zones. Occupancy data may not inform operations. Facilities issues may not be visible until they affect customers or associates. Energy savings may be pursued separately from safety, merchandising, or service goals. Sensors may exist but not contribute to broader store intelligence.

Connected facilities and smart lighting create an opportunity to make building systems part of the store operating model.

Smart lighting should not be treated as a standalone pillar. It is one practical example of how store infrastructure can become connected, controllable, measurable, and operationally useful.

Use cases Cisco enables

Occupancy-based lighting: Adjust lighting based on traffic, staffing, store hours, or zone activity.

Daylight harvesting: Reduce energy use when natural light is sufficient.

Task lighting: Support stocking, service counters, pickup areas, fitting rooms, or high-value departments.

Safety support: Improve visibility in parking, backroom, loading, or restricted areas.

Merchandising emphasis: Highlight promotional zones, endcaps, or product displays.

Facilities automation: Coordinate lighting with HVAC, occupancy, and store schedules.

Telemetry integration: Feed environmental and usage data into operational dashboards.

After-hours monitoring: Detect unusual occupancy, lighting, or facility activity outside expected patterns.

Technologies used

Cisco Catalyst PoE and PoE++ switching can power and connect lighting and sensor infrastructure in suitable deployments.

Cisco Meraki networking supports cloud-managed store connectivity and operational visibility.

Sensors and IoT systems capture occupancy, environmental, and facility signals.

Splunk can correlate facilities telemetry with store operations, energy data, security events, and occupancy patterns.

Cisco security and segmentation help protect facilities and IoT systems from broader network exposure.

AppDynamics and ThousandEyes can help assure supporting applications and cloud services where relevant.

Business impacts

Connected facilities and smart lighting create value by turning building systems into part of the store operating model instead of treating them as isolated facilities infrastructure.

A Lawrence Berkeley National Laboratory meta-analysis of lighting controls reviewed 240 energy-savings estimates across 88 papers and case studies, reinforcing lighting controls as a well-studied savings lever.

Retail TEV planning commonly isolates lighting-related energy before applying savings assumptions, because lighting can represent a meaningful controllable share of store utility spend.

Lower energy consumption when lighting responds to traffic patterns, operating hours, daylight, and zone activity.

Improved visibility and safety in pickup areas, parking lots, back rooms, loading zones, stocking areas, and service counters.

More useful occupancy and environmental data for operations, facilities, security, and sustainability reporting.

At an executive level, this value driver matters because facilities systems can support more than cost control. When connected properly, they contribute to safety, sustainability, operational visibility, and the broader store intelligence layer. Cisco helps provide the PoE, connectivity, segmentation, and telemetry foundation for that shift.

Illustrative example

A large-format retailer uses connected lighting to dim low-traffic zones during late evening hours while keeping pickup, checkout, and restocking areas fully lit. Occupancy sensors feed facilities data into an operations dashboard. The retailer saves energy while improving visibility where associates are actively working.

Value Driver 6: Connected Device Reliability

What challenge it solves

Store operations depend on connected devices: handhelds, scanners, tablets, POS terminals, self-checkout lanes, cameras, sensors, digital signage, ESLs, kiosks, and associate communication tools. When these devices fail or perform poorly, associates lose time, customers wait, and IT teams struggle to identify root cause.

The problem is often difficult to diagnose because the symptom appears in one place while the cause sits somewhere else. A handheld may appear broken when the real issue is Wi-Fi interference. A pickup workflow may slow down because of an API dependency. A digital sign may fail because of a content system issue. A POS delay may involve a WAN path, payment service, or local network condition. A camera outage may involve power, connectivity, policy, or cloud access.

Without connected observability, troubleshooting becomes reactive. Store teams report problems. IT investigates. Vendors are contacted. Time passes. The customer and associate experience suffers.

Connected device reliability gives retailers a way to see and resolve issues faster.

Use cases Cisco enables

Handheld performance assurance: Detect Wi-Fi, application, API, or device issues affecting associate tools.

POS and checkout resilience: Monitor connectivity and dependencies that support payment and transaction workflows.

Camera and sensor uptime: Ensure operational visibility systems remain available.

Digital signage assurance: Detect failed content delivery, connectivity issues, or device outages.

ESL update monitoring: Identify update failures and supporting network or application issues.

Incident correlation: Connect device symptoms to application, network, cloud, security, or API root causes.

Remote troubleshooting: Reduce truck rolls and store visits by diagnosing issues centrally.

Priority-based remediation: Focus IT response on issues affecting critical workflows.

Technologies used

Cisco wireless, switching, and SD-WAN provide reliable store connectivity for devices and applications.

Meraki cloud-managed infrastructure supports visibility and remote management across distributed store environments.

AppDynamics monitors application performance and business transactions.

ThousandEyes provides visibility into WAN, internet, SaaS, and cloud paths.

Splunk correlates telemetry across devices, applications, networks, security, and incidents.

Cisco security capabilities help protect devices, enforce policy, and identify suspicious behavior.

Business impacts

Connected device reliability creates value by reducing the operational drag caused when handhelds, scanners, POS, cameras, signage, ESLs, kiosks, and associate tools fail or perform poorly.

Cisco’s Meraki economic-impact materials cite a 40% reduction in network downtime and a 90% reduction in time spent on configuration changes in a commissioned Forrester TEI study.

Fewer store visits and truck rolls when IT can diagnose network, device, application, and cloud-path issues remotely.

Reduced MTTR when device symptoms can be correlated with Wi-Fi, WAN, API, SaaS, power, security, or application root causes.

Improved associate productivity when critical tools stay available for pickup, inventory, checkout, tasking, and customer service.

Clearer IT prioritization because incidents can be tied to affected stores, workflows, and business impact.

At an executive level, this value driver matters because device reliability is now business reliability. Cisco helps connect wireless, switching, SD-WAN, cloud management, AppDynamics, ThousandEyes, Splunk, and security telemetry so IT can move from reactive troubleshooting to proactive assurance.

Illustrative example

A grocery chain sees intermittent handheld failures in multiple stores. Associates think the devices are broken, but observability shows that the issue is tied to a specific SaaS API path during peak inventory updates. IT reroutes traffic and resolves the issue centrally, avoiding unnecessary device replacements and store visits while restoring associate productivity.

8. Priority 3 — IT Resilience, Trust, and Protection

In the connected store, IT resilience becomes business resilience.

A network outage is no longer just a technical event. It can stop payments, disrupt pickup, break loyalty redemption, disable digital signage, interrupt cameras, slow handhelds, delay associate workflows, and create customer frustration. An application slowdown may not appear as a full outage, but it can still damage conversion, productivity, and trust. A cyber incident can affect customer data, store systems, vendor access, payment environments, and daily operations.

AI-native retail increases this dependency. More devices, more applications, more APIs, more cloud services, more edge workloads, more customer data, and more automated workflows mean the store needs a stronger foundation. Every new connected capability can create value, but every new connection also adds complexity and risk if it is not secured, observed, and governed.

This is why IT can no longer be positioned as a back-office support function in the retail technology story. IT is the foundation that determines whether CX and EX improvements can scale. The same infrastructure that supports customer engagement also supports associate productivity, loss prevention, digital signage, smart lighting, fulfillment, contact center, observability, and cyber resilience.

For retailers, the strategic question is not simply, “Can the network stay online?” It is, “Can the technology foundation support the connected store without increasing fragility?”

Cisco’s role in this priority is to help retailers build the secure, observable, AI-ready infrastructure required to support modern stores at scale. That includes connectivity, identity, segmentation, threat detection, application assurance, network visibility, cloud path monitoring, telemetry correlation, and edge/cloud readiness.

Within this priority, five value drivers matter most:

Value Driver 1: Secure Connected Store Foundation

What challenge it solves

Retail stores depend on reliable connectivity, but many store networks were designed before the current density of cloud applications, mobile devices, smart cameras, sensors, digital signage, electronic shelf labels, self-checkout, and AI-enabled workflows.

Legacy store networks can become bottlenecks when retailers add more devices and more cloud dependencies. What used to be a relatively contained store environment is now a distributed technology node. A single store may support guest Wi-Fi, associate handhelds, POS terminals, payment devices, self-checkout, digital signage, cameras, sensors, smart lighting, ESL infrastructure, kiosks, contact center handoffs, pickup workflows, and multiple cloud applications.

If the foundation is weak, every connected use case becomes fragile.

The connected store requires a network foundation that is reliable enough for daily operations, flexible enough for new devices, secure enough for sensitive systems, and observable enough for IT to understand what is happening across hundreds or thousands of locations.

Use cases Cisco enables

Store Wi-Fi modernization: Support associate devices, customer engagement, guest Wi-Fi, and operational systems with reliable connectivity.

Cloud-managed store operations: Give IT teams centralized visibility and management across distributed locations.

SD-WAN resilience: Improve store connectivity to cloud, SaaS, and enterprise systems.

PoE device support: Power and connect cameras, access points, sensors, signage, ESL gateways, and selected smart lighting deployments.

Critical workflow prioritization: Protect traffic for POS, payment, pickup, associate tools, and other high-priority store systems.

Device visibility: Understand what is connected across store environments and identify unmanaged or unexpected devices.

Remote troubleshooting: Reduce dependence on store visits by diagnosing network and device issues centrally.

New use case readiness: Create a foundation that can support future connected-store deployments without rebuilding the store network each time.

Technologies used

Cisco Catalyst and Meraki wireless provide high-performance store connectivity for associates, customers, devices, and operational systems.

Cisco switching supports access, aggregation, PoE, and wired device connectivity across the store.

Cisco SD-WAN improves store-to-cloud and store-to-enterprise resilience by optimizing connectivity across WAN, internet, and cloud paths.

PoE and PoE++ switching can power and connect access points, cameras, sensors, signage, and selected smart lighting or edge infrastructure.

Meraki cloud management provides centralized visibility and simplified operations across distributed store environments.

ThousandEyes provides visibility into internet, WAN, cloud, and SaaS paths that affect store applications and customer journeys.

Splunk can correlate infrastructure, security, application, and operational telemetry to help IT teams see patterns across stores.

Business impacts

A secure connected store foundation creates value by making store infrastructure reliable enough, visible enough, and secure enough to support the next wave of connected retail use cases.

Cisco’s Meraki economic-impact materials report $7.3M in total benefits over three years, $1.7M in savings, and a 90% reduction in time spent on configuration changes in a commissioned Forrester TEI study.

Lower support burden when cloud-managed networking gives IT teams centralized visibility across distributed stores.

Fewer unnecessary truck rolls when network and device issues can be diagnosed remotely.

Improved readiness for cameras, sensors, signage, ESLs, smart lighting, associate devices, mobile POS, and AI-enabled workflows.

Better continuity for critical store workflows such as POS, payment, pickup, loyalty, inventory, and associate tasking.

At an executive level, this value driver matters because the network is no longer only infrastructure. It is the execution foundation for CX, EX, LP, retail media, facilities, security, and AI. Cisco helps retailers modernize that foundation without treating every new use case as a separate store rebuild.

Illustrative example

A specialty retailer rolls out mobile POS and clienteling tablets across stores. In locations with older Wi-Fi, associates experience intermittent disconnects that slow checkout and frustrate customers. A modernized Cisco foundation improves device performance, gives IT visibility into store conditions, and supports future digital signage, smart camera, and ESL use cases without requiring a separate infrastructure rebuild.

Value Driver 2: Zero Trust and Store Segmentation

What challenge it solves

Modern stores contain many types of connected users, devices, and systems: POS, payment devices, guest Wi-Fi, associate handhelds, cameras, digital signage, ESL gateways, HVAC, smart lighting, sensors, kiosks, vendors, support teams, cloud applications, and customer engagement platforms.

If these systems operate on overly flat networks or weak access policies, a compromise in one area can create risk across the store. A vendor account may have more access than it needs. A digital signage player may sit too close to sensitive systems. A guest network may not be adequately separated. A camera or IoT device may become a path into broader infrastructure. A compromised credential may expose more systems than intended.

The connected store expands the attack surface. Zero trust and segmentation help retailers expand connected capabilities without expanding risk at the same pace.

The goal is not to make store operations harder. The goal is to allow more devices, applications, vendors, and AI-enabled workflows to operate safely.

Use cases Cisco enables

Guest and corporate network separation: Prevent guest access from touching store systems.

IoT segmentation: Isolate cameras, signage, ESLs, lighting, sensors, HVAC, and other operational devices.

Payment environment protection: Restrict access to POS and payment-related systems.

Context-aware access: Validate users, devices, posture, location, and risk before granting access.

Vendor access control: Limit third-party access to only the systems, time windows, and functions required.

Policy consistency: Apply access and segmentation policies across distributed stores.

Threat containment: Reduce lateral movement and limit blast radius when a device, credential, or application is compromised.

AI and data governance support: Control access to systems and data that AI workflows may use.

Technologies used

Cisco ISE supports identity-based access control, device profiling, and segmentation policies across store networks.

Cisco Duo supports multi-factor authentication and device trust for users accessing applications and systems.

Cisco Secure Access helps secure access to private applications, internet destinations, and cloud services.

Cisco networking and segmentation capabilities enforce access boundaries across wired, wireless, guest, corporate, IoT, and operational systems.

Cisco XDR supports detection and response across security telemetry.

Cisco Umbrella / DNS-layer security can help block malicious destinations and reduce exposure to internet-based threats.

Splunk can correlate security, identity, network, and operational events to support investigation and response.

Meraki and Catalyst infrastructure support policy enforcement across distributed store environments.

Business impacts

Zero trust and store segmentation create value by allowing retailers to connect more devices, vendors, applications, and workflows without expanding risk at the same pace.

IBM’s 2025 breach-cost research reports a $4.44M global average cost of a data breach, reinforcing why segmentation, identity, and access controls matter as stores connect more systems.

Reduced blast radius when POS, payment, guest Wi-Fi, cameras, signage, ESLs, HVAC, sensors, and associate devices are segmented appropriately.

Stronger protection for customer data, payment environments, loyalty systems, and sensitive store applications.

Lower vendor-access risk when third parties are limited to the systems, time windows, and functions they actually need.

Safer expansion of IoT, AI, signage, cameras, and facilities systems because access is governed by context and policy.

At an executive level, this value driver matters because the connected store expands the attack surface. Cisco helps retailers enforce trust across users, devices, applications, IoT, vendors, and data flows so innovation can scale without leaving store systems overly exposed.

Illustrative example

A retailer connects new digital signage, ESL gateways, and smart lighting systems across hundreds of stores. Instead of placing all devices on a flat store network, IT segments each device category and restricts access to required services only. If one signage device is compromised, segmentation helps prevent access to payment systems, associate devices, customer data, and enterprise applications.

Value Driver 3: Observability as a Business Capability

What challenge it solves

In a connected store, customer and associate experiences depend on applications, APIs, networks, cloud services, ISPs, devices, and third-party platforms. Traditional monitoring may show that systems are technically online, but it may not reveal why a pickup workflow is slow, why an app coupon fails, why a handheld drops, why a digital sign is blank, or why checkout latency is increasing.

Retailers need observability that connects technical performance to business journey impact.

That means moving beyond the question, “Is the system up?” to more useful questions:

Can the customer redeem a coupon?

Can the associate complete a pickup?

Can the POS process payment?

Can the app load the product page?

Can the ESL update complete?

Can digital signage retrieve content?

Can the contact center receive context?

Can the camera system upload an event?

Can the order management API respond?

Which stores, customers, associates, or workflows are affected?

Without this visibility, IT teams troubleshoot in the dark, store teams absorb the friction, and customers feel the impact before the enterprise understands the root cause.

Use cases Cisco enables

Checkout journey assurance: Monitor payment, POS, network, and API dependencies that affect checkout.

Pickup and fulfillment monitoring: Track app, OMS, messaging, and store workflow performance.

Digital experience assurance: Monitor app, site, loyalty, offer redemption, and product information performance.

Store device incident correlation: Connect handheld, signage, camera, or ESL failures to root causes.

SaaS and internet visibility: Understand third-party path issues beyond the store network.

Incident prioritization: Prioritize issues based on customer, associate, and business workflow impact.

Executive reporting: Translate technical events into business impact language.

Proactive remediation: Identify degraded performance before it becomes a widespread customer or store issue.

Technologies used

AppDynamics monitors application performance, APIs, and business transactions that support customer and associate journeys.

ThousandEyes provides visibility into WAN, internet, SaaS, cloud, and third-party paths that affect store and digital experiences.

Splunk correlates telemetry across applications, networks, security, devices, and operational systems.

Cisco networking telemetry provides device and network performance context.

Meraki cloud management supports visibility into distributed store environments.

Webex platforms can provide interaction, contact center, and communication context where relevant.

Cisco security telemetry can add identity, access, and threat context to operational incidents.

Business impacts

Observability creates value by helping retailers connect technical performance to the customer, associate, and store workflows that depend on it.

Cisco Full-Stack Observability TEI materials report a 60% reduction in the average length of a major outage by Year 3 and a 90% reduction in time spent on war-room triage and resolution.

Faster diagnosis when application, API, SaaS, internet path, network, device, and security telemetry can be correlated.

Reduced MTTR and support effort by showing which systems, stores, and journeys are affected by degradation.

Better protection for checkout, pickup, loyalty redemption, digital signage, contact center, ESL updates, and associate tools.

Improved executive visibility because IT incidents can be explained in terms of business workflow impact, not only technical status.

At an executive level, this value driver matters because “green dashboards” are not enough when customers and associates are still feeling friction. Cisco helps retailers observe the full path from store to application to cloud so IT can detect, diagnose, prioritize, and resolve issues faster.

Illustrative example

A retailer sees a spike in pickup complaints in one region. Store teams think the issue is staffing. IT initially sees no major outage. Observability shows that a third-party API used by the order-status service is intermittently timing out through a specific internet path. ThousandEyes identifies the path issue, AppDynamics shows the affected transaction, and Splunk correlates complaint volume. The retailer resolves the issue before it becomes a national service problem.

Value Driver 4: Cyber Resilience and Risk Reduction

What challenge it solves

Retailers are attractive targets because they operate payment systems, customer accounts, loyalty programs, gift cards, ecommerce platforms, vendor integrations, supply-chain connections, and distributed store networks. Ransomware, credential theft, account takeover, API abuse, supply-chain compromise, IoT exposure, phishing, and shadow AI can all create operational and financial risk.

The connected store increases both value and exposure. More devices, more applications, more vendors, more cloud services, more data flows, and more AI-enabled workflows mean more places where attackers, misconfigurations, or weak policies can create harm.

Cyber resilience is therefore not only a security requirement. It is a business continuity requirement.

A cyber incident can disrupt stores, expose customer data, interrupt fulfillment, damage loyalty, create regulatory exposure, and slow operations. Retailers need to detect, contain, investigate, and recover quickly while keeping critical store operations moving.

Use cases Cisco enables

Credential and access protection: Reduce risk from compromised accounts, weak authentication, or unmanaged access.

Threat detection and response: Detect suspicious activity across endpoints, network, cloud, identity, and applications.

Store segmentation: Limit blast radius from compromised devices or systems.

Shadow AI and data governance support: Reduce unsanctioned data exposure and improve access controls where applicable.

Vendor risk containment: Limit third-party access to specific systems, time windows, and purposes.

Security telemetry correlation: Connect security events with operational, identity, network, and application context.

Incident response support: Improve investigation speed, containment, and executive visibility.

Resilience planning: Protect critical workflows such as POS, fulfillment, contact center, and store operations.

Technologies used

Cisco Duo provides multi-factor authentication and device trust to reduce credential-related risk.

Cisco Secure Access supports secure access to applications, internet destinations, and cloud services.

Cisco ISE helps enforce identity-based access and segmentation policies.

Cisco XDR supports detection and response across multiple security telemetry sources.

Cisco Umbrella / DNS-layer security helps block malicious destinations and reduce internet-based exposure.

Splunk correlates security, IT, identity, and operational events for investigation and response.

Cisco networking and segmentation provide enforcement points for access control and containment.

Business impacts

Cyber resilience creates value by protecting the connected store from disruptions that can affect operations, customer trust, payment environments, loyalty accounts, vendors, and data flows.

Sophos retail ransomware research reported a mean recovery cost of $2.73M in 2024, while IBM’s 2025 breach-cost research reports a $4.44M global average data-breach cost.

Retailers face persistent exposure from ransomware, credential theft, account takeover, phishing, API abuse, vendor access, and IoT compromise.

Reduced likelihood and impact of incidents when identity, device trust, secure access, segmentation, threat detection, and telemetry correlation work together.

Faster containment and investigation when security signals can be tied to users, devices, applications, stores, and operational workflows.

Protected customer trust when customer data, loyalty accounts, payment systems, and sensitive applications are governed more consistently.

At an executive level, this value driver matters because cybersecurity in retail is now a business continuity issue. Cisco helps reduce exposure, contain incidents, and improve response by connecting identity, segmentation, secure access, detection, DNS-layer protection, Splunk, and network enforcement.

Illustrative example

A retailer detects suspicious login behavior from a third-party maintenance account. Instead of broad access, the account is governed through identity controls, device trust, and segmented network policy. Security telemetry is correlated in Splunk, and the access attempt is contained before it reaches store systems. The incident becomes a manageable security event rather than an operational disruption.

Value Driver 5: AI-Ready Edge and Cloud Infrastructure

What challenge it solves

AI-native retail use cases increasingly depend on data and decisioning across the edge, cloud, and enterprise systems. Computer vision, queue detection, smart lighting, digital signage, product assistants, associate workflows, dynamic offers, inventory intelligence, and real-time tasking may all require low latency, local processing, secure APIs, resilient connectivity, and governed data flows.

Retailers that add AI workloads on top of brittle infrastructure risk creating new silos. One team deploys a computer vision pilot. Another deploys a digital signage platform. Another tests an associate assistant. Another adds sensors. Another adds customer messaging. If each project creates its own architecture, the store becomes harder to secure, harder to observe, and harder to scale.

AI-ready infrastructure gives retailers a reusable foundation for current and future use cases.

The value is not only the first AI deployment. It is the ability to deploy the next one faster, more securely, and with less rework.

Use cases Cisco enables

Edge video analytics: Support local processing for cameras and computer vision use cases.

Real-time store intelligence: Process traffic, queue, sensor, device, and operational signals.

AI-assisted customer journeys: Connect product, loyalty, inventory, messaging, and service systems.

AI-assisted associate workflows: Support handheld, tasking, and associate-assist experiences.

Dynamic content decisioning: Connect signage and merchandising to real-time store conditions.

Hybrid cloud connectivity: Support secure data movement between store, cloud, and enterprise systems.

Future AI workload readiness: Prepare stores for new AI use cases without rebuilding the foundation.

Governed data flows: Help ensure AI systems access the right data with the right controls.

Technologies used

Cisco networking, switching, and SD-WAN provide connectivity for edge, store, cloud, and enterprise workloads.

PoE infrastructure supports connected edge devices such as cameras, access points, sensors, signage, and selected smart infrastructure.

Meraki cameras and sensors provide physical-world data sources for store intelligence.

AppDynamics and ThousandEyes assure applications, APIs, cloud paths, and network dependencies that AI workflows rely on.

Splunk correlates data and telemetry across AI-related workflows, applications, devices, and operations.

Cisco security capabilities protect APIs, devices, users, data flows, and AI-adjacent systems.

Webex AI, Webex Connect, and Webex Contact Center support AI-enabled customer engagement, service workflows, and escalation.

Business impacts

AI-ready edge and cloud infrastructure creates value by giving retailers a reusable foundation for current and future AI-enabled store use cases.

McKinsey has estimated that generative AI could unlock $240B to $390B in value for retailers, and broader retail / CPG estimates reach $400B to $660B annually when use cases are fully implemented.

Faster rollout of use cases such as computer vision, queue detection, smart lighting, dynamic signage, associate assist, product Q&A, and operational automation.

Reduced infrastructure rework when new AI pilots can build on a common connectivity, security, observability, and data-flow foundation.

Improved governance because AI workflows can access store, edge, cloud, application, and customer data through controlled paths.

Greater strategic flexibility as retail formats, AI capabilities, device ecosystems, and customer expectations change.

At an executive level, this value driver matters because AI value does not come from isolated pilots. It comes from the ability to connect intelligence to execution repeatedly. Cisco helps create the edge, cloud, security, observability, and workflow foundation that lets retailers scale AI use cases without rebuilding the store architecture every time.

Illustrative example

A retailer wants to expand computer vision from loss prevention to queue detection, inventory visibility, and safety analytics. Rather than deploying separate infrastructure for each pilot, the retailer uses a connected edge and cloud foundation that supports camera data, secure segmentation, application monitoring, and workflow integration. Each new use case builds on the same foundation instead of creating another silo.

9. Cisco Connected Retail Architecture

The connected store operating model requires an architecture that can support business outcomes, not just technology deployment. Retailers do not need a product catalog disguised as a strategy. They need a clear view of the layers that must work together for the store to become more connected, intelligent, secure, and measurable.

Cisco’s role is strongest at the intersections: where devices connect to networks, where applications depend on cloud services, where customer engagement depends on messaging and contact center, where cameras and sensors create operational signals, where security policy protects distributed systems, and where observability connects technical performance to business impact.

The architecture should be understood as a shared operating foundation rather than a stack of isolated layers. The same connectivity layer that supports POS can also support cameras, associate devices, digital signage, ESLs, smart lighting, and customer engagement. The same observability layer that helps IT troubleshoot application issues can also protect pickup, loyalty, and checkout journeys. The same security controls that protect payment environments can also make it safer to add IoT, signage, and edge devices. The same Webex engagement layer that supports service recovery can also support product-originated conversations, pickup coordination, and contact center escalation.

The connected retail architecture gives retailers a way to scale innovation without recreating fragmentation each time a new use case appears. Detailed layer-by-layer architecture planning should follow in technical workshops or sales-enablement materials; the whitepaper’s purpose is to show how those layers work together to support the business model.

10. From Strategy to Measurable Value

A connected store strategy is only useful if it can be translated into measurable business value.

Connecting the whitepaper to TEV

This whitepaper defines the operating model and the value drivers. The companion TEV page translates those drivers into scenario-specific estimates, using customer inputs, benchmark ranges, deployment assumptions, and overlap controls. Keeping that separation lets this paper remain strategic while still giving sellers and customers a credible path from narrative to numbers.

For retailers, the opportunity is broad. A more connected store can improve customer engagement, reduce friction in omnichannel journeys, protect associate capacity, improve task execution, reduce shrink, strengthen safety, lower energy consumption, reduce support burden, improve uptime, protect digital experiences, and create new opportunities for retail media and first-party customer insight.

But the value story must be disciplined. Connected-store investments often influence multiple parts of the business at once. The same camera estate that supports loss prevention can also improve queue visibility and safety. The same messaging platform that supports pickup updates can also improve service recovery, contact deflection, loyalty engagement, and product-originated conversations. The same network foundation that supports POS can also support cameras, sensors, signage, ESLs, smart lighting, associate devices, and AI workloads.

That overlap is a strength, but it also requires careful measurement. A credible value case should distinguish between direct operating impact, strategic upside, and protected value.

This distinction matters. A retailer should not treat every enabled capability as immediate profit. Revenue opportunities should be converted using appropriate margin assumptions. Productivity improvements should distinguish between hard savings and reallocated capacity. Resilience and cybersecurity value should be framed as protected value, not new revenue. Retail media and first-party data opportunities should be tied to actual monetization and governance maturity.

Cisco Retail 2.0 should therefore move from strategy to value in three steps. First, identify the connected-store priorities that matter most to the retailer. Second, map those priorities to the store systems, workflows, and infrastructure required to execute them. Third, develop a customer-specific value model that separates direct impact, strategic upside, and protected value while using conservative assumptions and clear overlap controls.

The result is not a generic ROI claim. It is a structured value conversation. For the CIO, it shows how infrastructure modernization supports business outcomes rather than only technical refresh. For the COO, it connects store visibility and workflow orchestration to execution. For the CFO, it separates hard savings from strategic upside and protected value. For the CISO, it frames security as business continuity. For the Chief Customer Officer, it connects digital engagement to physical journey improvement.

The strategy defines what the store needs to become. The value model defines what that transformation is worth.

11. Summary: The AI-Native Retail Mandate

Retail’s next transformation will not be defined by isolated AI tools. It will be defined by operating models that make AI useful in the real world. That real world is the store.

The store is where customers decide, associates execute, inventory moves, media activates, services are delivered, losses occur, devices connect, and brand promises are kept or broken. It is also where fragmentation becomes visible first.

The AI-native era raises the standard. Retailers cannot simply add intelligence on top of disconnected operations and expect scaled value. AI requires context. Context requires connection. Connection requires secure, resilient, observable infrastructure and workflows that can act on what the business learns.

For customers, the connected store means journeys that feel more relevant, more intuitive, and easier to complete. For associates, it means clearer context, fewer avoidable interruptions, more reliable tools, safer workflows, and less time spent compensating for disconnected systems. For store leaders, it means better visibility into queues, tasks, fulfillment, shrink, safety, inventory, merchandising, and service flow. For IT and security leaders, it means a more observable, governable, resilient environment for the applications, devices, identities, APIs, cloud services, and AI workloads that now shape retail execution.

Cisco’s role is to help retailers build that foundation. Secure networking, Wi-Fi, switching, SD-WAN, PoE, smart cameras, sensors, ESL infrastructure, Webex Connect, Webex AI, Webex Contact Center, AppDynamics, ThousandEyes, Splunk, and Cisco security capabilities can help connect the physical and digital business into one operating environment.

The point is not to make the store feel more technical. The point is to make it work better. The AI-native retailer will not be the one with the most pilots, dashboards, or disconnected digital tools. It will be the one that can connect intelligence to execution across the store estate.

The connected store is how retailers get there.