Retail Analytics with Local AI – Personalization Without the Cloud

In today’s retail landscape, one size no longer fits all. Shoppers expect experiences tailored to their needs and tastes, both online and in-store. AI-driven personalization has become a critical differentiator for retailers, powering product recommendations, customized marketing, and real-time service enhancements. Research shows that customers reward this effort: 77% of consumers prefer brands offering personalized, data-based experiences (Retail 2024: Next-Level Personalization, AI and Harmony - Mood Media), and over half say their satisfaction increases as shopping experiences become more personalized (Retail 2024: Next-Level Personalization, AI and Harmony - Mood Media). Forward-thinking companies are also reaping the benefits – fast-growing businesses generate 40% more revenue from personalization than their slower-growing peers (40 personalization statistics: The state of personalization in 2025 and beyond | Contentful).

Yet, implementing these personalized experiences comes with challenges around data privacy, security, and cost. Many retailers have traditionally relied on cloud-based AI services to crunch data and generate insights. But with stricter compliance requirements and rising cloud costs, “local AI” – running AI on-premises or at the network edge – is emerging as an attractive alternative. In fact, over 70% of digital retailers in a recent survey believe AI-driven personalization will affect their business in 2024 (How AI-Powered Personalization Is Transforming Digital), and a growing number are exploring ways to do it without relying solely on the cloud. This article will explore why AI-powered personalization is so important for retail, how local AI can address privacy and cost concerns, and what business leaders need to know about adopting AI on their own terms.

AI-Driven Personalization: The New Retail Imperative

Personalization has quickly moved from a nice-to-have to a must-have in retail. Shoppers today are inundated with choices, and they gravitate toward brands that cut through the noise with relevancy. AI enables retailers to analyze massive amounts of customer data – purchase history, browsing behavior, loyalty activity, and more – to tailor each interaction. The result is a better customer experience and stronger business performance. According to industry reports, 92% of companies are leveraging AI-driven personalization to drive growth (40 personalization statistics: The state of personalization in 2025 and beyond | Contentful). Retailers that excel at using AI for personalization don’t just please customers; they sell more. For example, Amazon’s recommendation engine (powered by AI) famously drives a significant share of its sales by suggesting products each shopper is likely to want. Amazon’s success illustrates how effective personalization, fueled by high-quality data, can boost customer satisfaction and revenue (Top 5 AI challenges in e-commerce and retail).

Critically, consumers have come to expect this level of personalization. A study by Segment/Mood Media found that 77% of consumers prefer to shop with brands that use personal data to enhance their experience (Retail 2024: Next-Level Personalization, AI and Harmony - Mood Media). Shoppers notice when retailers don’t personalize – and it can hurt loyalty. In an era when switching costs are low and alternatives are a click away, failing to deliver relevant recommendations or promotions means leaving money on the table. It’s no surprise, then, that retail executives are prioritizing AI investments. Over 60% of retail leaders plan to increase their AI infrastructure investment in the next 18 months (AI Use Cases in Retail), and many rank personalization initiatives at the top of their agenda. Simply put, AI-driven personalization is becoming essential for retail competitiveness.

However, getting personalization “right” involves more than just algorithms – it requires handling customer data responsibly. High-profile data breaches and growing privacy concerns have made consumers more cautious about how their data is used. Retailers must balance personalization with privacy, ensuring that trust isn’t sacrificed in pursuit of customization. This is where local AI offers a compelling solution: it allows businesses to provide the tailored experiences customers demand while keeping sensitive data under tighter control. In the sections that follow, we’ll examine the benefits of local AI in detail and how it compares to the typical cloud-based approach.

The Case for Local AI: Privacy, Compliance and Cost Benefits

For years, cloud computing has been the go-to for AI solutions – centralizing data and tapping virtually unlimited compute power in off-site data centers. But this convenience can come at the expense of privacy, compliance, and cost control. Local AI (on-premises or edge AI) processes data on local devices or servers at the source, rather than sending it to the cloud. This approach is gaining traction as retailers face stricter data regulations and look to cut costs. Key advantages of keeping AI local include:

• Data Privacy & Compliance: By processing customer data on-site (in stores or company-owned data centers), retailers dramatically reduce the exposure of sensitive information to third-party cloud providers. Data isn’t continuously streaming over the internet to external servers, which minimizes the risk of leaks or breaches. This local-first approach directly addresses privacy concerns and helps retailers comply with regulations like GDPR, which mandate careful control over personal data (Edge AI vs Cloud AI: Understanding the Differences and Benefits) (webAI | Cloud AI vs. Local AI: Which Is Best for Your Business?). In essence, the mantra is “Your data stays with you.” Companies in finance and healthcare have long favored on-prem solutions for this reason, and now retailers are following suit to ensure customer data remains secure and compliant within their own walls.

• Lower Ongoing Costs: Cloud AI services often operate on usage-based pricing – the more data you send and process, the higher the bill. For a retail business with millions of transactions or frequent AI queries, these recurring costs can snowball. Local AI can offer cost savings by eliminating or reducing cloud usage fees. Once the up-front investment in hardware or edge devices is made, running AI workloads locally means you’re not paying a provider every time an algorithm runs. Additionally, processing data locally cuts down on bandwidth and data transfer costs, since large datasets don’t need to be uploaded and downloaded constantly (Edge AI vs Cloud AI: Understanding the Differences and Benefits) (webAI | Cloud AI vs. Local AI: Which Is Best for Your Business?). Many companies find that over the long run, a well-optimized local AI solution is more cost-effective for continuous, high-volume data processing than renting cloud computing power indefinitely.

• Faster Response (Lower Latency): In retail, speed matters – whether it’s a recommendation shown to a customer or fraud detection on a point-of-sale transaction, every millisecond counts. Cloud AI introduces inherent latency because data has to travel to a remote server and back. Local AI avoids that round trip. By keeping the computation on the premises (for example, in-store servers or edge devices near the point of data generation), retailers can get real-time insights and responses. This low latency is crucial for applications like interactive digital displays or self-checkout kiosks that rely on instantaneous AI decisions. Simply put, when AI is local, there’s no waiting for the cloud (webAI | Cloud AI vs. Local AI: Which Is Best for Your Business?). The result is a smoother, faster customer experience – think of a personalized offer loading immediately on a store app, or a shelf-scanning robot instantly adjusting to stock levels without needing to ping the cloud.

• Data Ownership & Control: Using third-party cloud AI platforms often means entrusting your data (and sometimes even your trained models) to an external provider. In some cases, the AI models in the cloud are “black boxes” or proprietary services that you can use but not fully own. Local AI flips that dynamic. Businesses retain full ownership of their data and AI models when they keep it in-house (webAI | Cloud AI vs. Local AI: Which Is Best for Your Business?). All the customer insights generated remain on the company’s servers, under its governance. This control can be vital for competitive differentiation – your algorithms and data become a unique asset that isn’t shared with or dependent on a vendor. It also reduces the risk of vendor lock-in. If tomorrow you want to switch tools or tweak an algorithm, you can, because you have possession of the data and the model. In an industry where consumer data and AI insights drive strategy, owning your intelligence can be a significant strategic advantage.

By leveraging these benefits, retailers can build personalization systems that are privacy-first, cost-conscious, and highly responsive. Of course, cloud computing still has its place – especially for training massive AI models or aggregating insights across regions – but many retailers are realizing that inference (the day-to-day running of AI models for predictions and personalization) can often be done locally. The result is a hybrid approach where the cloud might be used for heavy lifting or long-term data storage, while real-time decision-making happens on the edge. This balance can yield the best of both worlds: global learning with local execution.

Industry Momentum: Retailers Moving AI Closer to Home

The shift toward local AI in retail isn’t just theoretical – it’s happening on the ground. Industry reports and case studies show a clear trend toward hybrid and edge deployments. In a 2024 retail AI survey, over 50% of retailers said they prefer a hybrid approach (mixing cloud and on-premises) for their AI solutions ([State of AI in Retail and CPG Annual Report - 2024 | NVIDIA

](https://images.nvidia.com/aem-dam/Solutions/documents/retail-state-of-ai-report.pdf?ncid=pa-so-link-630560-vt23#:~:text=Implementation%20Approaches%207,percent%20assessing%20or%20piloting%20AI)). In fact, larger retailers (those with $500M+ in revenue) lean even more toward on-premise AI, with over 60% favoring a hybrid model that keeps critical AI workloads within their own infrastructure ([State of AI in Retail and CPG Annual Report - 2024 | NVIDIA

](https://images.nvidia.com/aem-dam/Solutions/documents/retail-state-of-ai-report.pdf?ncid=pa-so-link-630560-vt23#:~:text=expertise%20when%20implementing%20AI,of%20AI%20experts%2C%20working%20with)). This indicates that many retail leaders recognize the limitations of an all-cloud strategy and are pulling certain capabilities closer to their operations. Data security, latency, and control are cited as big drivers for this shift.

Several real-world examples underline the benefits of local AI. Consider the case of D.Phone, one of the largest mobile phone retail chains in China. D.Phone deployed an edge computing solution in its stores to power customer-facing applications. By running its sales and content applications on local in-store servers, D.Phone was able to reduce content download times to just seconds and avoid bandwidth bottlenecks that a cloud-only approach might face (AI at the Edge Report. Chapter 3: Applications & Case Studies). The result was a smoother shopping experience for customers (faster access to product info and promotions in-store) and lower network costs for the company. “Plug-and-play” edge nodes allowed D.Phone to roll out this capability across many locations, highlighting how scalable local AI solutions have become in retail environments.

Another example comes from the grocery sector. A major grocery chain worked with an AI solutions provider to implement an IoT-based thermal camera system for monitoring store conditions and equipment. This AI-powered system was deployed on-premises in stores for real-time analysis (rather than relying on cloud processing). The retailer gained immediate visibility into issues like cooler temperatures or foot traffic, enabling managers to respond quickly. The outcome was not only improved operations but also an estimated $195,000 in immediate ROI due to prevented losses and efficiency gains (The Potential of AI in Retail: Key Considerations, Benefits & Use Cases | Insight). Such case studies reinforce that local AI isn’t just about compliance – it can directly improve the bottom line.

From Europe to North America and Asia, retailers are embracing “local” AI deployments to complement their cloud strategies. Some are putting AI hubs in regional data centers or even within large flagship stores. These setups handle tasks like analyzing in-store video feeds, powering AR/VR experiences, or crunching POS data at day’s end – all without sending raw data off-site. The momentum is also fueled by tech advancements: today’s edge computing devices (from powerful GPUs to compact AI accelerators) make it feasible to run advanced machine learning models on location. Meanwhile, software providers are offering enterprise AI platforms that can be installed in a retailer’s own environment, rather than only accessed as a cloud service. This combination of demand and technology is creating a tipping point where “personalization without the cloud” is not only possible, but often preferable.

Of course, cloud AI isn’t going away – retailers will continue to use cloud platforms for many purposes. But the narrative is shifting. Instead of cloud being the default for every AI project, savvy companies now ask: “Does this need to be in the cloud, or can we do it locally to add value?” Increasingly, the answer is local. Next, let’s look at some of the specific retail applications where local AI is making a difference, from personalizing recommendations to catching fraud – and how it all works in practice.

Local AI in Action: Applications for Retail Success

What can retail organizations actually do with local AI? The answer: pretty much anything they can do in the cloud, and often faster and more securely. Here are some real-world applications of local AI in retail that are driving personalization, efficiency, and smarter decision-making:

• Personalized Product Recommendations: Perhaps the most visible use of AI in retail is the classic “customers who bought this also bought…” suggestions. Traditionally, e-commerce sites generated these recommendations in the cloud. Now, with local AI, retailers can bring that capability in-house. For example, an on-premises recommendation engine can analyze a shopper’s browsing and purchase history (all stored in the company’s database) and serve up tailored product suggestions in real time on the website or on a kiosk in-store. These engines use machine learning models to match customers with products they’re likely to love. A well-known example is Amazon’s in-house recommendation system, which leverages the company’s vast data to personalize every customer’s experience – contributing significantly to Amazon’s sales (Top 5 AI challenges in e-commerce and retail). Using local AI, even brick-and-mortar retailers can deploy similar tech on their point-of-sale systems or mobile apps without sending sensitive customer data to a third party. The result is higher engagement and basket sizes, as shoppers discover relevant products through AI-curated recommendations (Retail 2024: Next-Level Personalization, AI and Harmony - Mood Media).

• Demand Forecasting and Inventory Optimization: Retail has always been about carrying the right products, in the right quantity, at the right time. AI has revolutionized demand forecasting by analyzing historical sales, seasonal trends, local events, and even weather data to predict what will sell and when. When these predictive analytics run on local infrastructure, each region or store can generate its own forecasts quickly and securely. Predictive models can study sales data along with external factors to forecast demand, helping stores optimize inventory levels (Retail 2024: Next-Level Personalization, AI and Harmony - Mood Media). For instance, a chain might run an AI model overnight on its on-premises server to predict next week’s demand for each store, then automatically adjust orders and distribution – all without exposing sales data externally. Local AI ensures these insights are available with low latency (so they can even be updated intra-day if needed) and keeps proprietary sales data in-house. This leads to fewer stockouts (missing an opportunity to sell) and fewer overstocks (tying up capital in inventory), directly improving profitability.

• Fraud Detection and Loss Prevention: Retailers lose billions annually to fraud and theft – whether it’s fraudulent transactions with stolen credit cards or in-store shoplifting and organized retail crime. AI is becoming a powerful tool to combat these issues. By analyzing transaction patterns, AI systems can spot anomalies that indicate potential fraud, such as unusual purchasing patterns or return requests, and flag them instantly (AI Use Cases in Retail). When this analysis happens locally (for example, in a store’s secure server or at corporate HQ), alerts can be raised in real time to stop a suspicious transaction at the point of sale. Similarly, AI-driven computer vision can be used with security cameras to monitor store activity and detect theft or unsafe behavior. Edge AI vision systems in stores can identify incidents of shrinkage or out-of-stock situations as they happen, enabling staff to respond immediately (AI at the Edge Report. Chapter 3: Applications & Case Studies). Because the video processing is done on the edge device itself, no camera feeds are streamed to the cloud, preserving customer privacy. Retailers like Walmart and others have piloted in-store AI cameras that reduce theft by alerting staff to suspicious movements or scanning errors at self-checkouts. Local AI is ideal here due to the need for instant response and the sensitivity of surveillance footage. By catching fraud and theft early, businesses can reduce losses and build trust with honest customers and employees.

• Real-Time Pricing and Promotions: Pricing is another area being transformed by AI – and local data. Retailers are experimenting with dynamic pricing models that adjust product prices based on factors like local demand, time of day, or competitor pricing. With local AI, a store can run a pricing optimization model that considers its unique conditions (demographics, weather, events) and recommends price adjustments on the fly (AI-driven edge cloud: Personalizing retail experiences and optimizing operations | Retail Dive) (AI-driven edge cloud: Personalizing retail experiences and optimizing operations | Retail Dive). For example, if a sudden cold snap hits one region, an edge AI system at the regional level might quickly mark up winter coats (high demand) while marking down garden furniture. Because this can be done on local servers, each store or region can act autonomously and immediately, without waiting for cloud computations. Similarly, personalized promotions – like coupons tailored to an individual shopper’s habits – can be generated at the point of sale using local AI that matches a customer profile to the best current offer. These tactics drive higher sales and also move inventory more efficiently. Early adopters using localized dynamic pricing and promotion engines have seen both revenue and customer satisfaction improve, as prices feel more relevant and timely to shoppers (AI-driven edge cloud: Personalizing retail experiences and optimizing operations | Retail Dive) (AI-driven edge cloud: Personalizing retail experiences and optimizing operations | Retail Dive).

From customer-facing enhancements (like personalized deals) to backend optimizations (like stock management), local AI is proving its value across the retail enterprise. The common thread is real-time intelligence: decisions that used to take hours or days through cloud processing and batch reports can now happen in milliseconds on the sales floor or in the back office. The personalization angle remains front and center – whether it’s personalizing an offer, an assortment, or a service response – and it’s done in a way that safeguards data. Business leaders should take note of these use cases not only for the technology, but for the competitive outcomes: higher conversion rates, leaner operations, reduced fraud, and happier customers.

Challenges and Solutions When Adopting Local AI

Adopting AI at the edge or on-premises isn’t without its hurdles. Moving to a local AI model represents a shift in infrastructure and mindset. Business leaders should be aware of the common challenges and plan for ways to overcome them:

• Integration with Existing Systems: One of the first hurdles is integrating AI models into legacy retail systems – from POS terminals and inventory databases to CRM and e-commerce platforms. Retail IT environments can be complex, and adding new AI workloads locally means ensuring they can talk to other software, capture the needed data, and output decisions to the right channels (like a mobile app or checkout register). Integration challenges are frequently cited by retailers embarking on AI projects (Top 5 AI challenges in e-commerce and retail). Overcoming this requires a solid IT strategy: adopting middleware or APIs to connect systems, perhaps updating some legacy systems to be more AI-friendly, and incrementally rolling out AI alongside existing processes to test compatibility. Many retailers start with pilot projects in a few stores or a segment of the business to iron out integration issues before scaling up.

• Data Quality and Availability: AI is only as good as the data it learns from. If a retailer’s data is siloed, inconsistent, or of poor quality, localized AI models won’t perform well. “Bad or insufficient data” is a top obstacle for AI in retail (Top 5 AI challenges in e-commerce and retail). Companies must invest in data cleaning, consolidation, and governance. The advantage of doing AI locally is that you often can more easily tap into data right at the source (store-level systems, local IoT sensors, etc.), but it still needs to be the right data. Establishing a strong data infrastructure – maybe deploying a data lake or warehouse on-premises – and enforcing data standards (common customer IDs, consistent product taxonomy across channels, etc.) are critical steps. Some retailers partner with data management firms or use modern ERP systems as a foundation to ensure their AI has a steady diet of high-quality information.

• Talent and Expertise Gaps: There’s a well-known shortage of AI and machine learning experts in the job market. Retailers, whose core business has not traditionally been technology development, may struggle to hire and retain the data scientists and engineers needed to build custom AI solutions. Lack of AI skills internally is a major challenge for the industry (Top 5 AI challenges in e-commerce and retail). Additionally, maintaining on-premises AI infrastructure may require specialized IT skills (e.g. managing GPU servers or edge devices). To bridge this gap, business leaders can consider a few approaches. One is partnering with vendors or consultants that provide turnkey AI solutions which can be deployed locally. Another is upskilling existing staff – for instance, training savvy analysts or IT managers in AI through targeted programs. Interestingly, not every company needs an army of PhDs to leverage AI. Many modern AI tools are designed to be user-friendly or automated. As one IT services firm notes, you can choose to “use” AI by leveraging products with built-in AI capabilities (which requires minimal in-house development) versus “do” AI by building custom models from scratch for your unique needs (The Potential of AI in Retail: Key Considerations, Benefits & Use Cases | Insight). Often a hybrid approach works: use off-the-shelf AI components locally for common tasks, and focus your talent on a few high-value custom projects. The key is to be realistic about your team’s capabilities and to seek outside help when needed – whether through hiring, partnerships, or training.

• Upfront Infrastructure Costs: Shifting to local AI might mean investing in new hardware – edge servers in stores, upgraded data center machines, or IoT devices with AI capabilities. These capital expenses can be a barrier, especially for smaller retailers or those unsure of the ROI. Cloud services, by contrast, are usually an operational expense (pay-as-you-go) with low entry cost. To manage this, companies should start with a clear business case for each AI use case. Identify the expected return (e.g. reduced fraud losses, increased sales from better personalization, labor savings from automation) and use that to justify the investment. Often, a phased rollout can help financially – for example, purchase and deploy edge devices to a dozen stores, measure results, then expand further in batches. It’s also worth exploring whether existing hardware can be repurposed. Some modern POS systems or networking equipment have AI chips or modules that can be utilized with the right software. And as mentioned, a hybrid approach can mitigate cost – critical tasks can run on a few local devices, while less critical processing stays in the cloud for now. Over time, as hardware costs continue to drop, the barrier here is likely to lessen. Many retailers have noted that the cost savings on cloud bills and the value gains from faster insights quickly offset the initial spend on local infrastructure.

• Maintaining and Updating AI Models: Deploying AI models locally is not a “set and forget” endeavor. Models may need retraining as data patterns change (e.g., shifts in consumer behavior or seasonality), and software updates/patches will be required for both security and performance improvements. With cloud services, the provider often handles updating models or improving algorithms behind the scenes. In a local setup, the onus is on the organization to keep models fresh and accurate. This challenge can be met by establishing a robust MLOps (Machine Learning Operations) practice – essentially, applying the discipline of software DevOps to AI. This includes monitoring model performance, having pipelines to retrain models with new data, and seamlessly rolling out updates to all the edge devices or servers running the model. Tools exist to help with this, and many AI platforms (including open-source ones) support decentralized deployments with central management consoles. Some retailers schedule periodic model retraining (e.g. weekly demand forecast model updates using the latest sales data) and automate the deployment of the new model to all store devices overnight. Planning for maintenance from the start ensures that local AI deployments continue delivering value in the long run and don’t degrade due to “stale” models.

Despite these challenges, none are insurmountable. In fact, a lot of the heavy lifting to overcome them can be done with the right partnerships and planning. Vendors now offer on-premise AI appliances and edge solutions tailored for retail, which come with integration interfaces to common retail systems and sometimes even pre-trained models (for tasks like vision or anomaly detection). Engaging with the right technology partners can alleviate the need to build everything from scratch. Moreover, focusing on a clear set of use cases with strong ROI will rally the organization around the project, making it easier to justify investments and change efforts. Business leaders should also communicate the vision: adopting local AI is not just an IT upgrade, it’s a strategic move to protect customer trust (through privacy), to own your competitive differentiation (through proprietary insights), and to be ultra-responsive to market changes.

Finally, it’s worth noting that local AI doesn’t have to be an all-or-nothing proposition. Hybrid strategies can ease the transition. For example, you might keep using cloud AI for non-sensitive or less time-critical functions (like training large models or running website recommendations for general audiences), while deploying local AI for the high-priority, sensitive tasks (like in-store personalization, payments, and private customer data analysis). Over time, you can adjust this mix as your confidence and capabilities grow. The goal should be to gradually build up the in-house muscle for AI, so that you have maximum flexibility in choosing where to run each workload. The more options you have (cloud or local), the better you can optimize for privacy, performance, and cost on a case-by-case basis.

Conclusion: Embrace the Local AI Advantage – Your AI, Your Data

AI-driven personalization is set to remain one of the key drivers of retail success in the coming years. As we’ve discussed, the ability to deliver uniquely tailored experiences can boost customer loyalty, increase sales, and streamline operations. But achieving this doesn’t mean you must hand over your data (and a chunk of your budget) to a cloud provider. Local AI offers a compelling path forward: one where you reap the rewards of advanced analytics and machine learning while keeping control of your data, your costs, and your compliance risks. It truly enables the vision of “Personalization Without the Cloud.”

For business leaders, the message is clear. Your AI should be an asset that works for you on your terms. Imagine a world where your customer insights never leave your organization, where your IT team can fine-tune algorithms to perfectly fit your business needs, and where latency or internet outages never stand between your customer and a great experience. That’s the promise of bringing AI in-house. And with the maturation of edge computing and local AI platforms, it’s an achievable reality, not just an idealistic concept.

Of course, every organization’s journey will be unique. If you’re just starting out, consider conducting an audit of your current AI and analytics workloads – which ones involve sensitive data or could benefit from real-time speed? Those might be prime candidates for a local AI pilot. Talk with your CIO/CTO and data science teams about the infrastructure in place: do you have the foundational pieces (data pipelines, storage, hardware) to support an on-prem AI deployment? You might be closer than you think. Many modern retail software systems already run on local servers (e.g. store back-office systems); augmenting them with AI modules could be a natural next step.

To navigate the transition, engage in conversations about enterprise AI strategy with your peers and experts. This could mean consulting with AI solution providers who have done on-prem implementations, or learning from case studies of other retailers who successfully adopted local AI. The community of practice in this area is growing rapidly. Don’t hesitate to seek out insights – for instance, how a retailer in a similar sector handled data governance for local AI, or which edge computing device another company found effective for store analytics. Such knowledge exchange can save you time and resources.

We encourage you to take action: start with a small project that demonstrates the value of local AI, such as a store-level personalized marketing pilot or an AI-driven loss prevention camera system in one location. Measure the outcomes, iterate, and build the business case for wider adoption. Often, seeing is believing – when stakeholders witness faster insights and improved metrics, momentum for scaling up will follow.

Finally, keep the bigger picture in mind. Adopting local AI is not just a technology upgrade; it’s a strategic shift toward greater autonomy and trust. You’re saying that your customer data is so valuable that you’ll invest to protect and maximize it internally. You’re positioning your company as an innovator that can deliver cutting-edge experiences without compromising on privacy or incurring runaway costs. That stance can become part of your brand story – something you can even communicate to customers to build confidence (e.g., “we personalize your experience and we keep your data safe within our company”). In an age of data consciousness, that’s a powerful message.

As you move forward, remember that success in this endeavor is a journey, and we’re here to help guide you along the way. Feel free to subscribe for updates on the latest in enterprise AI and retail analytics, so you won’t miss insights that could inform your strategy. Whether you’re an executive mapping out a three-year technology plan or a manager looking to solve a tactical problem this quarter, staying informed is key. Let’s continue the conversation about what your business can do with AI – on your infrastructure and on your terms.

Your AI. Your Data. That’s more than a slogan; it’s a vision of empowerment. By harnessing retail analytics with local AI, you can deliver personalization that delights customers, all while safeguarding the data that makes it possible. The future of retail will be smart and personalized. Now is the time to ensure that your organization can provide those experiences with confidence and control. After all, nobody knows your business – or your customers – better than you do.