Enterprise AI: Build or Buy?
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AI agents in retail fall short on personalization not because of model limitations, but because of fragmented data access. The key to scaling personalized customer support is data abstraction. You don’t have to worry about yet another consolidation project.
Every retailer knows personalization drives revenue. The performance gap between generic and hyper-personalized campaigns is significant. Organizations that achieve true personalization at scale see conversion rate improvements of up to 60% over traditional approaches. And how are these tailored experiences created? It’s a mix of data, analytics, AI, and automation.
Even Amazon attributes up to 35% of its revenue to AI-powered recommendations (Hint: the business case is now settled. Nobody needs another slide deck proving that personalization matters).
Despite these results, most retailers deploying AI agents for customer support find that personalization stops at the surface. The agent can handle order tracking, process a return, and answer product questions competently. But ask it to recommend a product based on what the customer has actually bought before, or explain why this particular customer's loyalty discount isn't applying and the whole thing falls apart. The agent reverts to generic responses that could have come from any retailer's support page.
The industry's response has been to pursue better models. More sophisticated recommendation engines. More advanced NLP. More training data. But Amperity's 2025 State of AI in Retail report reveals the real constraint: while 45% of retailers use AI at least weekly, only 11% are ready to scale it across the business.
The report goes on to reveal that only 43% are applying AI to customer-facing experiences, and just 23% are using AI for identity resolution or data preparation. The core challenge is data that's siloed, incomplete, or fragmented.
The personalization gap in retail AI support isn't a model problem. It's a data architecture problem. AI agents can only personalize to the extent they can access and synthesize customer data across every touchpoint. When that data lives in disconnected systems, even the most capable model delivers generic responses.
The instinctive response to retail's personalization gap is to blame data fragmentation. Customer data is spread across the ecommerce platform, point-of-sale system, loyalty database, CRM, marketing automation tools, the mobile app, and social channels.
A single customer might exist as customer ID 45892 in the ecommerce system, loyalty member L-2847 in the loyalty database, and an email address in the CRM. The systems don't talk to each other so the diagnosis seems obvious. Consolidate the data, then personalize.
This is the thinking that has kept retailers stuck for years. It's also wrong. Fragmented data isn't the barrier. The barrier is the assumption that data must be unified before AI can use it.
Retailers pour millions into centralized platforms and analytics environments expecting that a single consolidated view will unlock personalization. For reporting and dashboards, it does. For AI agents that need to act on customer context in real time, it falls short. As Unframe's analysis of why unified data platforms miss the point for AI makes clear, the architecture that solved the analytics fragmentation problem wasn't designed to solve the AI context problem. Batch-extracted data that refreshes overnight can't tell an AI agent what the customer did ten minutes ago.
Amperity's research found that only 23% of retailers are using AI for identity resolution or data preparation. That means 77% are trying to personalize on top of data foundations that can't even confirm whether two records belong to the same customer. But the fix isn't another 12-18 month data platform implementation that attempts to merge every source into a single schema. The fix is an architecture that queries across existing systems at runtime, performing identity resolution at the moment the AI agent needs it rather than requiring pre-consolidated profiles.
The retailers winning the personalization battle are building data abstraction layers that query existing systems as they are. This is the federated approach to retail data. The abstraction layer provides a unified query interface so the AI agent can search across all customer data sources through a single request.
It performs real-time identity resolution, matching customer records across systems at query time rather than requiring pre-consolidated profiles. And it applies data quality scoring so the agent knows which data sources are most reliable for a given attribute. If the loyalty database has the most reliable purchase history but the CRM has the most complete contact information, the agent knows to trust each system for its strengths.
This approach has a practical advantage beyond speed of deployment. It doesn't require retailers to abandon their existing systems. The ecommerce platform, POS system, loyalty database, and CRM all continue operating exactly as they do today. The AI agent simply gains the ability to query across them.
It’s easy to talk about value in the abstract but let’s take a look at this in context. The difference data abstraction makes becomes concrete when you compare two customer support scenarios.
A customer contacts support about a defective product. Without data synthesis, the agent processes the return, sends a shipping label, and issues a refund. Interaction complete. The customer feels handled. Efficiently, perhaps, but not memorably. The experience is indistinguishable from every other retailer's return process.
With full data synthesis, the same agent accesses the customer's purchase history and sees this is a loyal customer who has purchased from this product line six times over the past two years. The agent acknowledges the customer's loyalty, processes the return with expedited shipping, proactively offers a replacement from the same product line (because the purchase pattern suggests they'll want one), applies a loyalty discount that the customer had available but hadn't used, and flags the product defect pattern for the merchandising team.
The second scenario generates measurably better outcomes. The customer feels recognized and valued. That's the experience they mention to friends. These experiences result in higher NPS, greater likelihood of repeat purchase, and unbreakable brand advocacy.
Deloitte found that 80% of U.S. consumers are more likely to purchase when brands offer personalized experiences. Additional research from the firm also shows that brands leading in personalization are 48% more likely to exceed revenue goals and 71% more likely to report improved customer loyalty compared to less mature peers.
As you can see, the revenue impact of personalized support isn't theoretical. It's measured and substantial. But it only materializes when the AI agent has architectural access to the full customer data landscape. This is a data infrastructure investment, not a model investment.
The retailers that will win the next phase of AI-powered customer experience aren't the ones with the most sophisticated models. They're the ones that solve the data architecture problem first (Hint: build access layers that let AI agents see the complete customer relationship across every touchpoint, in every system, in real time).
This doesn't require a multi-year data consolidation project. It requires an architectural decision to treat data access as the primary investment rather than model sophistication.
The model is a commodity. Every retailer has access to the same foundation models. The data architecture, which is how you connect those models to your customer data, is the competitive moat. That determines whether your AI support agent feels generic or genuinely personal.
If you’re ready to give customers a truly personalized experience via AI, let's chat.
