AI use is rapidly spreading across the retail industry. According to Amperity’s recent 2025 State of AI in Retail report, nearly half of retailers use AI on a daily or weekly basis. However, only 43% are using AI in customer-facing applications, and just 20% are applying AI to resolve customer identities or prepare data for personalization.
In short: retailers are spending more on AI, but they’re not seeing returns where it matters most - with the customer. In order to optimize their AI investments, retailers must go beyond isolated use cases and leverage AI for end-to-end customer experiences.
The root problem: input quality
AI gets a lot of attention for what it can do - generate product descriptions, optimize campaigns, predict churn - but those outcomes depend entirely on the quality of the data it’s fed.
For retailers, the inputs are often their customer data. With 58% of retailers admitting their customer data is fragmented or incomplete, it’s no wonder that only 21% feel very confident in their ability to both understand and act on customer behavior.
The issue isn’t just data quantity - it’s data quality, structure, and readiness. Legacy systems silo data with different channels recording customer behaviors in different ways. Customer data is also messy by definition: people use multiple emails, move addresses, or make typos. The result is a fragmented, unreliable view of the customer.
If customer data is siloed, poorly matched, or inaccurate, it doesn’t matter what your AI tools can do. The outputs won’t be worth putting in front of customers, and the insights will be unreliable.
The result? Retailers leave value from their AI investments on the table. They’re hesitant to leverage AI in customer-facing scenarios, opting to adopt AI use internally instead of on the front lines where it could deliver the most business value.
When the quality and scope of customer data is captured throughout the full customer journey - from pre-purchase through sale and follow-up - and organized in a streamlined, unified manner, AI outputs will immediately improve.
The bottleneck: identity resolution
Before AI can help personalize offers, predict customer behavior, or optimize experiences, it needs to know who it’s talking to. Retailers must be able to resolve identities - connecting behaviors, transactions, and preferences across channels, devices, and accounts into a single, unified customer profile.
As we previously explored, organizations struggle to work past identity resolution. The majority of innovation has happened in narrow AI applications and agents - not in the hard, messy work of unifying customer data.
Without identity resolution:
AI personalization engines target the wrong person.
Predictive models miss critical context.
Churn predictions flag incomplete segments.
LLM-based service bots misunderstand customer history.
The opportunity: transforming the data foundation
Retailers prioritizing AI transformation must first build an AI-ready data foundation. In practice, this strong data foundation provides the accurate, unified customer profiles; flexible data models; and governance and access controls that ensure responsible AI use.
Yet AI doesn’t just need good data inputs - it can also help create them. The future of AI in retail isn’t limited to customer-facing experiences. AI’s most urgent and valuable function today is improving the quality, organization, and readiness of customer data itself.
Agentic AI tools can be leveraged to lighten the load and improve the quality of customer data stitching. AI agents can:
Automatically identify tables with customer PII across databases
Standardize disparate data sources for matching and merging
Detect and correct bad or duplicate data
Accelerate identity resolution by analyzing billions of data points for hidden connections
This kind of AI-powered data foundation work transforms every downstream use case. When customer data is clean, connected, and up-to-date, AI can deliver on the promises of personalization, loyalty growth, and revenue lift.
The connective tissue: Amperity
With a strong data foundation in place, retailers can transform their AI investments from isolated experiments into impactful, end-to-end customer experience solutions.
Amperity’s platform is purpose-built to unify fragmented customer data and make it AI-ready. It acts as the connective tissue between customer data and AI initiatives to:
Unify data
Amperity connects omnichannel data sources - online purchases, in-store transactions, CRM logs, customer service interactions, loyalty accounts, third-party partner feeds - and ingests them into a centralized Customer Data Platform. Built-in semantic tagging ensures that emails, phone numbers, behavioral signals, and offline touchpoints are normalized and ready for matching.
Resolve identities at scale
Using hybrid AI and machine-learning matching models, Amperity’s Identity Resolution Agent stitches disparate signals into persistent, privacy-safe customer profiles. It flags duplicates, merges aliases (e.g. “Liz” vs. “Elizabeth”), and updates profiles as new data flows in - maintaining clarity even as customer behaviors and identifiers shift.
Fuel Next-Gen AI Experiences
Amperity does more than store data, it drives action. Customer segments flow natively into engagement tools (email, SMS, ad platforms), predictive models, content personalization services, and service intelligence systems. This ensures mess-free AI activation: building from a clean identity to curated segments, consistent messaging, and measurable results.
The takeaway
Retailers aren’t seeing the returns they expect from AI because they’re skipping the most critical step: fixing their customer data. AI-powered personalization, dynamic merchandising, and predictive service only work when built on clean, connected, AI-ready data.
The fastest, smartest way to accelerate AI’s business impact isn’t launching more bots or recommendation engines - it’s modernizing your customer data foundation.
To learn more, check out our 2025 State of AI in Retail report and explore how Amperity can help power your AI initiatives from the ground up.