Retailers are Betting Big on AI – but Fragmented Data Holds Them Back

Nearly every retailer is betting on AI. But despite daily use cases, only 11% say they’re confident they can scale it across the enterprise. According to Amperity’s 2025 State of AI in Retail report, 97% of retailers plan to maintain or increase AI investment over the next year, and nearly half say they already use AI tools daily. The problem isn’t the algorithms — it’s the data. From dynamic pricing engines and personalized marketing to predictive inventory planning, AI quickly becomes central to retail strategy and competitive differentiation across increasingly crowded consumer marketplaces.

AI promises to transform customer engagement and operational efficiency at a time when retailers are under immense pressure. Still, despite intent, there’s a stark gap between AI ambition and AI execution, creating missed opportunities, wasted investments and frustrated leadership teams struggling to see measurable business outcomes.

AI Without Identity Resolution: A Fragmented Future

Although a majority of retailers report using AI in some capacity, only 11% say they’re ready to scale it across enterprises. The core challenge is data that’s siloed, incomplete, or fragmented. Data fragmentation blocks the ability to unify customer identities. Without accurate identities, AI cannot recognize individuals across channels or predict their needs — making true personalization nearly impossible.

Amperity’s survey found that only 43% of retailers are applying AI to customer-facing experiences, and just 23% are using AI for identity resolution or data preparation. This gap explains why so many efforts fail to deliver meaningful value.

The lack of unified customer views doesn’t just slow AI adoption; it directly undermines growth opportunities. A recent Deloitte report highlights the stakes, finding that:

• 80% of U.S. consumers are more likely to purchase when brands offer personalized experiences
• Personalization can lift conversion rates by 16 percentage points
• Consumers spend 50% more with brands that personalize well, making data quality not just an IT concern, but a revenue driver

In a world where consumer preferences for personalization are at an all-time high, with 80% of global consumers expecting it, failing to unify customer data can hold back a brand’s growth strategy. AI without identity resolution doesn’t just miss the mark; it risks alienating customers.

Unified Data, Smarter AI

Retail isn’t short on use cases: predictive analytics, chatbots, dynamic pricing, generative content. None will consistently succeed without a unified, trustworthy foundation of customer data.

Identity resolution ensures that all the disparate signals, from point-of-sale systems, loyalty programs, mobile apps and third-party marketplaces, come together into a single, governed profile. This allows AI systems to:

• Personalize at scale – ensuring that every message, offer and recommendation is relevant to the individual
• Predict with accuracy – powering churn models, demand forecasts and next-best-action strategies
• Retain customer trust – by delivering seamless, respectful and privacy-safe experiences across channels

To put it simply, identity resolution turns AI from a novelty into a necessity.

The Cost of Getting AI Wrong

The risks of glossing over the identity resolution step are growing. With the need to deploy generative AI in visible, customer-facing contexts also comes concerns with practical implementation. A single misstep, like sending a renewal offer to a customer who just renewed, can damage brand loyalty.

A PwC report found that 32% of customers will walk away from a brand they love after just one bad experience. If AI delivers inconsistent or impersonal interactions, retailers won’t just fail to unlock ROI; they’ll lose customers outright. As the Deloitte report highlights, a majority of consumers now expect companies to understand their needs and expectations, raising the bar for every retailer experimenting with AI in highly competitive, margin-sensitive markets.

The Gap Between AI Ambition and Reality

Closing the gap comes down to three priorities:

1. Unify Customer Data Before Scaling AI

Brands need to audit their existing data systems for silos and inconsistencies. They should invest in customer data platforms (CDPs) and identity resolution solutions that can reconcile fragmented records and create real-time, actionable profiles.

2. Apply AI Where It Matters Most

Instead of deploying AI everywhere at once, brands should focus on high-impact use cases: personalized promotions, churn prediction, service automation and inventory optimization. Each of these is far more effective with unified data. For example, a loyal shopper may receive a churn-prevention discount on the same day they’ve just purchased. Without identity resolution, AI risks creating these damaging missteps.

3. Build Trust Through Privacy and Governance

While consumers expect personalization, they also expect privacy and secure systems that guard their information. Implementing privacy-safe data practices and transparent governance ensures AI enhances rather than compromises customer trust.

From Fragmentation to Transformation

The future of retail AI will not be determined by who adopts the technology fastest, but by who adopts it more responsibly and effectively.

When retailers unify fragmented first-party customer data like purchase history, behavioral signals and engagement patterns, they’re unlocking personalization at scale and realizing true AI ROI.

The difference is clear: AI built on disconnected data delivers scattered results. AI built on identity resolution delivers enterprise-wide transformation.

Going All-in

Fragmented data slows today’s progress — and threatens tomorrow’s relevance. AI done right isn’t just a technology upgrade; it’s a transformation in how retailers engage, earn trust, and grow.

With the AI market growing 42% annually to $733.7B by 2027, the winners won’t be those deploying the most models, but those building the strongest data foundations. Retailers who solve the data challenge first will be the ones who win with AI-powered customer experiences.

Progress over the next year will involve more investment, experimentation and deployment to position retailers to use AI effectively and efficiently while minimizing risk, strengthening differentiation and accelerating measurable returns.

As consumer expectations continue to rise, identity resolution is the strategic lever that will separate retail’s AI winners from those who fall behind.