Why retailers struggle with AI adoption and how data leaders overcome hidden challenges

Retailers are adopting AI, but barriers to AI-ready analytics remain

The retail industry is under immense pressure to adapt.

Customers are shifting more purchases online, and data sources are multiplying at record speed. 

To stay competitive, retailers must move faster, control costs, and modernize their decision-making—or risk being left behind. At the heart of this challenge lies data: the fuel that powers every team, project, and innovation. 

In the 2025 Retail and CPG in Focus Report, Strategy surveyed analytics leaders across 11 countries to measure AI+BI adoption—and to uncover what’s still holding retailers back.  The findings were clear: 

• 53% identified cost as the top barrier to scaling AI+BI platforms 

• 50% pointed to unrealistic expectations around speed-to-value, automation potential, and ease of implementation 

• 35% reported inconsistent answers from AI tools as a persistent blocker 

Together, these challenges reveal an industry that’s investing heavily in AI-ready analytics platforms, yet struggling to cut through the noise, jargon, and complexity that prevent true impact. 

3 hidden data challenges blocking AI adoption in retail

The above-mentioned data challenges are not the problem.

Instead, they are the result of limited knowledge, weak data management, and poor coordination.

1. Poor governance

Typically, irregularity in AI responses is caused by the following issues:

• Data silos: Retailers use multiple applications to collect customer, financial, and marketing data. If the apps don’t integrate well, critical insights get lost. 

• Poor metadata standards: Inconsistencies in KPIs, metrics, and definitions cause AI tools to confuse reporting terms and churn out incoherent results. 

• The absence of a shared semantic layer: When data access across various BI tools is unregulated, gaps in governance occur—increasing security risks and regulatory exposure. 

When definitions vary between departments—or when metrics are calculated differently across tools—the insights delivered by AI are quickly called into question. According to the report: 

• 35% of companies experience inconsistent answers to the same data questions 

• 31% lack a formal data strategy or semantic layer 

• Many respondents describe their systems as “fragmented,” with unclear ownership or stewardship 

Bottom line: AI-powered analytics tools rely on structured, consistent, and trusted data to generate accurate results. 

2. Lack of accessibility

Teams use data differently.

Analysts don’t just consume data: they model, clean, aggregate, and interpret it. They rely on precise data points and use BI tools to connect raw sources into meaningful dashboards.

Frontline employees, on the other hand, don’t necessarily “speak SQL” or do data modeling. Instead, they prefer quick, straightforward insights they can act on. 

This is where the problem lies: most traditional BI platforms don’t provide team-specific analytics—so users struggle with unclear expectations and inconsistent outputs. 

The report makes it clear: AI adoption only moves as fast as the trust behind it. 

3.  Underlying expenses

The cost of AI doesn’t end at buying a license. In fact, that’s just the start of a BI innovation process that—if unprepared for—can drive up expenses. Some factors include: 

• Data readiness: Messy, siloed, or incomplete data forces companies to spend heavily on cleaning, labeling, and integrating before AI can even be trained. 

• Integration: AI rarely runs standalone; it must be embedded into applications, e-commerce platforms, and operational systems—raising development and maintenance costs. 

• Costs of Adoption: Training employees to trust and use AI-driven insights isn’t free. If adoption lags, ROI suffers and sunk costs mount. 

In short: even when technology works, trust breaks down without structure, clarity, and realistic planning. 

Successful AI adoption in retail: how data leaders modernized their analytics

Despite the challenges, retail leaders are integrating AI-powered analytics into their daily workflows, delivering accurate, governed, and decision-ready insights to every department—without IT bottlenecks.  

How GUESS turned weeks of reporting into seconds

Global fashion brand GUESS faced a familiar set of challenges: data was trapped across systems, reports were slow, and access to analytics was limited to a small group of users. 

To respond faster to market signals and empower teams across its wholesale, retail, and digital channels, GUESS partnered with Strategy to modernize its analytics stack. The solution focused on: 

• Mobile dashboards that provided real-time access to sales, store performance, and product trends 

• AI chatbots that enabled non-technical users to query supply chain and loyalty data in natural language 

• Unified semantic governance to ensure answers were consistent across tools, teams, and roles 

The impact was significant: 

• Reporting cycles shrank from two weeks to seconds 

• Thousands of employees gained access to live data 

• AI became an active decision-making tool, not just a reporting layer 

“Strategy AI frees trapped data—data that we’ve worked hard to build, data that lives in assets across the company, but has been hard for people to access.”

— Bruce Yen, Vice President of Retail Applications, GUESS

How The Warehouse Group democratized data with AI

New Zealand’s largest non-food retailer, The Warehouse Group, tackled an equally complex challenge: during a period of recession and declining consumer spending, the company needed to make faster decisions, break down internal silos, and increase operational efficiency. 

Their approach began with data democratization. Working with Strategy, they focused on: 

• Self-service reporting tools that gave cross-team access to relevant insights 

• AI chatbots that surfaced real-time data in everyday language 

• A governed data fabric that ensured consistency, trust, and auditability across all systems 

The results were transformative: 

• AI tools became central to daily workflows in customer service, merchandising, and logistics 

• Data access expanded beyond analysts to all business teams 

• Governance guaranteed that everyone worked from a single version of the truth 

“Data is the lifeblood of our business—it’s the oxygen fueling everything we do.”

— Keryn McKenzie, Chapter Area Lead, Data, Insights & Services, The Warehouse Group

Rules for successful AI adoption

These two case studies share a few common traits that provide a framework for successful AI adoption: 

• A clear investment in data governance and semantic consistency 

• A focus on real-time access and usability—not just traditional BI dashboards 

• The ability to scale AI tools beyond analysts to frontline staff 

• Strong alignment between business and data teams 

What sets these retail leaders apart isn’t just their choice of AI+BI—it’s how they operationalize it across people, processes, and platforms.