How Do Retailers Structure Cross-Functional Teams for AI Product Work?

The difference between AI projects that succeed and the 85% that never make it past pilot stage often comes down to one thing: team structure. How do retailers structure cross-functional teams for AI product work? The answer separates winners from the rest. With the retail AI market projected to reach $40.74 billion by 2030, getting this right isn't optional - it's essential for survival.

The retailers winning with AI aren't just hiring data scientists and hoping for the best. They're building carefully orchestrated teams where merchandising experts sit alongside machine learning engineers, where domain knowledge meets technical capability, and where everyone understands both the business problem and the AI solution.

This guide breaks down exactly how leading retailers organize their AI teams, which roles matter most, and how cross-functional collaboration affects AI success in real-world implementations.

Why Cross-Functional Teams Matter for Retail AI Success

Here's a sobering statistic: a Fortune 500 retailer invested millions in an AI-powered demand forecasting tool, only to watch it fail completely. The reason? Regional managers ignored its recommendations because they weren't involved in building it. Without buy-in from all departments, the system was abandoned.

This isn't an isolated case. Two-thirds of executives report that generative AI adoption has created internal friction and conflict within their organizations.

The flip side tells a more encouraging story. Research from Harvard Business School found that AI-augmented cross-functional teams deliver far better results. When examining the top 10% of solutions, AI-enhanced cross-functional teams were three times more likely to produce breakthrough ideas.

The financial case is equally compelling. Firms using cross-functional AI teams report up to $3.50 in returns for every $1 invested. Organizations that effectively use cross-functional teams see technology adoption rates 34% higher than those with siloed approaches.

The Real Cost of Getting It Wrong

MD Anderson Cancer Center's collaboration with IBM's Watson for Oncology failed despite investing $62 million over four years. The core problem? Watson couldn't effectively access or integrate the hospital's siloed data systems. A cross-functional team with proper data engineering representation might have identified this issue in week one, not year four.

For retailers, the stakes are just as high. Over 60% of major retailers with annual revenues exceeding $500 million are integrating AI into their operations. Those using AI and machine learning technologies saw double-digit sales growth in both 2023 and 2024, with annual profit growing by roughly eight percent.

sbb-itb-ef0082b

Three Team Structure Models: How Retailers Structure Cross-Functional Teams

Not every retailer needs the same AI team structure. The right model depends on your company's size, AI maturity, and strategic goals. Here are the three primary approaches retailers use:

The Centralized Model (Center of Excellence)

This "star" organizational structure works best for smaller companies or those just starting their AI journey. A centralized AI team serves as the hub for all AI efforts across the organization.

How it works:

• A dedicated AI team handles projects for all departments
• Consistent standards and governance across initiatives
• Shared resources and expertise
• Clear accountability and reporting structure

Walmart created an AI Center of Excellence with strong executive support to drive AI adoption throughout the company. This centralized approach ensures consistent quality while building organizational AI literacy.

Best for: Retailers with fewer than 500 employees, those new to AI, or organizations wanting tight control over AI initiatives.

The Embedded Model (Distributed Teams)

In this approach, AI team members are embedded directly within business units. Data scientists work alongside merchandising teams. ML engineers sit with supply chain managers.

How it works:

• AI specialists report to business unit leaders
• Deep domain expertise develops naturally
• Faster iteration on business-specific problems
• Stronger stakeholder buy-in from day one

Best for: Large retailers with mature AI capabilities and distinct business units with unique AI needs.

The Hybrid Model (Most Popular)

52% of all retailers opt for a hybrid approach when implementing AI. Among retailers with annual revenues exceeding $500 million, that number jumps to 63%.

How it works:

• Core in-house team handles strategy, governance, and complex projects
• Embedded specialists work within business units
• External experts augment capacity for specialized needs
• Central oversight with distributed execution

Airbnb transitioned from a fully centralized data science team to this hybrid model. Data scientists are embedded as proactive partners with engineers, designers, and product managers while still reporting to central data science leadership.

Best for: Mid-to-large retailers balancing standardization with business unit autonomy.

Essential AI Team Members and Their Roles

Understanding how retailers structure cross-functional teams for AI product work starts with knowing which roles you need. Here's a breakdown of the essential positions:

Role	Responsibilities	Retail-Specific Focus
Data Scientists	Analyze data, develop predictive models, identify patterns	Demand forecasting, customer segmentation, price optimization
Machine Learning Engineers	Deploy and scale AI models for production use	Real-time recommendation systems, inventory algorithms
Data Engineers	Design data infrastructure and pipelines	Integrating POS, inventory, and customer data systems
Product Managers	Define scope, bridge business and technical teams	Translating merchandising needs into AI requirements
Domain Experts	Provide subject matter expertise, interpret results	Merchandisers, supply chain managers, store operations
AI Ethicists	Ensure ethical development, identify and mitigate bias	Pricing fairness, recommendation transparency

The Product Manager's Critical Role

The product manager is the bridge between business users, data scientists, and data engineers. They must understand the economic value drivers while structuring the problem-solving effort. In an AI-enhanced environment, cross-functional collaboration and influence are becoming vital leadership skills.

As one industry expert put it: "AI won't replace product managers. But product managers who know how to use AI will replace those who don't."

Why Data Engineers Often Come First

Here's something most guides won't tell you: the most common reason for AI project delay is data readiness. 61% of firms say their data is not ready for GenAI. The AI team often spends the first three to six months on data hygiene before any model building begins.

This means your hiring plan should prioritize data engineers, especially in the early stages. 66% of enterprise data goes unused due to siloed teams - a problem that skilled data engineers can solve.

How Cross-Functional Collaboration Affects AI Success in Retail

The research is clear: cross-functional collaboration dramatically affects AI success. Individuals working with AI delivered nearly 40% performance improvements. But AI-augmented cross-functional teams? They consistently outperformed every other configuration.

Beyond performance, collaboration affects team wellbeing. AI-augmented teams experienced a 64% boost in positive emotions, while AI reduced negative emotions like anxiety and frustration by approximately 23%.

Building Psychological Safety

Organizations that successfully navigate cross-functional AI development create environments that encourage intellectual humility and psychological safety. Team members need to feel comfortable acknowledging uncertainty and learning from failures.

Ways to build this environment:

• Encourage respectful communication across technical and business roles
• Listen to all perspectives, especially from domain experts
• Avoid blame when AI experiments don't work as expected
• Celebrate team and individual successes publicly

Knowledge Transfer Mechanisms

Effective knowledge transfer between team members with different specializations represents one of the most significant challenges in cross-functional AI development. Successful teams use:

• Formal documentation in shared repositories
• Shared ontologies and terminology guides
• Cross-training programs between technical and business roles
• Pair programming between specialists
• Regular cross-functional review sessions

A London-based tech company formed a cross-functional "AI Squad" with designers, engineers, and product managers working in daily iterations. Rather than adopting AI for its own sake, they identified specific pain points and experimented with AI solutions in short, rapid cycles.

AI Governance and Business Context

AI governance isn't just about compliance - it's about ensuring your AI initiatives align with business reality. Retailers need governance structures that provide business-specific accuracy while allowing for contextual refinement as conditions change.

Establishing an AI Governance Board

Leading retailers establish enterprise-wide AI Governance Boards with cross-functional representation:

Function	Role on Board
Legal	Compliance, regulatory requirements, contract review
Ethics	Bias detection, fairness standards, transparency
Technology	Technical feasibility, security, infrastructure
Business Units	Use case prioritization, ROI validation
Operations	Implementation feasibility, change management

This governance structure ensures AI contextual governance with organizational sight validation - meaning AI decisions are checked against real business conditions, not just technical metrics.

AI Support Beyond the Legal Department

Many retailers make the mistake of treating AI governance as purely a legal function. But effective AI governance requires support for cross-functional teams beyond the legal department.

This includes:

• Training programs for non-technical staff on AI capabilities and limitations
• Clear escalation paths when AI outputs seem incorrect
• Regular audits involving business stakeholders, not just technical teams
• Feedback mechanisms for front-line employees who interact with AI systems

Ensuring transparency and explainability in AI decision-making builds trust across the organization and with customers.

AI Capabilities for Cross-Functional Business Expansion

Once your cross-functional team structure is working, you can expand AI capabilities across the business. Here are high-impact AI project ideas for retail:

Merchandising + AI Teams

• Demand forecasting that incorporates weather, events, and social trends
• Automated assortment planning by location
• Dynamic pricing optimization
• Trend prediction from social media and search data

Supply Chain + Data Science Integration

• Inventory optimization across distribution centers
• Supplier risk assessment and monitoring
• Route optimization for delivery
• Automated reordering systems

Store Operations + AI Implementation

• Staff scheduling based on predicted foot traffic
• Shelf monitoring and restocking alerts
• Customer flow analysis and store layout optimization
• Loss prevention through computer vision

Customer Experience + ML Engineering

• Personalized product recommendations
• AI-powered customer service chatbots
• Sentiment analysis from reviews and social media
• Predictive customer lifetime value modeling

For businesses looking to implement AI in customer interactions, solutions like AI-powered phone answering can handle customer inquiries 24/7 while your team focuses on strategic initiatives.

Collaboration Tools and Platforms

The right tools make cross-functional collaboration possible. Here's what successful AI teams use:

Tool	Features	Best For
Slack	Messaging, channels, integrations	Daily communication, quick questions
Microsoft Teams	Chat, video calls, Office 365 integration	Enterprise environments, document collaboration
Asana	Task management, project tracking	Sprint planning, milestone tracking
Spoke.ai	AI-powered knowledge management	Cross-team knowledge sharing

AI Platforms for Cross-Department Collaboration

Beyond communication tools, retailers need platforms that enable cross-department collaboration on AI projects:

• Shared data platforms that give all team members access to the same information
• Model registries where data scientists can publish models for business users to access
• Experiment tracking systems that document what's been tried and what worked
• Business intelligence dashboards that translate AI outputs into actionable insights

Creating AI champions and fostering cross-functional collaboration will accelerate adoption and ensure AI is seen as an enabler, increasing adoption rates by an estimated 20-30%.

Agile Methods for AI Product Teams

Agile methodologies like Scrum and Kanban work well for AI projects because they accommodate the experimental nature of machine learning work.

Why Agile Works for AI

Common threads in successful AI case studies include a relentless focus on customer needs, iterative development to keep pace with AI's fast improvements, cross-functional teamwork, and careful attention to ethics and data quality.

With Scrum:

• Teams have short "sprints" to complete tasks
• Regular reviews assess model performance
• Retrospectives identify process improvements
• Business stakeholders see progress frequently

With Kanban:

• Visualizes the workflow from data prep to deployment
• Limits work-in-progress to prevent bottlenecks
• Enables continuous delivery of incremental improvements
• Adapts easily to changing priorities

Preparing for Agentic AI: The Future of Retail Teams

Agentic AI is expected to manage significant autonomous workflows within three to five years, with 90% of executives preparing for this shift. This demands a radically different team structure.

BCG predicts that "the merchant of the future will manage a team of AI agents: one negotiating contracts, one shaping prices, one allocating stock to stores, and one localizing assortments."

What This Means for Team Structure

You're no longer building tools for humans - you're building digital workers that require supervision. This shift requires:

• AI supervisors who monitor agent performance and intervene when needed
• Prompt engineers who design the instructions agents follow
• Integration specialists who connect agents to business systems
• Ethics monitors who ensure agents operate within acceptable boundaries

Retailers can move away from function-led hierarchies to become flatter and more cross-functional. Decision making shifts from slow approval chains to real-time action.

Ethical AI Development in Retail

Building trustworthy AI systems requires addressing ethical concerns proactively. For retailers, this means:

Reducing Bias in Retail AI

• Ensure diverse and representative training data across customer segments
• Implement algorithms to mitigate pricing and recommendation bias
• Audit AI systems for fairness across different customer groups
• Test models in different store locations and demographics

Data Privacy and Security

Protecting customer data privacy is critical in retail AI. Teams should:

• Implement strong data security measures for customer information
• Ensure transparency in how customer data is collected and used
• Obtain explicit consent for personalization features
• Provide customers control over their data

Measuring Cross-Functional AI Team Success

You can't improve what you don't measure. Here are the metrics that matter:

Metric Category	Specific Measures
Project Delivery	Time to production, sprint velocity, on-time completion rate
Model Performance	Accuracy, precision, recall, business impact metrics
Business Value	Revenue impact, cost savings, ROI per project
Team Health	Employee satisfaction, retention, cross-training completion
Adoption	User adoption rate, feature utilization, stakeholder satisfaction

Regular reviews let teams check their progress against these targets and adjust their approach as needed.

Common Challenges When Retailers Structure Cross-Functional Teams

Cross-functional AI teams face predictable challenges. Here's how to overcome them:

Challenge	Solution
Lack of collaboration	Co-locate team members, establish shared goals, create joint accountability
Communication breakdowns	Create shared terminology guides, hold regular cross-functional standups
Conflicting priorities	Establish clear governance, get executive alignment on priorities
Skill gaps	Invest in cross-training, hire external experts for specialized needs
Data readiness issues	Prioritize data engineering, budget three to six months for data hygiene
Resistance to change	Involve stakeholders early, demonstrate quick wins, communicate benefits

The Tiger Team Trap

"Tiger teams" - highly focused, multidisciplinary groups - can use AI to identify bottlenecks or risks, ensuring quicker resolutions. But there's a catch: tiger teams can't sprint forever. Each member has another role within the organization that will require the majority of their time.

Plan for transition from the start. Document everything, cross-train team members, and build sustainable processes that outlast the initial push.

Building Your Retail AI Team

Understanding how retailers structure cross-functional teams for AI product work is the foundation for AI success. The retailers seeing the best results share common characteristics: they bring together diverse expertise, create psychological safety for experimentation, and maintain strong governance without bureaucratic slowdown.

Start by assessing your current AI maturity and choosing the right team model - centralized, embedded, or hybrid. Hire data engineers early to address the data readiness challenge that delays most AI projects. Build cross-functional AI literacy so everyone can collaborate effectively.

Most importantly, remember that AI success isn't just about technology. It's about people working together across traditional boundaries to solve business problems in new ways.

For businesses looking to experience AI capabilities firsthand, try Dialzara's AI receptionist free for seven days. It's a practical way to see how AI can handle real customer interactions while your team focuses on building your broader AI strategy.