How a Fortune 500 retailer unlocked $34M in AI ROI with focused strategy

The client wanted to move beyond fragmented AI pilots and establish a unified, enterprise-wide strategy. They needed to identify which use cases actually drove value, create governance around AI implementation, and set up a roadmap for scaling projects across merchandising, supply chain, and customer experience.

Success would be measured not just by the number of AI initiatives launched, but by ROI, adoption, and faster time-to-value.

Despite significant investments, the retailer's AI program was underperforming. Projects lacked focus, governance, and integration—leading to wasted spend and failed pilots.

Key challenges included:

• Only 28% of AI projects succeeded, with $12M lost to abandoned pilots
• Absence of governance meant 67% of projects failed compliance checks
• 73% of ML models couldn't access required data due to siloed systems
• Lack of vendor evaluation expertise caused 54% implementation failures
• 61% of projects launched without measurable KPIs or ROI frameworks
• Competitors were achieving 3x better personalization metrics

CleverX mobilized a team of AI strategy consultants to design a structured approach—bringing rigor, governance, and ROI-driven prioritization into the client's AI program.

Expert recruitment:

• 17 consultants: 7 former Big Four AI leads, 6 ML architects, 4 change specialists
• Avg 8 years of AI deployment experience across retail and e-commerce
• Expertise spanned personalization, demand forecasting, and computer vision
• Hands-on experience with enterprise AI platforms and cloud migrations

Technical framework:

• AI maturity assessment across 50 capabilities and 8 dimensions
• Use case prioritization matrix evaluating 120 potential applications
• ROI modeling for 25 high-priority initiatives with risk scoring
• Governance framework aligned with compliance and corporate policies

Quality protocols:

• Stakeholder interviews with 80 executives and managers
• Current state analysis across tech, data, and people readiness
• Vendor scorecards assessing 15 platform providers
• Roadmaps with quarterly milestones over a 24-month rollout

The engagement structured discovery into focused phases, ensuring alignment between executives, data teams, and business units.

Weeks 1–2: Discovery & assessment

• Interviewed 80 stakeholders across 12 units
• Assessed 45 ML models and data pipelines
• Identified $8.2M in redundant AI investments

Weeks 3–5: Use case prioritization

• Evaluated 120 potential AI applications
• Selected 25 with combined ROI of $34M
• Designed 5 quick-win proofs of concept

Weeks 6–7: Roadmap design

• Built 3-year transformation plan with phased rollouts
• Designed org structure for 45-person AI Center of Excellence
• Governance framework with 20 standardized AI policies

Week 8: Executive presentation & change plan

• Delivered board-ready strategy to C-suite
• Rolled out comms plan to 5,000 employees
• Designed training for 200 business analysts

Efficiency gains:

With the streamlined AI strategy, the retailer significantly reduced project timelines and improved how resources were allocated across initiatives.

• Project timelines cut 14 → 8 months
• Vendor evaluations 58% faster with standardized scorecards
• Data integration accelerated 45% with unified architecture
• Portfolio optimization boosted resource use by 39%

Quality improvements:

The improved frameworks and governance practices directly enhanced model performance, data quality, and overall trust in AI-driven decisions.

• AI project success rate up 28% → 69%
• Model performance improved 41%
• Data quality scores rose from 52% → 81%
• Stakeholder satisfaction up 47%

Business impact:

By targeting high-value use cases, the company achieved measurable financial returns while also strengthening customer engagement and retention.

• $4.7M cost savings via supply chain optimization
• Customer lifetime value up 23% through personalization
• Inventory costs reduced by $2.8M with demand forecasting
• Conversion rates improved 31% with recommendations

Strategic advantages:

The transformation laid the foundation for long-term competitive advantage, creating scalable AI capabilities and new intellectual property.

• AI CoE managing 40+ initiatives enterprise-wide
• Reusable ML pipelines cut development time by 50%
• Partnerships with 3 major AI providers established
• IP portfolio expanded with 4 retail AI patents