Laying the Foundation for Scalable AI in Retail Software
Overview
A leading retail software company serving markets across the U.S. in a highly regulated industry was eager to discover how generative artificial intelligence (GenAI) and machine learning (ML) could increase revenue and streamline operations. However, the company didn’t know where to start with these cutting-edge tools, so they enlisted Brooklyn Data’s assistance, hoping to leverage our expertise with AI to drive innovation and groundbreaking change.
Challenge
This client was eager to explore how GenAI and ML could unlock new revenue streams and simplify growing operational complexity, particularly around compliance in their highly regulated industry. However, they faced uncertainty about where and how to embark on this exploration. Their internal data systems were fragmented, team expertise varied, and the company lacked a framework for responsible AI adoption. They needed to understand their current state, what initiatives could help them meet their strategic goals, and what it would take to scale AI capabilities strategically and sustainably across the organization. So, they contacted Brooklyn Data to help them overcome these challenges and develop a strategic roadmap for AI adoption.
The client’s challenges included:
- Understanding their current capacity for AI adoption
- Determining what AI initiatives could help them meet their goals
- Learning how to scale AI capabilities across their organization
Approach
Brooklyn Data led with a comprehensive AI & ML readiness assessment, measuring the company’s posture across five key pillars: infrastructure; governance and ethics; team capability; data quality and availability; and organizational alignment. We translated our findings into a clear, actionable roadmap, focused on maturing their AI capabilities over time while recommending ones that would deliver them immediate business value. This included identifying use cases with strong impact to value returns, such as real-time compliance monitoring using LLMs, addressing technical debt through cloud cost optimization and improved data engineering practices, and streamlining product offerings via statistical analysis.
Our approach included:
- Performing a comprehensive AI & ML readiness assessment
- Translating our findings into a clear, actionable AI roadmap
- Identifying AI use cases with strong impact to value returns
- Addressing technical debt through optimization
- Streamlining product offerings via statistical analysis
Solution
By grounding the engagement in technical and organizational realities, we helped this client transform AI from a vague aspiration into a concrete strategic advantage. With a firm understanding of their maturity level and a clear path forward, they are now equipped to confidently scale AI initiatives, backed by strong data foundations and business alignment.
Early wins — like reducing infrastructure costs and providing remediation for data readiness issues that hinder the client’s ability to extract value from their data — have already shown the value of our approach and built momentum for further AI investment. We also identified and remediated key data quality issues and demonstrated that the client can analyze customer purchases to streamline product offerings and create new ones. Their team now has a prioritized roadmap aligned to business goals, actionable next steps with prioritized short and long-term use cases, and the internal alignment needed to move forward with AI.
“One of the biggest challenges we faced with this client was developing impactful use cases in the face of some AI-data readiness challenges. Ultimately, we still found meaningful ways to use AI to help meet their business objectives because of the myriad ways that AI can be used.”
Results:
- A detailed AI & ML roadmap aligned to business goals
- Prioritized use cases with short-term ROI and long-term scalability
- An LLM-based approach to track evolving regulatory requirements in near real-time
- Cleaner data for powering AI
- Reduced cloud costs and increased data engineering efficiency
- Ways to analyze customer purchases to streamline current products or get ideas for new ones