Building the foundation for AI in supply chain

The decision

The company’s CEO and CTO viewed AI as an important 18- to 36-month product priority. Before committing significant engineering resources, they needed to understand where AI could create customer value, which use cases deserved attention, and whether the market was ready.

C Squared E conducted 15 in-depth customer interviews, surveyed 50 customers, assessed competitive activity, spoke with industry analysts, and evaluated potential AI applications in warehouse management.

What the evidence revealed

The research showed that customers were not yet thinking deeply about AI. Most had few well-developed ideas for how it could improve warehouse operations, and competitors had made little meaningful progress in the category.

More importantly, customers identified a more immediate problem: their data was not ready.

Operational data was often incomplete, scattered across multiple systems, inconsistently structured, and difficult to aggregate. Before customers could use AI for predictions or real-time decision support, they first needed a stronger data foundation.

The research also identified promising longer-term applications—including resource scheduling, staffing predictions, inventory forecasting, and operational decision support—but the evidence did not yet support major product-development commitments.

From insight to action

The findings caused leadership to rethink the sequence of its AI investments.

Rather than immediately building AI products, the company began exploring a data lake offering to help customers aggregate and normalize their information. It also developed a professional-services concept called Data Science as a Service (DSaaS) to help customers improve their data readiness while giving the company firsthand exposure to potential future AI use cases.

C Squared E then helped translate the strategy into a five-year business plan covering staffing, services revenue, product investment, operating costs, and the expected path to profitability.

Results

The CEO and CTO presented the findings and business plan to the board, which approved an initial investment in additional engineering resources.

The company also began promoting its DSaaS offering and secured an initial customer pilot within approximately a year.

Just as importantly, the research helped leadership avoid rushing into AI products before customers had the data, readiness, and demonstrated demand required to support them.

The resulting strategy was clear: strengthen the data foundation, learn through services, validate AI use cases, and invest in scalable products where the evidence supports it.