Retail organizations are not short on data. Most already have years of information stored across legacy systems. The real challenge is bringing that data together in a way that allows AI to reason over it and generate insights.

In this week’s episode of Modern Work Mondays, Nolan and Jeff explore both the work of unifying existing data and the emergence of an entirely new category of data that businesses have never really had access to before.

Bringing legacy data together

One of the first hurdles in any AI initiative is bringing data together across systems. That does not necessarily mean moving it from its original location. It means enabling AI to reason over it collectively to generate insights, reports, and support decisions.

This foundational step allows organizations to look across historical results and operational data. But it's only part of the picture.

The emergence of “novel” store data

Stores generate constant communication through written messages, digital channels, paging systems, phone traffic, and two-way radios. In a typical large big box store, that can amount to roughly 1,000 communications per day.

Historically, retailers did not really know what stores were talking about throughout the day. It was difficult to determine when issues were urgent or how frequently certain topics surfaced. That information was simply not aggregated at scale.

With AI, that communication data can be brought into the cloud and analyzed to better understand what’s happening inside stores from open to close.

From individual perspective to observable data

Operational reporting has often relied on individual summaries. For example, end-of-shift handwritten notes for the next manager reflect one person’s view of how the day went. Those notes take time and are shaped by personal effort and perspective.

Capturing communication data introduces a different approach. Instead of relying on a single recap, organizations can look at patterns in actual activity and make decisions based on real data rather than opinion.

AI as continuous support

No retailer is placing someone in every store to listen to radio traffic all day and document what happens. AI can do that continuously, open to close. It analyzes communication activity at scale and supplement store operations by surfacing patterns that would otherwise go unnoticed.

By bringing together legacy data and newly captured communication data, organizations gain a clearer view of how their stores are operating and can base decisions on observable data rather than individual interpretation.

Watch the full episode below.