As AI captures headlines and imaginations, many retailers wonder where to start. The answer isn’t more hype — it’s connected data. Here’s how Taiga helps C-stores move forward with confidence.

Last week, we explored how forward-thinking operators are paving the road toward an AI-enabled future. This week, we focus on what’s holding others back — and how retailers can overcome the FOMO (Fear of Missing Out) trap to start their own journey with confidence.

Our roadmap centers on three essentials that drive measurable progress:

• Invest in high-quality data infrastructure – the “fuel” your AI engine needs.
• Apply purpose-built models – solve real operational challenges today.
• Prioritize openness and flexibility – So you can easily adopt and connect new tools as they emerge.

Just days after we published our article, C-Store Dive’s 2025 C-Store Loyalty Benchmarking Report confirmed the urgency of the approach we detailed, showing that data readiness determines whether AI ambitions turn into results.

Last week, the Wall Street Journal published an article titled “Large Language Models Get All the Hype, but Small Models Do the Real Work.”

It captured something we see every day at Taiga: in the convenience industry, AI has not yet transformed businesses at scale. There is plenty of flash — much like a self-driving, flying concept car unveiled at an auto show — impressive in theory, but not built, tested, or ready for real-world performance.

Meanwhile, the operators making real progress are not waiting for the prototype to become reality. They are following a clear strategic path that starts with investing in data-driven infrastructure.  This allows them to leverage smaller, purpose-built models that deliver measurable results today.  Meanwhile, their business is ready to adopt the next generation of AI when it becomes available.

“What’s the ROI of your system?”
It’s one of the most common questions we hear.

The honest answer is: the returns are real—but often invisible at first glance. They’re buried in the hours your team no longer spends writing SQL queries, manually reconciling reports, or prepping Excel sheets and Power BI dashboards.

And yes, we help our customers make smarter, faster decisions that improve store performance. But tying those results directly to a single platform is tough. Was it the new system that lifted performance? Or some other initiative?

This uncertainty causes many organizations to delay investing in foundational data infrastructure. If the current setup delivers “good enough” reports and your team can cobble together what they need, why prioritize something new?

The real question is: What’s working in your store, with your customers, right now?

The article points to GLP-1 weight-loss drugs like Ozempic and Wegovy, which suppress appetite and reduce impulsive eating. But the truth is likely more nuanced—and more important to understand.
The shift in behavior is likely being driven by multiple overlapping forces:

• Lower-income consumers cutting back on discretionary purchases due to inflation and economic pressure.
• Higher-income shoppers making gradual changes to their eating habits in pursuit of health and wellness.
• GLP-1 users, whose reduced hunger is quietly altering purchasing patterns.

These forces may look similar in your top-line numbers—lower snack sales, fewer foodservice add-ons—but their underlying causes are very different. And if you’re not measuring at a granular level, you won’t know what’s really driving the shift—or how to respond.

So the key question becomes: Do you have the data to detect which of these trends are affecting your business?

And if PepsiCo is betting on emerging health-forward brands like Poppi, the follow-up is: Will these products work in your stores—with your customers?

Most organizations aren’t equipped to answer these questions. Without connected data across POS systems, loyalty programs, transaction history, and customer segments, it’s nearly impossible to isolate patterns or test assumptions.

You might see that sales are down—but:

• Is the dip broad-based, or focused in specific stores or regions?
• Is it impacting your core customers, or those who shop less frequently?
• Is it a matter of fewer trips, smaller baskets, or shifting category preferences?

In other words: Are you seeing signals—or just noise?

This is where data readiness becomes a strategic advantage. You need systems that allow you to segment customers, detect behavioral changes in real time, and test how new products like Poppi perform in specific cohorts. What works for a wellness-focused loyalty member in Austin may not land with a price-sensitive commuter in Toledo.

AI and BI tools are NOT magic solutions — they are only as good as the data infrastructure that supports them. Clean, organized, and accessible data is the foundation for reliable insights and business success.

Many c-stores are exploring bean-to-cup machines to serve freshly brewed coffee around the clock. With Taiga’s real-time data, you can:

• Test Market Performance Before Broad Investment:
  Pilot the machines in select locations to determine if the customer experience genuinely boosts overall sales, lifts higher-margin product sales, and drives repeat visits—helping you decide whether the innovation delivers long-term ROI or is merely a trendy experiment.
• Minimize Downtime:
  Monitor sales patterns to predict when bean refills are needed, triggering automated alerts so staff can promptly address any issues.

Many organizations are leveraging some form of data analytics, but fragmented data remains a significant hurdle. Without solving this fragmentation, it’s impossible to gain a holistic view of the business.

For example, one emerging trend is the use of electronic shelf labels to adjust prices and maximize margins. This pricing data is rich with insights but remains siloed without integration with other systems like loyalty programs, POS, and back-office platforms. The real value of analytics comes from connecting these data sources to uncover actionable insights.

Taiga’s platform is purpose-built for the convenience industry to tackle the data challenge head-on. Our off-the-shelf integrations connect directly to your store systems and aggregate, cleanse, and normalize the data in the cloud to enable seamless, real-time analysis and deliver actionable insights.

We understand that acquisitions often leave organizations with a mix of systems—four different back-office systems here, a handful of POS systems there, new car wash, diesel fuel—all operating within a single chain. While this complexity might seem like it would dramatically expand the scope of a solution, it doesn’t for Taiga.

Our platform is designed to handle these scenarios effortlessly because our integrations are part of our out-of-the-box solution. Whether your stores operate on multiple POS or back-office platforms, Taiga seamlessly connects to them, consolidating and normalizing your data without the need for costly, custom-built integrations.