How Majors Management Uses ResultStack’s AI to Optimize C‑Store Pricing, Inventory and Fuel Operations

The Problem: Operational Complexity at Scale

Convenience retail sounds simple on the surface. Stock shelves, price products, sell fuel. But at Majors’ scale, the complexity compounds fast.

Fuel pricing alone requires constant adjustments based on competitor moves, wholesale costs, and local demand signals. Inventory across hundreds of SKUs in hundreds of stores creates thousands of daily decisions. Labor planning across a national network means balancing cost efficiency against customer service quality — often with incomplete data.

Howard Hyche, Majors’ Chief Information Officer, put it plainly:

“Partnering with ResultStack gives us a real opportunity to tackle the inefficiencies that have lingered in our industry for far too long.”

That phrase — lingered for far too long — tells you something important. These aren’t new problems. They’re entrenched ones that legacy systems and manual processes have failed to solve at scale.

The Tool: What ResultStack Actually Brings

ResultStack’s primary focus is custom software development and digital transformation, but its AI capabilities are the core of this partnership. The firm’s technical stack includes:

• Machine learning for pattern recognition and predictive modeling
• Agentic systems capable of autonomous decision-making workflows
• Language model integration for natural language interfaces and reporting
• Predictive analytics to forecast demand, pricing windows, and inventory needs
• Real-time data architecture to process live operational signals
• Platform engineering to tie it all together into a unified system

This isn’t a plug-and-play SaaS tool. ResultStack builds custom solutions, which means the deployment at Majors is being engineered specifically for their operational environment — not adapted from a generic retail template.

ResultStack also brings relevant industry experience. The firm has previously worked with both Pilot Flying J and Cumberland Farms, two major players in the fuel and convenience space. That context matters. C-store operations have unique data structures, margin dynamics, and compliance requirements that generic AI platforms often underestimate.

Fuel Pricing Optimization

Fuel is the highest-frequency, highest-stakes pricing decision in c-store operations. Margins are thin, competitor pricing shifts constantly, and consumer sensitivity is high. AI-driven pricing models can ingest competitor data, wholesale cost feeds, and local demand signals to recommend — or automate — price adjustments in near real time.

At Majors’ scale, even marginal improvements in fuel margin per gallon translate to significant bottom-line impact across millions of transactions.

The Context: AI Is Becoming Standard in C-Store Operations

Majors isn’t alone in this move. The convenience retail sector is accelerating its AI adoption in 2026. Casey’s General Stores, Loop Neighborhood Market, Urban Value Corner Store, and Huck’s have all made notable AI investments this year.

The difference with Majors is the scope. Many operators are deploying AI for one specific function — pricing, or inventory, or customer analytics. Majors appears to be pursuing an integrated platform approach, using ResultStack to build a unified AI layer across multiple operational domains simultaneously.

That’s a more complex implementation, but it also creates compounding advantages. When pricing data informs inventory decisions, and inventory data informs labor planning, and all of it feeds into a real-time operational dashboard, the system becomes more valuable than the sum of its parts.

What’s Still Unknown

The announcement doesn’t specify implementation timelines, which AI capabilities will be prioritized first, or what measurable targets Majors has set. It’s also unclear how deeply the agentic and language model capabilities will be deployed versus the more established predictive analytics functions.

Custom AI deployments at this scale typically take 12 to 24 months to reach full operational maturity. Early wins are usually concentrated in the highest-data-density areas — fuel pricing and inventory — before expanding to more complex domains like labor optimization and customer experience personalization.

The results aren’t in yet. But the architecture being built here is worth watching.

What Other Operators Can Take From This

If you’re evaluating AI tools for retail or fuel operations, the Majors-ResultStack partnership surfaces a few practical considerations.

Breadth vs. depth is a real tradeoff. An all-in-one platform approach creates integration advantages but also implementation risk. Narrower, best-in-class tools for specific functions can deliver faster ROI with less complexity.

Industry-specific experience matters. ResultStack’s prior work with Pilot and Cumberland Farms isn’t just a marketing credential — it means the firm understands c-store data models, fuel pricing dynamics, and the operational rhythms of the industry. Generic AI platforms often struggle with sector-specific nuance.

Real-time data architecture is the foundation. Every AI capability in this deployment — pricing, inventory, labor, loyalty — depends on clean, real-time operational data. If your data infrastructure isn’t ready, AI tools will underperform regardless of how sophisticated the models are.

The Takeaway

Majors Management is making a calculated bet that AI-driven operations will become a competitive necessity in convenience retail — not a differentiator, but a baseline requirement. The partnership with ResultStack is designed to close the gap between where the industry is and where it needs to be.

Ben Farmer, ResultStack’s CEO, framed it well:

“That’s where the AI and machine learning capabilities we’ve invested in for years actually pay off.”

The real test will be execution. But the problem is real, the tools are credible, and the scale makes the potential impact substantial. For anyone tracking how AI is reshaping physical retail operations, this is a deployment worth following closely.