Cost is not the most common objection to AI adoption in fuel retail operations. It is age. The retail fuel business consists of a lot of regional players who manage a mix of old and new hardware. So, implementing AI that works on legacy fuel dispensing hardware is the number one concern.
- “Our equipment is too old for this.”
- “Our systems don’t support modern APIs.”
- “We would have to do a full site overhaul before any of this applies to us.”
These objections are understandable. They are also, in most cases, incorrect. Operators who believe them are losing out on the operational benefits their competitors are gaining.
“Legacy” Does Not Mean Unusable
McKinsey’s research on IoT value through 2030 estimates that IoT without AI has a value of anywhere from $5.5 trillion to $12.6 trillion in addressable economic value. The largest share is in B2B industrial settings and AIoT increases that value significantly. There are no technical reasons supporting the belief that legacy hardware disqualifies a site from participating in AIoT value. Many operators simply have a misunderstanding about how modern integration architecture works.
The objection assumes that AI requires modern hardware. In reality, AI requires data. And data is something that legacy fuel dispensers, ATG systems, and pump controllers have been generating for decades. The question is not whether the equipment can support an AI layer. The question is whether there is a bridge between the data that the equipment produces and a platform that can read it.
That bridge exists. It is called an edge gateway.
What “Legacy” Actually Means in Fuel Retail Infrastructure
In fuel retail, legacy hardware is the norm, not the exception. A Gilbarco Encore dispenser installed in 2010 runs on communication protocols that predate modern cloud connectivity. A Veeder-Root TLS-350 ATG system uses a serial interface designed in the 1990s. A submersible turbine pump controller may communicate on a proprietary protocol that its manufacturer has not actively supported in years.
None of this means the equipment is producing less data. These systems generate rich operational signals: pressure readings, flow rates, transaction records, error codes, inventory levels, power states. The data exists. It just lives behind a communication barrier that modern cloud platforms cannot cross directly.
When an IT team has tried to connect legacy forecourt equipment to a modern analytics platform and has been stymied by protocol incompatibility, the team is not facing an equipment problem. It is facing an integration architecture problem. And integration architecture problems are solvable without touching the equipment.
What the Interscope AI Platform Does With Legacy Data
The Bridgera Interscope AI Platform uses edge gateways as the translation layer between legacy forecourt equipment and the cloud intelligence layer. The edge gateway sits on-site, reads the native protocol of the existing equipment—that could be Gilbarco’s IFSF protocol, Veeder-Root’s TLS series serial interface, or the proprietary communications of a third-party pump controller—and translates it into a standardized data format that the Interscope AI platform can read.
You don’t need to replace dispensers or upgrade the ATG. We install the edge gateway alongside the existing equipment and it begins reading immediately.
McKinsey’s 2025 State of AI research found that 88% of organizations have deployed AI but only 39% are seeing enterprise-level EBIT impact. In legacy-heavy environments, the gap is often exactly this: the intelligence platform exists but the data cannot reach it. The edge gateway closes that gap. For a broader look at how AI-driven IoT connectivity management works as an architectural discipline, Bridgera’s connectivity management guide covers the approach in depth.
From the Interscope AI platform perspective, a dispenser communicating through an edge gateway looks the same as a modern dispenser with a native cloud connection. The health signals, transaction records, and error histories are all available for continuous analysis. The age of the hardware behind the gateway is operationally irrelevant.
Three Outcomes That Move First
Operations that deploy edge gateways against legacy forecourt infrastructure typically see early improvement in three areas:
- Operational visibility gain. Equipment that was effectively invisible from a network management perspective becomes fully monitored. The operator can see the full fleet for the first time.
- Maintenance model shift. Legacy equipment that was managed reactively because it could not be monitored remotely transitions to condition-based maintenance. The no-flow event becomes predictable rather than inevitable.
- Capital investment deferral. Equipment that was on the replacement schedule because “it can’t connect” remains in service with a monitoring layer added. The cost of the edge gateway is a fraction of a site overhaul or equipment replacement program.
What This Looks Like for a Network of Mixed-Vintage Sites
Connectivity issues are not uniform, especially for a fuel operation whose portfolio of sites span different construction eras and equipment generations. Your newest sites may have modern connected dispensers. The oldest sites probably have hardware that predates digital communications entirely.
Bridgera’s edge gateway architecture accommodates the full range of circumstances. Each gateway is configured for the specific protocol supported by the equipment at that site. The platform normalizes all of the ingested data into a consistent data format. The operations team sees a single, unified network view regardless of the underlying equipment vintage at each location. For operators who want to understand the full operational picture that legacy connectivity enables, Bridgera’s fuel management guide shows what the connected forecourt looks like end to end.
The sites with the oldest hardware are typically the ones that have been ignored. Edge gateways can correct that imbalance without requiring a huge capital program to reach hardware parity.
The Bottom Line
Legacy hardware is not a disqualifier for operational AI. It is a configuration challenge and one that edge gateways are specifically designed to solve. The Interscope AI platform reads the data that your existing equipment is already producing. The Bridgera JERA agent acts on that data. The equipment does not need to be new for the intelligence layer to work. It just needs to be connected.
Frequently Asked Questions (FAQ)
1. We have Veeder-Root and Gilbarco equipment that is 10 to 15 years old. Can Interscope actually connect to it?
Yes. Interscope uses edge gateways configured to read Veeder-Root TLS series ATG systems and Gilbarco dispenser controllers through their native communication protocols. You don’t need to make any hardware replacements.
2. What does the edge gateway installation actually involve?
The edge gateway is a compact device that we install at the site, typically in the equipment room or near the forecourt controller. It connects to the existing equipment through the native interface and transmits the data to the Interscope AI platform through the site’s Internet connection. We can accomplish installation in hours.
3. What happens if the site’s internet connectivity is unreliable?
Edge gateways include local buffering that stores data during connectivity gaps and transmits it when the connection is restored. The Interscope AI platform accounts for any gaps in its continuous analysis rather than treating the missing data as a failure.
4. Does connecting legacy equipment through a gateway expose it to cybersecurity risk?
Bridgera has designed its edge gateway architecture with security as a core requirement. The gateway does not open inbound network access to the legacy equipment; it reads from the equipment and transmits outbound data through encrypted channels. Nothing about the legacy system’s network exposure changes.
5. How do we prioritize which sites to connect first?
Bridgera begins with a data audit that identifies the sites where legacy connectivity gaps create the highest operational risk and the greatest potential upside impact from monitoring. We start there and scale based on your priorities.
About Bridgera
Operational Intelligence. Production-Ready AI.
Bridgera partners with operations-heavy enterprises to move AI beyond pilots and into real production systems. Through AI consulting, specialized talent, and scalable platforms like Interscope AI™, Bridgera embeds intelligence directly into the operational workflows that power the business.