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AI is accelerating expectations across B2B ecommerce - but it's also exposing issues that have been there for years. Product data gaps. Inconsistent stock visibility. Fragmented systems. What feels like something new is often just something old becoming harder to ignore.
That was the conclusion that emerged from two sessions at the IR B2B eCommerce Conference 2026: Practical AI in B2B and Data Done Right, both hosted by Biglight Co-Founder Steve Borges. Two different angles. The same destination.
AI in B2B is practical, not revolutionary
In the organisations that are further along, AI isn't being used to reinvent the experience. It's being used to make things work better, quietly supporting areas where complexity already exists, helping people make sense of data, reducing manual effort, and smoothing out interactions.
It shows up in small, practical ways: giving teams better context before speaking to a customer, helping customers interpret technical information, and removing friction from internal processes.
The ambition might be big. The value comes from focused, incremental improvements.
Trust is still the hard part
In B2B, relationships are long-standing. Customers are used to calling people they know. They rely on experience, not just information. That doesn't change overnight.
This is where many AI initiatives stall, not because the technology doesn't work, but because it's positioned badly.
Adoption happens when AI is clearly framed as a support layer, a way to improve speed and accuracy, a tool that enhances rather than replaces human interaction. Get that balance right and trust builds. Get it wrong and it doesn't land at all.
AI only works when the data is connected
The promise of conversational AI is simple, help customers move quickly from problem to solution. In practice, it only works when everything behind it is aligned.
When product data, stock, customer context and commerce systems are connected, the experience feels seamless. When they're not, things fall apart quickly. This is why standalone solutions struggle and they surface the problem rather than solve it.
The real constraint is data
A lot of what gets labelled as an "AI challenge" is something much more familiar.
Across both sessions, the same issues kept coming up:
• Product data varies across markets
• Stock visibility doesn't reflect reality
• Ownership is split across teams
• Data is treated as something to fix later
The examples were simple but telling. Products listed with incorrect specifications. Items showing as unavailable when they aren't. Translations that distort meaning. Entire categories disappear because of how a rule has been set up.
In every case, the system did exactly what it was designed to do. The data didn't.
The hidden cost of poor data
What makes this harder is how quietly it plays out. Poor data doesn't always cause obvious failure, it creates friction:
• Teams manually fixing issues behind the scenes
• Customers dropping off without explanation
• Conversion softening over time
It's easy to misdiagnose. Traffic gets blamed. The market is blamed. The platform gets blamed.
It's only when organisations connect data quality to revenue, missed sales, lost conversion, reduced visibility that the scale of the issue becomes clear. At that point it stops being a technical concern and becomes a commercial one.
AI raises the bar
AI doesn’t compensate for poor data, it exposes it. As discovery becomes more conversational, expectations shift. Data needs to be clear, consistent and structured in a way that holds up, not just for customers, but for the systems interpreting it on their behalf.
In that environment, data quality becomes either a competitive advantage or a visible weakness.
The fundamentals still win
The biggest gains in B2B ecommerce aren’t coming from adding more technology. They come from getting the basics right, aligning data, connecting systems so they reflect reality and designing journeys that work.
When those pieces aren’t joined up, it shows up in the experience and ultimately in performance.
Start with the problem. Understanding your customers, their journeys and the processes and data behind them will help you identify the right opportunities and invest in solutions (AI-powered or otherwise) that make a genuine difference.
At Biglight, we help organisations understand where those gaps exist and design and deliver improvements that make digital journeys simpler, more effective and easier for customers to complete.
If this sounds familiar, we’d be happy to have a conversation.
