Competitive advantage in supply chains now depends less on how much data companies can collect than on how quickly they can turn signals into action. Across modern networks, teams still spend too much time moving between EDI tools, ERP systems, emails, spreadsheets, supplier portals and support queues just to answer basic questions about failed invoices, missing orders or emerging partner risk.
That gap is widening as supply chains grow more distributed, more interdependent and...
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more exposed to disruption. Traditional automation, built around fixed rules and static dashboards, helped firms digitise routine work. But it was designed for an era when people could still manually interpret exceptions and coordinate responses before small problems became expensive ones.
The next step, according to OpenText, is an intelligent supply chain ecosystem in which AI sits inside the network itself, linking customers, suppliers, logistics providers, financial institutions and trading partners. The company argues that the real value of B2B integration is no longer limited to the secure exchange of purchase orders, invoices, shipment notices and remittance files, but extends to the insight that can be drawn from those transactions.
Every failed document, delayed response or onboarding bottleneck, OpenText says, contains useful context. The challenge is to convert that activity into something operational teams can use to understand what is happening, why it is happening and what should happen next.
Generative AI is central to that shift. OpenText says its Business Network Aviator is intended to give users conversational access to trusted product, operational and customer-specific information, rather than forcing them to search through documentation and support history. The company says the goal is to make answers available in natural language, while keeping them grounded in authoritative sources.
Its wider pitch mirrors a broader trend visible across the logistics and enterprise software sectors. DHL has described generative AI as a tool for faster onboarding, better customs and procurement workflows, predictive demand and more intelligent shipping operations. IBM, meanwhile, says AI can process vast volumes of data in real time, improve forecasting and support more autonomous decision-making, including through agentic systems that can act on internal and external signals.
OpenText says its approach is to bring that intelligence directly into day-to-day B2B operations. In its framing, the system would not merely respond to questions, but help users work more effectively across three areas: knowledge, insight and action.
That means giving staff immediate access to reliable guidance, helping them interpret patterns across partner activity and transactions, and reducing manual effort through recommendations, pre-filled tasks and safe automation of repeatable processes. In practical terms, the company says a user might ask which partners are producing the most errors, why a transaction failed or which suppliers have not sent expected advance ship notices.
The operational value becomes clearer in the kind of scenario supply chain managers know well: a critical supplier stops sending expected ASNs, teams only spot the problem after it has already begun to affect shipments, and the disruption cascades downstream before anyone has had time to respond. OpenText says an AI-enabled network should be able to surface that issue early, identify the transactions at risk and recommend next steps before the failure spreads.
The company also envisions AI becoming more proactive over time, with systems generating summaries of overnight activity, flagging anomalies automatically, supporting error analysis and initiating workflows where appropriate. In that model, the technology does not replace human expertise, but makes it easier to apply at scale across a wider ecosystem.
That said, the company acknowledges that operational AI in regulated, high-accountability environments cannot behave like an unchecked black box. It says such tools must remain explainable, secure and subject to human oversight, particularly where compliance and reliability are critical.
Looking further ahead, OpenText sees AI moving beyond conversational support towards agentic execution: monitoring transaction flows continuously, identifying anomalies before they trigger failures, validating transactions in flight and triggering recovery workflows. That would make intelligence a shared service across the network, rather than a separate layer sitting outside it.
The broader direction is clear. As firms look to manage greater complexity with fewer delays, the supply chain winners are likely to be those that embed AI into the systems where decisions are already made, rather than treating it as another standalone tool.
Source: Noah Wire Services
