AI is at the center of nearly every digital transformation conversation. Its potential to drive efficiency, insight, and scale is unmatched. However, AI is an expensive investment with no guaranteed outcomes, which can introduce risks to performance and data security. This is why choosing how and where to deploy AI is one of the most critical decisions facing product and technology leaders today.
And those choices matter. We’ve all seen the pressure on IT leaders to “adopt AI,” often without a clear use case or ROI. A recent Gartner report predicts that 40% of agentic AI projects will be scrapped by 2027, while a 2025 IBM survey of 2,000 global CEOs found that only 25% of AI initiatives have delivered the expected return on investment over the past three years. Ouch!
And in the world of B2B commerce, where reliability, consistency, and accuracy are critical requirements, not every AI solution delivers real value.
That’s why we’ve taken a more thoughtful approach. This guide isn’t about jumping on the AI bandwagon for the sake of “adopting AI”. It’s about how to harness AI where it improves outcomes, and avoid it where it compromises performance. From onboarding to analytics, we’ll explore where AI can enhance the buyer-supplier experience and why specific workflows must remain deterministic to meet real-world business requirements.
Ultimately, success with AI isn’t just about what you build; it’s about asking the right questions before you build anything.
Where AI Adds Value — And Where It Doesn’t
In B2B eCommerce and procurement, the promise of AI is not in replacing human oversight but in intelligently reducing friction across complex processes. When applied thoughtfully, AI can eliminate inefficiencies and highlight insights that would otherwise remain buried. But its value hinges on strategic application, not blanket adoption.
Before applying AI to any workflow, ask this important but counter-intuitive question: Will this add value without increasing the need for human validation downstream? While we expect or assume that AI will always enhance human productivity, there are many cases where the introduction of AI will do just the opposite. If the answer to that question is “no”, the benefits of automation may quickly erode under the weight of error-checking and rework.
Where AI Fails in High-Stakes Transactional Workflows
Despite the many advantages, it may be surprising that not every workflow is a good candidate for AI. Nowhere is this more evident than in automated transactional system integration, such as when a PunchOut cart from an eCommerce platform must be automatically converted without error into a purchase requisition in a procurement system. Or when an invoice from a supplier is expected by the buyer to be correctly transformed into the buyer’s data formats and specifications, while being fully enhanced with correct details from the original Purchase Order.
The core value proposition of the B2B system integration is to completely automate the process and reduce the cost and failure rate (errors) associated with traditionally non-integrated systems. The system-to-system transactional processes between buyer and supplier are foundational to revenue recognition, compliance, and customer satisfaction, demanding strict accuracy, consistency, and traceability.
Introducing probabilistic AI-driven logic into workflows that are expected to be completely automated, without error, creates uncertainty and risk. Expectations of errors in product identifier encodings, price transcriptions, or attribute mappings would require the re-introduction of humans “in-the-loop” to validate the output of the AI-”enhanced” process, thus eliminating the gains of implementing the system integration in the first place.
If you’re considering AI in these workflows, the key question is: Can we afford any ambiguity in how the system makes decisions? For processes that support revenue recognition, auditability, and customer trust, the answer is almost always no.
That’s why automated transactional system integrations in B2B must remain deterministic by design, relying on structured rules and verified data rather than probabilistic outcomes.
Notably, we’ve recently seen posts by others in the procurement space who are claiming 100% automation with AI. But when you dig into it, they still need a human-in-the-loop to validate what was automated. How can they claim 100% automation when a human is still required?! iPaaS vendors like TradeCentric already deliver true 100% automation (the kind that doesn’t require a human-in-the-loop!) for PunchOut return carts, PO integration, and Invoice integration.
9 Practical Ways AI Can Improve the Buyer-Supplier Experience
While some workflows must remain rule-based, many others benefit from AI-driven assistance. The best question to ask is: Where can AI give your team leverage — enhancing speed, insight, or efficiency — without adding risk?
AI is especially useful as an assistant to digital workers:
- To help create digital content such as product descriptions and images.
- To help set up and validate the configuration for system integration.
- To help create and validate data transformations, such as mappings of product categories or units of measure.
- To deliver more powerful digital search experiences from product search to analytical insights, all based on conversational natural language queries.
- To make suggestions, such as vendor or product recommendations.
- To help validate that data and content adhere to specific rules or standards.
- To offer predictive analytics, such as identifying demand patterns or flagging potential supply chain disruptions.
- To identify processing anomalies that deserve immediate or expert attention.
- And of course, Agents. When these capabilities are enabled as agents to support conversational experiences, they empower eCommerce, procurement, and IT teams to remove significant busy work from their tasks and make proactive decisions that drive continuity and savings. In most cases, that’s the real advantage of AI: not replacing people, but empowering them to make smarter, faster decisions that drive results.
Responsible Adoption is Your Competitive Edge
B2B leaders who treat AI as a strategic tool will unlock its true value. That means applying AI to remove workflow complexity and to uncover meaningful insights, while preserving human oversight and deterministic logic where trust, accuracy, and scale are non-negotiable. The future of AI in commerce isn’t about automation for its own sake; it’s about making every step of the buyer-supplier journey smarter, faster, and more resilient.
