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If AI agents start choosing suppliers on behalf of your customers, would your company qualify?
Not in a marketing sense, and not in a brand-awareness sense. In a machine-readable, data-validated, algorithm-tested sense.
Agentic commerce is introducing a structural change in B2B digital trade. Discovery moves to AI assistants, comparison becomes instant, and transactions can execute without a human in the loop, sometimes from trigger to purchase order in seconds. Instead of a procurement manager browsing portals or requesting multiple quotes, an AI system interprets intent, evaluates options across multiple platforms, and recommends, or even completes, the purchase.
Manufacturers and distributors shouldn't think of this as plugging in a new AI feature. The bigger challenge is preparing your infrastructure for a completely different type of decision-making. When algorithms start qualifying suppliers based on structured data, reliability signals, integration compatibility, and measurable value, just being visible won’t be enough anymore.
The real question is operational: Are your systems ready to be evaluated by machines?
The traditional B2B buying funnel was predictable: awareness, consideration, negotiation, purchase. Each stage left room for influence, messaging, and relationship building. Sales reps had time to make their case.
But as both the Digital Commerce 360 analysis and Michael Komasinski’s Web Summit 2025 session on agentic commerce pointed out, AI agents compress that entire process. Instead of a layered funnel, what remains looks more like: Intent signal → AI comparison → Transaction.
Here's what that looks like in practice.
A mid-sized manufacturer of industrial pumps runs predictive maintenance across its facilities. Sensors flag that a specific bearing component will need replacement within 12 days. Normally, a procurement manager would log into multiple supplier portals, request quotes, compare lead times. That process might take a day, maybe two.
With an AI purchasing agent, the process triggers automatically. It checks contract pricing across approved distributors, real-time inventory levels, historical on-time delivery rates, warranty terms, shipping costs, and expected delivery windows.
Within seconds, the agent ranks suppliers, identifies the best combination of price and reliability, and places the order. No browsing, no manual comparison, and no drawn-out consideration phase to speak of.
This is exactly the kind of funnel compression described in the Web Summit session. Intent is captured, AI performs comparison across platforms, and the transaction follows.
Now here's where it gets uncomfortable for some suppliers. If one distributor's API returns outdated inventory data, or if pricing rules conflict with ERP logic, that supplier may not even make the shortlist. The algorithm simply moves on. Evaluation becomes structured, data-driven, and nearly instant.
For distributors competing mostly on price and availability, this compression creates real pressure. Perfect price comparison and automated optimization, both highlighted as defining traits of agentic commerce, increase margin tension and expose operational weaknesses right away.
The funnel hasn't shortened. In many cases, it's gone entirely.
One idea from the Web Summit session stood out above the rest: the future of commerce won't be determined by who wins consumer attention, but by who wins algorithmic trust.
That reframes competition entirely. In traditional commerce, platforms controlled visibility: SEO rankings, marketplace placements, and advertising budgets determined who appeared first. In agentic commerce, algorithms control qualification: AI agents evaluate suppliers based on structured data, reliability signals, pricing logic, and measurable performance. Persuasive copy doesn't register.
This change has given rise to a concept people are calling Agent Engine Optimization, or AEO. Think of it as the next cousin of SEO. Just as SEO required structured metadata and fast page performance, AEO requires machine-readable product data, accessible APIs, standardized attributes, and consistent performance indicators. AI-driven comparison reduces the influence of traditional persuasion and increases the weight of operational transparency. In practical terms, that means margin pressure gets worse as price comparison becomes instantaneous and spans the entire supplier landscape.
For B2B brands, this calls for a different competitive mindset, as being visible is no longer the finish line. You need to be machine-preferable.
That said, not everything becomes automated. Identity-driven purchases, particularly in luxury or experience-based retail, will remain human-centric. Even in industrial markets, long-term partnerships and sourcing decisions still rest on trust built over years of working together.
The danger sits in commoditization. If your differentiation isn't clear to an algorithm, you risk being reduced to a line item in a comparison table.
The takeaway here is straightforward. Algorithmic trust is earned through clarity, consistency, and performance.
Strategy conversations about agentic AI matter. But the real preparation is operational. If AI agents are going to evaluate your business, they need access to clean, structured, reliable information. Most B2B organizations aren't there yet.
Here are the foundational areas businesses should look at closely:
Are your product specifications standardized and machine-readable? Are units consistent? Are attributes complete? In many ERP systems, data inconsistencies are manageable for human buyers who can ask a clarifying question or make a phone call. AI agents can't tolerate ambiguity. Incomplete or inconsistent data will simply exclude you from consideration.
Agentic commerce assumes AI-to-AI communication. That requires accessible, secure APIs. If your ecommerce platform or ERP can't expose pricing, availability, lead times, and order status programmatically, AI agents will struggle to transact with you. An API-first mindset becomes a mandatory, not a nice-to-have feature.
B2B environments are rarely simple. ERP, PIM, CRM, OMS, pricing engines, and warehouse systems all need to communicate reliably. Agentic AI amplifies integration weaknesses. Real-time pricing discrepancies or outdated inventory feeds undermine trust immediately. The more automated the purchasing process becomes, the less room there is for internal misalignment.
AI agents will optimize for reliability just as much as price: on-time delivery rates, fulfillment accuracy, dispute resolution speed, and service-level compliance may all become algorithmic inputs. Operational excellence becomes something machines can see and measure.
Perfect price comparison increases margin pressure. To protect profitability, companies need to make their differentiation explicit. That might include specialized product configurations, technical support integration, subscription replenishment models, sustainability credentials, or performance guarantees. If your value isn't structured into data, algorithms can't recognize it.
But no need to panic, preparation doesn't require a full system overhaul overnight. It starts with audits and step-by-step changes: identify data gaps, assess API coverage, evaluate integration stability. Small, focused efforts that build toward readiness.
With all the talk of automation, it's worth separating what's real from what's exaggerated.
Agentic AI will automate routine purchases and comparison-based decisions, but it won't eliminate complex human negotiation or long-standing sourcing relationships.
Large capital equipment purchases, multi-year supply agreements, and collaborative engineering projects still depend on human trust and consultation. Brand storytelling, reputation, old connections and credibility still carry weight.
For mid-level decision-makers in B2B, this should be reassuring. Agentic AI doesn't erase your sales teams or your relationships. It changes where efficiency is expected. Routine reorders may become automated and contract compliance checks AI-verified. Price benchmarking may become instantaneous, but partnership, creative problem-solving, and service differentiation still require people.
Understanding what won't change helps organizations invest wisely. Preparation should focus on transactional infrastructure while continuing to strengthen the parts of your business that only humans can deliver.
Preparing for agentic AI in B2B commerce doesn't require predicting every future scenario. It requires building capabilities that stay valuable regardless of how fast adoption moves.
Both the Digital Commerce 360 industry analysis and the Web Summit discussion point to the same operational reality: organizations need to prepare for AI-to-AI transactions and structured evaluation.
Here are the practical steps worth considering:
These efforts align directly with the session's key takeaways: data readiness, defensible differentiation, and trust infrastructure.
What's worth noting is that these improvements generate value even before widespread agentic adoption takes hold. They reduce manual errors, clean up digital transactions, and strengthen system resilience, so preparing for algorithmic trust is far from speculative. It's really just disciplined digital maturity that happens to pay off now and later.
The organizations that adapt early won't necessarily look radically different from competitors on the surface. Their advantage will sit in invisible readiness. When AI agents begin evaluating suppliers at scale, and industry analysts increasingly expect they will, these companies will already meet the technical and operational criteria required for qualification.
Preparing for agentic commerce is not about chasing a trend. The same discipline that improves integration, data governance, and reliability today will determine competitiveness tomorrow.
Agentic AI in B2B commerce is closer than most companies think, an emerging layer already forming on top of existing digital systems. It rewires discovery and compresses the funnel, shifting real power toward algorithms.
The challenge is clear. If tomorrow's buyer is assisted, filtered, or even represented by AI, will your systems qualify? Winning algorithmic trust doesn't mean abandoning brand, relationships, or strategy. Your infrastructure just needs to speak fluently to machines while continuing to serve people.
The companies that prepare now will be the ones algorithms choose first, and in a world where decisions happen at machine speed, being second on the list might mean not being on it at all.
Have questions or need assistance with your project? Contact our team, and we’ll be happy to help.