Four words still printed on millions of B2B product pages are quietly destroying revenue: Call for a Quote. To a human buyer, those four words are friction. To an algorithmic buyer, they’re a closed door. There’s no price to read, no lead time to evaluate, no API to query. Just an invitation to email a stranger and wait three days. 

This is what invisibility looks like in the agent economy — and it’s already costing distributors billions. 

In a previous article, I argued that your next customer is an algorithm. That businesses without machine-readable data become invisible to AI agents. That digital transformation is no longer about going online — it’s about becoming queryable. That argument was the macro view. This one is the forensic view of where that invisibility starts costing real money: your quote desk. 

The Bottleneck You’ve Been Living With 

Let’s be precise about what "Call for a Quote" actually costs. 

A recent survey of 200 B2B manufacturing decision-makers found manual sales and quoting processes are draining an average of 5% of revenue annually, with 88% of respondents reporting lost deals as a direct result. Buyers say they want quotes within a day; the average delivery time is closer to three. 

In the agent economy, three days isn’t a delay. It’s a forfeit. 

The mechanics behind those four words are almost identical across thousands of distributors. An RFQ arrives by email, web form, or PDF — unstructured. An inside rep parses it manually, retypes part numbers, opens the ERP to check inventory, opens the CRM to check contract pricing, routes for approval if the discount exceeds threshold, drafts the quote in Excel, and emails it back. When the buyer accepts, someone re-keys the order into the ERP. 

Every step is a place where momentum dies. Every step is a place where an algorithmic buyer has already moved on. And the leverage works both ways: McKinsey’s pricing research finds that a 1% price improvement typically translates into an 8.7% increase in operating profit, assuming no loss of volume. For a $500 million distributor running at 5% operating margin, even a one-point improvement in price realization is the difference between a $25M and a $27M year — won or lost one quote at a time. Every percentage point of margin that distributors leak to slow, inconsistent, or manual quoting compounds straight through to the bottom line. The operational tax around the quote desk makes that leak almost certain: 71% of manufacturers still take a full day or more to produce a single quote manually, and approval bottlenecks are the top reason for lost deals. 

Most of that leak runs through the quote desk. 

Why CPQ Couldn’t Fix This 

The honest answer is that the industry has been trying to solve this for two decades. Configure-Price-Quote software was supposed to compress quote cycles from days to hours. In some cases it has. In most, it hasn’t. 

The reason is buried in McKinsey’s research on the topic: email and informal channels still account for the majority of transactional volume in manufacturing and distribution — regardless of how much has been invested in structured digital order channels. CPQ was designed for a world where the buyer comes to a portal and configures a structured request. The reality is that buyers send PDFs, scribbled BOMs, forwarded supplier emails, and contractor takeoffs. Traditional CPQ chokes on that input. 

There’s a second, deeper problem: B2B pricing complexity is combinatorial, not rule-based. Every additional customer tier, contract, region, and SKU multiplies the rule space. Writing more rules doesn’t solve the problem — it makes the system more brittle. 

This is Digital Transformation 1.0 thinking applied to a problem that requires Digital Transformation 2.0 architecture. DT 1.0 assumed humans would type into forms. DT 2.0 assumes machines will query data. The quote desk is where that gap between the two becomes a P&L issue. 

Enter the Agents — On Both Sides 

Here’s the part most distributors haven’t internalized yet: the agent economy isn’t a one-sided phenomenon. It’s not just buyer agents disrupting sellers. It’s buyer agents and seller agents — and the quote desk is where they meet. 

On the buyer side, procurement organizations are deploying agents that issue RFQs autonomously, score supplier responses against weighted criteria, and recommend shortlists for human approval. The major enterprise procurement platforms have all shipped these capabilities. Forrester predicts that 20% of B2B sellers will be forced to engage in agent-led quote negotiations this year alone.6 

On the seller side, a new generation of platforms is deploying agents that parse inbound RFQs, match SKUs, pull pricing and inventory, and draft responses without a human in the loop. 

This is the operational landing site for the protocols I wrote about last time. MCP is how an agent queries your specs and inventory. A2A is how a buyer’s procurement agent talks to your quoting agent. AP2 is how the resulting order gets paid for. These aren’t theoretical standards. They’re the plumbing of the next quote-to-cash cycle, and the quote desk is the first place they touch revenue. 

Five Forces Reshaping the Quote Desk 

The market isn’t moving in a single direction. Five distinct forces are converging on the quote desk simultaneously — some are tools distributors can deploy, some are forces they don’t control, and some are tectonic shifts in buyer expectations. 

Force 1: Inbound RFQ-parsing agents (a tool to deploy).

These attack the email problem CPQ couldn’t solve. They handle the messy, unstructured input that breaks template-based automation: handwritten notes, PDFs, contractor takeoffs, forwarded supplier emails. A new category of agent-native quoting platforms now reads unstructured email, matches text to SKUs, drafts the response, and writes it back to the ERP without re-keying, collapsing quote turnaround from hours or days to minutes. This is the most immediately actionable force on the list because it doesn’t require changing what the buyer does — it changes what happens after the buyer sends the email. 

Force 2: AI-native pricing intelligence (a tool to deploy).

Where Force 1 fixes the input side, this fixes the brain. A new generation of pricing platforms isn’t replacing pricing rules — it’s replacing brittleness. The old CPQ generation tried to encode every combination of customer, contract, region, and SKU as explicit logic; the new generation learns from outcomes. McKinsey tracked a $15 billion B2B distributor that built agentic capabilities onto its analytical AI foundation and captured 50 basis points of margin improvement on top of 200 basis points already delivered by traditional AI — compressing value realization from years to weeks. 

Force 3: Buyer-side procurement agents (a force you don’t control).

This one isn’t a strategy a distributor runs — it’s a force happening to distributors whether they’re ready or not. Every major procurement and source-to-pay platform is converging on the same direction: procurement organizations that anticipate, source, and execute without continuous human direction. If your only quoting channel is "email a human," you’re literally unparseable to one of these systems. You get filtered out before consideration. The Forrester prediction cited earlier — 20% of B2B sellers facing agent-led quote negotiations this year — is this force showing up at the front door. 

Force 4: The rep-free buyer expectation (a tectonic shift).

Underneath the technology, buyer behavior has already changed. Gartner research finds 67% of buyers in B2B buying groups prefer a rep-free experience, and a 2025 survey of 646 B2B buyers found 45% used AI during a recent purchase. This isn’t a strategy distributors choose to adopt — it’s the demand-side reality their strategies have to answer to. Platforms like OroCommerce and commercetools have integrated AI assistants on top of their B2B portals because the buyer is no longer willing to wait. The goal isn’t to deflect customers from sales reps; it’s to give the buyer a price the moment they need one, before they ask elsewhere. 

Force 5: The data foundation problem (the precondition for all four above).

None of the other four forces work without it. Bolt-on AI on top of fragmented stacks won’t survive contact with reality — agents are only as good as the data underneath them. Platforms like OroCommerce are showing what the alternative looks like: unified data architectures purpose-built for B2B distribution, where clean product information, real-time inventory, exposed pricing logic, and machine-readable contracts are first-class citizens rather than afterthoughts. SKU resolution comes first: an agent can’t even attempt a price lookup if it can’t unambiguously identify the product. Pricing logic comes next, then inventory, then contract terms. Done in that order, each layer enables the next — and each one earns its own ROI before the next investment is required. The distributors who win Forces 1 and 2, and adapt to Force 3, and meet Force 4 will be the ones who treated Force 5 as the prerequisite rather than the cleanup project. 

Most distributors today are reactive to Force 1: AI helps the rep draft a quote faster. Leaders are operationalizing Forces 1 and 2 together — auto-quoting within guardrails for standard SKUs and standard customers. Force 3 is starting to land in pilots. That’s the trajectory the agent economy is on. 

What This Means for B2A Readiness 

The argument I made elsewhere — that the agent economy demands a machine-readable data foundation — becomes concrete here. Walk into your quote desk and ask six questions. 

• Can an agent retrieve a price for any SKU in our catalog without human intervention? 
• Is our pricing logic exposed via API, or trapped in a senior rep’s head? 
• Does our inventory feed update in real time, or in nightly batches? 
• Are customer-specific contract terms machine-readable, or living in PDFs in someone’s email? 
• Can our system respond to an RFQ in milliseconds, or do we still measure response time in hours? 
• If a procurement agent queried our catalog tomorrow, would we appear on the shortlist — or would we be invisible? 

If the honest answer to most of these is "no," your quote desk isn’t just slow. It’s the canary in the coal mine for your entire B2A readiness. Every other workflow that depends on machine-readable data — order entry, replenishment, contract renewal, cross-sell — has the same gaps you’ll find here. The quote desk is where you discover them first because that’s where the buyer’s urgency is highest. 

The Quote Desk Is Where It Starts 

In agentic commerce, there is no Page 2. There’s also no "Call for a Quote." When a procurement agent runs a sourcing query across twelve distributors, the ones that respond at API speed make the shortlist. The ones that take three days don’t. They never did. 

The good news is that of all the workflows in a distribution business, the quote desk is the easiest one to instrument first. The volume is there. The rules are codifiable. The ROI is measurable in weeks, not quarters. And the upgrade path moves you directly toward the architecture the agent economy demands — clean product data, real-time inventory, exposed pricing logic, machine-readable contracts. 

Your next customer is an algorithm. The first place that customer will judge you isn’t your website, your brand, or your sales team. 

It’s your quote desk. 

You spent a decade transforming for digital buyers. The quote desk is where you find out whether you’re actually ready for algorithmic ones.  Ready to build an agent-ready quote desk? Let’s connect.