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March 28, 2026 · The Bloomfield Team

Why Your Best Customers Are Getting Quotes Faster From Your Competitors

A purchasing manager at an industrial equipment OEM in Wisconsin sends the same RFQ to four machine shops on Monday morning. By Tuesday afternoon, two shops have responded with detailed quotes. By Thursday, the purchasing manager has shortlisted those two shops and is negotiating delivery terms. The other two shops submit quotes on Friday. The purchasing manager thanks them, files the quotes, and moves on.

This is not a hypothetical. This is the pattern that purchasing managers at OEMs, contract manufacturers, and distribution companies describe when they talk about how they select vendors. Speed is the first filter. Price and capability matter, but they only matter for the shops that made it through the speed filter.

If your shop is consistently quoting in four to five days while competitors are quoting in one to two, you are being filtered out of opportunities before your price or your quality record is even considered.

What Changed in the Past 18 Months

The quoting speed gap between shops has widened dramatically since early 2025. A growing percentage of manufacturers, currently estimated at 20 to 25% of shops under 200 employees, have deployed some form of AI-assisted quoting tool. These shops are not quoting faster because they hired more estimators or because their estimators work longer hours. They are quoting faster because the information-gathering phase of the quoting process, which typically consumes 60 to 70% of the total quoting time, has been compressed from hours to minutes.

When a shop's estimator can open an RFQ and immediately see the five most similar jobs the shop has run, with complete cost data, setup times, cycle times, and production notes, the estimator moves directly to the decision-making portion of the quote. Material pricing is current. Historical accuracy for similar geometries is displayed. The estimator builds the price with full context in 60 to 90 minutes instead of spending a full day on research followed by a rushed pricing decision.

The data on this is clear. Manufacturers that respond to RFQs within two days win roughly 35% of the bids they submit. At five or more days, that drops to about 12%. The shops using AI-assisted quoting are operating at the two-day pace. The shops that have not adopted these tools are still at four to five days. The gap in win rate between these two groups is where your customers are going.

How Purchasing Managers Actually Decide

Understanding why speed matters requires understanding how a purchasing manager works. A typical purchasing manager at a mid-size OEM handles 80 to 150 open purchase orders at any given time. They are managing supplier relationships, tracking deliveries, resolving quality issues, and sourcing new parts. When they send out an RFQ, they need a quote back quickly because the engineering team is waiting for cost data to finalize the bill of materials, or the production planner is waiting for lead times to commit to a customer delivery date.

The purchasing manager does not have the luxury of waiting for every shop to respond before making a decision. They operate on a first-qualified-first-considered basis. The first two or three quotes that arrive from qualified suppliers become the evaluation set. If those quotes are in the right range on price and lead time, the purchasing manager selects a vendor and moves on to the next item on the list.

A shop that quotes on Friday for an RFQ sent Monday is not competing against the two shops that quoted on Tuesday. It is competing against the possibility that the purchasing manager has not yet placed the order, which becomes less likely with each passing day.

For repeat customers, the dynamic is even more decisive. A purchasing manager who has received fast quotes from a competitor three times in a row starts sending that competitor the RFQs first. Over six to twelve months, the preferred vendor list reshuffles based on responsiveness. Your 15-year customer relationship does not disappear overnight, but the volume allocation shifts toward the shops that make the purchasing manager's job easier.

The Competitor You Should Worry About

The competitor that is taking your customers is not necessarily a bigger shop with more machines. In most cases, it is a similar-sized shop that has invested in better quoting infrastructure. They have the same ERP. They run similar machines. Their quality certifications match yours. The difference is that their estimator has a tool that does in 10 minutes what your estimator does in 3 hours.

That tool is an AI quoting system built around their specific operation. It connects to their ERP and accesses their full job history. When an RFQ arrives, it retrieves the most comparable past jobs and presents them with actual production data. The estimator starts the quote with a foundation of real numbers instead of building from memory and spreadsheets.

The investment was likely between $40,000 and $80,000. The payback period was one to three months. The ongoing cost is minimal. And the advantage compounds every month because the system learns from every new job, making the next quote faster and more accurate than the last.

The most concerning aspect for shops that have not adopted these tools: you do not see the loss happening. You see the same number of RFQs arriving in your inbox. You submit quotes at the same rate. The win rate decline is gradual, perhaps 1 to 2 percentage points per quarter, and it is easy to attribute to market conditions, pricing pressure, or customer budget changes. The real cause, that your quotes are arriving after the decision has already been made, is invisible without measuring quote-to-response time against win rate.

Measuring Your Exposure

Pull the following data from your operation to quantify how exposed you are to the quoting speed gap.

Average quote turnaround time. From the moment the RFQ hits your inbox to the moment the quote is sent to the customer. Measure this across the last 90 days. If you are above 3 days, you are at risk. If you are above 5 days, you are actively losing bids to faster shops.

Win rate trend over the past 12 months. Track the percentage of quotes that convert to orders, by quarter. A declining trend that cannot be explained by pricing changes or market conditions suggests that speed, or the lack of it, is a factor.

Customer concentration changes. Has the percentage of revenue from your top five customers shifted? Are existing customers sending fewer RFQs? Are they splitting orders that used to go entirely to your shop? These are signals that a competitor has entered the preferred vendor rotation.

Time breakdown per quote. Ask your estimator to track, for one week, how they spend their time on each quote. What percentage is research and information gathering versus actual pricing and decision-making? If research consumes more than 40% of the total quoting time, that is the gap an AI tool closes.

Closing the Gap

The quoting speed gap is a process problem, and process problems are fixable. The fix involves giving your estimator access to the shop's complete production history at the moment they open a new RFQ, so the research phase that currently takes hours is compressed to minutes.

The data to do this already exists in your operation. Your ERP contains years of job records with costs, times, and production details. Your quoting files contain pricing history and estimator notes. Your supplier correspondence contains material pricing. The problem is that none of these sources were designed to work together in the moment the estimator needs them.

An AI quoting tool built for your operation connects these data sources, structures the information around the quoting workflow, and delivers the context the estimator needs before they ask for it. The estimator still makes every pricing decision. They make it faster and with better information.

The implementation timeline for a focused AI quoting tool is typically 8 to 12 weeks from project kickoff to production deployment. The data preparation requirements are manageable for any shop with three or more years of ERP history. The cost ranges from $40,000 to $90,000 depending on scope, data complexity, and integration requirements.

Against those numbers, consider the revenue impact of moving from a 12% win rate at 5-day turnaround to a 25% win rate at 1.5-day turnaround. On 40 RFQs per month at $20,000 average job value, that shift represents $104,000 per month in additional revenue. The tool pays for itself before the first monthly invoice.

The Clock Is Running

Your competitors who have deployed AI quoting tools are getting faster every month. The system improves as it processes more jobs. The estimator becomes more effective as they learn to work with the tool. The customer relationships solidify as the purchasing manager builds habits around the vendors who respond quickly.

Every month you wait, the gap widens. The customers you are losing today will be harder to win back in 12 months, because they will have established working relationships with the shops that quoted faster. The work that could have been yours will have been produced, invoiced, and reordered from someone else.

The quoting process at most manufacturing shops has not fundamentally changed in 20 years. The same estimator opens the same RFQ, digs through the same ERP records, checks the same spreadsheets, and builds the same kind of quote they have always built. What has changed is that some of their competitors have compressed that process from days to hours. The shops that match that speed will keep their customers. The shops that do not will watch the volume shift, gradually and then suddenly, to the shops that respond first.