Stop Guessing: How AI Can Streamline Quoting for Small to Mid-Sized Manufacturers
For many small and mid-sized manufacturers, quoting isn’t just part of sales. It’s a daily grind that pulls people away from their real jobs.
Sales is chasing drawings. Engineering is reviewing specs. Operations is checking capacity. Purchasing is calling suppliers for updated material pricing. Someone digs up the “last similar job” and tries to make the numbers fit. Meanwhile, the clock is ticking and the customer is waiting.
Even after all that effort, quotes are often inconsistent. Sometimes wildly so.
That happens because most quoting processes are built on a shaky foundation. They rely on old assumptions, disconnected spreadsheets, tribal knowledge, and routing data that hasn’t been updated in years. At that point, quoting becomes less about calculation and more about making an educated guess.
This is where AI can actually be useful.
It won’t replace your estimators. It won’t fix broken data overnight. But it can help manufacturers use the data they already have to reduce friction, improve accuracy, and respond faster without cutting corners.
The real issue: quoting depends on data that doesn’t talk to itself
Accurate quotes depend on a lot of moving parts working together:
- Material costs that change constantly
- Labor standards and cycle times
- Setup and changeover assumptions
- Scrap and yield rates
- Machine availability and capacity
- Routings and work center logic
- Outside processing and freight
- Overhead and burden rates
Most of that information exists somewhere. It’s just rarely in one place.
Instead, it’s scattered across ERP systems built for accounting, spreadsheets only one person understands, old quotes that no longer reflect reality, and email threads full of one-off decisions. Add in a healthy dose of “we’ve always done it this way,” and you get what many shops live with every day.
This is the hidden factory behind quoting. It’s invisible on the shop floor, but everyone feels the pain.
What AI is actually good at when it comes to quoting
Quoting is a pattern problem.
Every manufacturer has quote history. Every manufacturer runs similar parts, similar routings, and similar customers, even when the work feels custom. Those patterns are buried in years of quotes, jobs, and production results.
AI can help surface those patterns and turn them into practical guidance.
Here’s what that looks like in real terms.
1) Cleaning up RFQs and getting quotes moving faster
Many quoting delays happen right out of the gate.
RFQs show up in different formats. Drawings are buried in PDFs. Bills of material are incomplete. Critical notes live in email threads.
AI can help by pulling key information out of RFQs and attachments, flagging missing data before a quote gets stuck in engineering, and standardizing part descriptions, revisions, and customer naming. It can also help route quotes to the right people faster.
The payoff is fewer back-and-forth emails and less manual cleanup before real work even starts.
2) Learning what actually drives cost from your own history
A common quoting shortcut is “what did it cost last time?” That’s also one of the fastest ways to lose margin.
AI can look across historical quotes and completed jobs to identify where estimates consistently miss the mark, such as material volatility, routing steps that run longer than planned, labor assumptions that don’t match reality, or scrap rates that are always underestimated.
Instead of relying on memory or averages, manufacturers get a feedback loop that improves quotes over time based on what actually happened on the floor.
3) Bringing consistency to estimates across the team
Two experienced estimators can quote the same part and land in very different places. Assumptions vary. Risk tolerance varies. Time pressure varies.
Over dozens of quotes a week, that inconsistency shows up as unpredictable win rates and surprise margin erosion.
AI can help flag quotes that are out of line with similar jobs, suggest standard labor or setup assumptions by part family, and provide recommended pricing ranges instead of a single number.
The goal isn’t to override experience. It’s to capture it and apply it consistently.
4) Quoting faster without turning it into guesswork
Speed matters. Customers don’t wait.
In many shops, the fastest quotes are also the riskiest because they rely on rough assumptions and incomplete inputs. AI can help shorten turnaround time while improving accuracy by pre-filling routings and labor estimates based on similar past work, checking current material pricing, and flagging missing cost elements before a quote goes out.
It can also prompt estimators with simple reminders that get missed under pressure, like secondary operations, inspection time, or packaging.
The result isn’t just faster quotes. It’s faster quotes you can stand behind.
5) Closing the gap between quoting and execution
Quoting shouldn’t stop when the customer says yes.
In many companies, the quote lives in one system and production planning lives somewhere else. That disconnect leads to rework, re-keyed data, jobs running differently than expected, and margin slipping away unnoticed.
With better data alignment and AI-assisted quoting, the assumptions made during quoting can carry into execution. Routings improve with every completed job. Variances get captured automatically. Teams learn where estimates are consistently off.
Over time, quoting becomes something that gets smarter, not just faster.
“Our data isn’t good enough” and why that shouldn’t stop you
This is the most common concern, and it’s a reasonable one.
Many manufacturers feel stuck because their ERP data isn’t clean, routings aren’t standardized, labor standards are outdated, and too much information lives in spreadsheets.
The reality is that AI doesn’t require perfect data to deliver value. One of its strengths is helping manufacturers see what data they already have, where the biggest gaps are, and which inputs matter most to clean up first.
You don’t need to fix everything at once. You need to start somewhere.
Quoting can be a real advantage if you treat it that way
Quoting isn’t just a transactional step anymore. It affects speed, margin, planning, and customer confidence.
Manufacturers that consistently win tend to have quoting processes that are repeatable, grounded in data, fast without being sloppy, and tied to real-world costs and capacity.
AI is becoming a practical tool to help get there, especially for companies that are tired of quoting draining time, tying up key people, and creating margin surprises after the job is done.
If you want to reduce quoting time, improve accuracy, and build a process based on real data instead of outdated assumptions, we can help.
A short conversation is often enough to identify where better data alignment and AI can deliver quick, practical wins.
About the Author: Frances Phan, Data & AI Analyst, Catalyst Connection
Frances is a Specialist at Catalyst Connection, leading initiatives in data and AI solutions to improve efficiency, workforce outcomes, and sustainability for manufacturers. She began her career as a People Strategy Partner in Southeast Asia, where she led data-driven workforce strategies and saw how people’s decisions directly shaped factory performance.
After earning her STEM MBA in Business Analytics from the University of Pittsburgh, Frances sharpened her technical expertise in predictive modeling, data visualization, and automation. At Catalyst Connection, she brings this blend of strategy, analytics, and AI to help small and mid-sized manufacturers to scale impact with smart data practices.
What sets Frances apart is her ability to bridge people strategy and advanced data solutions. She designs predictive models, intuitive dashboards, and AI-driven tools that leaders can act on – then translates the numbers into clear, actionable stories that move executives, teams, and frontline workers alike. Her passion lies in making data human: using insights not just to optimize operations, but to create more sustainable, resilient organizations.