Slow Quotes Leaving Presses Idle? A Practical Look at AI Estimating for Label Converters

Press speed has long ceased to be the only bottleneck in a print shop. Every time I visit a plant, press operators are waiting on prepress for job tickets, while prepress is waiting on sales to confirm quotes and substrates. Back and forth, half a day is gone. Over the past few years, I often tell clients that upgrading print equipment is no longer about hardware speed, but software intelligence

At LOUPE Americas, HiFlow's system for label and packaging converters sent a very clear signal: embed AI inside the ERP to tackle the messiest part of the process, quoting

ERP (Enterprise Resource Planning): A software framework that integrates inventory, finance, production scheduling, and order management. In a print shop, it connects front-end sales quotes to back-end job tickets and material tracking, acting as the operational nerve center of the factory

HiFlow CEO Mariusz Sosnowski pointed out a common industry headache: traditional estimating relies heavily on cross-department handoffs, endless email chains, and manual data entry. This does not just slow down quoting, it also hides huge operational costs

Right now, AI is best at the grunt work of data extraction and matching. For label and packaging converters, every incoming job arrives with a long list of specifications: dimensions, dies, substrates, and coating finishes. In the past, people had to type these into the system one by one

Bring in an AI module like HiFlow Quote, and the practical workflow looks like this:

1. Intent and spec extraction: A customer sends an RFQ email with an attached PDF and text descriptions. The system automatically pulls out dimensions, run lengths, target delivery dates, and special finishing requirements

2. Automated matching to inventory and presses: Armed with these extracted specs, the system checks existing substrate inventory and determines whether running the job on a digital or conventional press is most cost-effective

3. Generating structured estimates: Running 24/7, the system instantly outputs a standard estimate with a full cost breakdown. Sales reps only need to give it a final review before sending it off to the client

For designers and print buyers, they get accurate pricing much faster, without waiting two days for a revised budget just because they adjusted a single dimension

That depends on your share of short-run jobs and your labor costs for estimating. Based on our experience advising MINDS on custom and mid-to-high-end commercial printing, the turnaround time for front-end quotes directly determines how busy the presses stay on the floor

If you are still on the fence, look at the clear contrast between manual and AI-assisted estimating:

・Traditional quoting: Relies on veteran sales reps' memory, eats up time in cross-department checks, cannot take orders after hours, and easily misses special specifications

・AI-assisted quoting: Automatically breaks down specs, calculates machine and material costs in real time, operates around the clock, and sharply cuts the risk of manual typos

Before rolling out an AI ERP module like this, make sure your internal item numbers and naming conventions are standardized. If your sales reps and warehouse crew call the same paper by different names, no software can save you. That is just the cold reality

Production bottlenecks in print shops are rarely about press speed, they stem from inefficiencies in front-end quoting and data entry

The main application of AI in ERP is automatically parsing RFQ emails to generate structured estimates around the clock

Internal naming rules for materials and finishing methods must be unified before implementing the system, or the AI will have nothing reliable to match against

Seeing overseas label converters use AI for quoting does not mean you should rush out and buy a new system tomorrow. The real takeaway is this: audit how much time your sales team spends every day transcribing specs and chasing down order status. Identify those manual handoffs and clean up your data flow with your existing forms first. Only when your data is structured will you have the foundation to adopt AI

・HiFlow to Showcase Next-Generation ERP and AI for Label Converters at LOUPE Americas