Estimators spend significant time opening emails, reviewing specifications, downloading PDFs, comparing files and manually entering job information before applying the expertise that actually protects margin. The cost shows up in slower response, backlogged RFQs and skilled employees spending time on administrative work that technology could increasingly handle.

That suggests a different standard for ERP, AI and automation: technology should take on more of that routine operational work— so skilled people can spend their time on pricing exceptions, evaluating options, and protecting margin. That is also where the next evolution of ERP begins: specialized AI Agents that can interpret information, execute defined workflow steps and move work forward inside the system.

THE FIVE COST PRESSURES

1. Workforce Shortages: Let the ERP take on the routine work
U.S. manufacturing could need 3.8 million workers by 2033, with as many as 1.9 million positions going unfilled if workforce challenges aren’t addressed. More telling for label converters: according to NAM’s 2026 survey an estimated 2.8 million will come from retirements. For a converter, losing an experienced estimator isn’t simply losing a person. It’s losing years of accumulated judgment—the knowledge that makes it possible to price unusual jobs quickly and accurately.

The label industry’s labor challenge is usually framed as a hiring problem. But there is another question worth asking how much employee time is being consumed by work that the ERP itself could be doing?

The business value of AI is clearest here: taking on defined routine work so people have more time for work that requires judgment and experience. In a tight labor market, productivity increasingly depends not only on who a converter can hire, but on how much of that routine work the ERP can simply absorb.

2. SKU Proliferation and GS1 Sunrise 2027: Managing Complexity
Every new SKU variant—different sizes, claims, seasonal versions or customer requirements—adds another set of details estimating has to get right. As SKU counts climb, so do the quotes, specifications, pricing decisions and data points moving through the operation.
LOUPE’s program includes a dedicated session on GS1 Sunrise 2027 compliance, another example of how changing requirements add data and process complexity. More variants and changing barcode requirements increase the opportunity for outdated information, duplicate entry and errors when information is managed across disconnected systems.

The answer isn’t asking estimators and production teams to process more information faster. It’s giving the ERP itself more of that work to do: a connected data foundation, with automation and AI capturing, interpreting and moving information through the workflow. The advantage is being able to handle more products and variations without the manual workload growing at the same rate

This is where the flexibility of a system offers real promise. An ERP built to support multiple products, machines, technologies and production requirements makes adding a new product line or adjacent packaging capability easier, faster and more cost-effective. A converter adding in-line folding carton should not automatically have to introduce another disconnected system if the ERP can support those workflows.

The larger question is whether today’s ERP can support what the label converter is becoming—not only the business it is today.

4. M&A: One Company Should Not Operate Like Three
Industry consolidation adds another layer of complexity. Acquired plants may continue using different estimating methods, systems and data structures long after the transaction closes. The result can be inconsistent quoting, limited capacity visibility and teams reconciling information across locations instead of working from a shared operational picture.

The business may own three plants, but its workflows can still behave like three separate companies. That’s also where the labor-shortage pressure and the multi-plant pressure meet.

A combined company with three plants on three different systems doesn’t have three times the estimating capacity — it has three different answers to the same customer’s RFQ, no shared visibility into which plant actually has capacity for a rush job, and an estimating team that’s now reconciling pricing logic across acquisitions instead of quoting new work.

As consolidation continues, the ability to bring locations, processes and information into a one business platform becomes an operational capability, not just an eventual cleanup project.

AI works best as part of a connected operational foundation — not something layered on top of disconnected processes and expected to fix them. The real distinction is between AI that advises and AI that acts. An assistant may tell an employee what to do next. An AI Agent can perform a defined task inside the workflow.

Modern ERP provides that connected foundation. AI builds on it by taking on work that traditionally required employee time — extracting and structuring incoming information, processing documents, creating estimates, and moving data through the workflow. The opportunity isn’t simply adding AI to an older system. It’s pairing a modern, connected ERP with AI so that more of the routine, manual work happens inside the system itself — not on your team’s desks.

What Operational Readiness Looks Like Heading Toward 2030
The cost landscape for label converters has changed. Substrate, ink, adhesives, freight and equipment still matter. But increasingly, so does the cost of the work required to keep a more complex and growing operation moving.

That brings the five pressures back to where we started: estimating. As workforce shortages, SKU proliferation, diversification, consolidation and aging systems increase complexity, the answer isn’t simply asking estimators to price more jobs faster. It’s taking more of the work out of the process before pricing even begins.

A modern AI-powered ERP should increasingly move from capturing information to acting on it—using automation and specialized AI Agents to interpret, process and advance defined workflows. Every hour your best estimator spends preparing to estimate is an hour they’re not protecting margin, managing exceptions, evaluating production options or responding to the next opportunity. By 2030, the advantage won’t just be estimating faster. It will be freeing skilled people for the work that actually moves profitability.

These Conversations Are Happening at LOUPE

The themes are visible throughout the LOUPE Americas 2026 program: the talent landscape, AI in packaging manufacturing workflows, practical AI implementation, flexible packaging and folding carton diversification, GS1 Sunrise 2027, M&A, and the hidden cost of disconnected packaging operations.

HiFlow will be part of that conversation, including HiFlow’s Steve De Benedetto moderating “How AI is Rewiring Packaging Manufacturing Workflows.” At Booth 3131, HiFlow will demonstrate how ERP, automation and AI can take on more of the routine operational work — while giving converters better visibility and room to evolve.

Heading toward 2030, the five pressures reshaping label converting are also expanding the job of your ERP. The real opportunity isn’t AI for the sake of AI. It is an AI-powered ERP that does real work—using specialized AI Agents to take on more of the repetitive operational tasks people have traditionally handled by hand, so your team’s time goes where it matters most.

See HiFlow at LOUPE Americas, Booth 3131, September 15–17 in Chicago.