The Impact of AI and Data on Package Printing, Converting

Anyone who isn’t familiar with data management and AI has probably been living under a very large rock the past few years. There is no denying the impact these technologies have on every aspect of our lives, from personal interactions to business transactions. The label and packaging printing industry is no exception.

Andy Paparozzi, chief economist at PRINTING United Alliance, notes that the impact hasn’t been confined to just one area either.

“Every mission-critical function from quality control to supply chain management, and from customer-preference analysis to personalization based on those preferences, benefits from artificial intelligence and superior data management — i.e., the creation, maintenance, and accessibility of relevant, robust databases,” Paparozzi says.

“I don’t know if everybody realizes how fast this is changing,” notes Steve Metcalf, co-founder and CEO of IMAGINE AI LIVE. “But I would start by saying that [in the printing and packaging industry], there’s already a big expertise in managing data for clients. If you’re printing labels for pharma, for example, you’ve got to ensure they are all labeled correctly. There’s this huge amount of data and trust that goes on, that’s given to printing and converting houses. It’s a perfect world for this new sort of capability coming their way with generative AI.”

Amy Servi-Bonner, vice president, Consulting, Applied AI and Printing Technology, PRINTING United Alliance, stresses the need to distinguish between AI tools and strategy.

“As enthusiasm for AI tools accelerates, it’s important to distinguish between using AI and building an AI strategy,” she says. “While rapid experimentation can surface opportunities, long-term value comes from intentional design aligning AI initiatives to business outcomes, governance, and data ownership.”

On the point of data, Paparozzi notes there are three major “buckets” it can be classified into. Managing that data is a given in all of them, but AI is making huge strides in how exactly that is accomplished.

1. Structured data such as financials, market demographics, and customer profiles that fit neatly into conventional computer spreadsheets.
2. Unstructured data such as audio files, video files, and text files that spreadsheets do not handle well.
3. Semi-structured data such as data generated by Internet of Things (IoT) sensors, which is a mix of the two.

Paparozzi says AI can help analyze all of it, helping ensure packaging printers are making the best possible decisions across the board, instead of just relying on a “gut feeling” or guesswork.

As to where it’s going next? That’s hard to predict, especially with how rapidly the AI space is evolving — seemingly every day. That’s not to say there aren’t specific areas to keep an eye on.

Paparozzi notes there are few key areas of innovation he’s watching closely: “Operations — including quality control and predictive maintenance — because our markets are too competitive to pass inefficiencies to customers; automation to boost productivity and production speeds, and to overcome chronic labor shortages; market analysis and forecasting to support consistently superior decisions companywide; and cybersecurity and risk management, because as one participant in PRINTING United Alliance research says so well, ‘No matter how well your business is doing or how well it is run, it can turn in an instant.’”

What does that mean for the average label and packaging printer, and how can you prepare for something — much less implement it — if it is changing so rapidly? First, start small; don’t try to do everything at once. Pick one area of your operation and analyze it — see where the inefficiencies are. Ask staff what tasks they hate doing, that are repetitive or frustrating. What kinds of overlaps are there with other tasks? Where is data being input multiple times to move through your systems?

Servi-Bonner emphasizes that jumping into AI without clear plans and people in place can actually have an effect opposite of what was intended.

“Many early adopters are discovering that deploying tools without a defined strategy can create fragmentation, dependency on vendors, and unclear ROI,” she says. “In contrast, organizations that start by establishing a clear AI roadmap grounded in data readiness, security, and operational priorities are better positioned to scale AI responsibly and retain ownership of their intellectual property.”

From there, you can start building on the process. What tasks or departments link to the ones you’ve just added AI analytics and automation to? Can those also be automated, or benefit from their own AI analytics tools? You can eventually have systems that hook into every part of the business — but don’t try to start off at that point, or you will quickly get overwhelmed. Create a plan for AI before you invest in a single tool, and know exactly what you are getting and — more importantly — why you are getting it.

Next, the old adage of “garbage in, garbage out” proves even more critical when it comes to AI and data analytics. You can only get good results from implementing these tools for inventory management, production workflow, predictive maintenance, or any host of tasks if the data provided is good to begin with. If you’ve got incomplete data, errors, or a situation where the same set of data is different in two separate systems, you’re not going to get the most out of AI. Before even exploring AI tools, make sure your data is clean, and your policies about inputting and maintaining that data are spelled out in detail to everyone in your organization.

Finally, pick one person to be your data and AI “guru.” It could absolutely be a full-time job to keep up with new innovations, much less figuring out how to implement them in a logical way. However, having a designated person to answer staff questions, make recommendations on AI tools to invest in, and ensure the accuracy of AI outputs is critical to getting it right. Depending on the size and scope of your operation, you might need to assign several people to different parts of this process, but the fact remains: You need to be clear about who is taking responsibility for it.

Metcalf notes that you can “turn anybody into a data analyst now, and then spin up the apps around them to support them. And maybe it just becomes a stepping stone to getting some greater capability down the road — I use the term burner apps, like you can literally create a burner app now that gives you a taste of what you can do. And maybe that’s not the end game. Maybe you spend a few hours on it and say ‘That wasn’t quite right.’ But now you have a better sense of what you can do.”

That, he stresses, means even if you ultimately decide to explore a solution from different vendors, you have a much better idea of what exactly you need, rather than just picking an AI tool at random and hoping for the best.

All said, the benefits of AI, especially in the data management space, are building up daily.

Paparozzi notes, “Embracing AI and being data-driven allows companies of all types and sizes to be more productive by automating companywide low-value-added tasks they could never automate before, freeing time for activities that create the greatest value for clients, employees, and the company; and to enhance market analysis and forecasting — critical in an industry in which the gamut of opportunity is expanding but the margin for error is shrinking.”

So, what final advice do the experts have when it comes to diving into AI and data management?

“For packaging printers, the smartest path forward isn’t chasing AI tools, it’s deciding what problems are worth solving first,” Servi-Bonner stresses. “Identify where margin leaks, rework, compliance risk, or decision delays exist, then evaluate how AI supports those priorities. AI should strengthen core systems and processes, not create parallel workflows that can’t be governed or measured.”

For Metcalf, it’s all about finding a community to bounce ideas off of and get excited with.

“Let’s say you’re in Chicago, or Kansas City, or Boston, or wherever,” he says. “It doesn’t matter — any size city, look up your peer groups. Local AI groups are mushrooming up everywhere. Spend time with other AI people. Figure out how other people are applying it and using it. They all have monthly meetings where AI-minded people are getting together, and they’re learning from each other. They’re using each other as sounding boards and for inspiration.”

Paparozzi emphasizes the fact that you can’t hesitate; refusing to engage with AI is no longer an option.

He says: “Take the advice of PRINTING United Alliance State of the Industry participants: ‘You can’t sit on the sidelines with regard to AI because it is going to fundamentally change our business, so make sure you are learning about it. It isn’t going away;’ ‘The big winners in our industry will master AI at all levels of the company;’ and ‘We feel very strongly about our organization learning about and using AI because if we don’t, our competitors are going to pass us by.’”