Before the internet, if you wanted to find something out, you asked someone who knew, or you went to a library. If you wanted to reach a customer in another country, you consulted your rolodex (remember those?) and wrote them a letter.
The idea that any of that could happen instantly, from a device in your pocket, would have sounded like science fiction to most people working in business in 1985. And then, within the space of a decade, it didn’t just change how businesses operated, it changed what businesses were possible.
AI is that kind of seismic change. The kind that rewires the underlying logic of how work gets done, how decisions get made, and where competitive advantage actually comes from. We’re still in the early throes but spend enough time on LinkedIn and you’ll be awash with AI generated posts, conference keynotes, or trade press features with an overabundance of ‘transforming’, ‘shifting’ and ‘shaping’. This notwithstanding, a lot of AI development is genuinely very exciting, and some of it is well worth paying attention to. The challenge is working out which bit.
For the print and packaging industry, there’s a tendency for the AI conversation to get pulled towards the most clear and visible uses: writing company social media posts, producing image concepts, or promotional graphic design work. Smaller businesses are of course using AI for these purposes, but it’s not where the most consequential developments are happening for businesses in this sector.
The AI that will define competitive advantage in print and packaging over the next decade is the kind that Peak’s Richard Potter has called ‘boring AI’.
It doesn’t generate a striking visual or write a product description. Instead, it makes a production line run better, reduces waste before it happens, or turns a four-hour estimating process into two minutes. It’s embedded directly into workflows rather than sitting on top of them, and it builds up over time in ways that are difficult to reverse-engineer by competitors.
Not all AI is created equal.
Before we get into the specifics, let’s step back for a moment. When most people say ‘AI’, they’re picturing the same thing: a chat window, a prompt, and an answer. That’s understandable, because it’s the version that’s been most visible and most heavily marketed.
But treating AI as a single, monolithic thing is a misnomer, and it’s part of why so many industry conversations about it end up in the wrong place.
The AI most people interact with daily is generative AI. That group includes tools like ChatGPT (OpenAI), Claude (Anthropic), and Google Gemini. These are large language models trained on enormous volumes of data. Give them a prompt and they generate a plausible response. They are impressive, but they are also, at their core, just sophisticated pattern-matching systems. They produce outputs that look like answers because they’ve learned what answers tend to look like, which is a very different thing from actually knowing.
Then there’s agentic AI, which is where the conversation is heading fast. Where generative AI responds to a single prompt, agentic AI is designed to orchestrate goals across multiple steps, making decisions and taking actions along the way with limited human oversight. Think of it as the difference between asking someone a question and delegating a task. Agentic systems can gather information, analyse it, act on it, and iterate, often across different software platforms simultaneously. The practical applications are significant, but so are the governance questions it raises.
Beyond those two, there are other forms such as Predictive AI, which uses historical data and machine learning to forecast what’s likely to happen next, whether that’s equipment failure, demand fluctuation, or supply chain disruption. Analytical AI surfaces patterns in large datasets that would take humans weeks to find manually, while Workflow AI operates inside defined business processes, automating specific steps within a controlled, auditable structure, and is particularly adroit at the kind of repetitive, rule-based tasks that drain operational capacity.
This taxonomy matters for print and packaging businesses is because Generative AI is what gets talked about, but the other kinds are what will actually move the needle on operational performance, margin, and regulatory compliance. Understanding the difference is the starting line for making good decisions about where to invest in AI.
From the pressroom to the boardroom, AI is already putting in the hard graft.
It’s easy to talk about AI in concept, but It’s more useful to talk about what it’s actually doing in businesses that look like yours.
In prepress and artwork management, AI is compressing timelines that have historically been a significant source of delay and cost. Version comparison tools can scan two iterations of a print-ready file and surface every change, including those that a tired human eye might miss at 2am before a press deadline. Colgate-Palmolive has reported cutting artwork development time by 60 to 70% across major SKU runs using AI-assisted artwork management systems.
On press, vision systems powered by machine learning are catching defects that even the keenest eye would miss. Think misregistration at tolerances invisible to the naked eye, colour drift across a long run, or barcode readability that falls just outside GS1 specification. The more sophisticated implementations are beginning to feed data back into press parameter adjustments in real time, closing the loop between inspection and control.
In structural packaging, the application of machine learning to material performance modelling is particularly intriguing. Where an engineer might test a handful of substrate configurations for a new transit-ready pack, an AI model can simulate thousands of combinations, accounting simultaneously for compression strength, stacking efficiency, moisture resistance, and the recyclability credentials required under incoming EPR obligations. Nestlé’s R&D teams are using exactly this kind of approach to identify high-barrier packaging materials by training models on the molecular properties of existing substrates. The output is a shortlist of material candidates that would have taken months to reach through conventional trial-and-error.
And at the supply chain and production planning level, AI-driven demand forecasting is addressing one of the most persistent headaches in packaging: overproduction. A converter producing short-run digitally printed packaging for FMCG clients operates with very little buffer between order placement and delivery. Machine learning models that incorporate point-of-sale data, promotional calendars, and seasonal indicators are helping some businesses to reduce excess inventory by close to a third, while maintaining fulfilment rates that would have been difficult to achieve through manual scheduling. That’s happening right now, in businesses operating at scales from regional converters to global CPG manufacturers.
If we cut the fluff, what will print and packaging's AI revolution ACTUALLY look like?
Print and packaging has always operated with tight margins, complex workflows, and very little tolerance for error. A misprint is a material cost, a scheduling knock-on, and a client conversation that nobody wants to have. Waste is a profitability issue and, increasingly, a compliance risk as Extended Producer Responsibility (EPR) and the Packaging and Packaging Waste Regulation (PPWR) tighten their grip.
These are exactly the kinds of problems that operational AI is well suited to address, and the data is starting to reflect that. Research from Alliance Insights found that among print businesses actively using AI, the most cited benefits were increased production efficiency (39%), improved quality and consistency (33%), and freeing staff from repetitive tasks (30%). One commercial printer cut a four-hour distribution estimating process to two minutes, consistently within 5% of manual accuracy. Another used AI-powered accounts payable matching to redirect staff away from routine invoice processing and towards exception handling and client services.
None of this makes headlines that’ll stop a CFO in their tracks, but as one executive in the Alliance Insights research observed: ‘Even 10 minutes saved each day adds up.’ Across a production facility running multiple shifts, that arithmetic becomes extremely significant.
Regulation turns the optional into the essential.
The regulatory environment is making all of this potential more urgent. EPR compliance, PPWR implementation, and the mounting scrutiny around recyclability claims create data requirements that are beyond what manual processes can deliver, or at least reliably maintain.
Think about when a brand needs to model the end-of-life recyclability of a substrate across multiple collection infrastructures, demonstrate material reduction targets against a baseline, or evidence packaging performance in a way that satisfies both a procurement team and an incoming regulatory audit. The businesses with AI-integrated data workflows have considerably more headroom here than those trying to manage it through manual spreadsheets and institutional memory.
There’s also a risk dimension worth acknowledging. Businesses that can’t evidence their compliance position with clean, traceable data aren’t just at a competitive disadvantage, they’re exposed to risk. AI-integrated reporting systems help mitigate that exposure by making data collection, categorisation, and audit-readiness a continuous process rather than a pre-inspection scramble.
Businesses that are building this capability now, even in incremental steps, are creating an advantage that will multiply as compliance requirements tighten. The ones that haven’t started are not going to be in a comfortable position.
The pragmatic view.
AI will not replace the expertise in your print business. It will not replace the knowledge of a packaging engineer who understands how a niche substrate grade behaves on a press configuration, or the judgement of a team that knows how to handle a complex client brief. Nor will it replicate the kind of B2B marketing that builds authority in technically demanding markets.
What it can do is remove friction from processes that don’t require human judgement and amplify the capability of the people who do the parts that matter. The print and packaging businesses getting the most bang for their buck with AI are those treating it as a tool with specific jobs: well-defined, governed, and measured against real outcomes.
Getting groundwork started is easier than we might think. Begin with one problem worth solving, and codify what success looks like before you start, because the businesses seeing the strongest returns from AI adoption are the ones with oversight built into the process, not retrofitted. Build the data infrastructure to support it and keep people in the loop. And while you’re doing all of that, make sure the story you’re telling about it in the market is written by people who actually understand what it means. That part, at least, still requires a human brain at the wheel.
Think B2B Marketing works with businesses across the print and packaging sector to develop marketing strategies that are technically credible, commercially focused, and grounded in genuine sector expertise. If you want to talk about how to position your business as the market moves, get in touch.
