If you attended the Flexible Packaging Association’s (FPA) 2026 Annual Meeting, you probably noticed something. Conversations about artificial intelligence (AI) were happening everywhere, not just in some of the formal sessions. AI was front and center. And the discussions were not about whether AI matters; they were about what to do with it.
That shift is significant.
For years, the flexible packaging industry has watched the AI conversation unfold from a comfortable distance. We are a hands-on industry. We build things. We solve real problems for real supply chains. When AI showed up promising to “transform everything,” a healthy dose of skepticism was warranted.
But here is where we are now: The early hype has settled, the experiments have played out, and a much more practical conversation is emerging. Not “What can AI do?” but “How does AI actually help my strategic, commercial, leadership, and operations teams do their jobs better?”
That is a question worth answering. And it is one our industry is uniquely positioned to tackle.
The Experiment Phase Is Over
A recent EY survey found that only 20% of CEOs said AI meaningfully exceeded their expectations last year. That is not a failure of the technology. It is a failure of approach. Most companies bolted chatbots and copilots onto existing workflows and hoped for magic. When the magic did not materialize, they chalked it up to AI not being ready.
The reality is more nuanced. AI is ready. But scattershot adoption is not a strategy. Companies that treated AI as a series of one-off experiments—a tool here, a pilot there—are now sitting on a collection of disconnected capabilities that do not add up to much.
Meanwhile, the macro environment is not getting easier.
The World Bank projects global economic growth will slow to 2.6% in 2026. Supply chains remain volatile. Regulatory landscapes are shifting fast. And every packaging company I talk to is navigating the same tension: Do more, know more, move faster with teams that are already stretched thin.
This is where AI stops being a nice-to-have tool and starts being a necessity. But it only works if it is applied with intention.
Start With the Problem, Not the Technology
The most common mistake I see companies make is starting with the tool. Someone in leadership reads about generative AI, a vendor demo looks impressive, and suddenly the organization is trying to figure out where to plug it in. That is backward.
The companies getting real traction are starting with their actual pain points and working backward to the right application of AI. When you take that approach in flexible packaging, four functional areas consistently rise to the top.
1. Strategic teams: Seeing around corners
Strategy teams are drowning in information.
Market signals, legislative developments, sustainability mandates, merger and acquisition (M&A) activity, macroeconomic shifts—the volume of inputs that should inform strategic decisions has grown exponentially. No human team can monitor all of it in real time.
This is where AI can genuinely multiply strategic capacity—not by replacing the thinking, but by doing the monitoring, filtering, and connecting of dots that humans cannot do at scale. Imagine your strategy team walking into a quarterly planning session already briefed on every relevant regulatory change, competitor move, and market signal from the past 90 days, with all the information synthesized, prioritized, and contextualized for your specific business.
That is not science fiction. That is the kind of capability that is available now, and it turns strategic planning from a reactive exercise into a proactive one.
2. Commercial teams: Intelligence that drives revenue
Sales and marketing teams in our industry often operate with a significant information gap. They know their own products. But staying current on what is happening in their customers’ industries—new product launches, leadership changes, supply chain disruptions, regulatory headwinds—is a full-time job nobody has time for.
AI changes that equation. When commercial teams have continuous access to relevant market intelligence, conversations with prospects and customers shift from transactional to consultative. Your sales rep does not just show up with a capabilities deck; they show up knowing that a customer’s key competitor just filed a patent, or that a piece of legislation is about to affect a prospect’s category.
That kind of informed engagement builds trust and shortens sales cycles. It also gives marketing teams the insight they need to create content and campaigns that resonate with what buyers need right now, not what they needed yesterday.
3. Leadership: Aligning on what matters
One of the quieter challenges in any organization is alignment. Executive teams are making decisions based on different information, different timelines, and different interpretations of the market. When everyone draws on their own sources and forms their own viewpoint, misalignment is inevitable.
AI-driven intelligence platforms can serve as a shared foundation—a single source of curated, relevant market context that the entire leadership team draws from. When the CEO, the head of sales, the vice president of operations, and the research director are all looking at the same synthesized intelligence, the quality of strategic conversations improves dramatically. Debates shift from “What’s happening?” to “What should we do about it?”
That is a subtle but powerful shift, and it doesn’t require a massive technology overhaul. It requires the right information delivered in the right way to the right people.
4. Operations teams: Anticipating instead of reacting
If the past few years have taught operations leaders anything, it is that surprises—a raw material shortage, a regulatory change in a key market, a supplier’s financial trouble—are expensive. By the time these show up in your operational reality, you are already in response mode.
AI enables a fundamentally different position. Continuous monitoring of supply chain signals, geopolitical developments, and regulatory pipelines enable operations teams to spot disruptions before they hit. The goal is not to eliminate surprises entirely, but to reduce the number of times your team gets caught off guard.
For an industry that prides itself on reliability and execution, such an early warning system is not a luxury. It is a requirement.
The Knowledge-Transfer Problem
Our industry, like much of manufacturing, is facing a significant knowledge-transfer challenge. Experienced professionals are retiring, and the institutional knowledge they carry about markets, customers, competitors, and regulatory nuances is walking out the door with them.
Training and upskilling new talent are essential, but they are slow and capital-intensive. AI doesn’t replace that institutional knowledge, but it can capture and scale the kind of awareness that used to live only in the heads of your most experienced people. A junior analyst with access to AI-powered market intelligence can operate with a level of context that previously took a decade to build.
Using AI is not about replacing people. It is about ensuring your people have the foundation they need to contribute at a higher level and faster.
Why Adoption Is the Real Challenge
Technology is not the hard part. The hard part is adoption. Getting teams to integrate AI-driven intelligence into their daily workflows is where most initiatives stall.
The companies that succeed treat intelligence delivery the same way they treat any other critical workflow. It has to be accessible, digestible, and embedded into tools people already use. Whether that is email, a customer relationship management (CRM) system, or a mobile app, if someone has to go looking for the insight, they will not.
The other piece is relevance. Generic AI outputs do not change behavior. Intelligence curated for your industry, competitive set, and strategic priorities does. When sales reps open their morning briefing and see something directly relevant to the customers they will meet that afternoon, that is the moment AI becomes a daily habit rather than a quarterly experiment.
What’s Next: A Collaborative Path Forward
What excites me most about where we are as an industry is the appetite for practical progress. The conversations at FPA events have moved well past “Should we care about AI?” to “How do we make this work for our teams?” That’s exactly the right question.
The most meaningful progress will come from collaboration among technology partners, industry associations like FPA, and the member companies that are doing the hard work of integrating new capabilities into their operations. We are all figuring this out together, and the organizations that lean in now will set the pace for the rest of the decade.
The era of experimenting with AI is winding down, and the era of putting it to work across strategy, sales, leadership, and operations is getting started. The flexible packaging industry has never been one to sit on the sidelines. I do not expect that to change now.
Chelsey Quick is vice president, business solutions, at Industry Intelligence Inc., which has been serving the flexible packaging community since 1999.
