The U.S. print industry is facing a familiar squeeze. Expenses are climbing faster than prices—costs are up about 4.4%, but prices have only risen by 3.0%. To make matters worse, finding good help is harder than ever. 72% of printers struggle to hire staff, and many shops are paying 10% to 20% more just to keep their current team. It’s no wonder leaders are looking at AI and automation. When money is tight and workers are scarce, anything that makes the job easier looks like a winner.And to be fair, the early results can be VERY impressive.
Some print providers report using AI tools to cut complex shipping distribution estimates from four hours down to two minutes, while still maintaining accuracy within about five percent of manual calculations. In other cases, teams have uploaded historical proposal data into private AI environments and reduced RFP turnaround from a week to under an hour. Back-office automation is also catching duplicate payments and driving down accounts-payable error rates—from about 0.5% to under 0.1%—while freeing up the equivalent capacity of several full-time employees.On paper, those numbers look like a breakthrough. Faster estimates. Faster proposals. Fewer accounting errors. Less administrative friction.
But speed, by itself, is not the same thing as profit…

The False Positive of Speed

One of the quiet traps in early AI adoption is what I sometimes call the “AI sugar rush.” The first improvements are very visible. Work gets faster. Tasks that used to take hours suddenly take minutes. It feels like a massive win—and in many ways it is. But if leadership doesn’t decide in advance what that saved time is for, the gain can disappear almost as quickly as it arrived.
Consider a simple example. An estimator discovers a workflow that saves three hours a day using AI to draft freight calculations or summarize RFP requirements. That sounds like reclaimed capacity. But if no one has defined how that time should be reinvested, it often dissolves into a familiar pattern: more emails, more small requests, more internal busywork.
The day becomes busier, not more valuable.
This is a classic version of Parkinson’s Law—work expanding to fill the time available for its completion. In this case, AI expands the amount of work that can fit into a day unless leaders intentionally direct the freed capacity toward something more strategic.
That distinction is one reason we are beginning to see a separation emerge between organizations experimenting with AI and organizations actually benefiting from it.
Industry research shows that AI leaders treat the technology as a structured business initiative, not just a collection of helpful tools. About 46% of leading adopters have a documented roadmap tied to specific business priorities, compared with only 9% of laggards. The leaders assign accountability for outcomes, build internal guardrails, and require human verification for critical outputs. In other words, they treat AI like a capability that must be managed—not a shortcut that runs on autopilot.

The Higher-Value Move: Expand Capability

The recent UC Berkeley research discussed widely this year adds another important layer to the conversation. The takeaway is not that AI is inherently good or bad. It is that speed changes behavior. When people gain speed but lack direction, they often just move faster in the same direction they were already going. The organization becomes more efficient at doing the same things, instead of becoming capable of doing better things.That is why the most effective AI strategies in print shops tend to follow a two-part pattern.
First, AI removes the low-value friction that drains margin: drafting documents, summarizing information, formatting data, and turning messy notes into structured first drafts.Second—and this is the critical step—leaders deliberately use the time savings to expand human capability.That might mean strengthening estimating processes so knowledge does not live only in one person’s head (hint = try creating a Custom GPT for this!). It might mean improving onboarding so new hires ramp up faster (like aggregating disparate files into a NotebookLM for new hires to reference “Tribal Knowledge”). It might mean developing new service offerings or building clearer standard operating procedures that reduce costly handoff errors. When used this way, AI does not replace expertise. It multiplies it.The tool accelerates the first pass. The human team still owns judgment, customer relationships, and the craft that makes a shop trusted rather than interchangeable. But that outcome only happens when leaders protect the time AI creates instead of quietly filling it.