Case Study: Commercial Printer Uses AI to Amplify Productivity and Client Value

Artificial intelligence is reshaping how commercial printer providers get work done. But behind the headlines and hype, most printers are not pursuing radical transformation. Instead, AI is being applied in practical, often incremental ways as companies look to improve efficiency, reduce friction, and better support their people and processes. That real-world activity is documented in the Alliance Insights research study, AI Adoption in the Printing Industry: From Curiosity to Competitive Advantage, which combines an industry survey with in-depth executive interviews to capture how printers are actually using AI today and where they see it heading next.

This article is part of a series that shares a common introduction but highlights a different commercial printer case study in each installment.

Presented here is one of three expanded case studies developed from executive interviews conducted as part of the study; shorter versions of these profiles appear in the report. To encourage open and candid discussion around strategy, culture, and risk, all participants requested anonymity. Together, the series offers a grounded look at how commercial printers are moving deliberately from early experimentation to purposeful, value-driven AI adoption.

Across these commercial printer case studies, AI adoption is neither uniform nor rushed. Instead, it is intentional, problem-driven, and deeply tied to leadership behavior. The companies making the most progress are not chasing tools, they are defining rules, testing narrowly, and expanding only when value is proven.

Other case studies include in this series:

• Strategically Building an AI-Powered Future, Commercial Printer with $20million to $40 million in annual revenue
• Transforming the Front End—Using AI to Streamline Operations, Commercial Printer with $17 million in annual revenue

Case Study: Using AI to Amplify Productivity and Client Value

For this commercial printing company with annual sales of $40 million to $60 million, AI adoption began not with technology, but with a question: If the customer were sitting at the table, where would they feel the most friction? That perspective has shaped a disciplined, customer-centric approach to AI, one focused on reducing manual effort, improving follow-through, and amplifying employee performance rather than replacing it.

“We started with a very simple goal,” the executive explained. “How do we augment employee performance? And how do we do that without disrupting our core operation?”

From Curiosity to a Roadmap

The company began implementing AI tools in early 2024. Rather than experimenting broadly, leadership created a roadmap, recognizing that the fast-moving AI landscape could easily lead to distraction or tool sprawl. The roadmap prioritized projects that were low risk, quick to deploy, and scalable. Rather than rolling tools out to hundreds of employees at once, leadership deliberately started with a small group of internal advocates.

“We wanted people who were technologically curious,” the executive said. “The ones asking questions, pushing boundaries, and willing to test things.”

Those early advocates came from a mix of departments including marketing, administration, customer service, and logistics, rather than IT alone. Their role was not just to test tools, but to provide feedback and help build momentum organically across the organization.

Early Tools, Practical Wins

Among the first tools deployed were AI note-taking applications, generative AI assistants, and developer-focused tools. Rather than mandating a single solution upfront, leadership asked the advocate group to test multiple options and report back. One meeting-capture and task-summary tool emerged as the preferred choice due to its integrations and ease of use.

The impact was immediate. Meeting notes, action items, and follow-ups became easier to track, improving accountability and follow-through, an issue leadership had identified as critical to both customer trust and internal coordination.

AI was also applied to coding, compliance, and contract-related work, where natural language models proved particularly effective at accelerating review and reducing repetitive effort.

Empowering the Front Line

One of the most beneficial use cases was language translation on the production floor. Employees could speak or type in their preferred language, review the translated version, and then submit accurate information into internal systems. This allowed staff who were previously hesitant to provide detailed input to communicate more clearly and confidently.

Another major win came from logistics. Historically, freight quotes required multiple handoffs, slowing response times and pulling supervisors away from value-adding work. The company built an AI agent trained on negotiated carrier rates and shipping options. Employees could now request shipping costs and transit times in real time, in their own language, and respond to customers almost instantly.

“That freed people up to focus on what really matters on the shop floor,” the executive noted.

Learning Where NOT to Apply AI

Not every early experiment succeeded, and leadership sees that as part of the learning process. Estimating, for example, initially appeared to be a strong candidate for automation. But the complexity of projects spanning print, mail, fulfillment, and logistics proved challenging for AI to fully grasp.

Rather than forcing the issue, the company reclassified estimating as a lower-priority, higher-complexity project. “We realized we weren’t ready for AI to take that on end-to-end,” the executive said. “So instead, we focused on tools that support estimators, like shipping and reporting, rather than trying to replace them.”

That experience led to a more structured roadmap, organized by tiers based on complexity, value, and client impact. Projects are now flagged based on whether they directly benefit customers, helping leadership prioritize development.

One of the most successful tier-one projects emerged directly from a client conversation. During a performance review, a customer asked not just how the company performed but why a small percentage of orders missed service-level targets.

Rather than manually analyzing spreadsheets, the team used AI to automate reporting and uncover patterns. The AI didn’t just flag failures; it identified near misses, timing trends, and labor-related insights that helped operations teams make targeted adjustments.

“That’s when it clicked,” the executive said. “This wasn’t just automation. It was insight.”

Governance, Security, and Trust

From the beginning, leadership emphasized data security and responsible use. Employees are trained never to input personal, client, or proprietary data into open AI systems. Content creation is limited to trained staff, and access controls are managed centrally.

The company is also preparing for future challenges, such as employees bringing their own AI subscriptions into the workplace.

“That’s going to require training,” the executive said. “People need to understand when they’re working on company business, they need to be using company tools with proper data controls.”

A Living Roadmap

Today, the AI roadmap is a living document, updated as new opportunities arise from staff feedback and client needs. While the review process isn’t formalized in the traditional sense, leadership remains actively involved.

“This is moving too fast to delegate completely,” the executive said. “If leadership isn’t personally engaged, it won’t succeed.”

The company currently has a small but growing group of advocates and active users, and leadership expects adoption to accelerate as those advocates demonstrate value to their peers.

Advice to Peers: Start with Purpose

For other commercial printers, the executive’s advice is clear: start with a roadmap, start with the customer, and start small. Off-the-shelf tools like meeting note takers and document summarization offer quick wins with minimal risk.

“Leadership matters,” the executive emphasized. “If the business owner or leadership team isn’t learning it themselves, employees won’t see the value.”

The ultimate takeaway: AI isn’t about replacing judgment. It’s about scaling it. Used thoughtfully, it allows leaders and teams to focus less on low-value tasks and more on the work that truly creates value for customers and the business.

For more insights on the research study AI Adoption in the Printing Industry: From Curiosity to Competitive Advantage, see the following resources: Survey Reveals Keys to Staying Competitive in AI, Lessons Learned from Recent AI Study, and Why Talking About AI and Leading With AI Garner Different Results.