Case Study: Strategically Building an AI-Powered Future

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:

• Transforming the Front End — Using AI to Streamline Operations, Commercial Printer with $17 million in annual revenue
• Using AI to Amplify Productivity and Client Value. Commercial Printer with $40 million to $60 million in annual revenue

Case Study: Strategically Building an AI-Powered Future

Integrating AI is not about chasing hype, reports a commercial printer with annual revenue of $20 million to $40 million; it’s about removing friction, improving efficiency, and using data with purpose.

AI adoption isn’t a single initiative—it’s a phased, step-by-step effort guided by pragmatism, governance, and a focus on removing friction from everyday work. Leadership views AI as inevitable but believes the biggest risk isn’t moving too fast or too slow; it’s adopting without guardrails or a clear business purpose.

“We’re not using it to replace people,” the company president said. “We’re using it to work smarter and everything still requires human review.”

That mindset guides the company’s AI journey, which focuses on practical experimentation, early wins in marketing and estimating, and longer-term goals around process optimization, forecasting, and lead scoring.

AI Roots: Marketing and Content First

The company’s first meaningful use of AI came in marketing. Tools such as ChatGPT were used to draft blog posts, outline SEO strategies, support research, and generate content frameworks aligned with brand guidelines. AI-assisted imagery was also introduced to accelerate creative iteration.

One early experiment stood out: a podcast entirely scripted and voiced by AI. “It was shockingly good,” the president admitted. “It had this cool, hip banter. I hate to admit it.” The result helped build confidence internally and demonstrated that generative AI could deliver quality output—when paired with human oversight.

From the start, leadership established a non-negotiable rule: nothing goes out “raw.” All AI-generated content must be edited, verified, and aligned with the company’s tone and standards. “AI is not a decision-maker,” the president said. “It’s a helper.”

Ground Rules and Governance

Early experimentation reinforced the need for governance. The company developed “do’s and don’ts” guidance and is now formalizing a broader AI policy. Core principles include:

• Never input client, personal, confidential, or proprietary information into open tools
• Always verify outputs against trusted sources
• Treat AI as an assistant, not authority
• Require human review for anything externally shared

Early encounters with incorrect AI-generated statistics reinforced the need for skepticism and validation. Rather than slowing adoption, these guardrails enabled broader experimentation by clearly defining acceptable use.

Early Wins: Process Mapping, Reporting, and Estimation

Beyond marketing, some of the most compelling early wins have come in operations—particularly estimating and logistics.

One standout use case involved distribution estimating for publication work. Using AI, the estimating team calculated carton configurations, shipping methods, and cost comparisons across multiple carriers in minutes. A process that typically took four hours was completed in about two minutes, with results landing within roughly 5% of a manually produced estimate. “That got our attention,” the president said.

AI is also being used to accelerate process mapping following a recent ERP implementation. With the help of Microsoft’s Copilot, the company is documenting workflows from quote through invoicing. “It helped determine which functions can run concurrently and which need to wait,” the president explained. “It just helps us get there faster.” Department owners still review and refine the maps, but AI provides a strong starting point.

Another area of success is profitability analysis. AI tools are now used to analyze margins by job type, customer, and product category, helping identify anomalies faster and refine estimating strategies. Leadership emphasized that AI surfaces patterns and questions—but humans make the decisions.

Cautious Expansion: What’s Next?

While encouraged by early results, the company remains intentionally cautious. The company reports that tasks tied to financial control, scheduling, and automation will only be pursued once data quality and process discipline are fully trusted.

Looking ahead, leadership is evaluating several next-stage opportunities, including:

• Lead scoring: Prioritizing inbound inquiries based on AI evaluation.
• Campaign performance analysis: Automating reviews of outbound marketing effectiveness.
• Pricing strategy simulation: Modeling “what-if” scenarios to optimize pricing during slow periods.
• Inventory optimization: Using AI to predict procurement needs and reduce reliance on manual spreadsheets. “We want to go deeper into procurement automation,” the president said. “AI could tell me when I’ll need 80,000 pounds of paper in July. Right now, that’s all spreadsheets.”

Security, Culture, and the Human Factor

Leadership encourages AI experimentation but within limits. Departments use AI with oversight from IT, and the president himself often demonstrates use cases to spark ideas. “We’ve allowed experimentation but not necessarily free experimentation,” he said.

One priority is shifting employees away from fear and toward curiosity. “If you're not using AI you're either ready to retire, or should move on,” the president said bluntly. “You need to at least understand it.”

Advice to Peers: Start with Purpose

The president’s advice to other commercial printers is pragmatic: be cautiously optimistic, start small, and lead from the top. “Use AI to write a marketing plan for your business,” the president suggested. “You’ll be amazed.”

Above all, leadership must take the lead. “The business owner should be the first person experimenting,” he said. “That’s how you know what direction to go.”

As the president summed it up: “AI is shockingly intelligent—but also really dumb. You need a human at the wheel.”

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.