• AI is neither alive nor sentient, but it can be difficult to define and measure concepts like thinking and sentience.
  • AI systems can “evolve” beyond their original programming in ways that their developers don’t understand because they entail so much data and computations.
  • It’s crucial to consider the differences, benefits, and limitations of rules-based and AI programming to determine the appropriate approach for the precision-oriented printing business.

By Greg Cholmondeley


It’s difficult to avoid the topic of artificial intelligence (AI) in the news this year, and the hype surrounding the subject is spectacular. At any moment, you’re likely to hear industry experts predicting that AI might someday drive our cars, steal our jobs, fight wars for us, or fight wars against us. We even have senior executives pushing for a moratorium on AI research—as if that will help! While AI is undoubtedly an exciting topic, let’s take a moment to strip away some of the hype and consider the ways it could impact the printing industry.

Thinking vs. “Learning”

While AI is highly complex code, it is still just software. Arthur C. Clarke’s third law states, “Any sufficiently advanced technology is indistinguishable from magic,” and AI systems can certainly seem magical. We use terms like learning, thinking, problem-solving, and understanding to describe AI operations, which contributes to anthropomorphism. Of course, AI is not alive, nor is it sentient. At the same time, however, concepts like thinking and sentience can be difficult to define and measure.

Back in 1950, mathematician Alan Turing contemplated whether digital computers would ever be able to think. Turing punted on the question because he couldn’t find a meaningful way to define or measure thinking. Instead, he proposed a test that he termed “the imitation game.” In it, he asked, “Are there imaginable digital computers that would do well in the imitation game?” Turing’s definition of an imitation game involves someone having a text conversation with both a person and a computer. The computer would pass the test if the tester were unable to determine which conversationalist is human and which is a computer. Programs like ChatGPT can now either pass Turing’s test or will soon be able to, but this simply means that they can mimic human behavior. These programs still can’t think in the human sense, so we’re going to need a new test.

This isn’t to say that AI isn’t a valuable tool, nor does it mean that these systems can’t get out of control and wreak havoc. Our world is largely controlled by software, and AI systems build their models and generate new code based on what they’ve read (much like a student whose research involves scouring the Internet). In addition, people are using AI to generate and post new content that sounds legitimate—and this content will, in turn, be used to help AI systems “learn.”

What About Evolving?

One of the most challenging aspects of AI is that it “grows” and “adapts” beyond its original programming. I apologize for using those terms, but the result is that AI systems “evolve” beyond their original programming in ways that their developers don’t understand because they entail so much data and computations. Sam Bowman, Associate Professor of Linguistics, Data Science, and Computer Science at New York University, wrote a fascinating paper entitled “Eight Things to Know about Large Language Models.” In it, he made the following observations about large language models (LLMs) used by AI software:

  • LLMs become more capable with more computing power and data, even without innovation.This unexpected result is unlike any other programming style, where programmers must code new features and capabilities. AIs can do this simply with more data and computing power without new coding.
  • LLM behaviors emerge unpredictably as they become more capable.Unlike all other programming styles based on “IF this THEN do that,” LLMs can quickly and unpredictably alter their responses and capabilities with no additional coding.
  • There are no reliable techniques for steering LLM behavior.LLMs are not controllable. Programmers try to put “guard rails” in place to steer AIs, but human term-programmers and AIs always manage to get around them.
  • The inner workings of LLMs and unknown.As previously stated, these self-adapting systems become so large and complex that no one knows precisely how they work after a while.
  • LLMs can express different values than their creators or training text.Just as LLM behaviors are unpredictable, so are their interpretations of their training content. Again, no one can know how these complex systems work after they have evolved.
  • LLMs can exceed human performance.This is one of the most significant benefits (and yet scariest aspects) about AI. It can perform analyses that humans or traditionally programmed computers could never do. This is especially true for applications with large data sets and variables. AI is responsible for many recent breakthroughs in medicine and science.
  • Brief interactions with LLMs can be misleading. LLMs often appear to learn about and use representations of the outside world.

Approaching AI in the Print Industry

It is crucial to consider the differences, benefits, and limitations of rules-based and AI programming to determine the appropriate approach for the precision-oriented printing business. Rules-based programming is precise and gives the same result for the same input conditions (unless you code in a random number generator). This is ideal for financial calculations and many forms of prepress automation. For example, Xerox FreeFlow Core provides efficient prepress automation for specific job types and is all driven by rules-based programming. Nevertheless, rules-based programming becomes increasingly complex and challenging as the number of variables and options increases; coders must account for every possible condition. Thus, while different workflows can be automated, determining which one to use for each incoming job is still a human-operator decision.

With all its faults, AI programming dramatically simplifies handling large data sets and variables. Specifically:

  • Translating a free-text email in any language into a job ticket or even determining whether the email is a job request would be nearly impossible for rules-based programming. There are just too many possibilities. Even so, off-the-shelf AI systems can quite easily do a good job.
  • Programming job routing and scheduling can become quite complex for handling an ever-changing array of jobs, deadlines, media inventories, press availabilities, and other factors. AI-driven approaches are well-suited for doing this sort of work. The AI software evolves and adapts, so the initial programmers don’t need to consider every eventuality.
  • Business and workflow analysis of customer, shop, and press data can involve millions or even billions of records. Many traditional systems can provide dashboards to help people visualize their shops, but identifying potential issues and likely causes while making recommendations is beyond what traditional coding can provide. In contrast, AI software is ideal for this work.
  • Design, layout, and development of marketing materials and campaigns typically involve fixed libraries of pre-built templates, workflows, and special coding. AI software could develop these using branding guides and best practices examples.

The Bottom Line

Does AI have the potential to destroy humanity? While this scenario is possible, it isn’t worth losing sleep over. Will we have fully autonomous, AI-driven print shops in our lifetimes? Probably not, but we will certainly be using AI to become more efficient in the future. It is our opinion that AI software will increasingly help printers and in-plants address labor issues like aging workforces and unavailable skilled workers by assisting with print-oriented tasks. We will see AI used in customer service, job onboarding, prepress automation, estimating, design, scheduling, and business analysis. Despite all their promise, AI solutions need constant verification and management because they can become unpredictable. Almost every workflow automation software vendor we meet is investigating how to utilize AI in their offerings. During the coming two years, we will likely see print automation advancements that can change the world.

A recognized expert on workflow automation, strategic planning, software solutions, and the printing industry, Greg Cholmondeley directs Keypoint Intelligence's Production Workflow consulting service. He is a frequent speaker and panelist at industry events as well as a published author.