• Written by Muhammed Abdulla NC, CEO

The dashboard for the print production workflow at a mid-sized packaging firm sits idle on a screen, its charts and graphs a reminder of the data that is being collected but not fully utilised. This is a common sight in the printing and packaging industry, where the potential of AI to transform day-to-day work is still being explored.

Where AI has actually landed in printing and packaging so far

According to the US Census Bureau's Business Trends and Outlook Survey, 12.4% of US manufacturers, the Census sector that includes printing and packaging, reported using AI in the previous two weeks. This adoption is largely limited to functions whose output is text, such as estimating, scheduling, and customer service, rather than those that handle physical work like production and maintenance. The office functions are where AI has genuinely changed the day-to-day work, enabling staff to automate routine tasks and focus on higher-value activities.

A shift supervisor at a printing firm, for example, can use AI to quickly generate reports on production schedules and material usage, freeing up time to focus on more strategic tasks like optimizing production workflows and improving quality control. This is in line with the broader trend of AI adoption in US businesses, where 14.3% used AI in sales and marketing in the previous six months, compared to just 1.7% in production of goods.

Setup and makeready losses

One area where AI can make a significant impact is in reducing setup and makeready losses, which are a major cost factor in job-shop economics. By enabling staff to ask the data in the print MIS, prepress workflow, estimating, and scheduling a direct question instead of waiting for a report, AI can help identify areas for improvement and optimize production workflows. This can lead to significant reductions in waste and downtime, and improve overall efficiency.

A category manager at a packaging firm, for example, can use AI to quickly analyze data on substrate usage and identify opportunities to reduce waste and optimize material usage. This can involve analyzing data on production schedules, material usage, and quality control to identify areas for improvement and optimize production workflows.

Quoting accuracy

AI can also improve quoting accuracy by enabling staff to quickly generate quotes based on up-to-date data on material costs, production schedules, and other factors. This can help reduce errors and improve customer satisfaction, as well as enable firms to respond more quickly to changes in the market. Currently, quoting often involves stitching together data from two or more systems, which can be time-consuming and prone to error.

By providing an answer in the moment, AI can help reduce the time and effort involved in generating quotes, and improve the accuracy of the quotes themselves. This can be particularly important in the printing and packaging industry, where firms often have to respond quickly to changes in customer demand and market conditions.

Substrate waste

Reducing substrate waste is a key challenge in the printing and packaging industry, but it is harder than it looks. While AI can help analyze data on substrate usage and identify areas for improvement, it requires a high degree of data quality and integrity to be effective. This means that firms need to have robust data collection and management systems in place before AI can be used to reduce substrate waste.

Additionally, reducing substrate waste often requires changes to production workflows and processes, which can be difficult to implement and may require significant investment in new equipment and training. While AI can provide insights and recommendations, it is not a silver bullet for reducing substrate waste, and firms need to be realistic about what it can achieve.

Why most adopters have not changed anything underneath

According to the US Census Bureau's Business Trends and Outlook Survey, 64.3% of US businesses already using AI made no change of any kind to adopt it. This is a concern, as it suggests that many firms are not getting the full benefits of AI, and may be simply using it to automate existing processes rather than transforming their businesses. In the printing and packaging industry, this can limit the potential of AI to improve efficiency, reduce waste, and improve customer satisfaction.

This is why it is so important for firms to think carefully about how they implement AI, and to consider what changes they need to make to their underlying systems and processes to get the most out of it. This may involve investing in new equipment, training staff, and changing production workflows, but it is essential to achieving the full potential of AI.

What a sensible first quarter looks like for a printing and packaging business

A sensible first quarter for a printing and packaging business looking to adopt AI might involve focusing on a single question, such as "how can we reduce setup and makeready losses?" or "how can we improve quoting accuracy?". This would involve identifying a single system, such as the print MIS or prepress workflow, and a single team, such as the estimating or scheduling team, to work with.

By focusing on a specific question and system, firms can quickly generate insights and recommendations, and start to see the benefits of AI. This can help build momentum and enthusiasm for further adoption, and provide a foundation for more widespread implementation. For more information on how to get started with AI, contact us or visit our production page.

What this does not do, or when it is the wrong choice

AI is not a replacement for human judgment and expertise, and it is not a solution to every problem. In some cases, the costs and complexity of implementing AI may outweigh the benefits, or the data quality and integrity may not be sufficient to support AI-driven decision making. Firms need to be realistic about what AI can achieve, and to carefully consider whether it is the right solution for their specific needs.

For example, if a firm is struggling with basic data collection and management, it may not be ready for AI. Similarly, if a firm is looking to completely automate a complex process, AI may not be the right solution. In these cases, firms may need to focus on building their underlying systems and processes before adopting AI. For more information on the potential pitfalls of AI adoption, see our blog on AI and IT investment in printing.