Why AI matters at this scale

Matthews International is a diversified global provider operating in three core segments: Brand Solutions (graphics, packaging, merchandising), Memorialization (bronze memorials, cremation equipment, mausoleums), and Industrial Technologies (marking equipment, storage solutions). Founded in 1850 and now employing over 10,000 people, the company blends manufacturing of physical goods with design and brand services. At this enterprise scale, with complex global supply chains and a product mix spanning from consumer goods to cemetery products, operational efficiency and data-driven decision-making are critical for maintaining margins and competitive advantage.

For a company of Matthews' size and vintage, AI presents a transformative lever to modernize legacy processes, unify insights across disparate business units, and inject agility into manufacturing and design workflows. The sheer volume of transactions, design files, and supply chain movements generates vast data, which, if harnessed by AI, can optimize everything from raw material procurement to personalized customer offerings. Without such innovation, large industrial firms risk falling behind more digitally-native competitors and disruptors in adjacent spaces.

1. Design & Production Automation

A high-ROI opportunity lies in applying generative AI to the Brand Solutions segment. AI-powered design tools can rapidly generate concepts for packaging, signage, and point-of-sale materials based on textual brand guidelines, accelerating the artwork creation process from days to hours. This reduces reliance on manual design labor for routine projects, allowing creative teams to focus on high-value, complex commissions. Computer vision systems on production lines can further automate quality checks for printed graphics and etched products, reducing waste and rework costs.

2. Demand Forecasting for Memorialization

The Memorialization business, with products like bronze plaques and granite markers, faces unique demand drivers tied to demographics and pre-need sales. Machine learning models can analyze historical sales data, regional mortality trends, and pre-need contract pipelines to generate highly accurate production forecasts. This predictive capability allows for optimized inventory of raw materials like bronze and stone, significantly reducing carrying costs and minimizing stockouts during peak demand periods, directly protecting revenue.

3. Intelligent Sales & Service Optimization

With a large, decentralized sales force serving diverse markets, an AI layer on top of CRM and ERP systems can identify cross-selling and upselling opportunities. For example, a customer purchasing brand graphics for retail might also be a candidate for industrial marking equipment. AI can analyze account histories and market signals to prompt sales reps, increasing wallet share. Natural language processing can also enhance customer service for the Memorialization segment by quickly routing inquiries from grieving families to the appropriate specialist.

Deployment Risks for Large Enterprises

Implementing AI at a 10,000+ employee organization like Matthews carries specific risks. Integration complexity is paramount, as AI tools must connect with legacy ERP (e.g., SAP) and manufacturing execution systems without disrupting production. Data governance is a major hurdle, requiring the consolidation of siloed data from business units with vastly different operational models into clean, accessible data lakes. Change management at this scale is daunting; shifting the mindset of a traditionally industrial workforce and leadership accustomed to physical craftsmanship toward data-centric decision-making requires sustained training and clear communication of AI's value. Finally, cybersecurity risks escalate as more systems become interconnected and data-rich, necessitating robust investment in securing AI models and their data pipelines.