When the first industrial robot was installed in a General Motors factory back in 1959, few people could have predicted how far the ripple effects would travel. That 2,700-pound machine was the beginning of a revolution that would reshape not just manufacturing, but virtually every industry that followed. Today, robotics and automation power everything from hospital medication dispensing to logistics warehousing to construction — and the lessons learned along the way are more relevant than ever.
At Friedman Corp, we work closely with businesses navigating complex operational challenges. That’s why the findings from Hexagon’s Advanced Manufacturing Report — a study conducted by Forrester Consulting — caught our attention. The report focuses on three pillars that manufacturing has wrestled with for decades: data, collaboration, and automation. These aren’t manufacturing-specific issues. They’re universal business challenges with hard-won lessons we can all apply.
You’re Not Alone in Your Data Struggles
In 2006, British mathematician Clive Humby famously compared data to oil — valuable in its raw form, but only truly powerful when refined. Nearly two decades later, most organizations are still struggling with the refining part.
The Advanced Manufacturing Report found that 98% of manufacturing organizations face significant challenges with their data — particularly when it comes to sharing it across teams to work toward common goals. If that number seems high, consider this: a simple search for “common data challenges for businesses” returns nearly 2 billion results. This isn’t a manufacturing problem. It’s a business problem.
Construction is a telling example. Commercial construction projects generate an average of 197 million data assets per project. Without smart systems to collect, organize, and analyze that information, projects run over budget, miss deadlines, and fall short of specifications. The industry’s response — adopting tools like Building Information Modeling (BIM) and real-time field-to-office platforms — mirrors the same moves manufacturers made years earlier.
The takeaway for any business: breaking down data silos and turning raw information into actionable insight is not optional — it’s foundational. Better data access leads directly to better collaboration, higher quality outcomes, and stronger sustainability performance.
Automation as a Collaboration Tool — Not Just an Efficiency Play
Collaboration has long been held up as a soft skill — something you improve through culture and communication training. But the manufacturing sector has learned something important: collaboration also breaks down because of structural and operational problems, not just interpersonal ones.
Siloed departments, misaligned project goals, and redundant manual tasks are collaboration killers. In fact, 35% of manufacturing leaders in the study identified a lack of automation for repetitive tasks as a direct barrier to productivity and collaboration. When people are buried in low-value work, cross-functional teamwork suffers.
The financial services sector illustrates how automation solves this problem beyond the factory floor. By automating key steps in the mortgage origination process, banks have shrunk approval timelines from 46 days down dramatically — cutting costs, reducing errors, and freeing staff to focus on the customer relationship instead of paperwork. Integrated portals and AI-driven chatbots have made communication smoother and the overall process faster.
The lesson here is that automation isn’t about replacing people — it’s about removing the friction that keeps them from doing their best, most collaborative work.
AI’s Promise Runs Through Your Data Foundation
The conversation about artificial intelligence and automation is accelerating across every sector. The Advanced Manufacturing Report found that 52% of manufacturing leaders expect to benefit most from AI-driven design optimization and generative design to augment human innovation. The potential is real — Forrester has estimated that AI could contribute between $2.6 trillion and $4.4 trillion in annual economic value across 63 different use cases.
Customer service is already seeing dramatic results. In one documented case, deploying generative AI in customer operations improved issue resolution rates by 14% per hour, cut handling time by 9%, and reduced both agent attrition and escalations by 25%. The tool proved particularly valuable for less experienced team members, elevating their performance to be comparable with seasoned veterans.
But here’s the important caveat that manufacturing already knows well: AI is only as good as the data it runs on. As McKinsey puts it, your data and its underlying foundations are the determining factors for what’s possible with generative AI. Without clean, well-organized, accessible data, the most sophisticated AI tools will underperform. The work of fixing your data infrastructure isn’t glamorous — but it’s the prerequisite for everything that comes next.
Three Principles Every Organization Can Act On
Manufacturing’s digital transformation journey — spanning more than six decades — offers a hard-won roadmap. Here are three principles that translate across industries:
- Data Mastery: Invest in making your data accurate, integrated, and actionable. Precision data management is the foundation for everything else — smarter decisions, better AI outcomes, and faster innovation.
- Collaborative Environments: Use technology intentionally to tear down silos. Real-time platforms, shared dashboards, and open collaboration tools create the conditions for teams to actually work together — regardless of geography or department.
- Automation as a Catalyst: Think of automation not just as a cost-saving measure, but as a tool that clears the path for higher-value human work. When repetitive tasks are handled by machines, your people can focus on what they do best.
What This Means for Your Business
At Friedman Corp, we see these dynamics play out across the organizations we serve. The companies that are pulling ahead aren’t necessarily the ones with the biggest technology budgets — they’re the ones that have gotten disciplined about their data, intentional about collaboration, and strategic about where automation can create the most leverage.
Manufacturing didn’t get there overnight, and neither will you. But the path has already been walked. The question is whether you’re ready to follow it — or whether you’ll wait until you have no choice.
