Automation Missteps
The Top 3 Things Companies Are Doing Wrong – and How to Fix Them
Automation Is Booming – But So Are Mistakes
The promise of automation is enticing: lower labor costs, faster cycle times, and relief from chronic hiring shortages. U.S. companies installed more than 44,000 industrial robots in 2023, an increase of 12% from the prior year. Yet for all the headlines, adoption is far from universal. Only 8.3 % of U.S. manufacturing firms currently use robots, and barely 1% of nonmanufacturing firms have incorporated them. With nearshoring bringing more production back to the U.S., those numbers are likely to climb.
The catch? While automation adoption is accelerating, most organizations are still learning. And from what I’ve seen over the years, it’s a bit like buying a sports car and then discovering you still need to pass your driving test. Companies quickly realize they can’t just hit the accelerator and expect everything to work itself out.
In this article, I’ll share the three most common mistakes I’ve seen in years of working with automation, as well as how to avoid them. We’ll cover why chasing labor savings alone backfires, how poor data strategies undermine ROI, and why failing to redesign the operating model keeps companies stuck in “pilot purgatory.”
#1 - Don’t Focus Solely on Labor Costs. Space Is Money Too
When evaluating an automation project, leaders naturally compare the labor cost of a worker with the cost of a robot. But this narrow view overlooks another scarce resource: real estate. Many machines take up far more floor space than a human performing the same task. Without accounting for space, I have seen companies buy automation that suboptimizes the full P&L because the ROI did not include the lost capacity due to the machine using more floor space compared to the manual option.
I’ve seen this with packing stations and box building equipment. A company installed automated box‑building equipment to support a rate increase at packing stations. Because the automation assembled boxes faster than a person, it looked like a win on paper. However, when the equipment was installed, it took up the space of 5 pack stations. These 5 pack stations put out more in volume than then rate increase of the equipment, and so for the same amount of floor space, the total capacity of the building was now lower. Because the total capacity was now lower, the fixed cost per unit rose more than the savings in variable per unit cost. All in all, the company invested capital, time, and labor into a solution that ended up costing them more money than it saved.
The Fix: I focused on improving their Automation Review process. When calculating ROI, I added a “rate per square foot” metric alongside the labor cost comparison. This simple addition forces the conversation beyond headcount savings and ensures automation improves both fixed and variable costs. It also pressures vendors to design vertical (rather than sprawling) systems and sometimes confirms that people plus ergonomic tools remain the best option.
#2 - Don’t Neglect Your Data. It’s the Backbone of Automation
Data is the fuel that powers modern operations. With automation comes the promise of a large amount of new data about your processes. Sadly, it’s often the most neglected part of an automation project. As a result, leaders lack an understanding of their data sources to generate reliable metrics from their new machines. The result: misleading dashboards, faulty decisions, and wasted investment.
I spoke with a colleague about a company that implemented automation without a proper data strategy. The team mistakenly summed equipment‑uptime data rather than averaging it. Their dashboard showed ~90% Overall Equipment Effectiveness (OEE). In truth, each subsystem was running at around 18% OEE. Because there was low understanding of the new data sources, the wrong metric went unchallenged, and leadership celebrated a successful pilot while the system continued to underperform for months.
The Fix: Convene your technology and operations teams to build a foundational data strategy. Identify the critical metrics you need (e.g., throughput, downtime, quality, operator interventions) and then ensure that you understand the raw data from your automation and how the raw data is being transformed into your operating metrics. Validate your calculations early by doing Gemba walks and working directly with operations. The investment is critical to driving the teams forward and achieving the expected ROI.
#3 - Don’t Postpone Operating Model Changes. Plan Before the Pilot
Automation touches every part of your business: hiring, training, labor planning, maintenance, shift design, business reviews, and more. Too many organizations treat the pilot as a self‑contained test and leave operating‑model changes for later. This is how you end up in “pilot purgatory.” Companies often spendtwo to three yearsstuck in pilot purgatorybecause they did not plan for enterprise level impact.
I once inherited a “production-ready” piece of equipment from a research team. It was deemed “ready to launch” because it passed the production testing plan. But running equipment to meet volume and cost targets is quite different than running equipment in a test environment, and I learned quickly just how much needed to change to get this equipment to meet our business needs. Learning from this experience to help companies scale automation effectively, I work across the organization to re-engineer the business. For example:
HR: Do we need to change our job descriptions and requirements? Do we need to adjust breaks and lunch schedules to match machine cycles and preventative maintenance schedules?
Maintenance: What new skills do technicians need? Do we know the preventative maintenance and calibration plans? Where are our parts residing to balance uptime and safety stock cost?
Data & Analytics: How are we building our data pipelines? What metrics do we need to create?
Labor Planning: How will our expected rate change? How are we building this into our labor planning models?
Operations: How are we training our workforce? Do we need to change incentives? How frequently do we need to review each metric? What are our escalations plans? If an escalation occurs, does the owner know what to do?
The Fix: Before scaling, gather leaders from every function and ask: “How will this automation change your work?” Map out new standard work, roles and training plans, review cadences, etc. The sooner you align people and processes, the faster you unlock ROI.
Conclusion: Build Automation the Right Way
Industrial automation is no longer optional; it’s a competitive necessity. The upside for most companies is enormous - if they avoid common mistakes.
Ready to ensure your automation investments deliver measurable results?
Assess your floor space metrics. Calculate throughput per square foot for your existing process and compare it with potential automation solutions.
Audit your data. Identify gaps in your current systems, get to know your new data streams, and prioritize integration before launching new automation pilots.
Redesign your operating cadence. Bring together leaders from every function and map how automation will change roles, training, and decision‑making.
At Cade Operations Consulting, we help organizations build and scale automation smarter, faster, and with confidence. Contact us today to discuss how our proven frameworks and real‑world expertise can accelerate your automation journey.