About the Expert
Alex Cutchey,
Senior Policy Manager and AI Advisory Committee Member
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Artificial intelligence is moving quickly, but at Geronimo Power, we are approaching it with both curiosity and care. As a member of the company’s newly formed AI Advisory Committee, I am part of a cross-functional effort to help guide the safe, practical, and effective use of AI across the organization. The committee was created to evaluate tools, share best practices, and support colleagues as they explore where AI can strengthen the way we work.
Recently, I had the opportunity to present on the history of AI during the committee’s first companywide Lunch and Learn on the topic. The session was designed to open the door to a broader conversation: what AI is, how it is evolving, and how we can use it responsibly in our day-to-day work. One theme stood out: AI may feel new, but the larger story of automation is not. By looking back at how earlier technologies changed work, we can better understand how this next wave may shape the path ahead.
When a new technology emerges, the first questions usually focus on disruption: What will it change? What will it replace? What will happen next? Those are fair questions. But history offers a more nuanced answer.
For centuries, automation has reshaped work by moving repetitive or routine tasks to technology, while people have shifted their time toward work that depends on judgment, creativity, relationships, and decision-making.
AI is not the beginning of this story. It is the next chapter in a much longer one.
A Brief History of Automation
The effort to use technology to reduce manual effort is as old as industry itself.
In the late 1700s, mechanization transformed mills, mines, and manufacturing. Water and steam power took on physically demanding work that had previously required significant human labor. Productivity increased, industries expanded, and people found new ways to contribute beyond the most repetitive tasks.
More than a century later, the assembly line revolutionized manufacturing. When Ford introduced the moving assembly line in 1913, the time required to build a Model T dropped from more than 12 hours to roughly 90 minutes. Production scaled dramatically, and automobiles became accessible to far more people.
The next major shift came with computers. During the 1950s and 1960s, organizations began using mainframes to handle calculations, payroll processing, and bookkeeping. Machines performed the math, but people still made the decisions. Accountants, analysts, and finance professionals did not disappear. Their work evolved toward interpretation, oversight, and strategy.
The 1980s and 1990s brought personal computers and spreadsheets into everyday workplaces. Calculations that once took hours could be completed in seconds. The arithmetic became automated, but human expertise remained essential for understanding the results and deciding what to do next.
In the early 2000s and 2010s, the internet and workflow tools transformed how information moved through organizations. Processes became easier to track, documents became easier to manage, and communication became nearly instantaneous.
Now, in the 2020s, AI is helping with another category of work: reading, drafting, summarizing, identifying patterns, and organizing knowledge.
The technology has changed. The pattern is familiar.
The Pattern Behind Every Wave
Across more than two centuries of technological change, three patterns appear again and again.
1. The Cost of a Task Comes Down
Automation makes certain types of work faster, less expensive, or easier to complete.
The assembly line is a classic example. As productivity increased, the cost of producing vehicles fell dramatically. The same dynamic appears in almost every major technological advancement. When routine work requires less time and effort, organizations can accomplish more with the same resources.
2. Work Moves Up Rather Than Disappearing
One of the most persistent myths about automation is that it simply eliminates work. In reality, technology often changes the nature of work rather than removing the need for people.
Consider the ATM. Many predicted that bank tellers would become obsolete once machines could dispense cash. Instead, branch operating costs fell, more branches opened, and teller roles evolved toward customer service, relationship management, and financial guidance.
The routine task was automated. The human contribution moved to where people could add greater value.
3. Change Takes Longer Than Headlines Suggest
New technologies rarely transform organizations overnight.
Electricity, for example, took decades to produce meaningful productivity gains across industry. Factories did not become dramatically more efficient simply because a new power source became available. Leaders had to redesign workflows, rethink processes, and determine how best to use the technology.
That lesson matters today. The value of innovation comes not from the technology alone, but from how thoughtfully people choose to apply it.
What This Means for AI
AI follows many of the same patterns as the technologies that came before it, while also introducing something distinct.
Previous waves of automation primarily focused on physical labor or rules-based processes. AI can support knowledge work by helping people read information, extract insights, draft content, summarize documents, and organize ideas.
The key word, however, is help.
AI is not a substitute for judgment. It is a tool that can accelerate parts of the process so people can focus more time on thinking, deciding, and creating.
Another important difference is accessibility. Unlike many previous technologies, AI often requires very little investment to begin exploring. You do not need a large implementation or a major systems overhaul to see value. In many cases, it starts with a single question, a single draft, or a single task.
People remain responsible for the outcome. AI can assemble information, suggest language, or produce a first draft, but human review is what determines whether the result is accurate, useful, appropriate, and aligned with the organization’s standards.
Where We See the Opportunity
For many organizations, the greatest opportunity may be found in the most routine parts of the day.
Document reviews, recurring reports, information gathering, meeting preparation, email drafting, and administrative triage are all examples of work that can consume valuable time without always requiring the full depth of someone’s expertise.
When AI helps reduce the effort spent on those repetitive tasks, it creates more capacity for the work only people can do: building relationships, solving complex problems, making strategic decisions, and applying the context that comes from experience.
That is the real promise of AI: not replacing people, but helping people spend more time on the work that matters most.
At Geronimo Power, that is where the opportunity is most meaningful. As our teams continue advancing complex projects and navigating a fast-changing energy landscape, AI can help create space for higher-value work while keeping people at the center of judgment, accountability, and decision-making.
History suggests that the biggest benefits will come to organizations that pair new tools with thoughtful adoption, clear expectations, and trust in the people closest to the work.
