Over my 27-plus years in technology, I've watched some remarkable changes in how businesses operate. We've gone from people manually entering information into systems, to applications sharing data, to robotic process automation (RPA) handling repetitive tasks. Today, we're entering another chapter with AI agents capable of performing increasingly complex work, making decisions within defined boundaries, and coordinating with other agents to accomplish things that once required entire teams of people.
And I have to say, it's pretty amazing. But there's something I think we should be talking about more often. As we continue to automate more of the work humans have traditionally performed, what does that mean for us in the long run?
RPA Was Just the Beginning
For years, RPA has helped organizations automate repetitive tasks. Think about the employee who spends hours copying information between systems, processing transactions, validating records, or generating reports. RPA allowed us to create software robots that followed defined instructions and performed many of those activities without someone sitting at a keyboard.
The benefits were obvious. Processes became faster, errors could be reduced, and employees could spend more time on work that required judgment and experience. It wasn't perfect, and RPA certainly had its limitations, but it helped demonstrate how much work could be automated when we understood the underlying process.
Now we're seeing something much bigger. Tools like Claude and other AI platforms are helping move automation beyond predefined scripts and rigid workflows. Instead of simply telling a software robot which buttons to click, we can increasingly give an AI agent a goal, access to approved tools, and instructions about what it is allowed to accomplish.
That's a significant change in how we think about automation.
From Individual Tasks to Orchestrated Agents
What fascinates me most is the evolution from automating individual tasks to orchestrating entire workflows using multiple agents.
Imagine a business process that traditionally required several employees. One person receives a request, another validates information, someone else researches an issue, another makes a recommendation, and finally someone completes the transaction.
In the past, we might have automated a few individual steps using RPA. Today, we can design systems where specialized AI agents work together. One agent gathers information, another evaluates it, another prepares an action, and another checks the results before the workflow continues.
When designed properly, these systems can perform work at a scale and speed that would have been difficult to imagine just a few years ago. Traditional RPA still has an important role here, particularly when interacting with older systems or executing predictable actions. AI agents can add flexibility and reasoning capabilities, while orchestration coordinates the activities and human oversight remains part of the design.
We're no longer talking about simply automating a task. We're talking about automating the coordination of work itself.
And that's where things become both exciting and a little concerning.
Those Tasks Used to Belong to People
Here's something worth considering. Many of the activities we're now assigning to AI agents were once someone's daily responsibilities.
The employee who reviewed documents, researched discrepancies, routed assignments, reconciled information, or prepared recommendations was doing work that contributed to the organization. In many cases, those responsibilities were also how people learned the business, developed judgment, and eventually moved into more senior positions.
Today, we can increasingly automate portions of those responsibilities, sometimes combining the work of several traditional roles into a single coordinated system.
From a business perspective, the opportunity is enormous. We can reduce delays, improve consistency, handle larger volumes, and potentially deliver better service to customers.
But from a human perspective, I think we need to ask a different question.
If we automate the work people traditionally used to learn their jobs, how will the next generation develop the experience needed to lead?
That question deserves more attention than it's getting.
What Happens to the Next Generation?
Think about how most of us developed our careers. We started somewhere. We performed repetitive tasks, made mistakes, asked questions, learned from more experienced colleagues, and gradually developed a deeper understanding of the business.
Not every task was exciting. Some were downright tedious. But those experiences often taught us things we couldn't have learned from a textbook or training course.
Now imagine an organization where many entry-level responsibilities are handled by AI agents. The work gets completed, productivity improves, and operating costs may decline.
But where does the next generation of employees begin?
How do they develop the judgment to recognize when an AI agent makes a questionable recommendation? How do they understand a process they've never actually performed? And how do we develop future managers and leaders if we eliminate too many of the opportunities that once helped people gain practical experience?
I'm not suggesting we preserve inefficient processes just to give people something to do. That wouldn't make sense either.
I'm suggesting that as we redesign work, we also need to redesign how people learn, grow, and contribute.
Technology Has Always Changed the Workforce
This isn't the first time technology has changed how people work, and it certainly won't be the last.
Manufacturing automation, personal computers, the internet, cloud computing, and modern SaaS platforms have all changed the responsibilities of employees and the skills businesses need. Some jobs disappeared, others evolved, and entirely new careers emerged.
I believe AI will create opportunities we haven't fully imagined yet. There will be growing needs around agent orchestration, governance, security, process design, system integration, and the human judgment required to oversee increasingly autonomous technology.
But we should also recognize that transitions aren't always easy. New opportunities don't automatically appear in the same locations, at the same salaries, or for the same people whose jobs are changing.
Telling someone whose responsibilities have been automated that technology will eventually create new jobs doesn't necessarily help them today.
That's where leadership matters.
The Responsibility of Leadership
I believe good technology leadership has always been about more than implementing systems. It's about understanding the business, helping people succeed, and making decisions that create lasting value.
As organizations adopt AI agents, leaders should be asking more than how much work can be automated or how much money can be saved.
We should also be asking how employees will adapt, what new skills they'll need, where human judgment remains essential, and how we preserve opportunities for people entering the workforce.
We also need to remember that AI agents aren't employees. They don't carry personal accountability, understand every business consequence, or automatically recognize when a technically correct action might be the wrong business decision.
People remain responsible for the outcomes, even when software performs the work. That means security, governance, auditability, and appropriate human oversight must be part of the design, not something we add after a problem occurs.
The goal should be to use technology to make our organizations better, while also helping our people become better.
Progress Should Include People
I'm genuinely excited about where AI and automation are taking us. The possibilities are extraordinary, and I believe we're still in the early stages of understanding what coordinated AI agents will eventually accomplish.
But I also believe we have a responsibility to think beyond the next productivity improvement or quarterly cost reduction.
What will our workforce look like five or ten years from now? Will we create new opportunities for people to grow, or will we unintentionally eliminate the experiences that helped previous generations become experts? Will technology give employees more meaningful work, or will the pressure to automate everything leave fewer opportunities for people to contribute?
I don't pretend to have all the answers, and I don't think anyone does.
What I do know is that technology has always been most valuable when it solves real problems and improves people's lives.
So yes, let's embrace RPA, AI, Claude, intelligent agents, and the remarkable orchestration capabilities now becoming available. Let's eliminate unnecessary work, improve our processes, and build better products and services.
But let's also remember the people who helped build the businesses we're now automating, and the generations who will need opportunities to build careers in the future.
Because the real measure of progress shouldn't simply be how much work we can automate. It should also be how much opportunity we create for the people who come next.
And that's something I think is worth stopping for a minute to consider.
Thanks,
Michael Cronin
Website: https://www.michaelcronin.info
LinkedIn: https://www.linkedin.com/in/michaeltcronin/details/experience/
