This article explains how AI should be implemented as a workforce expansion tool — not a job replacement strategy — and how organizations can restructure roles around managing AI systems.
“People, Ryan… and people will never go out of business.”
Michael Scott’s rare genius shines on us again as we look ahead to how AI will shape our hard working teams.
Let me be clear: AI SHOULD NOT REPLACE JOBS. It should expand them.
The Low-Code-Bros will NEVER advocate for teams to be cut based on our automations. Rather, roles should transform.
Junior team members should have elevated roles with more responsibility. They shouldn’t be replaced- but rather marshalling teams double the size with double the energy.
The New Org Chart: AI as the First Universal Junior Hire
While Janice from accounting was at home brewing her herbal tea, her AI agents were processing her emails in double time. Her Claude account was scraping new emails as they came in and pinging her with urgent messages.
Her Perplexity account was deep-researching the new Accounting methodology she heard about on LinkedIn.
Round and round her agent fleet goes, the new junior members of the Accounting department.
When you manage AI agents in your role, total responsibility shifts from Creator to Editor. Where you once created spreadsheets for your staff, you now oversee their creation and make adjustments for human taste where needed.
Teaching team members unfamiliar with AI how to manage these “junior” agents can come with pushback.
Employees struggle to start conversations with a large language model (LLM), or identify a key workflow they could easily pass off to an AI.
Moreover, there’s a real learning curve to leading junior team members that know everything about the world, but almost nothing about your business.
But there’s a simple trick. If you’re new to LLMs, simply start conversations with the AI like you’d talk to an employee.
AI are great at 2 things: research and creation.
Where do your teams lean into those? Start there, and give the AI a task like you’d give to a new hire on your team.
The workflow for AI agents looks like: receive a task -> decide whether it’s creative or research-based -> if so, send to AI for a first pass -> Re-prompt or edit manually where necessary.
Will they always get it right away? Nah. But that’s where human managers will start to separate themselves. Explaining to AI agents task directions is a new key soft skill for any position.
What “Promotion” For AI Managers (AKA your entire staff) Actually Means
Congratulations! Each of your employees have officially been promoted to managers!
No, but seriously- if you’re framing this shift in any other way, you’re going to get pushback from your team.
Start with the “Why.”
“We’re introducing AI because we believe our team is best-in-class and we want to scale it.”
And by the way, “scaling” is the appropriate term here- if you have a “Worst-in-class” team, get ready to see more of it!
Your newly implemented AI Managers should now be in a position to exercise new skills: delegation, QA, and reporting come to mind- all because of their ability to delegate to an LLM.
In addition to their newfound knowledge of AI processes, these soft leadership skills that allow them to successfully deploy AI will upgrade their careers anywhere they go.
The New Skill: Managing Digital Teammates
The first thing you learn when working with AI and large language models is: systems are your friend!
I mentioned before that poor teams lead to poorer teams with AI. Well, so do poor systems.
Data integrity, process formalization… these aren’t just buzzwords in 2026, but actual problems that extrapolate out when trying to automate. So get your systems and your data right!
This has historically NOT been as much an issue, because your messy folder on your work computer has only been accessed by YOU.
If an AI agent goes to find documents on your laptop, how will they discern between “budget sheet 1,” “budget sheet (final),” and “budget sheet (master)?”
If your data nomenclature is poor, your instructions will have to be that much more extensive. Do the dirty work now and clean up your files.
Once you have that in place, redefine roles. Who is actually managing if AI is a core process in the business? A content marketing strategist’s AI agents will look a lot different that the CFO’s.
Structurally, everyone stays in the same position (no need to revise job titles just because you roll out AI), but delegation to even an associate-level now comes with a need for total comprehension.
You wouldn’t delegate a task to a director who half-understands the goal. When everything is scaled out, even a slight miscommunication can lead to a large-scale problem to solve later.
This can now occur at all levels of your organization.
Also, roll out a formal AI management program! It doesn’t have to be the Low-Code-Bros, but anywhere people can get clear instructions on how to lead their new AI employees will add value.
Most importantly, get training in place that puts the people in the driver’s seat. Empower your teams to explore, learn new things, and fail smartly within safeguards.
Once tools are set up to run independently with their managers, slowly pivot toward an integrated approach, where AI actually talk to each other.
Simple pass-offs work great initially, but then layer on simple follow ups and build in a safeguard to avoid a expensive mishaps.
AI adoption is all about how you frame it. If you frame it as the replacement technology, teams will scoff.
If you frame it as a step toward leadership, it’s easier to grasp.
If you’re interested in more thoughts on guiding your team through AI adoption, give us a shout at kyle@low-code-bros.com.
