You do have to ask yourself: why bother with AI and agents when you already have a perfectly tailored, perfectly adequate software solution in place for a specific use case? A solution that works entirely without AI, no less.
Shift planning, for example.
There's no shortage of software for planning shifts. From relatively simple solutions to complex, highly customizable specialist tools for specific industries – it's all out there. And because there are so many providers, licensing costs are usually quite reasonable.
So why would you even think about an AI agent when an off-the-shelf piece of software does the job just fine?
Especially since you'd have to build that agent first. Tailored to your own needs. With access to all kinds of data. With everything that comes with it.
Let's walk through a – completely hypothetical, of course – scenario.
Daniela and the 5-Minute Plan
Once a month, Daniela is responsible for planning a 4-shift operation. Each shift runs 8 hours, with a 2-hour overlap. She uses a fantastic piece of software for this (note: I know it's fantastic 😉).
This software factors in holidays and absences from the HR system, various rules and policies for distributing shifts, required additional qualifications per shift, and much, much more.
Creating the initial plan usually takes Daniela less than five minutes before she sends it out to all scheduled employees.
Great.
And yes: an AI agent isn't going to make that any faster.
30 Seconds Later …
The real effort starts for Daniela roughly 30 seconds after she hits send.
Maria emails to say she'd like to swap one shift with Martin and another with Marianne.
Hans shows up at her door asking if he could take on more night shifts – they're unpopular with most colleagues anyway. He also lets Daniela know he could really use the extra pay.
Julian has submitted a leave request for next month.
Carina doesn't want to work the same shift as Markus.
Markus asks if it would be possible to only work early and day shifts for the first two weeks because he's currently getting his driver's licence.
And so on.
Of course, none of this arrives at once. It trickles in over days or weeks.
The Real Problem Isn't a Planning Problem
Daniela tries to accommodate everyone. She relies on keeping team morale high – it also makes it easier to fill gaps on short notice when emergencies come up.
So she spends hours rearranging shifts, checking alternatives, rethinking dependencies, and defusing conflicts. Most of the time it works out. But sometimes it doesn't. And then comes the question:
"Why could you accommodate Markus but not me?
I'm the one who jumped in twice last month when you needed someone at short notice."
Daniela tries to explain. Honestly, even she often can't fully recall why a particular request didn't work out. Too many changes. Too many knock-on effects. Too much context to keep track of.
And shift planning isn't even her main responsibility. It's supposed to be something she "quickly takes care of on the side." But because team cohesion matters to her, she puts in the time – not uncommonly in the evenings or on weekends.
It's frustrating.
Especially when she ends up scheduling herself for an extra shift just to avoid a difficult conversation.
The Impulse for Change
At this point, in conversation with her software provider, it becomes clear: the problem isn't the planning itself. That works brilliantly.
The problem is everything that happens afterwards:
the many human side conditions,
the wishes, conflicts, approvals and rejections,
and the question of how to explain decisions after the fact.
Together, Daniela and the software provider decide to tackle exactly this. Not with a new planning engine – but with an additional AI agent that complements the existing solution.
Division of Labour, Not "AI Replaces the Human"
Daniela provides the domain expertise:
What kind of requests typically come in?
Where do discussions arise?
Which decisions need to be traceable later?
The software provider takes care of the technical side:
Connecting to the existing systems,
accessing the relevant data,
integrating without introducing new friction points.
The AI agent is deliberately given a clearly defined and limited role: it doesn't create shift plans of its own and it doesn't make autonomous decisions.
How Requests and Changes Are Captured
There's a central inbox where employee requests and feedback are collected. The AI agent reads this inbox, structures the contents, and maps them to the existing shift plan.
In addition, Daniela can log requests directly through the software interface – for instance, when someone stops by in person or she sorts something out over the phone. For the agent, it makes no difference: everything ends up in the same context.
Leave as a Clear, Documented Decision Point
Leave requests continue to be submitted exclusively through the existing system and approved or rejected by Daniela.
When leave is approved, the AI agent is automatically triggered. It analyses the impact on the current shift plan and surfaces necessary adjustments as well as possible options within the existing rules – without making any decisions itself.
When leave is rejected, the agent records that decision too. Not to judge it, but to have context later:
Both remain traceable – approvals as well as rejections.
What the AI Agent Does – and What It Doesn't
The AI agent consolidates information, makes dependencies visible, and ensures nothing gets lost.
It doesn't evaluate.
It doesn't decide.
But it doesn't forget either.
The Real Impact
After a few months, the effect becomes apparent. It's not the number of requests that has changed – it's how they're handled.
Rejections are accepted more easily. Follow-up questions become more matter-of-fact. Decisions are traceable.
Daniela has to explain less, justify less, and keep far less in her head.
The actual planning still only takes a few minutes. The difference lies in everything that comes after – and in the relief it brings to her daily work.
Conclusion
The value of AI agents doesn't lie in replacing existing software or reinventing entire processes. Quite the opposite: well-established applications can often be complemented by such agents surprisingly easily – without touching proven logic.
This kind of AI agent leverages existing events like approvals, rejections, and changes. The technical effort remains manageable, and the value materialises quickly.
What emerges from this combination is something very practical: the existing software remains the reliable foundation for rules and planning. The AI agent adds context, memory, and explainability.
That creates relief – professionally and emotionally. Decisions become more transparent, discussions more objective, and the sense of fairness grows. Ultimately, it even has a positive impact on motivation and collaboration within the team.
Not autonomous.
Not replacing.
Supporting.
And that's exactly how AI agents can meaningfully complement existing software solutions – pragmatically, with quick impact, and with tangible benefits in everyday work.
