AI tool fatigue is starting to feel like standing in front of an old telephone switchboard.
One request comes in. You connect it to one model. Another task needs a different model. Research goes here. Writing goes there. An image model handles the visual. An automation moves the result. An agent checks what happened. Another system stores it.
Then something fails, and you become the switchboard operator again.
Different AI models suit different kinds of work, and people who work closely with AI know it. One model may be stronger at research. Another may write with better judgment. Another handles code, images, long documents or data analysis more reliably.
There is no single model that wins every task.
That freedom is useful. It is also becoming exhausting.
We were promised less work
The pitch behind workplace AI was simple: save time.
In many cases, it does. OpenAI reported in 2025 that surveyed enterprise users saved an average of 40 to 60 minutes per day. It also found that 75% believed AI improved the speed or quality of their work.
But those gains do not account for every minute spent deciding:
- Which model should I use?
- Should I use regular chat or deep research?
- Does this require an agent?
- Which app has the latest file?
- Did the automation run?
- Which subscription includes this feature?
- Can I put company data into this system?
- Why did this model produce a different answer yesterday?
This is the hidden work of AI adoption, and nobody puts it on the invoice.
The output may take 30 seconds. Choosing, prompting, checking, moving and correcting it can take 20 minutes.
The switchboard operator never disappeared
Early telephone switchboards connected calls through a central operator. The caller did not need to understand the wiring. The operator received the request, found the right destination and made the connection.
That is close to where AI is heading.
Right now, many of us are acting as our own operators. We receive the task, judge its complexity, choose a model, provide context, connect other tools and inspect the result.
The growing list might include language models, search tools, image generators, meeting assistants, transcription software, workflow automations and specialized agents.
Each product can be useful on its own. Together, they can create a new management job.
You wanted help writing a proposal. You ended up comparing three models, copying context between two apps and checking which one invented a source.
You wanted meeting notes. You spent time reviewing permissions, cleaning the transcript and moving tasks into your project system.
You wanted an automated workflow. Now someone has to watch the automation.
We have reduced the labour inside certain tasks while adding labour around them.
One model for everything is the wrong answer
The obvious response would be to choose one AI platform and force everything through it.
That sounds tidy. It also ignores how AI systems actually perform.
A short customer-service classification does not need the same computing power as a contract review. A basic summary does not need the same reasoning capacity as a financial scenario. Image analysis, software development and document research each place different demands on a model.
Using the largest model for every request can increase cost and response time without producing a meaningful improvement. Using a small model for complex work can lower the quality of the result.
