If you now find yourself spending your morning choosing which AI tab to click first, you aren’t the only one. Over the past two years, countless knowledge workers and their teams have transitioned from “WOW this is amazing!” to AI Tool Overload. And by this, I don’t mean simply “we have too many tabs open.”
I mean that many people are logging on later, burning out earlier, and complaining that AI workflows that promise 10X efficiency aren’t working quite that well. And the reason isn’t a lack of adoption, bad training, or any one product. The reason is the number of tools.
I don’t know who coined the term stack, but we should all be familiar with it. It basically means, “the combination of software and applications an organization uses, as well as its technology and infrastructure.” We’ve long been using software stacks, but AI makes it easier for teams to stack too many overlapping tools. You can see what a stack is in the latest theMITmonk video.
It’s not uncommon today for marketing to have a brand voice AI tool, research to have an AI-powered search agent, a transcription AI with an embedded AI assistant for meeting notes, a CRM with an embedded AI to write a follow-up email, and for each browser to have two or three different AI extensions for quick summarization tasks.
These are all good tools. No, they are great tools. You might be using one tool to write marketing copy because of its strong brand voice feature, another to search for facts, a third to transcribe meetings, and a fourth to draft follow-up emails to a contact.
That’s fine, but the issue isn’t in using one product or even three. The problem is when it turns into four, five, or six that all rely on human effort to manage their use and output: the AI tool itself, the work the tool produces, and the decision for when to use that tool.
When you have more than one AI product or workflow, the human cost of adoption isn’t only about learning how to write a new prompt; the cost of adoption also means managing logins and workspaces, prompts and parameters that don’t match, and knowing which is the “source of truth” in the data, as you manage them all.
The Problem is You’re Doing More Work by Doing Less
Research, too, backs this up. Harvard Business Review summarized data on AI “brain fry” in a recent report, showing that while productivity increased with the adoption of AI, there was a point at which the use of AI became counter-productive. After three AI tools or agents, the data shows that self-reported productivity and decision fatigue actually began to decrease.
There is some evidence for this in studies about context switching, the cost that occurs every time knowledge workers switch between apps, tabs or windows. Context switching is taxing because of what we’re leaving behind: our focus, our train of thought, which tab was last active, which document was most recently changed.
But what if you weren’t just switching to a different tool, you were also switching from doing work to evaluating what work you were actually doing and which version was the most recent? The problem isn’t just the tool switch, but the human judgment it also calls for.
It’s an interesting, if not inevitable, trade-off: the first and second tool might have been a net productivity benefit for each task, the third a small net benefit, and the fourth and fifth likely cost you more time than they saved.
Avoiding AI Decision Fatigue
People ask me all the time, how do I avoid AI decision fatigue? But it is volume of decisions, not syntax of any single one.
You will tend to make decisions in chunks with a manual process: pick a direction, plot out a plan, execute. With several AI tools on top of that you are adding dozens of additional decisions:
- Which tool should I use for this?
- Is the output good enough or should I re-generate?
- Is the citation trustworthy or should I double-check with another tool?
- Do I move this draft to the writing tool or leave it where it is?
Every decision costs you a fraction of mental energy. A couple of them, sure, it’s negligible. Hundreds every day, not so much.
That’s the thing no one wants to confront: AI tools can make discrete pieces of work faster while making the whole day harder from a cognitive perspective. You become a reviewer rather than a producer, and the kind of review-heavy work that’s the most exhausting of all.
Fragmented work
The other impact of too much AI tool usage is the fragmentation of your workflow. Your end-to-end process isn’t smooth from start to finish anymore, it’s a patchwork:
- A meeting occurs in Zoom.
- An AI notetaker generates a transcript from it.
- Highlights are summarized with a general-purpose chatbot.
- Action items get ported to your task app’s AI assistant.
Later, someone asks, “What did we decide?” Do you check the transcript tool? The AI summary? The project board? It turns out different tools each have partial perspectives on the same situation.
That’s why so many are increasingly asking whether they are better off with a simpler, manual workflow in some cases. It’s not the “AI tool stack vs manual processes” debate that’s about sentiment.
It’s about the reliability and predictability of your process. You may be able to get work done faster with an AI tool stack, but if it’s scattered and fragmented rather than cohesive, then the manual method can give you better end results.
Subscription bloat
The other issue with too many AI tools: managing your subscriptions.
A growing number of people now pay for:
- A handful of general-purpose chat tools
- A specialized assistant for writing or design
- An AI meeting recorder
- An AI-powered CRM or project management module
Each tool is often billed at $10–$30/month, so that doesn’t sound like that much. But put enough of them together and this is what AI subscription bloat looks like: when you are effectively paying multiple vendors to re-sell you the same models while not even using half the features you’ve paid for.
The financial cost is one thing, but you are also paying in terms of:
- The time to learn each interface
- The hassle when it comes to renewing, downgrading, or cancelling
- The danger of a work-in-progress trapped in a tool you no longer use
When you and the team audit your stack, you can expect to spot both over-capacity and redundant capabilities. We encounter the problem of tool overload in our to-do list before we see it on our schedules.
Here’s why we get there. Most of these issues originate with the same order-of-operations problem: we’re thinking about tools first, not about our workflows.
Usually it works like this: first, you see a great demo or post. Second, you use the tool for the first time and it gives an amazing first result. Third, you add it to your stack “just for this use case.” Then you repeat this a few or a dozen times. Suddenly you’re just sprinkling AI onto things, like “here’s an AI for emails, and an AI for decks.”
Nobody has designed an AI-enabled workflow that spans all of a person’s productivity needs, so the stack grows horizontally instead of vertically.
Or it could go the other way.
- Design the work.
- Find the few tools that can reliably do it for you.
This isn’t the same as having zero AI tools, or a single AI tool to rule them all. This is choosing a lean, small, and deliberately mapped set of tools for the actual bottlenecks in your repeatable workflows.
For a solo consultant, a lean AI stack could mean just:
- A single primary chat interface for writing, brainstorming, and general reasoning
- A single integrated tool for meeting notes and action items that feeds directly into their task manager
- A single tool for a specific capability they actually need, like code generation, design, or analytics
For a small team, it could start like this:
- Identify your slowest workflows. Where do hand-offs get stuck? Where do people spend time copying and pasting? Where are decisions being bottlenecked to one person or two?
- Identify which steps are actually good for AI, such as pattern-matching, summarizing, drafting, classifying, or distributing tasks. Not every process needs a model to do it better; some just need a more coherent process.
- Use as few tools as possible to do those things well end-to-end. In many cases, this means leaning more on the AI capabilities built into the tools you already use, rather than using entirely new point tools.
Here are a few practical guidelines that can help anyone trying to build an efficient AI-enabled workflow without exhausting their team:
- Design AI around flows, not features. Map out the end-to-end process from the initial trigger to the final outcome. At each point in the chain, determine where AI actually provides value in the process flow, and which single tool will own that function.
- Don’t go over the brain limit. For any individual, there usually isn’t a good reason to heavily rely on more than three distinct AI tools at the same time. The vast majority of daily workflows should go through just a handful of familiar and predictable paths.
- Where your outputs end up matters. Pin down a single source of truth for the human-reviewed product, whether that’s your docs, your CRM, or your ticketing software. Your AI stack should feed into that system, not fight against it.
- Audit your subscriptions regularly. Treat AI subscriptions like an ongoing practice rather than a one-off cleanup effort. For each tool, ask what specific action it speeds up, how much time it saves each week, and whether a tool you already have could do the same job.
Exploring without being overrun
None of this means you should avoid exploring other AI tools. Experimentation is how you uncover better-fitting solutions. You just need to explore deliberately without letting exploration devolve into unmanaged proliferation.
A sensible way to do that is to batch your experiments. Block off a small amount of time, say one afternoon per month, to explore or evaluate substitutes for a particular part of a particular task, like research or slide deck writing. Take a few notes about what genuinely felt faster or clearer in terms of a practical task.
Curated directories can make this easier by surfacing equivalent tools in parallel without the distraction of social media. If you’re trying to see a range of options available within a category, say for AI note-taking or coding assistance, going to a catalogue like TopCollection.ai is a more pragmatic way to get your bearings than randomly signing up for the latest tools that appear in your feed in a given week.
The point is that you should try new tools against a defined work process and an existing benchmark, rather than in a vacuum.
From fatigue to sustained leverage
The hard truth about many teams experiencing AI fatigue is that they haven’t done anything wrong. They’re after higher productivity, they’ve followed the trend, and they’ve implemented what looked at first glance to be useful. But this pattern isn’t sustainable.
AI will perform better if you give it a couple of clean, clearly defined lanes.
This is achievable with a slimmed-down AI suite that’s organized around well-thought-out tasks. The stack becomes easier to navigate because workers know what tools to deploy and why.
It stays free of subscription bloat because only what aligns with the most pressing bottlenecks is maintained while the rest is discarded. It avoids fragmentation because artifacts get delivered to the same location, not spread across a dozen different views.
All of this eventually comes around in a less dramatic fashion than with a brand-new AI tool. Less open tabs. Less micro-decision-making. Less confusion over where you left something. More periods of substantive, concentrated effort, with the AI in turn fulfilling its part behind the scenes.
The promise of AI has always been about doing more of what really matters with less drag, not about using every available AI tool possible. Getting there isn’t a question of what the next version of the model can do. It’s about what your stack looks like, and what you’re willing to leave it to do without.
