The Technical Context
I read a text by Siddhant Khare and got a very familiar feeling: the problem isn't that AI is weak, but that too many layers have been built around it. When AI implementation in a team boils down to ten tabs, three agents, and constant re-checking, the work genuinely becomes harder.
Khare's thesis is simple and very true to life: AI generates ideas and drafts faster than a human can validate them. This is where I usually advise clients to pause at the start. If the output stream grows but the decision-making loop isn't redesigned, you don't get acceleration; you get a new form of chaos.
This is also supported by recent data from 2026. BCG, via Fortune, reports that productivity increases with up to three AI tools, but starts to decline after four. The reason isn't magical: mental effort, fatigue, and information overload increase because a person has to act as a dispatcher for a whole zoo of models.
What strikes me most here isn't 'fatigue' as a buzzword, but the 'judgment tax.' AI can easily spit out 10 versions of text, code, or research, and then I have to figure out which one won't break the product, introduce a hallucination, or create tech debt a week later. It's this tax on judgment that eats away at the benefits.
That's why the idea that 'the more AI tools, the better' dies upon first contact with real work. A single strong scenario with good AI integration is almost always more useful than five semi-integrated services that require the team to manually copy-paste between them.
Impact on Business and Automation
For businesses, the takeaway is very down-to-earth. The winning teams are those that limit their tech stack, assign clear roles to tools, and eliminate manual jumps between them. The losers are those who buy everything and call it 'automation with AI.'
I would look at three things: how much time people spend on verification, how many context switches there are in a single process, and where AI actually completes a task rather than just creating another draft. If there are no answers, you don't have automation yet; you have expensive hustle.
At Nahornyi AI Lab, we typically solve this exact problem: not 'adding another AI,' but building a workflow that reduces the team's workload. Sometimes the best move isn't a new agent at all, but cutting out half of the unnecessary decision points.
If you feel your team is no longer speeding up but is instead getting tired of AI software, let's break down the process step by step. At Nahornyi AI Lab, I help build AI automation without this circus of tabs—so the system removes routine tasks instead of adding another layer of overload.