The 80/20 AI Paradox: Beyond the Hype to Human-Centric Solutions

25

September

2025

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There’s a pattern I’ve come to recognize with almost every generative AI tool I use: it gets me 80% of the way there, almost instantly. Whether it’s drafting an email or generating code, the early output has something magical about it. Yet, that other 20% is where meaningful effort becomes essential. The process of fine-tuning the result through quick tweaks often feels like the futile exercise of pushing a rope. The initial magic wears off to expose the need to bring one’s own knowledge to bear in order to successfully arrive at the final target.

This personal experience aligns closely with my own experiences within my professional practice. I work at an AI consultancy that helps businesses navigate the complexities of artificial intelligence. A common scenario is managers or business owners coming to us with a broad question: “What can we do with AI?” They’ve heard the buzz and are eager to apply what they heard.

However, after we guide them through a design sprint to clearly define their requirements, a funny thing often happens. The problem they experience rarely requires an advanced form of an AI model at its core. Instead, what is really needed is a more streamlined business process. As Cedric Muchall (2022) points out in his book Bedrijf Bamischijf, companies often fall into the trap of adopting trendy solutions instead of addressing the fundamental pitfalls of how they are organized.

AI tools can enhance a step in that new process, like summarizing reports or automating data entry, but it’s a supporting actor, not the main star. The core solution is almost always about rethinking the workflow, not just plugging in an AI. The technology becomes a small part of a much larger, more human-centric solution.

For me, this highlights a critical misunderstanding in the corporate space. The temptation is to look for a technological fix, but the real gains come from first understanding the fundamental problem. It seems the hardest part isn’t the AI implementation, but the human process of figuring out what to build in the first place.


Muchall, C., & Toma, L. (2022). Bedrijf Bamischijf: van onzinnig “bedrijfje spelen” naar zinnig organiseren.

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The destructive effects of generative AI

11

September

2025

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Recently, a tremendous shift in the use of technology has taken place. When ChatGPT was first introduced, people treated it as an interesting novelty that you could use to create entertaining content. However, as generative AI became increasingly advanced, people started using it for a broader spectrum of uses. (Liang et al., 2025) In this moment in time, an overwhelming number of people use generative AI daily. (Beshay & Beshay, 2025) While generative AI is technologically very exciting, I think we should all proceed with great caution and be more aware when we use it.

The first reason for this is the massive burden that generative AI imposes on the environment. The enormous number of resources it takes to maintain the status quo is immense. (Zhuk, 2023) In an era where we are already being challenged with environmental issues on many fronts, minimizing the impact of generative AI on the environment should prove to be significant, and compared to other environmental issues, an easy win. (Berthelot, Caron, Jay, & Lefèvre, 2024)

Not only are we suffering from the effects of generative AI on a global scale, but also in our personal lives. I think there would be great benefits in limiting our usage of generative AI. By using generative AI as a personal companion, we can lose touch with reality. (Fang et al., 2025) Generative AI tends to react in a way that validates whatever we say. (Sharma, Liao, & Xiao, 2024) So, if we are faced with different opinions in real life, a feeling of detachment can arise. (Idem.) In addition, there are more implications on a personal level, such as a negative impact on attention (Zhai et al., 2024).

While generative AI is a tool that can be very effective in a work environment, I think we should refrain from using it excessively. It is still a very novel technique, so long-term effects have not been studied yet. However, it is a fact that it impacts the environment negatively. I think it is also safe to say that not relying on generative AI too much will positively impact our brain health.

References:

Beshay, & Beshay. (2025, April 3). 1. Artificial intelligence in daily life: Views and experiences. Pew Research Center. https://www.pewresearch.org/internet/2025/04/03/artificial-intelligence-in-daily-life-views-and-experiences/

Berthelot, A., Caron, E., Jay, M., & Lefèvre, L. (2024). Estimating the environmental impact of Generative-AI services using an LCA-based methodology. Procedia CIRP, 122, 707–712. https://doi.org/10.1016/j.procir.2024.01.098

Fang, C. M., Liu, A. R., Danry, V., Lee, E., Chan, S. W. T., Pataranutaporn, P., Maes, P., Phang, J., Lampe, M., Ahmad, L., & Agarwal, S. (2025, March 21). How AI and human behaviors shape psychosocial effects of chatbot use: a longitudinal randomized controlled study. arXiv.org. https://arxiv.org/abs/2503.17473

Liang, W., Zhang, Y., Codreanu, M., Wang, J., Cao, H., & Zou, J. (2025, February 13). The widespread adoption of large language Model-Assisted writing across society. arXiv.org. https://arxiv.org/abs/2502.09747

Sharma, N., Liao, Q. V., & Xiao, Z. (2024). Generative Echo Chamber? Effect of LLM-Powered Search Systems on Diverse Information Seeking. Roceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’24), 1–17. https://doi.org/10.1145/3613904.3642459

Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: a systematic review. Smart Learning Environments, 11(1). https://doi.org/10.1186/s40561-024-00316-7

Zhuk, A. (2023). Artificial intelligence impact on the environment: Hidden ecological costs and Ethical-Legal Issues. Journal of Digital Technologies and Law, 1(4), 932–954. https://doi.org/10.21202/jdtl.2023.40

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