The Algorithm is Almost Never the Problem

📰 Medium · Data Science

The algorithm is rarely the problem in AI adoption, but rather the lack of understanding and unasked questions, learn how to ask better questions to succeed with AI

intermediate Published 13 Apr 2026
Action Steps
  1. Read the WHO's updated guidance on AI ethics and governance to understand the importance of responsible AI adoption
  2. Identify the key questions to ask before adopting AI solutions, such as what problem are we trying to solve and what are the potential risks and benefits
  3. Develop a framework for evaluating AI vendors and models to ensure they align with your organization's goals and values
  4. Establish a cross-functional team to oversee AI adoption and ensure that all stakeholders are aligned and informed
  5. Continuously monitor and evaluate the effectiveness of AI solutions and make adjustments as needed
Who Needs to Know This

Data science and AI teams can benefit from this article as it highlights the importance of understanding the problem and asking the right questions before adopting AI solutions, and how this can lead to successful AI adoption

Key Insight

💡 Asking better questions and having a deeper understanding of the problem and the AI solution is key to successful AI adoption

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💡 The algorithm is almost never the problem in AI adoption, but rather the lack of understanding and unasked questions #AI #DataScience

Key Takeaways

The algorithm is rarely the problem in AI adoption, but rather the lack of understanding and unasked questions, learn how to ask better questions to succeed with AI

Full Article

Title: The Algorithm is Almost Never the Problem

URL Source: https://medium.com/@keyegon/the-algorithm-is-almost-never-the-problem-ee425eb87d34?source=rss------data_science-5

Published Time: 2026-04-13T17:11:55Z

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# The Algorithm is Almost Never the Problem | by Erick Kiprotich Yegon | Apr, 2026 | Medium

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# **The Algorithm is Almost Never the Problem**

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_A guide for health leaders before saying “yes” to AI_

_I’ve spent 17 years building health systems across sub-Saharan Africa. AI fails not because of bad code — but because of better questions left unasked._

I want to start with something that might sound counterintuitive coming from someone who has spent years building and deploying AI systems in community health programmes across sub-Saharan Africa.

The algorithm is almost never the problem.

The organisations struggling most with AI adoption are not struggling because they chose the wrong model or the wrong vendor. They are struggling because they said yes before they understood what they were saying yes to. And the ones succeeding are not succeeding because of better technology — they have better questions.

This is what the WHO’s updated guidance on AI ethics and governance is really about, even if it takes 9
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