5 Articles About AI Worth Your Time

Natasha Salmi

03 Dec 2025 — 2 min read

Articles featured: An Opinionated Guide to Using AI, OpenAI Research Paper, MIT Research via Forbes, Business Costs of Poor AI Data Quality, ICO Guidance

I waded through the AI slop flooding the internet (yes, over half of new articles are now AI-written) to find pieces that will actually help you work smarter.

What makes these different? They’re not technical pieces written by engineers for engineers, or product marketing dressed up as thought leadership. They're written by real people for real people and based on quality research and data.

Here's what's worth reading:

1. Which AI Should You Actually Use?

An Opinionated Guide to Using AI

Ethan Mollick cuts through the confusion with a practical guide on picking the right chatbot for your needs. No fluff—just clear recommendations on when to use ChatGPT vs Claude vs others based on your specific tasks.

Takeaway: Match the tool to the job. One AI for everything means suboptimal results.

2. How People Really Use ChatGPT

OpenAI Research Paper

OpenAI analysed usage patterns across 700 million users and found something surprising: ChatGPT's primary value is helping people make better decisions in knowledge-intensive work, not automating tasks away.

Takeaway: Most people treat AI as a co-pilot, not a replacement.

3. Why 95% of AI Pilots Fail

MIT Research via Forbes

MIT research pinpoints why most GenAI initiatives never scale: organisations bolt AI onto existing workflows instead of redesigning the work itself.

Takeaway: Valuable AI integration requires friction and re-thinking entire processes.

4. The £406 Million Data Quality Problem

Fivetran Survey

Poor data quality costs organisations an average of £406 million when used to train AI models—because AI amplifies whatever you feed it.

Takeaway: AI tools won't fix poor data foundations.

5. UK Government Guide on AI Security

ICO Guidance

The ICO's new guidance translates data protection compliance into practical steps for assessing security and data minimisation when using AI—no technical background required.

Takeaway: Understand your data protection obligations before feeding data into AI systems.

The common thread is the hard work isn't using AI day-to-day. It's doing the organisational groundwork to ensure you get real value from it.

See you next Wednesday!

Natasha and the ClimateAligned team

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