The Hidden Cost of AI Enthusiasm

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— — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — Last week, I sat in a meeting where a client’s excitement about AI was palpable. They wanted to implement AI everywhere — in their data pipelines, their analytics, their customer service. Their enthusiasm reminded me of a project I led years ago during the big data wave, when everyone wanted Hadoop clusters regardless of whether they needed them.

As we dug deeper into their actual needs, I noticed a familiar pattern emerging. Just like in the big data era, the focus was heavily on the technology itself rather than the problems it could solve. The team had already picked out several AI tools but hadn’t clearly defined their use cases or considered the foundation needed to make AI successful.

This brought me back to a painful lesson from that Hadoop project. The organisation had built an impressive cluster, but six months later, it sat largely unused. Why? The team hadn’t invested enough in the fundamentals — data quality, team skills, and clear business objectives. The technology wasn’t the problem; the approach was.