
Companies are missing the mark with AI projects. The headlines are not great.
- Gartner Predicts 30% of Generative AI Projects Will Be Abandoned After Proof of Concept By End of 2025
- Research by the RAND Corporation estimates more than 80% of AI projects fail, which is twice the rate of failure of non-AI projects.
- Deloitte shows some benefits accruing such as uncovering ideas and insights with ROI generally positive, and yet, barriers due to mistakes with real-world consequences.
- BCG’s 2025 survey indicates only “25% of executives are seeing significant value from AI”, with projects vulnerable due to unclear use cases and poor data readiness.
What’s going on? And more to the point; can product people help improve these outcomes?
Perhaps there are efforts to solve tech problems, but not always customer problems. From a Product person’s perspective, it’s time to re-visit basics and use cases. In the add AI scramble, there’s confusion about our shiny new toys. You see it in the drive to do anything with them. You see ridiculous job requirements posted from tech to product asking 5 – 10 years experience deploying AI. (For traditional ML, that may be fair. But they usually mean GPTs/LLMs, which have only been around a handful of years.)
Let’s reconsider our new capabilities from customer use case perspectives with focus on LLMs/GPTs. While also growing, we’ll skip traditional ML as it’s a separate and better understood category. (Things like regression models, decision trees, or clustering algorithms used in fields like finance, logistics, or medical diagnostics, which have established frameworks and decades of practical application.)
Why should we try a look from use case perspectives? Because for those of us that work in Product, instead of panicking figuring out how to check off the “We’re doing AI” into a checkbox in an Annual Report or a marketing piece, it might be useful to have clarity of purpose. We’ll focus on Generative Pre-trained Transformers (GPTs) and the Large Language Models (LLMs) over traditional ML or multi-modal GPTs.
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