Enterprise implementation of AI is turning out to be a rocky road. In May 2026, Starbucks abandoned its high-profile AI-based inventory system, after nine months of failing to get it to work. The “Automated Counting” system was deployed at 11,000 Starbuck stores, rolling out from September 2025. Staff were provided with handheld tablets to scan…
A series of studies have now shown that corporate AI projects are failing at a disturbingly high rate. MIT in August 2025 found that 95% of AI projects fail to deliver any economic return. The same was found at the same 95% failure rate by Boston Consulting Group in September 2025. Similar, albeit slightly lower,…
The technology media tends to focus on the latest foundation models for AI, products like ChatGPT, Gemini, Claude, DeepSeek etc. These are general-purpose models that can be adapted to specialist uses. What gets less attention in the press is that a whole industry of such specialist tools has sprung up to take advantage of the…
For the last three years or so, generative AI, powered by large language models (LLMs) have been pushed by vendors and consultants as the ultimate automation tool. Previous waves of AI have struggled to make a lasting impact outside of niche applications, as IBM found out with its Watson AI tool. A much-publicised flagship Watson…
One mantra regarding large language models (LLMs) is that bigger is better. Parameters are the learned weights of a model, while tokens represent the pieces of text used to train it. The more training tokens an LLM sees, the more fluent the answers it produces. This assumption that bigger is better is what has driven…
I saw a disturbing image today. Not something from a war zone or a horror film, but a post on Linkedin: Microsoft have, in their wisdom, introduced a large language model (LLM)-driven AI helper to Excel called Copilot. After entering “=copilot( )”, you can type in any text you like within the brackets, and Excel…