You are two weeks from shipping the AI feature. The model is trained. The endpoint is deployed. The product manager has written the release notes. And then someone asks: “has anyone actually checked whether the rollback plan is documented?” That question, asked two weeks before launch, is manageable. Asked two days after launch when the model starts producing […]
You ran the assessment. You sat down with your team, worked through the questions honestly, and found something you did not expect to find. Maybe it was the data, years of records with no labelling, no outcome signal, nothing a model can learn from. Maybe it was the use case, three people in the room […]
The conversation in your last board meeting probably included the phrase “we need to be doing more with AI.” Your investors are asking about it. Your competitors are announcing it. And somewhere in the middle of all that, you are trying to figure out whether your organisation is actually in a position to build something that works. The honest […]
Somewhere in the last eighteen months, a decision got made in a lot of product teams that sounded reasonable at the time. The question was “should we add AI to this feature?” and the answer was “yes, we should use one of the big language models.” Nobody in the room pushed back. GenAI was what everyone was […]
Your team has decided to add ML to the product. Now someone in the room says “we need to predict this” and someone else says “no, we need to classify it” and a third person is sketching a forecasting model on the whiteboard. The meeting ends without a decision. This confusion is not a technical problem. It […]
You have sat through three AI demos this quarter. Two of them involved GPT. One involved a neural network diagram nobody in the room fully understood. And somewhere in all of it, your team is trying to answer a very practical question – which customers are most likely to churn next month? That question does […]
Building a GenAI prototype is not the hard part. The demo works. The outputs look convincing. The team is excited. The hard part is everything that comes next. According to Gartner’s April 2026 analysis of GenAI project failures, at least 50 percent of GenAI projects are abandoned after proof of concept. Of the projects that do proceed, […]
Ask most engineering teams when they chose between RAG and fine-tuning and the honest answer is – before they fully understood the problem they were solving. A proof of concept gets built with whichever approach the team was most familiar with. That approach either works or does not. If it does not, the other approach […]
There is a distinction that most SaaS teams building GenAI features do not make early enough, and it costs them significantly when they discover it in production. The distinction is between users trusting your feature and your feature being trustworthy. A GenAI feature can earn user trust quickly. The outputs sound confident. The interface feels […]
Most organisations that have experimented with generative AI in their product and engineering teams share a version of the same experience. The pilot looked promising. Code was being generated faster. The demos impressed. Then adoption stalled, the productivity numbers came in lower than projected, and the ROI question went unanswered at the next quarterly review. […]