You shipped the chatbot to take pressure off your support team and give users instant answers. The dashboards looked healthy at first. Sessions were happening. Then you looked closer at what those sessions actually contained, and the picture changed. Most people open the chat, type one or two messages, and leave. Seven out of ten […]
You have the budget approved. The board is excited. Someone on the team has already built a demo over a weekend that looked convincing enough to get everyone nodding. Now the pressure is on to turn that demo into a real feature inside your product, and the quiet worry sitting underneath the excitement is the one […]
You hired an AI consulting firm. They ran workshops. They interviewed your team. They produced a comprehensive AI strategy presentation with a technology roadmap, a list of recommended vendors, and a prioritised list of use cases. The engagement closed. You have a deck. Six months later, nothing has been built. This is the most common outcome of AI consulting engagements. […]
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 […]
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 […]