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. […]
Most content about multi-agent orchestration describes what it is. This article describes where it actually works in production business software, what the coordination looks like inside each use case, and which ones are worth prioritizing for a first build. The distinction matters because not every workflow benefits from multi-agent orchestration, and not every use case that sounds appealing is production-ready. […]
Most AI chatbots sound impressive for the first 30 seconds and then lose the user. They answer questions, but they do not help people move toward a decision, a booking, or a purchase. That is the difference between a chatbot that looks good and a chatbot that actually drives business value. For product and platform […]
For years, enterprise automation has largely operated on a predictable principle: define the rules, structure the workflow, and let the system execute repetitive tasks efficiently. That model helped businesses streamline a wide range of operational processes. Teams automated approvals, ticket routing, invoice processing, notifications, onboarding flows, and customer support escalations through predefined logic built into […]
Enterprise AI projects usually begin with excitement. A team experiments with a large language model, uploads a few internal documents, asks some questions, and suddenly the possibilities feel enormous. Employees can retrieve information conversationally. Customer support responses become faster. Internal search appears dramatically smarter. For a brief moment, it feels like the organization has solved […]
Most businesses hear “AI chatbot” and “AI agent” as if they mean the same thing. They do not. A chatbot is usually built to answer questions and guide users through a defined conversation, while an AI agent is designed to take action, make decisions within boundaries, and complete more complex tasks. For product and platform […]
The first wave of enterprise AI adoption was largely centered around individual agents. A business would identify a repetitive workflow, connect a language model to a retrieval system or a few operational tools, and deploy an AI assistant capable of handling a specific set of tasks. In many early-stage implementations, this worked well enough. Teams […]