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 […]
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 […]
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 […]
Enterprise AI conversations have changed dramatically over the last year. A short while ago, most discussions revolved around model capabilities. Businesses were fascinated by how large language models could generate content, summarize information, write code, or answer questions conversationally. Now the conversation is becoming more operational. Companies are asking a much more practical question – […]
AI agents are quickly moving from experimental technology to operational infrastructure. Businesses are no longer exploring AI only for chatbots or internal productivity experiments. They are beginning to evaluate how AI systems can coordinate workflows, interact with enterprise tools, automate decision-making, and reduce operational overhead across departments. That shift has created a new challenge. Finding […]
When a chatbot fails, the instinct is to blame the technology. The model is not smart enough. The platform is too rigid. The answers are too generic. In most cases, the technology is not the problem. The flow is. A chatbot flow is the logic that determines what the bot does next at every point […]
The most common reason multi-agent systems fail in production is not that the underlying models are wrong. It is that the coordination between agents breaks down. Individual agents may perform well in isolation. The architecture connecting them does not hold under real conditions. The MAST study, presented at NeurIPS 2025, analysed 1,642 execution traces across seven state-of-the-art multi-agent frameworks. Failure rates ranged […]
For most of the last decade, enterprise AI systems were largely observational. They generated predictions, surfaced recommendations, summarized information, or helped employees retrieve knowledge more efficiently. Even when automation systems became more advanced, the final operational action was usually still initiated by a human user somewhere in the workflow. That boundary is now beginning to […]