An AI assistant that produces a wrong answer creates an information risk. An AI system that calls the wrong tool can create a business event. This is the architectural boundary that matters when we move from conversational AI to tool-using assistants and agents. The model may interpret a request, select a tool and prepare its … Read more
Testing an AI application before release is essential, but it does not establish that the application will remain reliable in production. Enterprise AI systems operate in a changing environment. Users discover new ways to ask questions. Knowledge sources are revised. Prompts and retrieval settings evolve. Model providers release new versions. Business processes and policies change. … Read more
Many organizations have already defined principles for responsible artificial intelligence. They want AI systems to be fair, transparent, secure, reliable and subject to human oversight. They create policies, review boards and risk classifications. These are important foundations, but they do not by themselves control what happens when an AI application receives a request in production. … Read more
Traditional software and generative AI solve problems in fundamentally different ways. Traditional business applications follow explicit rules. Given the same input and state, they normally produce the same result. Generative AI works probabilistically. It interprets context and produces a response based on learned patterns, which means the result can vary even when the input appears … Read more
Artificial intelligence discussions often begin with models. Which model is the most capable? Which one has the largest context window? Which one performs best in reasoning, coding or multimodal tasks? These are valid questions, but they can also lead organizations toward an incomplete understanding of enterprise AI. A powerful model may be an essential component, … Read more