Generative-Ai

The most common reason a generative AI proof-of-concept fails to reach production has nothing to do with the model. It is a sequencing mistake: teams build the retrieval pipeline before anyone has agreed on what “correct” means. There is no accuracy threshold, no agreed evaluation set, and no defined pass/fail criterion. The PoC produces output. The output looks plausible. Then it goes to a domain expert who finds edge cases the demo never surfaced, and the project enters a revision cycle that has no natural end. ...

Companies are spending $30,000 to $150,000 on agentic AI systems for problems that a $50/month generative AI API subscription would have solved. The reverse happens too: teams settle for a ChatGPT wrapper on a process that runs 500 times a day with 12 external system dependencies – and wonder why it never scales. The gap between generative AI and agentic AI is specific and technical. It is also consistently misrepresented. According to Gartner, 33% of enterprise software applications will include agentic AI by 2028, up from less than 1% in 2024 – but a significant portion of current “agentic AI” pitches are generative AI with a better interface. Buying the wrong category costs you either budget or months of engineering time. ...

For B2B founders, operators, and commercial leaders, the useful question is not whether agentic AI or generative AI is more advanced. The useful question is: which one removes a measurable constraint in your business? If the bottleneck is producing, reviewing, or summarizing information, generative AI may be enough. If the bottleneck is work getting stuck between systems, approvals, queues, spreadsheets, inboxes, and people, agentic AI is usually the more relevant pattern. If the workflow needs both judgment and execution, you are probably looking at a hybrid system. ...