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A bank receives an instruction to move $4.8 million from a corporate treasury account. The request does not come directly from a human user. It comes from an AI agent operating inside the company’s finance stack. The bank has to decide whether the instruction is a valid delegated action, a misconfigured workflow, or a compromised […]

When you walk into most enterprise AI conversations today, the discussion starts with the model. Is it smart enough? Is the demo impressive enough? Does the feature set cover enough ground? But talk to buyers in operational, customer-facing, or regulated work and the questions shift. What job does this system do? What is it allowed […]

One of the most overlooked questions in an AI SaaS evaluation is not “what features are included.” It is “what happens when we need this vendor to stop?” That question sounds defensive until you watch a buying team realize what the demo is really showing. A customer-service platform is no longer only summarizing tickets or […]

As frontier models converge for a growing class of work, raw AI capability explains less of the difference between products. That is a narrower claim than it sounds. Model quality still matters. Accuracy matters. Latency matters. Long-context reasoning, code generation, multimodal performance, tool use, and safety behavior can decide whether a product works at all. […]

A demo only has to impress once. An AI product has to work every day. The first version looks magical in a conference room: it answers the clean prompt, completes the happy path, and suggests that broader autonomy is one launch away. Then real users arrive. They ask incomplete questions, use old terminology, need exceptions, […]

AI makes it easier to produce plausible work. That makes it more important for PMs to know which work should survive. A PM can now generate a research synthesis, mock a prototype, draft a PRD, summarize support tickets, ask an agent to update a system, and compare five product directions before lunch. The visible output […]

The easier it gets to add AI, the more valuable it becomes to know where AI does not belong. Most teams no longer struggle to access models that can summarize, draft, classify, route, recommend, answer, and act well enough to produce impressive demos. They struggle to turn that capability into something users trust, repeat, and […]

Many companies are not building AI automation capabilities. They are rebuilding the same hidden machine in different departments. Finance funds an invoice automation project. Legal buys contract AI. Product teams add a research synthesis tool. Compliance experiments with policy review. Each team writes a separate business case, evaluates separate vendors, defines separate workflows, and argues […]

When you work on AI in a regulated environment, you will hear the same question again and again: can the model say the same thing twice? The question is not wrong. It just aims too low. The real standard for regulated AI is whether the workflow can prove what happened. Consider a finance team. They […]

When you are responsible for putting AI agents into production, you will face a governance question that sounds like security but turns out to be about operating standards. A policy tells a team what an AI agent is allowed to do. It does not tell the team whether the agent deserves to keep doing it. […]

When you watch a customer support agent resolve tickets end to end in a demo, the workflow looks complete. It reads the complaint, checks the account, drafts the response, applies the credit, and closes the case. The room nods. Then the product reaches production and the real questions begin. Which refunds can it issue? What […]

The dangerous question in agentic AI is no longer, “Can the agents talk to each other?” It is, “Who gave them permission to decide?” That question sounds simple until a workflow goes live. A customer support agent triages a complaint. Another agent retrieves account history. A third drafts the response. A fourth recommends a credit […]