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  • AI Agent Governance: The Control Plane for AI Work

    AI Agent Governance: The Control Plane for AI Work

    The market wants to know how much an AI agent can handle on its own. Enterprises, on the other hand, care about whether they can accept the agent’s actions. In high-risk enterprise workflows, the most successful systems will not be the ones that act alone. Instead, they will be the ones whose actions a company… Read article →

  • Know the agent before it moves the money

    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…

  • The AI product is the workflow

    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…

  • The new AI SaaS lock-in is operational

    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…

  • The AI moat is moving to the last mile

    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.…

  • AI products earn autonomy one workflow at a time

    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,…

  • The last-mile AI strategy test

    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…

  • Stop rebuilding the hidden machine

    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…

  • Stop asking whether the model is deterministic

    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…

  • Permission is not governance

    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.…

  • Autonomy is the reward for getting control right

    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…

  • Who gave the agent permission to decide?

    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…

  • AI will kill low-judgment product work, not product management

    AI will not replace product managers. It will replace the parts of product management that never required much judgment. That distinction matters because the current debate is too broad. One side says PMs are safe because product work is human, strategic, and cross-functional. The other says PMs are exposed because AI can draft specs, summarize…

  • Shadow workflows are your best AI roadmap

    If you work in a large organization, you already know this, even if you have never named it. Most organizations have two versions of the same workflow. One appears in the process map. It has swim lanes, systems of record, approval paths, owners, and service-level expectations. The other lives in the work itself: the spreadsheet…

  • Your AI strategy is not an AI strategy if it starts with AI

    The worst AI question is also the most common one: “How do we use AI?” It sounds responsible. It sounds urgent. It sounds like the kind of question a leadership team should ask when the board, investors, or an executive offsite demands proof that the company is moving fast. But it points the organization in…