AI Automation

Feedback is addictive. Give an AI agent a tool, an API, a database, a browser, a simulator, or a workflow environment, and the temptation is obvious: let it keep poking the world until something works. It tries. It observes. It corrects. It tries again. Compared with a model sitting alone in a prompt box, imagining every possible transition in its head, this looks much healthier. Less hallucinated planning, more contact with reality. Very grown-up. ...

Clicking the right button should not be an intelligence test. For humans, a webpage is usually manageable. We scan the visible screen, ignore the footer, dismiss the newsletter trap, and find the search box without treating every hidden <div> as a philosophical object. Web agents are less lucky. They see a modern page as a swollen mixture of visible text, invisible attributes, nested containers, event handlers, accessibility metadata, layout debris, cookie banners, product cards, promotional links, and enough frontend residue to make “just use the DOM” sound like a mild punishment. ...

Memory is easy to oversell. Give an AI agent a database, a longer context window, and a few inspirational phrases about “learning from experience,” and suddenly everyone in the room starts talking as if the system has developed institutional wisdom. It has not. At best, it has a slightly more organized attic. ...

Search looks simple until it becomes a workflow. A human analyst can open ten tabs, notice which source contradicts which, remember that one earlier search result changed the meaning of the question, and decide whether the next move should be another search, a calculation, or a final answer. An LLM agent can also open tabs, call tools, browse pages, run code, and produce a final answer. The difference is that the agent often does all of this with the discipline of a caffeinated intern who has been told that “more context” is the same thing as “better memory.” ...

Runbooks are underrated. Not the glossy strategy kind. The real kind: “check this first, then open that system, then verify the thing that usually breaks, then escalate only if the next signal appears.” Most operational work is not heroic reasoning. It is structured repetition under partial information. This is exactly where many LLM agents still look strangely amateur. They can describe a process beautifully, then fail to follow it. They can hold a long context window, then ignore the one action that would move the task forward. They can retrieve prior examples, then drown themselves in irrelevant steps. Very impressive. Very expensive. Occasionally useful. ...

A good employee does not only finish the task. A good employee leaves behind a better way to do it next time. Most enterprise AI agents do not. They solve a ticket, answer a question, call a tool, browse a page, generate a report, and then politely forget the operational trick that made the task work. The transcript may be logged. The result may be saved. But the capability itself usually evaporates into the great corporate compost heap of “learnings”. Very nourishing. Not especially executable. ...

TL;DR for operators Entry-level automation is usually discussed as a headcount issue. That is too crude. The sharper operational question is whether automation changes which juniors get access to which experts. A firm can keep the same number of junior roles and still damage its future skill pipeline if more of those roles move away from high-quality mentors. ...

TL;DR for operators The useful question is no longer “Can an LLM write code?” It can. Often quite well, occasionally with the confidence of a junior developer who has just discovered Stack Overflow and caffeine. The better question is: which parts of the software development lifecycle can be safely handed to an agentic workflow, and under what controls? ...

TL;DR for operators A recent case study from If P&C Insurance is useful because it does something most AI automation stories conveniently skip: it follows the work after the model is deployed.1 The company used an LLM to identify specialised claim parts in insurance claims, a task that had depended on human claim handlers and specialist knowledge. In offline evaluation, the fifth model iteration built around GPT-4o-0806 reached 81% recall in English, above the company’s 70% human baseline. That sounds like the usual “AI beats humans” headline. Mercifully, the paper is more interesting than that. ...

TL;DR for operators Month-end close is not where small firms discover their love of manual labour. It is where invoices arrive half-labelled, clients reply with attachments named final_final_real.xlsx, and a senior accountant spends expensive hours doing work that is intellectually closer to sorting laundry than advising a business. The practical AI opportunity for small accounting and professional service firms is not “give everyone a chatbot and hope the profession becomes futuristic by Friday.” The better architecture is a cost-aware, privacy-first workflow: classify the task, remove or mask sensitive data where possible, retrieve the right firm knowledge, route the easy work to cheap or local tools, escalate uncertain cases to stronger models, and keep humans in charge of outputs that affect filings, financial statements, tax positions, or client advice. ...