A few weeks ago, in a Monday planning session, my LLM agent (Claude, in this particular instance) informed me — politely, and with excellent reasoning — that I did not have enough hours in the week to get everything done that I had planned.
I start every week by running an LLM planning session across all of my workspaces, my calendar and my inbox, making sure everything that needs to happen that week is actually planned. This time, like every other time, I asked the agent to lay out all of it. It came back with a completely booked week and a pile of things that did not fit. Content production alone ran to several hours of my time, blocked across the calendar like a series of meetings with myself, and once everything else was arranged around those blocks the conclusion was simple math: the work exceeded the week.
Technically, it was not wrong. But there was a nuance.
I looked at what it had actually scheduled, and here it was. It had filled my calendar with two-hour blocks — drafting, research, and a good many other things — and it had reserved a Tuesday morning for a task that, as far as I could tell, did not require me to be awake.
So I typed into the prompt: Claude, stop. You are thinking too much like a human, and I need you to think like AI.
Let me pause there for a second. I was telling an AI to stop thinking like a human and start thinking like an AI. The irony was not lost on me.
Here is what I actually wrote, unedited:
feel like you sometimes organize the schedule too much like old-way human would, there are tasks where I’m absolutely needed to make decisions, but there are also tasks which can run on schedule without my participation - for example, I do a content Q&A session, and you or codex can automatically convert that Q&A output into a draft that I review and make changes, there are probably other ways how we can optimize our processes, in the end that’s exactly what we’re selling to our clients - efficiency. Let’s be efficient ourselves.
You can probably guess the response:
I did schedule this like a human calendar: I gave you a block for every stage, including stages that don’t need you. That’s the wrong shape, and it’s the thing we’d charge a client to fix.
And then it immediately proposed a new workflow that saved me several hours a week.
Notice what did not change. No model got smarter. No tool was added, no subscription upgraded, no vendor engaged. What changed was an assumption underneath the schedule — the assumption that a task, in order to get done, must occupy a block of a particular person’s time. My agent had inherited that assumption from the old way of thinking, which is unsurprising when you consider that these models are mostly trained on data from the past.
Which brings me to the point of this piece. To get the most out of your AI agents, you have to think differently from the way you have been thinking in the past. And that is much harder than it sounds, because the old thinking does not announce itself. It arrives disguised as a perfectly reasonable schedule.
Many companies face the same problem I was facing. The instinct is to go task by task or department by department, handing out AI roughly the way you would hand out parking spaces. Sales gets a tool. Finance gets one. Legal and operations get something too. In most companies I talk to, someone on each of those teams is already using a chatbot — ChatGPT, Claude or Copilot — to help with drafts. It is tidy, it is easy to approve, and it maps beautifully onto the org chart — which is precisely the problem. The org chart tells you who reports to whom. It has never once told you where the work gets stuck.
In my last piece, What Happened to IoT?, I suggested a test: take a process that matters and separate the parts that genuinely require you from the parts that only require someone. Here is where that test leads once you point it at a company instead of a calendar.
Imagine a fifty-person engineering firm that lives on requests for proposal. Four groups touch every bid. Sales owns the relationship and the win. Finance owns price and margin. Legal owns terms and risk. Operations owns whether the thing can actually be built by the date on the cover page.
Now imagine each of them is using an excellent AI agent that optimizes for what its department is measured on.
Sales — an aggressive timeline. Finance — lean staffing. Legal adds terms that push risk back to the client. Operations assumes it can reuse a design from a similar project last year.
Four locally optimal answers. One proposal that doesn’t fit together. The date sales promised depends on the design operations assumed it could reuse, the price finance set assumed staffing that date cannot support, and the terms legal added are ones this client will not sign.
Traditionally this gets reconciled the human way. Everyone gets in a room, or the general manager works down a list of calls. It works. But multiply that by every bid and it becomes clear what it costs: days of senior attention, most of it spent discovering contradictions that were created a few hours earlier by people who had no way of seeing each other’s work.
So what does designing around the workflow look like instead?
It starts with one shared picture of the job — the workflow. Today the bid lives as four documents in four inboxes. Instead, the price, the timeline, the staffing plan and the terms are facts about one workflow that everybody who has access reads from the same place.
Then put an AI agent over the top whose only job is to monitor the whole thing at once. Just to notice, early, when two groups have written down things that cannot both be true. Today that takes a reconciliation meeting. But an AI agent can catch the inconsistencies the moment they happen. And not only can an agent spot inconsistencies early, it can also help you decide which similar and repetitive tasks within the workflow can be handled by AI agents automatically and which ones require human intervention, and as a result save you and your team hours, days, and eventually money.
If you sell configurable products you may be thinking this is what CPQ (Configure, Price, Quote) software has done for a decade, which is partially true — encoding price and margin rules is exactly what those systems do well. But CPQ exists to produce a correct quote, and it is generally a seller’s tool. The stronger products do reach into operations — they can carry lead times and order status. However, producing a valid quote and reconciling four departments’ commitments against one another are different jobs, and the second one is usually nobody’s.
Delivery commitments and final approvals to a client stay with a person, every time. Starting with the workflow is the reframe: human review stops being spread thinly across forty small approvals and concentrates where a commitment is actually being made.
If you want to try this, pick one workflow that matters and ask three questions. Where does the work actually get stuck? Why does it get stuck there? And what is that delay costing you?
The rest of it comes down to a sentence I keep repeating: humans need to define what good looks like. If you review something, you owe the workflow an explicit account of what evidence you need, what thresholds matter, and what counts as an exception. Do that, and the work arrives assembled and a decision takes minutes. Skip it, and you remain the constraint, no matter how many agents are running underneath you.
My planning agent and I have reached an understanding. It takes the 2 a.m. shifts; I take the parts where someone has to be accountable. It has also stopped trying to book those for six in the morning, which is a relief, because I would not generally trust anything I approve at six in the morning.