Why Most People Use AI Wrong — And How to Fix It

Most businesses that "adopt AI" do the same thing. They buy a tool, bolt it onto a process that was designed for humans, and wonder why nothing changed.

They're asking the wrong question. They're automating at the wrong level — asking "can AI do this job?" when the real question is "which steps of this job can AI own?"

There's one framework behind every workflow I redesign. It's been hiding in plain sight for decades — fighter pilots use it, robots use it, and now every major AI lab has independently converged on it. Once you see it, you can point it at any task in your business and know exactly where AI fits.

Every Workflow Is Secretly a Loop

Every workflow — every single one — is a loop with six steps:

• Sense — gather the inputs. An email arrives, a lead fills a form, an invoice lands.
• Interpret — classify it. What kind of thing is this? Urgent, routine, junk?
• Propose — generate the candidate action. A draft reply, a recommendation, a route.
• Decide — approve, edit, or reject. This is the judgment step.
• Act — execute it. Send, file, book, pay.
• Iterate — learn from the corrections so next time is sharper.

Here's the insight. In the pre-AI world, you optimized workflows by training people or buying software for whole tasks. All or nothing. Now the unit of design has shrunk. You can place a machine at any one of these six steps — independently.

Work stops being a job description and becomes a loop where humans and AI each own the parts they're best at.

That's the shift from the pre-AI world to the post-AI one: we move from "all or nothing" — whole job descriptions handed to a person or a tool — to breaking work into steps and sub-steps, each a place where you and AI work side by side. It's also a stepping stone: as trust builds, more of those steps hand off to AI entirely — but that's for another day.

This Pattern Isn't New

If this sounds familiar, it should. The pattern has a deep lineage.

Fighter pilots know it as the OODA loop — Observe, Orient, Decide, Act. John Boyd, military strategy, 1970s. Roboticists call it Sense–Plan–Act. IBM built self-managing systems on MAPE-K — Monitor, Analyze, Plan, Execute over a shared knowledge base.

And the kicker: OpenAI, Anthropic, Google, Microsoft, Meta — wildly different products — have all converged on the same perceive-reason-act cycle for their AI agents.

When decision science, robotics, and five competing AI labs all land on the same shape independently, that's not a coincidence. It's the common substrate of intelligent action — how any agent operates, whether it's a pilot, a thermostat, a chief of staff, or an LLM.

The Second Axis: Who Runs Each Step

The loop tells you what the steps are. The other half of the framework is who runs each one — and that changes over time.

Three modes. Think self-driving car levels:

• Human-in-the-loop — AI proposes, human approves every time. New, high-stakes, unproven steps start here.
• Human-on-the-loop — AI acts, human monitors and can intervene. For proven steps where speed matters.
• Human-out-of-the-loop — AI runs autonomously, human reviews in aggregate. Low-stakes, high-volume, well-trained.

The rule that matters: autonomy is earned per-step, not switched on globally.

You start everything human-in-the-loop. You watch one number — the human's edit rate. How often does the human change what the AI proposed? When that rate falls toward zero, that step has earned its autonomy. That's not a feeling. That's data.

And the human always keeps a kill switch. Always.

For example: the first step you hand over might just be "open my inbox each morning and summarize the newsletters sitting in it." Read-only, low-stakes — if it gets one wrong, nothing breaks. Watch it for a couple of weeks. Once the summaries are accurate and you trust them, hand it the next step: archive those newsletters automatically. Now it's taking an action, not just reading — but only because the read step earned it.

Three Real Examples

Same loop, three workflows. Watch where the line between human and machine sits.

Email Inbox

Fifty emails a day, two hours of your life.

• Sense: AI reads everything as it arrives. Machine job, day one.
• Interpret: AI tags it — client, urgent, newsletter, junk. Machine, day one.
• Propose: AI drafts the reply in your voice. Machine.
• Decide: You approve or edit each draft. Human — for now.
• Act: Send, archive, file. Machine, once approved.
• Iterate: Every edit you make teaches the next draft.

Three months in, your edit rate on routine scheduling replies is near zero — so that category graduates to autonomous. Client negotiations? Still human-in-the-loop. Maybe forever. That's fine. That's the design.

Lead Follow-Up

Sales team, or a solo realtor — same loop.

• Sense: New lead hits the CRM from a form, a call, a referral.
• Interpret: AI scores it — budget, timeline, fit. Hot, warm, cold.
• Propose: AI drafts the personalized first touch and a follow-up sequence.
• Decide: Human approves hot-lead messages; warm-lead nurture runs on-the-loop.
• Act: Messages go out, meetings get booked.
• Iterate: Which messages got replies? That feeds the next scoring and drafting round.

Notice the nuance — different autonomy modes within the same workflow. Hot leads get human judgment. Routine nurture doesn't need it.

Invoice Processing

Back office. Nobody's favorite job.

• Sense: Invoice lands by email or upload. AI extracts vendor, amount, due date, line items.
• Interpret: Match to a PO, flag duplicates and anomalies — "this is 40% above last month."
• Propose: Code it to the right account, queue the payment.
• Decide: Human approves anything over a threshold or flagged. Under $500 and clean? Straight through.
• Act: Posted to the books, payment scheduled.
• Iterate: Every correction tightens the matching.

The threshold is the autonomy ladder, expressed in dollars.

How to Run This on Your Own Business

Pick one workflow that eats your week. Then:

1. Name it — and the trigger that starts it.
2. Walk the six stages — write down what currently happens at each, and who does it.
3. Find the insertion points — Sense and Interpret are usually the first wins. Cheap pattern-matching. High-judgment Decide steps stay human longest.
4. Set the autonomy mode per step — default everything to human-in-the-loop.
5. Define the feedback mechanism — how do corrections get captured? Skip this and you've bought automation, not intelligence. The system never improves.
6. Phase it — ship the lowest-risk, highest-savings step first, usually Sense plus a daily summary. Prove it. Then expand.

The deliverable is one page: six stages, current owner, target owner, autonomy mode, feedback loop. That map is the redesign.

The Shift

The question is no longer "will AI take this job?" It's "which steps of this loop can AI own — and how does the human move up to supervising the loop instead of grinding through every step?"

Your role migrates from doing the work to designing the loop, handling the exceptions, and deciding when a step has earned its autonomy. That's higher-order work, by design.

And because of that Iterate step, the loop compounds. The organizations that win the next decade aren't the ones with the most AI tools. They're the ones whose loops get better every single week.

Pick one workflow. Map the loop. Move the line.