Amit Kothari
Amit Kothari CEO of Tallyfy · Workflow AI Expert

Important steps, not every step

In brief

A process that documents every keystroke is unusable, and writing it kills the project before it ships. The skill is capturing only the steps that carry risk, judgment, a handoff, or a decision, naming one owner each, and letting the obvious parts stay obvious.

Summary

  • The exhaustive process is the broken one - nobody reads a 150-step monster, and the act of writing it down kills the project before anyone runs it once. Length is not rigor.
  • Capture only the steps that carry weight - the ones with risk, a judgment call, a handoff, or a decision. The rest can be implied, because the people doing the work are not idiots.
  • One owner per step, not a committee - a step that belongs to “the team” belongs to no one. Name a person. See how we think about process documentation

A useful process is short enough to be trusted and run. That is the whole argument, and the rest of this post earns it. We build Tallyfy, so we’ve watched a lot of teams try to write down how their work actually happens, and the failure mode is almost always the same. It’s not that they document too little. It’s that they try to document everything, the project collapses under its own weight, and the real work goes back to living in someone’s head.

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Picture the wall. A team we worked with mapped one core process across what felt like a 15-foot stretch of conference-room wall, sticky note after sticky note, arrow after arrow, until the whole thing looked less like a workflow and more like a transit map for a city nobody wanted to live in. It was an impressive artifact. It was also useless, and here is the part that took a while to see: the bulk of that wall was the rare worst-case exception, the 1% that almost never happens, not the 99% path the team runs every single day.

That is the trap.

The giant exhaustive map is usually a monument to the edge case.

The exhaustive process is the broken one

Length isn’t rigor. The instinct to capture every keystroke feels responsible, like you’re being thorough, but a 150-step document is a janky thing nobody opens twice. We’ve seen plenty of these. They start clean and then suffer scope creep with every edit, until one person finishes the heroic burst, leaves, and the document calcifies because touching it is a small expedition. The work moves on. The document doesn’t. Within a few months the written process and the lived process have quietly drifted apart, and you now have a map that lies.

What caught us off guard, watching this happen across industries, is how often the exhaustive version is actively worse than a rough one. A short process that captures the four steps that matter gets read, trusted, and followed. A long one that captures all forty gets skimmed, then ignored, then contradicted by reality. The reader cannot find the load-bearing step inside the noise, so they stop looking. You wrote more and communicated less.

There is a famous exercise that makes this concrete. Tom Wujec, a designer who has run it with thousands of people, asks them to draw how they make toast. Sounds trivial. It is not. People draw wildly different diagrams of the same five-minute task, and the clearest, most useful ones are never the most detailed. They are the ones with a handful of nodes and clean links between them. The cluttered drawings, packed with every fork and crumb, communicate the least. A model that tries to show everything shows nothing, because the human looking at it cannot hold all of it at once. That is not a toast problem. That is every process document you have ever been handed.

So the question is not “how do we capture more?” It is “which steps actually carry weight, and which ones can we trust people to figure out?”

Which steps actually matter

Four kinds of step earn a place in the document. Anything that carries risk, where doing it wrong is expensive or hard to undo. Anything that needs judgment, where a person has to weigh something rather than follow a rule. The handoffs, where work passes between people and the ball gets dropped. And the decisions, the real forks where the path splits. If a step is none of those four, it is probably obvious, and obvious steps do not need writing down.

Take quoting or estimating, a process almost every business runs in some form. The step where someone checks the customer’s specs against what you can actually deliver carries risk and judgment, so write it, and write it well. The step where the quote moves from the estimator to whoever approves it is a handoff, so name that one. But “open the spreadsheet”? “Type the customer’s name”? Leave them out. The person doing the work knows how to open a spreadsheet. Writing that down insults them and buries the two steps that would have saved the deal.

This is where the conditional logic in your process earns its keep, by the way. The 1% exception does not belong on the main path crowding out the normal flow. It belongs behind a rule: if this rare condition is true, then show this branch, otherwise stay on the happy path. That way the exception exists for the day it happens without making every reader, every day, wade through a worst-case scenario they will almost never hit. The exhaustive wall-map mashes the 99% and the 1% together at the same visual weight. A good process keeps the common path clean and tucks the rare one out of sight until it is needed.

We said obvious steps do not need writing down. Let’s be more careful, because that oversimplifies it. Obvious is relative to the reader. A step that is obvious to a ten-year veteran is a cliff to a new hire on day three.

The real move is to know who the document is for. If it is for the people already doing the work, trust them and stay lean. If it is really an onboarding tool for someone brand new, you write more of the basics, but even then you flag which steps carry weight so the newcomer knows where to slow down. The skill is the same either way: separate the load-bearing steps from the scaffolding.

Name one owner, not a committee

Every step that matters needs a name attached to it. One person. Not “the team,” not “operations,” not “whoever’s free.” A step owned by a group is a step owned by nobody, and the gap between “someone should do this” and “Maria does this” is where work goes to die. We’ve watched handoffs fail for years not because the step was undocumented but because the step had no clear owner, so each side assumed the other had it.

This is the quiet reason the exhaustive maps fail twice over. They have no owners because you cannot assign 150 steps to real people without the whole thing becoming a bureaucracy. Accountability collapses when the unit is too small. A process built from a handful of weighty steps, each with a single named owner, is enforceable. You can look at it and ask, plainly, did Maria’s step happen? That question has an answer. “Did the team execute the workflow?” does not.

One named owner per step also makes the process trackable in a way a wall of sticky notes never is. When work runs through something like Tallyfy instead of a static document, each step has an assignee and a status, so the handoff is not a hope, it is a tracked task with a real owner. The document stops being a description of work and becomes the work itself. That is the difference between a process you describe and a process you run. One sits in a drawer. The other holds people accountable.

Does naming one owner per step solve every coordination problem? No. People are out sick, roles change, someone owns a step and still drops it. But “one named owner” is the floor, not the ceiling, and for what it’s worth, a process that cannot even clear that floor has no chance at the harder stuff above it.

AI raises the stakes, it does not lower them

Here is where this gets sharper in 2026. The temptation, now that AI can draft a process for you in seconds, is to let it generate the exhaustive version, all 150 steps, because the writing is suddenly free. Letting the machine write more feels like a no-brainer. Resist it anyway. A bloated process does not become useful just because a model wrote it instead of a person. It becomes a bloated process faster.

AI runs whatever process you hand it, exception and all, at full speed. Feed an agent a 150-step monster where the one load-bearing judgment call is buried at step 94, and it will dutifully grind through the 93 steps of scaffolding around it, missing the point you actually needed a human or a careful rule to catch. The clarity that matters for a human skimming a document matters even more for a machine executing one. A process that names the four steps that carry weight, and assigns them, gives an AI agent a real map. A transcript of everything a human does gives it noise to choke on.

The biggest lesson we’ve learned building Tallyfy is that the discipline doesn’t change when the tools get smarter. If anything, pinning down the few steps that carry weight becomes worth more, because now both people and AI are reading the same process, and both of them perform exactly as well as that process is clear. A broken, bloated workflow handed to an AI just breaks at scale. A lean, owned one gives the machine something it can actually run.

So write less. Capture the steps that carry risk, judgment, a handoff, or a decision. Put one name on each. Trust the people doing the work to handle the obvious parts, because they are not idiots, and let the rare exception live behind a rule instead of on the main road. A process you can trust in one read and run the same afternoon beats a transcript nobody finishes. For more on getting this right, our take on why nobody follows your SOPs comes back to the same root: the goal was never to document everything. It was to document what matters, and run it.

About the author

Amit is the CEO of Tallyfy. He has 25+ years of practical experience in technology, entrepreneurship, and operational efficiency. He's been hands-on with AI-first engineering and changing Tallyfy to AI-native workflow automation since Claude Code was first released. He's also an Entrepreneur in Residence at WashU's Skandalaris Center, created the OneDay (Woolf) AI curriculum for their accredited MBA and consults with clients who need help with AI via Blue Sheen. He graduated with a Computer Science degree from the University of Bath. He's originally British and lives in St. Louis, MO.

Find Amit on his website , LinkedIn , or GitHub . Read Amit's bio →

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