Preparing for AI, and how to tell if you are ready
Readiness is four things you either have or you do not.
Ready for AI means four things: a task that repeats, data that arrives on its own, someone who owns the output, and a before number. With all four, you can ship in a quarter.
Is it a fit?
A task with a name
One process you can describe with an input and an output.
An input that arrives
It lands from a system, not from somebody exporting a file.
A baseline
Hours or errors measured today, before anything changes.
Start here. Six questions score whether this task should be automated at all.
Score the taskWork it out in a minute
Eighteen questions across six areas. Names the one weakness holding the others in place, and the first ninety days in order.
The plan
Do these six, in order
A time against each one and a way to tell it is finished.
Name one task
The process, who runs it now, and roughly how many hours a month it takes.
1 hour · Done when the task fits in a sentence with an input and an output
Take the baseline before anything changes
Hours, error rate or queue age today, measured rather than estimated.
2 hours · Done when a number exists that somebody else could check
Trace how the input arrives
Follow it back to a system. If a person exports it, that is the part to fix first.
half a day · Done when the input can arrive daily with nobody touching it
Name the owner for after go live
One person whose job includes this once the build is finished.
30 minutes · Done when the name is written down and that person knows
Decide where the wrong ones go
Who sees exceptions, how fast, and what they are allowed to do about them.
1 hour · Done when an exception has a route and a time limit
Write the one page policy
What may go into a model, what may not, and who to ask when it is unclear.
1 hour · Done when it fits on one page and the team has read it
What you get
One task, not a strategy
Readiness is measured against something specific. Name the process, who runs it now, and how many hours a month it eats. A company that cannot name one task is not ready for anything yet.
Data that arrives without a person
The input has to land from a system rather than from somebody exporting a file on Friday. Where a person is the pipeline, the pilot works and production does not.
A named owner after go live
Somebody whose job includes this once the build is finished. With no name, the system runs until its first bad week and then quietly stops being used.
A number from before you start
Hours, error rate or queue age, measured rather than estimated. Skip it and you will never be able to say whether it worked, only whether people liked it.
A route for the ones it gets wrong
Every model is wrong sometimes. Readiness means knowing who sees the exceptions and how quickly, decided before anybody argues about accuracy.
A policy people can follow
One page: what may go into a model, what may not, and who to ask. Long policies get ignored, and no policy at all gets you usage you cannot see.
What readiness means
Most readiness checklists ask about culture, strategy and data maturity, then produce a score. A score is not a decision. The useful question is narrower: is this company ready to put AI on one named task, this quarter.
Then the owner. Pilots are almost never killed by the model. They die because nobody week changed, so when the system had a bad day there was no one whose job it was to care.
Questions we get
Is our company ready for AI agents?
Agents ask for more than AI generally does: work that arrives constantly, rules that cover most cases with judgment only at the edges, and output somebody can check afterwards.
Where the steps never vary, a plain workflow is cheaper and steadier than an agent.
Do we need to clean our data first?
Not all of it. You need the one input the first task uses to arrive on its own and be right. A company wide data project before a first result is how a year disappears.
Clean what the task touches and leave the rest until something needs it.
How long does it take to get ready?
The task definition and the baseline are usually an afternoon. The data path is days to weeks. The owner is a conversation.
The policy is one page and one hour, and it is the item most often left undone.
We tried before and it did not stick. What now?
Check which of the four was missing. In most cases it is the owner or the baseline, and the model was fine.
That is worth knowing before anybody buys a different tool.
The money-side readiness pages are preparing for a lender and preparing for an audit. The other readiness page is preparing for a raise. Once you are ready, the buying question is ChatGPT Enterprise or custom AI.
More in the guides and every answer in one place.
Read next
What to build once you are ready, the rules to write first, and the reason most of these never ship.
Shaheer leads the work, with engineers, writers, filers and analysts behind him. C-suite operations for a San Francisco AI company, Six Sigma on the process side, Anthropic certified on the Model Context Protocol, ten years across eight industries. See what we have built