AI Readiness
Most organizations are not ready for AI. That is fine, and it is fixable.
The gap between a board asking about AI and a project that actually ships is almost never the AI. It is the data underneath it, and the manual processes nobody has fixed yet. This is how to work out where you stand before spending anything.
Eight questions worth answering honestly
If you cannot answer most of these, that is the finding. It is more useful than a pilot that fails eighteen months from now.
Where does your reporting data actually come from?If the answer involves more than three systems and a person who reconciles them, that reconciliation is the first problem.
Do two departments ever produce different numbers for the same thing?Conflicting definitions break AI faster than bad models do.
How current is the number leadership sees when they open a report?If the answer is measured in weeks, decisions are being made on history.
Which repeatable processes still run on people?Compliance reporting, approvals, document and procedure management. These automate before anything else.
Can you trace where a figure came from?If an auditor asked, could you show the lineage? Regulated environments need this before AI, not after.
Who owns data quality today?If the honest answer is nobody, that role has to exist before tooling helps.
What decision would you actually make differently?A use case that does not change a decision is a demonstration, not a project.
What is the cost of being wrong?This determines how much validation the use case needs, and whether it belongs anywhere near a regulated process.
What readiness actually looks like
Your data is consolidated and trusted
One version of the numbers the business runs on, with the conflicts between source systems resolved rather than papered over.
The obvious manual work is already automated
Automation is the step before AI, not a lesser version of it. Organizations that skip it end up applying AI to a broken process and scaling the breakage.
Reporting is current and someone owns it
If the report is stale when it is opened, no model on top of it will help.
You have a use case tied to a decision
Specific, measurable, and attached to something the business would do differently. Not a category like customer service.
How AIS approaches it
We start with a conversation about what leadership needs to see and cannot currently get, then work backwards into the systems. Frequently the conclusion is that an automation and data project delivers most of the value, and the AI layer becomes a smaller and much safer piece of work than expected. We would rather tell you that early than sell you the larger version.
Two published engagements where the automation and data work came first:
Want a second read on where you stand?
Bring us your answers to the eight questions. We will tell you honestly what has to be true before AI is worth spending on.
Start an AI readiness conversation