AI readiness: what companies confuse with actual preparation
Why the gap between interest in AI and operational readiness to deploy it is much larger than it appears after a conference or a demo.
Conversations about AI in business are happening on two parallel tracks right now. The first is conferences, capability demonstrations, case studies from large companies, and a general tone of "if you are not moving, you are falling behind". The second is actual attempts to implement something specific and running into the reality that infrastructure, processes and data are not ready.
I see both tracks. And the gap between them is significantly larger than it appears at the start of the conversation in most companies.
What counts as readiness and what does not
Companies that consider themselves "AI-ready" often mean this: there is executive interest, there are a few people with the right words in their CVs, and there is a desire to launch a pilot. That is not readiness. That is the starting point.
Operational readiness looks different. It means:
- a clear data source for the specific task planned for automation;
- a process for labelling or accumulating training examples;
- agreement on who will evaluate model quality and by what criteria;
- infrastructure for deploying and monitoring the model in production;
- an understanding of what happens when the model is wrong.
Most companies beginning their first AI project have none of these five items ready. That is normal - but it means there are several months of preparatory work before "deploying AI" that rarely gets mentioned at conferences.
Why a demo and a pilot are different things
Demonstrations run on clean, specially selected data in controlled conditions. That is exactly why they look convincing.
A pilot in a real environment encounters something else: data in the form it actually exists - with gaps, duplicates and inconsistent formats; users who do not trust the automated decision and do things manually "just in case"; integrations into existing systems that nobody has touched for years.
A good pilot is not "show that the technology works". It is "check whether it works specifically in our conditions, with our data and our people". Those are different questions.
What makes sense to do before the first project
A few steps that reduce the risk of disappointment:
First - choose a task with a measurable result. Not "improve the customer experience", but "reduce incoming request processing time from 4 hours to 1 hour". Without a concrete success criterion, it is impossible to honestly assess the outcome.
Second - inventory the data for that task before the technical work begins. Where does the data come from, how complete is it, who owns it, can it be used.
Third - assign a business-side owner of the result. Not the IT team, but a person from the business who cares about the result and will participate in quality evaluation.
Fourth - agree on stopping criteria. If results are not achieved after three months - what happens? This is not pessimism, it is risk management.
A practical benchmark
A simple readiness test for a first AI project: can you, within one hour, bring together in one meeting the business person who owns the task, the person who owns the data, and the person who will build the solution - and all three are talking about the same thing?
If not - preparation is not yet finished.