Five AI questions worth answering before the year ends

The end of the year is a good moment to stop and honestly answer questions that the daily rush keeps pushing aside. This year the main topic for most managers I worked with was AI - not as a theoretical possibility, but as a concrete task with budgets, pilots, results, and some disappointments.

I want to offer five questions I find useful for an honest self-assessment before the next year.

Question 1: What launched this year is actually running in production?

Not "what we tried" and not "what looked promising in a demo". What is running in real use, regularly, with a clear and measurable effect?

If the answer is nothing, or "it is hard to say", that is important information. It is not a verdict on the direction as a whole. It is a signal to understand why pilots are not making it into production, and to address that before launching new ones.

Question 2: Where did data remain the main constraint?

In almost every AI conversation, at some point it becomes clear that the real barrier is not the model or the technology - it is the data. Wrong data, poorly organised, wrong format, insufficient history.

If that is true for your projects - what specifically was done in 2025 to improve data quality? If nothing - data will be the constraint in the next year too.

Question 3: Who in the company owns AI initiatives?

I frequently see a situation where AI projects exist in several places at once: the CTO wants one thing, marketing launched its own, HR is testing a third. No coordination, duplicated approaches, competing priorities.

Ownership is not "who is responsible for delivery". It is "who sees the full picture and makes prioritisation decisions". If that person does not exist, AI investment will keep dispersing.

Question 4: How is the company managing AI-related risks?

This question has two sides. On one side - the risks of employees using AI: what data they are sending into models, what decisions are being made on the basis of AI output without verification. On the other - external risks: AI in the hands of attackers is changing the threats the company faces.

If this was not discussed in 2025, it is accumulating, not disappearing.

Question 5: What do you want to be able to say about AI at the end of 2026?

This is the strategic question. Not "which features will we roll out" but "how will the company be different if the AI initiatives work". Greater output with the same headcount? Quality that was previously out of reach? New products or segments?

If there is no answer, that is fine - but then there is no criterion for evaluating decisions in the coming year.

A good year-end review is not a tick-box exercise. It is a moment when uncomfortable questions can be asked and honest answers obtained - while budgets and plans for the next year are still open. For a topic that changes as fast as AI, that matters.