For many years, project managers have tried to collect more data to support their decisions. Project plans, progress reports, risk registers, performance indicators, and meeting notes are all part of this effort.
With artificial intelligence (AI), it is now much easier to analyze, compare, and summarize this information.
But there is an important question: Does having more data really mean making better decisions?
I don't think it always does.
One challenge I have experienced in project management is uncertainty about whether data is up to date.
A report may look correct, but the information behind it may no longer reflect the current situation. Different people may also interpret the same information in different ways, depending on their responsibilities and experience.
For example, a technical team may see a problem differently from a business team.
When I face this kind of situation, I prefer to review the issue with the relevant subject matter expert (SME).
My goal is not only to solve the current problem. I also want to understand why it happened and what we can improve to prevent it from happening again.
AI can support this process by finding patterns, comparing information, and suggesting possible causes. However, it cannot automatically guarantee that the data is current or that its explanation is correct.
One area where I see real value in AI is software development and testing.
After making changes to an application, I have used AI to check whether users can perform specific actions based on their permissions.
These AI-powered checks made it easier to observe the impact of changes.
However, passing individual checks does not mean that the complete system works correctly.
I believe there is still significant room for improvement, especially in end-to-end (E2E) testing. A user may successfully complete one action while another part of the process still has a problem.
This is an important difference between having more test results and having better evidence.
The real question is not how many checks we perform, but whether we are testing the right risks.
I regularly follow new technologies, tools, and approaches. When I face a problem, I try to understand different solutions before selecting one.
Sometimes I use more than one AI tool to compare ideas and review the solutions they suggest. This helps me learn and improve my existing knowledge.
But this approach can also create problems.
When several AI tools continue suggesting improvements, a simple solution can become unnecessarily complex. More features, additional components, and new technical approaches may look useful, but they can also increase maintenance effort.
This is where understanding the product roadmap becomes important.
Because I know the direction of the product, I can decide when a simple and sufficient solution is better than a more advanced one.
We do not need to build every possible future feature today.
AI can generate different options, but people still need to decide which option fits the product's goals, resources, and priorities.
I believe AI is changing the project manager's role.
We can now spend less time preparing reports, summarizing meetings, and comparing possible solutions.
However, saving time is only valuable if we use that time well.
Accepting an AI-generated analysis without questioning it may help us make decisions faster, but not necessarily better.
A detailed and convincing AI response does not always mean that its assumptions are correct.
Project managers therefore need more than the ability to use AI tools. They also need to ask good questions, validate information, work with subject matter experts, and understand the possible consequences of decisions.
Sometimes the most valuable decision is not to introduce a new technology, but to recognize that the current solution is good enough.
AI can significantly improve how project managers access and analyze information. It can help us identify risks, compare alternatives, and perform checks more efficiently.
But more data does not guarantee better decisions.
If information is outdated, different perspectives are ignored, or AI suggestions are accepted without review, more analysis can also create new problems.
For me, the real value of AI is not transferring decision-making responsibility to technology. It is using technology to explore more options and make better-informed choices.
Good project management is not about having the most data or the most complex solution. It is about making the right decision, at the right time, with enough reliable information.
Cover photo by Tania Melnyczuk on Unsplash.