One of the biggest promises of artificial intelligence (AI) in project management is speed. We can analyze information, generate different solutions, and complete some technical tasks much faster than before.
However, there is an important difference: Solving a problem faster does not always mean making a decision faster or delivering the solution sooner.
Sometimes, working with AI can even have the opposite effect.
AI tools make it easy to explore different solutions. We can also use several tools to compare suggestions, identify possible problems, and consider other approaches.
I use this method in my own work. Comparing suggestions from different AI tools helps me discover new options and question technical decisions more effectively.
However, I have also noticed that this process sometimes takes more time than I originally planned.
Each new alternative creates new questions. Could we use a better architecture? Should we choose another technology? Do we need to prepare for possible future requirements?
These questions can be valuable. But trying to answer all of them may delay the decision.
As AI makes analysis easier, project managers need to become better at deciding which alternatives deserve further attention.
In software projects, it is often possible to build a more advanced solution. But that does not automatically mean it will create more value for users.
When I evaluate a solution, two important factors are user needs and technical debt.
I do not want to make a solution unnecessarily complex just because a more advanced option is available. At the same time, I do not want a short-term decision to create significant technical debt in the future.
Finding the right balance is important.
For example, a feature may become valuable in the future but may not be necessary today. In this situation, I prefer to design the current solution so that it can be extended later, instead of developing the complete feature immediately.
I find this especially valuable when the flexibility does not create additional cost or significant complexity.
There is an important difference between building everything for the future and preparing a system for future changes.
A good technical decision is not about developing every possible feature today. It is about creating a solution that can evolve when needed.
AI can also improve software development and quality control processes.
Identifying the cause of a problem, generating possible fixes, and performing certain checks can now take less time.
However, especially in complex applications, developing a solution is only one part of the process.
We still need to evaluate compatibility with existing systems, user permissions, integrations, test results, and deployment requirements.
In my own work, I am developing an approach to manage checks across different applications through a common process and track their results. The goal is not only to perform checks but also to make quality issues more visible.
However, solving a problem quickly with AI does not mean that the solution can be deployed immediately.
In systems with many dependencies, deployment can still take considerable time.
Therefore, when we discuss speed in project management, we should be clear about which part of the process is becoming faster.
Reducing development time is not the same as reducing end-to-end delivery time.
Another challenge when working with AI is deciding when to stop reviewing a solution.
We can ask different AI tools to review the same solution again and again. Each review may produce another improvement suggestion.
But this process needs a stopping point.
My approach is to move forward when there are no critical issues and the solution meets the current need.
This does not mean ignoring other suggestions. I add relevant improvements to the backlog and, when appropriate, include them in the changelog.
This allows us to keep improvement opportunities without making every suggestion a requirement for the current delivery.
The advantage is that we can avoid unnecessary delays while keeping future improvements visible.
Not every suggestion needs to be implemented immediately. Some improvements are critical for the current release, while others may create more value later.
Understanding this difference is not only a technical decision. It is also a project management decision.
AI allows project managers to explore more alternatives, compare technical suggestions, and evaluate risks more systematically.
I can see this change in my own work.
Today, I can compare different approaches more easily than before. It is also easier to question technical recommendations and review possible risks separately.
However, I do not believe this means that project managers should replace technical teams.
Instead, having access to more information can help us have better discussions with our teams.
The important point is not to implement every suggestion generated by AI. It is to understand which suggestions support user needs, the product roadmap, and project priorities.
Sometimes we need more research. Sometimes we need to accept that we have enough information and move forward.
AI has the potential to make analysis and problem-solving faster in project management. However, generating more alternatives does not guarantee faster decisions or shorter delivery times.
Moving forward with a sustainable solution that meets user needs and has no critical issues may create more value than trying to implement every possible improvement.
Project managers therefore need more than knowledge of new technologies. They also need to consider technical debt, risks, future product needs, and delivery requirements together.
AI can give us more options and faster analysis. But deciding which solution is good enough and when to move forward remains a human responsibility.
Cover photo by Sindy Süßengut on Unsplash.