Software, like any other product, can become obsolete. Vendors run out of budget, features become irrelevant, and customers, as a result, find newer, more interesting solutions.
This is not an uncommon scenario. What’s new is that artificial intelligence is becoming one of the most dangerous software killers today.
The reality is that many online programs fall behind their competitors and can not keep pace, not because they are poor, but because they don’t fit into the vendor’s AI strategy.
As a product founder and CEO with many years of experience, I think that it’s a mistake to believe that AI tools will completely replace software or humans. Smart algorithms undoubtedly help, but they are not a panacea for all situations.
Nevertheless, AI is forcing vendors to shut down working products. Why does it happen, and what is really behind these solutions?
I’ve been involved in project planning and management for a long time, so I’ll use them as a basis for my reasoning.
Why vendors use AI as an excuse to retire tools and push migrations
The number of new software solutions keeps growing. A reliable report claims that the global project management software market is expected to grow at a CAGR of 11.1% during 2026-2033.
At the same time, the true reasons for the sudden discontinuation of popular tools are not always obvious at first glance.
Many experts simply implement AI.
Here are some reasons why:
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- Integrating AI into legacy architecture is challenging. Many products implement smart algorithms so quickly. It seems like it is a magic button that can be added to a workflow in 15 minutes. However, to operate smoothly and accurately, AI requires a unified object model, access to structured data, and solid cloud infrastructure. Products developed 10-15 years ago struggle to adapt. Their optimization often requires excessive resources.
- Values change. A familiar interface ceases to be a product. It becomes a shell around an agent. In this context, the familiar program and its operating logic are not improved but replaced.
- Algorithms require data consolidation. Vendors often reduce their product portfolios or combine several tools into a single system with a shared repository. This allows AI assistants to see the context rather than blindly selecting tasks from different products.
- AI increasingly absorbs budgets. Developing, computing, and operating algorithms is rather expensive. Funding often comes from budgets allocated for supporting legacy products that aren’t part of the new strategy. It turns out that these products exist only formally, but their development has actually stalled for several years.
- The AI economy is incompatible with the “buy once and install” model. If you pay for a classic license, you make a one-time payment for code that then runs for free. AI doesn’t work that way. Every model call costs the provider money. The more often you use an assistant, the more you cost the provider.
- People are as important as money. More and more, managers are sending engineers to work on new AI solutions. A legacy product without a team becomes more obsolete automatically.
What does this look like in practice?
Let’s explore the example of the popular Microsoft Project.
Last year, Microsoft announced that support for Project Online will end on September 30, 2026. Projects, portfolios, related data, and reports will be permanently unavailable after this date.
The company itself explains this move by acknowledging that the legacy architecture was limiting development, so it redirected investments to Planner, Microsoft 365 Copilot, and the Project Manager agent. So, they decided to invest in a new generation of tools powered by AI, built to be more intuitive, scalable, and productive.
It means you won’t see MS Project Online anymore because it’s not suitable for building AI.
Users will feel this change and the impact of this new strategy. Many of them have chosen this software for several years to come. But the reality is that today, the lifespan of software is defined by the vendor’s AI roadmap.
It turns out that the AI-first approach is becoming a determining factor for many.
What does an AI-first approach actually mean for teams transitioning to these tools?
In this reality, a new AI-based product gets a budget, a team, and scheduled releases. Legacy software goes into maintenance mode, then into retirement. This is the vendor’s decision, as they are the ones who choose where to invest in development.
For teams, the situation is different. They were not asked about the transition. But they do have definite deadlines and commitments.
Can such a transition go smoothly? My experience shows that it rarely does.
A similar example comes to mind: Atlassian’s shift in focus from Jira Server and classic on-premises solutions to an AI-focused cloud.
For years, many teams had been working with on-premises servers (Jira Server and Data Center). However, the vendor decided to completely phase out support for Server versions and classic standalone architectures, forcing users to migrate to the cloud. The company officially explained this by redirecting all engineering resources and finances to cloud services and the implementation of AI assistants.
In this case, we again see teams finding themselves in a situation where proven functionality is becoming obsolete. The vendor imposes a new solution, and users who refuse to switch to it lose support for their familiar software.
The migration itself becomes a massive project. When moving from an old project management tool to a new AI system, a team must upload data, verify milestones and baselines, reconfigure fields, permissions, and reports, train staff, and much more. For a period of time, the two environments must operate in parallel, which can last several weeks or months.
This approach only partially corresponds to real-world planning.
AI serves as a great helper at the initial stage and in performing routine tasks. It automates the processes of preparing work breakdown structures, describing statuses, identifying risks, and formulating tasks.
However, a truly effective plan must include data on the budget, dependencies, critical paths, and resource utilization. Experience shows that many AI-based products often simplify this classic structure.
Furthermore, accountability for deadlines and decisions remains with a project manager, not the algorithm.
The risk of building a planning stack on a product whose future is tied to a vendor’s AI roadmap rather than your own needs
Migrating to a new system can come with obvious risks.
First and foremost, there’s the loss of control over your existing planning strategy. As you can see from the examples I gave above, it actually becomes dependent on someone else’s roadmap.
Essentially, a vendor is the one who sets the end-of-life date for the existing solution or the list of features that will remain after the transition.
You may be focused on managing projects two years in advance, but the vendor only confirms support for the next release cycle. It means you’re building a foundation that doesn’t belong to you.
Furthermore, some features may be simplified or even disappear after the transition. Data migration may be inaccurate, some information can inevitably be lost, and familiar integrations may suddenly stop working.
The price of the new solution will increase, as AI features will only be available in more expensive plans.
Moreover, no one really thinks about the degradation of the planning system itself.
The developed plan may seem so convincing that team members won’t argue about estimates, dependencies, and other nuances. This is unfortunate, as future failures are often revealed precisely in such disputes.
How to evaluate an AI-first replacement you didn’t choose, instead of just accepting the default
When teams choose an AI-first approach not by choice but simply accept it by default, they face a particular problem.
It can work like this: a vendor suddenly sends a notice, announces a date after which the old software will stop working, and offers a migration. Often, this is accompanied by a lucrative discount or other incentive.
Teams usually don’t have time to doubt or argue, so they simply agree.
If you encounter this situation, try to view the proposed replacement not as an upgrade, but as a purchase of new software. Remember, you’re not renewing a subscription, but rather changing a system that nonetheless contains all your deadlines.
Evaluate the transition based on your goals. Record what your team members do in the legacy software every day (create task hierarchies, distribute workloads, add dependencies, compare actual results with the baseline, calculate budgets, prepare reports, etc.).
Then be sure to ask the vendor whether the new product would still be useful if all AI features were turned off tomorrow. A negative answer will mean you’re buying a promise, not a solution.
Calculate the cost of transition. Compare the cost of ownership for two years ahead, taking into account your AI implementation plans.
Finally, test the algorithms on your own data, not on a demo project.
These simple but important steps will help you make a crucial decision. You may be able to proceed with the migration without any hesitation. Otherwise, you’ll have to go to another vendor who doesn’t hold your future planning hostage to their AI strategy, or who will do so in a more user-friendly manner.
