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Author : Srishti Sharma – Product Marketer
Gartner predicts that by 2026, more than 80% of software products will include foundational AI capabilities. That means AI won’t be seen as an “add-on” or novelty; it will be part of the default expectation for how digital products behave. There will be products that could be personalised with AI. Others may use it to automate tasks, detect patterns, or generate content. One thing is certain, though: the paradigm is already shifting, and it is transforming product team thinking, planning, and building.
Roles are also being transformed by this change. The demands of the product managers also change, particularly in the field of AI in product management, where it is no longer purely a matter of creating functionality but of creating systems that learn, develop over time, and become better as the user uses them. The problem that product teams currently have is that they require individuals who are capable of seeing how AI can be used to create value, what the limitations are, and how one can design intelligence in a responsible way.
Key Takeaways:
AI is weaving itself into almost every industry healthcare, finance, retail, education, cybersecurity, and entertainment. Manual logic that used to be used in products has been replaced by machine learning models. It can be personalization, forecasting, automation, or conversational interfaces; either way, AI is now a product strategy and not a feature.
It means that the roadmap of a lot of products in the future will consist of:
The companies that are investing more in AI in product management are hiring differently, planning differently, and building differently.
It is not a matter of whether AI will have an effect on product management – it is a matter of to what depth and at what speed.
Job boards today show a growing volume of roles like:
These roles appear familiar but different. The product management pillar is still on: customer knowledge, need focus, solution design, and business alignment. These new roles introduce demands in the areas of:
Product management in AI is not decelerating in demand; it is speeding up.
The product leaders of the next generation will require a slightly different toolkit. Not very technical, but technically fluent.
The following are some of the core competencies that will influence the position.
You do not have to construct a model, but you must know how one works. This includes:
Such terms as ‘precision’, ‘recall’, ‘false positives’, ‘confidence scores’, and ‘bias’ gradually enter daily conversations.
The classical product thinking is centred on features.
Product thinking with the help of AI is orientated toward predictions, outcomes, and learning loops.
Instead of writing a requirement like “Show recommended content”
The AI/ML product manager frames: “How should the system decide what to recommend based on user behaviour and similarity patterns?”
This mindset shift is at the heart of AI in product management.
Data is the fuel behind AI-based products.
PMs must understand:
Success isn’t just feature adoption, it’s model improvement over time.
AI is bringing up issues that never existed:
Future PMs will regularly face tradeoffs where the right decision isn’t always the smartest one technically.
Working with data scientists, ML engineers, research teams, and cloud inference specialists may become part of daily work. The role becomes collaborative in a new direction – one that combines human judgement with automated intelligence.
Today, AI is shaping products in ways that go far beyond convenience. Some practical areas include:
These changes are gradually being embraced in every industry, and with time, the users will not value them but demand them.
The most apparent concern is that AI models will automate research, documentation, prioritization and decision-making, rendering the PM position less important.
However, the trend that is developing is different.
Repetitive operating tasks such as summarizing customer feedback, writing first drafts of PRDs, or creating reports will probably be taken over by AI.
However, judgement, empathy, tradeoffs, moral reasoning, and business alignment remain in need of a human being.
Instead of replacing PMs, AI is becoming a powerful partner – one that helps product teams think better and move faster.
If someone wants to transition into an AI/ML product manager role or strengthen their understanding of product management in AI, the steps don’t require a full technical degree. Instead, a steady path might look like this:
Small momentum matters more than mastery.
The future may involve roadmaps that constantly evolve. Products may shift from being delivered versions to ongoing intelligent systems. A feature won’t be done when it ships – it’ll be done when the model performs consistently well.
Traditional product managers planned features. Future product managers design intelligence.
And that transition is already underway.
Technology has reached a moment where products are learning faster than teams expect. The future of AI in product management won’t belong to those who know the most frameworks or tools, but to those willing to stay curious and rethink what a product can become when it’s powered by intelligence instead of instruction.
AI in product management refers to using artificial intelligence to build smarter products, automate workflows, and support data-driven decision-making in product strategy and development.
No, you don’t need to code, but you should understand how machine learning works, where it adds value, and how to evaluate model performance.
Key skills include data literacy, machine learning fundamentals, ethical AI understanding, user empathy, and the ability to work with technical AI teams.
Traditional PMs ship features, while AI PMs design systems that learn, adapt, and improve over time based on data.
AI is automating repetitive tasks, but product managers are still needed for judgement, strategy, prioritization, and ethical decision-making.
