At Teacher Booker, we’re passionate about applying cutting-edge technology to help schools and educators thrive. As part of our ongoing exploration into how AI is transforming the way we build products and deliver value, we asked Iryna Milchynska, our Product Manager, to share her thoughts on where things are heading.

What follows is a thought-provoking perspective on how artificial intelligence is reshaping not just the tools we use, but the very nature of product management teams, roles, and user expectations. Whether you’re deeply technical or just curious about how AI is driving the future of work, this piece provides a glimpse into where things are going — and what it takes to stay ahead.

Product management has always been demanding work. Between conducting market research, creating prototypes, and ensuring product quality, product managers juggle multiple complex tasks that traditionally required significant time and resources. AI is changing these processes, which are at the core of any technology company, in practical, immediately impactful ways.

But this isn’t all just high-level fluff about revolutionary change or replacing human judgment. Rather, it’s humans using intelligent tools to handle repetitive tasks more efficiently, which in turn allows product managers to focus on strategy, user needs, and business impact. Here’s how AI is making product management work more effective across three key areas.

Market research used to mean weeks of manual work: browsing competitor websites, reading hundreds of user reviews, analysing feature sets, and trying to piece together meaningful insights from scattered information. AI tools now handle much of this heavy lifting.

Modern AI can analyse hundreds of competitor applications and extract key features, pricing strategies, and user feedback patterns in hours rather than weeks. You can ask specific questions like “What are the most common user complaints about scheduling supply teaching staff?” and receive detailed analysis based on thousands of actual user reviews and ratings.

For example, when researching competition for a new feature, AI tools can create comprehensive comparison tables that include feature lists, pricing models, user ratings, and specific feedback themes. This analysis covers more ground than manual research and identifies patterns that can easily be missed when reviewing competitors one by one. AI processes huge datasets quickly, and can spot trends across multiple sources simultaneously. This means human decisions are based on comprehensive market intelligence rather than manual research, with the limitations that can bring.

The key is that AI handles data collection and initial analysis, while product managers focus on interpreting insights and making strategic decisions based on this information.

Moving from ideas to testable prototypes has historically involved multiple handoffs between product managers, designers, and developers. Each step introduced potential miscommunication and delays. AI tools shorten this cycle hugely and help connect the dots more straightforwardly.

Current AI capabilities allow product managers to describe user flows and interactions in natural language, and receive working prototypes that stakeholders can actually use. Now we can build interactive prototypes that demonstrate how features would look, feel and work in practice, rather than being limited to communicating via Post-It notes on a whiteboard.

For example, when developing a new interview scheduling flow for an ATS product, instead of writing detailed specifications that each team member might interpret differently, you can describe the intended flow to an AI tool and receive a functional prototype within hours. Teams can then test this prototype, gather feedback, and iterate before committing development resources.

This approach reduces miscommunication and allows for user testing much earlier in the development process. Ideas can be validated with real user feedback before significant time and resources are invested in development.

The result is more accurate requirements, fewer revision cycles, and better alignment between initial concepts and final products.

Traditional quality assurance happens after features are built. But AI tools enable proactive quality assurance by identifying potential issues, edge cases, and user experience problems during the planning phase.

AI can analyse proposed user flows and identify scenarios that might cause problems, in advance – for example, cases where users have unusual data volumes, different device capabilities, or unexpected usage patterns. This analysis helps surface issues that human reviewers might overlook during initial planning.

For instance, when designing a file-sharing feature, AI analysis might identify edge cases like: what happens when shared files are deleted during download, how the system handles users with limited storage space, or how the interface works for users with accessibility needs.

AI tools also review product specifications for consistency and completeness. They can identify missing requirements, conflicting functionality, or gaps in user journeys before development begins.

This proactive approach means fewer issues discovered during development or after launch, resulting in smoother user experiences and much more efficient development cycles.

These AI capabilities are accessible through various tools that integrate into existing workflows. Most are designed to be user-friendly and don’t require technical expertise to operate effectively – and they are proliferating right now.

The key to success is starting with specific problems rather than trying to implement AI everywhere at once. Identify which aspect of your current process takes the most time or causes the most frustration, then find AI tools that address that specific challenge.

For market research, tools that aggregate and analyze competitor data can provide comprehensive insights quickly. For prototyping, platforms that generate interactive mockups from text descriptions can speed up the concept-to-feedback cycle. For quality assurance, AI that reviews specifications and user flows can catch issues before they become expensive problems.

Implementation doesn’t require becoming an AI expert. These tools are designed to augment existing product management skills, not replace them.

The combination of faster research, quicker prototyping, and proactive quality assurance has measurable business benefits. Product teams can validate more ideas, iterate more quickly, and ship features with fewer post-launch issues.

Efficiency gains importance as user expectations continue to rise. Modern users expect polished experiences and rapid improvements. Teams that can research, prototype, and refine ideas quickly are better positioned to meet these expectations.

The competitive advantage comes not just from speed, but from the ability to test more approaches and make decisions based on better information (and fewer assumptions). AI tools provide the bandwidth to explore multiple solutions and validate them with users before committing to development.

AI in product management is all about practical efficiency gains, not dramatic role changes. Product managers still need to understand users, make strategic decisions, and solve business problems. AI simply provides better tools for gathering information, testing ideas, and identifying potential issues.

For those starting out on their product management journey, AI tools create a more manageable learning environment. Instead of being overwhelmed by the tactical demands of research, prototyping, and quality assurance, new product managers can dedicate more energy to developing strategic thinking, user empathy, and business acumen – the core skills that define successful product management.

The teams that integrate these tools most effectively will have the most time for strategic thinking, user research, and creative problem-solving. These are the aspects of product management that drive real business value.

Success comes from viewing AI as a capability enhancement rather than a replacement for human judgment. The goal is to spend less time on repetitive tasks and more time on the decisions and insights that create better products, while building the expertise needed to excel in this dynamic field.