The Product Owner’s AI Guide – AI Assistants for a PO
Right now, a product owner would probably benefit from thinking about at least 10–15 different AI assistants to make such a broad and demanding role manageable. But there’s no need to panic — you can always ask AI how AI should be used, and move forward one challenge at a time.
At the moment we’re all experiencing a bit of AI overload. Everywhere we turn we hear how artificial intelligence and AI assistants are about to reshape every job description, every process, and the world in general. One of the domains that is constantly predicted to change dramatically is software development. Some paint a picture where a future development team might consist of a product owner, one developer, and a collection of AI helpers — where previously there used to be a full team. (In highly regulated environments with a lot of legacy systems though, this image will probably never quite be true.)
There’s certainly lots of truth in the excitement — and yet it can also become a little tiring.
Still, the role of the product owner or product manager may be exactly the kind of job where this message resonates most. For as long as agile product development has existed, the product owner has been the turbocharged role — the person who ends up trying to cover a bit of everything with their last remaining energy. That includes understanding users, translating business metrics into product features, and ensuring that technical implementation details — like accessibility or security — meet a sufficient level of quality.
So let’s assemble the AI helper army that product owners both desperately need and genuinely deserve. What might it look like right now?
Built-in AI inside tools is the fastest path to happiness
One useful starting point in a product owner’s AI strategy is to divide AI helpers into two categories - AI features built directly into applications and separate AI assistants.
Many of the tools closest to a product owner — for example Jira and other backlog management tools — already provide AI capabilities directly inside the application. That means there’s often no need to build custom assistants for these tasks.
Jira AI features (March 2026)
Creating tickets from conversations in linked Teams or Slack channels
AI-assisted ticket updates based on observable progress in the work
Creating tickets from Confluence content and automatically generating subtasks
In addition, Jira allows AI agents to access the data that the user already has permissions for. In practice this means you can connect almost any AI helper to it — if you’re willing to do a bit of tinkering.
Azure DevOps AI support (03/26)
AI capabilities in Azure DevOps currently rely mostly on extensions. Among these, Copilot4DevOps is particularly useful for product owners. It can help, for example, with breaking down epics into user stories, generating acceptance criteria, and identifying dependencies between backlog items.
Beyond backlog tools, even prototyping tools like Figma now have built-in AI features — although these are typically used more by UX designers than by product owners themselves.
And of course it’s worth mentioning that the startup rocket Lovable has largely transformed the ability to create working applications without traditional coding. It’s not perfect, but it’s an excellent resource for teams that need quick working prototypes or MVPs — or when requirements are relatively simple.
Role-based tools vs. process-based tools
Although we’re focusing here on AI tools that a product owner can directly adopt themselves, it’s worth keeping a process-level perspective in mind as well.
Around every product owner there is a lot of activity: customer support, development work, content creation, and more. With a bit of encouragement from the product owner, many of these roles can start benefiting significantly from AI tools — while employees breathe a sigh of relief as the most tedious routine clicking disappears.
Of course this often requires encouragement from management and some centralized coordination within the organization. Still, product owners can influence quite a lot through their own example.
In many organizations, role-specific AI guidelines and assistant libraries are currently being built at high speed. Slowly but surely, they’re also starting to help people adopt these tools.
A product owner could easily have 10–15 AI assistants
But let’s get to the point. To get the most out of their time — and to stay sane under the weight of such a broad responsibility — a product owner could realistically have 10–15 AI assistants helping with different parts of the job.
Here are some of the most useful ones.
User understanding
Usage data analysis. There’s usually one major problem with data-driven user behavior analysis: product owners rarely make full use of the collected data in their day-to-day work unless there’s a particularly strong reason to do so. Tools like ChatGPT Agents and other agent platforms make it possible to create regularly running agents. In practice, an analytics assistant can monitor usage data continuously according to agreed rules and report findings on a regular schedule.
Synthesizing user insights. When gathering user understanding, one of the most expensive and time-consuming phases has traditionally been summarizing the insights from user research — surveys, interview notes, and similar material. AI has already been saving time here for quite a while. Its weak spot has been counting how often a specific theme appears in qualitative data and the quality there still varies. But dedicated tools like Thematic or Dovetail solve that problem nicely. This can save both days of work and thousands of euros.
User personas. In some projects or phases of product development there simply isn’t the time or budget to collect fresh user insights. In those situations, simulation may be useful. This means relying on secondary data — not data that directly describes the exact environment of your product, but rather the market context in general. Even so, secondary data is often far better than having nothing at all. AI can generate personas based on research and competitor analysis. These can then be turned into a GPT persona panel that can comment on feature or epic ideas.
Roadmaps and feature prioritization
Roadmap generation. When creating a product roadmap for the first time, AI can generate an initial proposal based on things like organizational OKRs, user feedback, and competitor analysis. You probably shouldn’t accept the proposal as-is (and it may not even look particularly pretty), but it can still provide a very useful starting point for discussion.
Feature prioritization. To carry the insights from roadmap thinking into actual feature prioritization, a product owner can ask for prioritization help from the AI built into their backlog tool. Fully outsourcing prioritization to AI still requires some experimentation and a certain lack of fear toward technology. Ultimately the product owner must still define the final priority order and discuss it with the team — but AI can certainly help structure the thinking.
Writing user stories
Breaking epics into user stories. Breaking epics into user stories is often partly mechanical work — although it also involves decisions and insights that the team should make together under the guidance of the product owner. An “epic splitter” AI assistant can generate an initial suggestion of what user stories an epic might contain once the user need and expected business impact are understood. (Jira and Azure DevOps already offer this kind of functionality.)
Acceptance criteria. Writing acceptance criteria is similarly partly important thinking work — and partly tedious mechanics. An AI assistant can be given the user story and the team’s definition of done, so it avoids repeating things already covered there. The product owner can then focus their thinking on editing and on the most important aspects of the specific use case. Based on acceptance criteria it’s also easy to generate test plans, especially when considering likely risk areas of the product. Again, this kind of support already exists in Jira and through extensions in Azure DevOps.
Managing the team’s work
Ticket routing. If the product owner’s team is also responsible for maintaining the product, they probably receive a constant stream of requests and inquiries. An AI assistant can analyze, classify, and route incoming requests from sources like shared inboxes or databases, ensuring that the team doesn’t waste time manually triaging them. Many ticketing systems are already starting to offer these capabilities directly inside the tools.
Project managementa
Updating risks. Risks — or in agile terms, identifying impediments — are beloved topics of steering groups and business stakeholders. You might have a couple of AI assistants for this. One could analyze Jira data, while another monitors external benchmark information related to the project’s domain. User support requests can also provide useful signals during the maintenance phase. And to make sure the work actually happens, the task can simply be scheduled as an automated agent.
Detecting dependencies. If multiple related product development projects within the same organization share a ticketing system like Jira, a dependency assistant could be extremely helpful. It can scan for cross-system dependencies and detect schedule changes that affect multiple products. The AI features in Jira and Azure DevOps are not particularly strong here yet, but tools like ChatGPT Team can already support building something like this — which can be well worth it in complex product ecosystems.
Administrative tasks. A product owner’s job also includes an endless stream of project administration tasks that no one really should be doing manually anymore. Writing meeting invitations or producing various summaries are perfect examples of tasks that AI can take off the product owner’s plate.
Content
Content editing. If a product owner also happens to act as the editor-in-chief of a content-oriented digital service, editing the structure and content of the service can become a surprisingly labor-intensive part of the job. AI can be extremely useful for simplifying content structures, summarizing texts, and translating content — unless the field requires particularly sensitive domain language.
Market research
Competitor monitoring. One of the difficulties in product development is that user expectations and competitor innovations keep moving the goalposts while the product is being built. For example, on the ChatGPT Agent platform it’s possible to create a competitor monitoring agent that regularly produces updates about market developments and signals that the product team should consider in their decisions.
Future scenarios. In addition to what is currently happening in the market, it’s also worth paying attention to possible futures. A scenario generator can help identify important signals and turn them into potential scenarios that might influence the direction of the product roadmap.
Don’t worry — focus on what matters right now
The assistant army described above may sound like quite a lot. Don’t worry — no one expects a product owner to build the entire lineup in a week waiting for future needs. And that wouldn’t make much sense anyway, because AI capabilities are evolving quickly all the time.
In practice, a product owner can still focus on whatever matters most at the current stage of the product lifecycle. The key is simply remembering that not everything needs to be done manually anymore. Whenever something looks like hours or days of work, it’s worth first asking how AI could help. And when facing difficult problems, you’re no longer alone without someone to bounce ideas off.
Over time, product owners will almost certainly benefit from organizations taking more centralized responsibility for employee AI needs and offering shared libraries of AI agents by role. Employees could adopt assistants built by colleagues and improve them further. This is already happening, for example, at Yle, where internal AI tools are being developed for journalists.
Until that kind of future becomes widespread, though, each of us can still start helping ourselves. And while building and experimenting, it’s always worth asking AI at every turn :D
Because the content of this blog post evolves continuously, it is updated regularly every two months.
Last updated: March 6, 2026.