How product ops teams can move from busywork to big-picture strategy with agentic AI.
In product operations today, AI is everywhere… but often in the wrong places. Embedded into individual tools like Jira or Confluence, AI tends to solve isolated problems: a chatbot here, a smart assistant there. But product operations teams don’t solve in silos. Their job is to orchestrate across systems, people, and strategy. So how do you harness AI to improve the whole workflow, as opposed to just doing one thing better?
Enter AI agents.
In a recent Productboard webinar, Ross Webb (Founder, Product Team Success) and Graham Reed (Head of Product Operations, HeliosX Group) showed how agentic AI—intelligent workflows built on a trained foundation of your business context—can save product ops teams hours each week, reduce cognitive load, and empower more strategic decision-making.
Here’s a glimpse of what they covered, and why you’ll want to watch the full session to see it in action.
Watch “The Future of Product Operations: Empowering teams with AI Automation” on-demand.
AI in most product tools today feels like a sprinkle on top. It might auto-summarize a meeting or rewrite a story description, but it doesn’t connect the dots.
What Ross and Graham argued is simple but powerful: product ops teams shouldn’t be solving individual problems. They should be designing holistic workflows that support the entire product lifecycle.
That’s what makes AI agents different. They don’t just act once. They reason across systems. They work towards outcomes. And, when trained well, they become like a teammate who never sleeps.
“Think of it as automation with a driver’s license,” Ross said. “It’s not just doing tasks. It’s understanding the goals, pulling in the context, and acting across tools.”
A few high-impact use cases Ross and Graham discussed include:
The common theme? Each use case reduces manual effort while improving decision quality. A one-two punch every product team wants.
Yes, you can use ChatGPT. But as Ross emphasized, that’s just the surface. To build an effective AI agent, you need:
“The quality of the information you feed it matters,” Ross reminded. “You have to train the workflow. It won’t be perfect on the first go.”
To move beyond theory, Ross walked the audience through a real-world example of an AI-powered workflow he built using open tools and real company data. The use case? A weekly product update that would normally require hours of manual work—gathering status updates, analyzing product usage, cross-referencing with goals, and surfacing insights. With his agentic AI workflow, all of that happens automatically.
The first step was creating a “brain” for the system. Ross pulled together the core strategic documents of the company—its mission, vision, product goals, and OKRs—all stored in Google Docs. He imported this documentation into a vector database (Supabase, in this case), a key difference from traditional SQL storage. This setup allows the AI agent to retain memory, retrieve context, and reason across sources. Without this foundational step, the agent wouldn’t understand what “success” looks like for the business or how to prioritize the information it’s gathering.
“Since we did this—just a week or so—she’s saved hours and hours. And more importantly, she’s using that time to talk to stakeholders, push new ideas forward.”
Once the brain was in place, Ross connected a set of tools that product teams already use in their day-to-day work: Productboard for roadmapping and prioritization, Linear for tracking development progress, PostHog for user analytics, and a feedback tool with built-in sentiment analysis. Each system had a specific role in the workflow, feeding data into the central AI engine.
He built the entire workflow using n8n, a low-code automation platform that allowed him to create custom flows across these tools. The agent was programmed to run automatically—say, every Sunday evening—and by Monday morning, a fully-generated status update would land in the product manager’s inbox.
The output wasn’t just a simple digest. It was a weekly-generated executive report that included:
In the demo, the system flagged a major drop-off in a comment-posting funnel inside the app, classifying it as a critical insight. Based on the company’s goals and KPIs, the AI recommended investigating the issue immediately and suggested next steps: initiate user research, analyze feedback, and explore whether the comment flow itself needed optimization.
For the product manager using this system, the impact was immediate.
Instead of spending hours toggling between tools, compiling data, and trying to spot patterns, she could begin each week focused on the work that matters: shaping strategy and driving outcomes. “Since we did this—just a week or so—she’s saved hours and hours,” Ross said of the PM testing the tool. “And more importantly, she’s using that time to talk to stakeholders, push new ideas forward.” And that’s the promise of agentic AI in product operations: not just automation for automation’s sake, but intelligence that lets you operate at a higher level.
This isn’t plug-and-play.
Building a real, reusable agent takes effort, technical support, and stakeholder buy-in. Not all tools will integrate easily. Not all product teams will be ready. But the cost of not exploring this? Wasting 10+ hours a week on manual work that AI can already do.
So what can you do today?
🎥 Watch the full webinar replay. Get the full behind-the-scenes look at how agentic AI is changing product operations.
