Artificial Intelligence (AI) is an industry mainstay that’s altering how we build, launch, and manage products. But there’s a new flavor on the block: Agentic AI in Product Management, an advanced form of AI that not only processes data or generates content but also acts autonomously in pursuit of objectives.
During a recent Productside webinar, our resident AI expert, Dean Peters, and host Roger Snider explored a critical question: Is Agentic AI a friend or a foe to product managers? Drawing on live demos, real-world examples (including a memorable fruit-salad analogy), and audience Q&A, they offered a fresh look at how PMs can harness AI’s decision-making prowess—without getting replaced.
In this post, we’ll explore the concepts they discussed:
- What sets Agentic AI apart from traditional or generative AI
- Concrete ways AI-driven product strategy can speed up (and shake up) product workflows
- Why ethics, guardrails, and transparency matter now more than ever
- How to adapt your business model to stay relevant in a market that expects outcomes, not just features
If you’ve been wondering whether the rise of AI means you’ll be out of a job—or whether it’s your secret weapon—this is the breakdown you need.
What Is Agentic AI?
Most product folks have kicked the tires on generative AI (think ChatGPT) for tasks like generating user stories or brainstorming marketing copy.
Agentic AI, however, goes several steps further by adding autonomy, agency, and accountability—sometimes called “the three A’s.” As Dean Peters explained in the webinar, Agentic AI systems don’t just spit out answers; they:
- Plan tasks independently
- Adapt to real-time data
- Act with minimal or zero human oversight
In other words, Agentic AI for Product Managers isn’t waiting around for your next prompt. Like a teenager sent to the grocery store, it has the authority to make on-the-fly decisions, switch tactics if Plan A fails, and stay accountable for the outcome. According to Dean:
“Agentic AI isn’t just reasoning. It’s also acting. It’s designed to take action based on context and objectives, minimizing human input.”
The result? AI moves from a passive support role to an active participant in your product’s lifecycle.
Beyond Generative AI: Autonomy in Action
During the webinar, Roger Snider used a playful example of sending his teenage daughter to buy fruit. She had the freedom to pivot from strawberries to blueberries if stocks were low—demonstrating autonomy, agency, and accountability.
That same logic can apply to AI-driven product management tools:
- Autonomy: The system operates out of your direct line of sight, evaluating options and forging ahead when it’s sure of the next move.
- Agency: It has the latitude to make decisions beyond a single set of rules. The AI might identify new opportunities (e.g., “No strawberries? Let’s try berries that are in stock—or find a store that has them.”).
- Accountability: It’s built with guardrails so that if something goes wrong, the AI’s “owner” (a company, a product manager, or a developer) can audit the process, see what happened, and correct course.
In other words, the AI doesn’t just generate a to-do list. It executes tasks—much like Tesla’s self-driving system or an automated AI-powered market research platform that “decides” when and how often to email prospects.
From Outputs to Outcomes: Why Agentic AI Changes the Game
Traditional AI helps with discrete tasks (predicting churn, generating text, or finding patterns in data). Agentic AI, though, can orchestrate entire workflows. Based on the webinar, here are a few key scenarios:
- Autonomy for Market Research and Insights
Market research can eat up a product manager’s week. An Agentic AI for market research can pull data from multiple news sources, competitor sites, and social channels, then synthesize it—automatically. In the webinar’s Q&A, Dean mentioned how some solutions generate “synthetic user personas,” effectively running simulations and user tests without human intervention. Product managers can then focus on strategic analysis instead of drowning in raw data.
- Automatic Stakeholder Management
Building alignment is core to successful product management. Certain AI-driven tools for stakeholder management now track stakeholder conversations (email, chat logs) and predict potential “red flags.” If a stakeholder’s sentiment sours, the AI can reach out proactively, propose solutions, and notify the PM in real time. This is more than simple automation—it’s an AI that learns from behavior and acts before you even realize there’s a problem.
- Decision-Making and Roadmap Prioritization
Thanks to AI-powered decision-making, Agentic AI can juggle multiple data points—like feature usage, user feedback, and revenue metrics—and rank the backlog accordingly. The system might even propose entire epics or solution trees. In the webinar, Dean demoed a flow where the AI independently chose to target a specific segment (“working parents”) after scanning a fictional email from “the boss.” While you’d still verify the data and weigh business objectives, the heavy lifting is off your plate.
Potential Pitfalls: Ethics, Drift, and Over-Reliance
No technology is without risks, and Agentic AI amplifies certain concerns:
- Data Drift: Over time, models can become stale or biased. You’ll likely need an “agent to monitor your agent,” ensuring it’s using relevant data and hasn’t veered off course.
- Compliance and Guardrails: In regulated industries—think healthcare or finance—fully autonomous decisions can be risky. AI-powered governance systems can keep your AI from going rogue.
- Transparency: Users and stakeholders need to know when AI is making decisions. Without a transparent “show your work” approach, trust can erode quickly.
PWC estimates that 40–70% of knowledge work (including product management tasks) could be automated by AI. But Roger Snider noted in the session that while AI can “pull the trigger” on routine tasks, it can’t replace leadership or strategic vision. That’s still on you.
Agentic AI: Friend, Foe, or Co-Pilot?
So, do you need to worry about losing your role to an AI agent? According to Dean Peters, the real danger lies in ignoring how quickly AI is evolving. If your entire job revolves around manually writing user stories or generating PRDs, you could find yourself outdated.
However:
- Visionary PMs are more crucial than ever, bringing strategic thinking, user empathy, and cross-functional leadership that no AI can replicate.
- Ethical Stewards are needed to set guardrails, ensuring AI doesn’t create legal or reputational nightmares.
- Data-Aware Leaders who can harness AI’s insights—and feed it the right data—will outpace competitors.
To borrow Dean’s phrase from the webinar, “Agentic AI makes a great co-pilot—but it still needs a pilot.”
Mastering Zero-to-One Product Management Starts Here
Agentic AI is redefining what it means to manage products. By understanding how to leverage its autonomy, agency, and accountability, product managers can move from task execution to strategic leadership, using AI as a co-pilot—not a replacement.
- Download our Productside Playbook or templates and tools to optimize your product management process with AI.
- Watch our next webinar to explore real-world strategies for integrating Agentic AI into your product workflows.
- Enroll in our AI Product Management course to master AI-driven decision-making and stay ahead in a market that demands more than just features.
What are the biggest challenges you’ve faced in pricing decisions? Share your thoughts in the comments or connect with us on LinkedIn.
