Synthetic Intelligence (AI) is an business mainstay that’s altering how we construct, launch, and handle merchandise. However there’s a brand new taste on the block: Agentic AI in Product Administration, a sophisticated type of AI that not solely processes information or generates content material but additionally acts autonomously in pursuit of targets.
Throughout a latest Productside webinar, our resident AI professional, Dean Peters, and host Roger Snider explored a important query: Is Agentic AI a good friend or a foe to product managers? Drawing on stay demos, real-world examples (together with a memorable fruit-salad analogy), and viewers Q&A, they supplied a recent have a look at how PMs can harness AI’s decision-making prowess—with out getting changed.
On this publish, we’ll discover the ideas they mentioned:
- What units Agentic AI aside from conventional or generative AI
- Concrete methods AI-driven product technique can pace up (and shake up) product workflows
- Why ethics, guardrails, and transparency matter now greater than ever
- Tips on how to adapt your enterprise mannequin to remain related in a market that expects outcomes, not simply options
For those who’ve been questioning whether or not the rise of AI means you’ll be out of a job—or whether or not it’s your secret weapon—that is the breakdown you want.
What Is Agentic AI?
Most product people have kicked the tires on generative AI (assume ChatGPT) for duties like producing person tales or brainstorming advertising copy.
Agentic AI, nonetheless, goes a number of steps additional by including autonomy, company, and accountability—typically referred to as “the three A’s.” As Dean Peters defined within the webinar, Agentic AI techniques don’t simply spit out solutions; they:
- Plan duties independently
- Adapt to real-time information
- Act with minimal or zero human oversight
In different phrases, Agentic AI for Product Managers isn’t ready round in your subsequent immediate. Like a young person despatched to the grocery retailer, it has the authority to make on-the-fly selections, change techniques if Plan A fails, and keep accountable for the end result. In keeping with Dean:
“Agentic AI isn’t simply reasoning. It’s additionally performing. It’s designed to take motion primarily based on context and targets, minimizing human enter.”
The consequence? AI strikes from a passive assist position to an lively participant in your product’s lifecycle.
Past Generative AI: Autonomy in Motion
Throughout the webinar, Roger Snider used a playful instance of sending his teenage daughter to purchase fruit. She had the liberty to pivot from strawberries to blueberries if shares had been low—demonstrating autonomy, company, and accountability.
That very same logic can apply to AI-driven product administration instruments:
- Autonomy: The system operates out of your direct line of sight, evaluating choices and forging forward when it’s certain of the following transfer.
- Company: It has the latitude to make selections past a single algorithm. The AI may determine new alternatives (e.g., “No strawberries? Let’s attempt berries which might be in inventory—or discover a retailer that has them.”).
- Accountability: It’s constructed with guardrails in order that if one thing goes unsuitable, the AI’s “proprietor” (an organization, a product supervisor, or a developer) can audit the method, see what occurred, and proper course.
In different phrases, the AI doesn’t simply generate a to-do listing. It executes duties—very like Tesla’s self-driving system or an automated AI-powered market analysis platform that “decides” when and the way typically to electronic mail prospects. degree of ambiguity will be overwhelming. However Scott thrives in it, always validating assumptions and iterating on insights.
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From Outputs to Outcomes: Why Agentic AI Modifications the Sport
Conventional AI helps with discrete duties (predicting churn, producing textual content, or discovering patterns in information). Agentic AI, although, can orchestrate complete workflows. Primarily based on the webinar, listed below are just a few key situations:
- Autonomy for Market Analysis and Insights
Market analysis can eat up a product supervisor’s week. An Agentic AI for market analysis can pull information from a number of information sources, competitor websites, and social channels, then synthesize it—mechanically. Within the webinar’s Q&A, Dean talked about how some options generate “artificial person personas,” successfully operating simulations and person assessments with out human intervention. Product managers can then give attention to strategic evaluation as a substitute of drowning in uncooked information.
- Automated Stakeholder Administration
Constructing alignment is core to profitable product administration. Sure AI-driven instruments for stakeholder administration now observe stakeholder conversations (electronic mail, chat logs) and predict potential “pink flags.” If a stakeholder’s sentiment sours, the AI can attain out proactively, suggest options, and notify the PM in actual time. That is greater than easy automation—it’s an AI that learns from conduct and acts earlier than you even understand there’s an issue.
- Determination-Making and Roadmap Prioritization
Because of AI-powered decision-making, Agentic AI can juggle a number of information factors—like characteristic utilization, person suggestions, and income metrics—and rank the backlog accordingly. The system may even suggest complete epics or resolution bushes. Within the webinar, Dean demoed a circulate the place the AI independently selected to focus on a particular phase (“working mother and father”) after scanning a fictional electronic mail from “the boss.” When you’d nonetheless confirm the info and weigh enterprise targets, the heavy lifting is off your plate.
Potential Pitfalls: Ethics, Drift, and Over-Reliance
No expertise is with out dangers, and Agentic AI amplifies sure considerations:
- Information Drift: Over time, fashions can grow to be stale or biased. You’ll possible want an “agent to observe your agent,” making certain it’s utilizing related information and hasn’t veered off target.
- Compliance and Guardrails: In regulated industries—assume healthcare or finance—absolutely autonomous selections will be dangerous. AI-powered governance techniques can maintain your AI from going rogue.
- Transparency: Customers and stakeholders have to know when AI is making selections. With no clear “present your work” strategy, belief can erode rapidly.
PWC estimates that 40–70% of data work (together with product administration duties) could possibly be automated by AI. However Roger Snider famous within the session that whereas AI can “pull the set off” on routine duties, it can’t exchange management or strategic imaginative and prescient. That’s nonetheless on you.
Agentic AI: Good friend, Foe, or Co-Pilot?
So, do it’s essential to fear about shedding your position to an AI agent? In keeping with Dean Peters, the true hazard lies in ignoring how rapidly AI is evolving. In case your complete job revolves round manually writing person tales or producing PRDs, you might end up outdated.
Nonetheless:
- Visionary PMs are extra essential than ever, bringing strategic pondering, person empathy, and cross-functional management that no AI can replicate.
- Moral Stewards are wanted to set guardrails, making certain AI doesn’t create authorized or reputational nightmares.
- Information-Conscious Leaders who can harness AI’s insights—and feed it the best information—will outpace rivals.
To borrow Dean’s phrase from the webinar, “Agentic AI makes an incredible co-pilot—however it nonetheless wants a pilot.”
Mastering Zero-to-One Product Administration Begins Right here
Agentic AI is redefining what it means to handle merchandise. By understanding leverage its autonomy, company, and accountability, product managers can transfer from process execution to strategic management, utilizing AI as a co-pilot—not a alternative.
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February 20, 2025
