- AI agents built on enterprise context and connected to the tools teams already use help product organizations break down silos, uniting feedback, engineering progress, and market signals to drive faster, more informed decisions.
- Glean’s Work AI platform gives product teams secure, permission-aware agents that streamline critical workflows like sprint planning, feature prioritization, and product adoption — delivering actionable insight without compromising governance or control.
- By turning disconnected data into a connected knowledge layer, Glean helps product leaders make better decisions, shorten development cycles, and improve customer experiences —building more aligned and effective product organizations.
Every product team is responsible for connecting customer feedback, engineering progress, and market demand. But today, that context often lives across too many places — Jira tickets, Slack threads, and customer surveys. Product managers spend more time piecing information together than shaping strategy, and clarity gets lost in the process.
AI for product management helps bring that clarity back. Intelligent agents, grounded in enterprise context, help teams plan, build, and launch with greater focus and speed.
Understanding AI for product management
Product management depends on continuous context — the information that guides discovery, prioritization, and delivery. Traditional tools capture pieces of that story but rarely connect them. AI for product management bridges those gaps by bringing intelligence directly into the systems teams already use.
Unlike static dashboards or workflow automations, AI agents can reason through complex, cross-functional inputs. They interpret unstructured data, uncover relationships between signals, and summarize the “why” behind every decision. That makes them ideal for work that depends on judgment, such as evaluating customer feedback to planning releases.
When built on a platform like Glean, these agents operate on trusted, permission-aware data across Jira, Confluence, Slack, and design tools. They deliver the context behind every decision while keeping security, governance, and accuracy intact.
Here’s how those capabilities appear in everyday product work.
Core capabilities of valuable AI agents
The most effective AI agents don’t just answer questions, they deliver results you can trust. That requires more than a powerful model. It takes real business context, secure access to company systems, and clear governance. The capabilities below make that possible.
- Natural language understanding: Teams can ask questions like, “What are the top customer requests driving this quarter’s roadmap?” and get a supported answer that links directly to relevant research, tickets, and release notes.
- Contextual reasoning: Agents can analyze feedback, compare usage data, and explain outcomes based on what’s happening inside your organization.
- Multi-system access: When agents connect to the tools product teams already use they can deliver insights without requiring teams to search across systems.
- Built-in governance: Role-based access, permission-aware indexing, and audit logs help ensure that sensitive product data stays protected and that every answer reflects the right level of visibility.
Glean’s Work AI platform supports all of these capabilities by design. With 100+ connectors and real-time permission syncing, it gives product teams a foundation to build agents that are not only powerful, but also secure, scalable, and reliable.
How AI agents are transforming product workflows
AI agents give product teams a smarter way to work — one that reduces friction, automates repetitive tasks, and supports faster, more confident decisions. By grounding intelligence in a company’s real product data and documentation, agents can streamline complex workflows, highlight key insights, and keep information consistent across systems. They’re designed for the way modern product organizations operate: collaborative, fast-moving, and detail-oriented.
Here are a few of the challenges agents can help solve:
- Accelerating development cycles: Compile active tickets, design handoffs, and blockers across tools to create a single view of progress.
- Enhancing feature prioritization: Aggregate feedback from customers, engineers, and business stakeholders into clear, data-driven recommendations.
- Improving product adoption and engagement: Analyze product usage data and feedback trends to help teams understand how customers experience new features.
6 strategic use cases for AI agents in product management
These six examples show what’s possible when product teams use Glean to build their own AI agents. Each one is designed to address a specific workflow challenge — helping teams shorten development cycles, make smarter prioritization decisions, and improve adoption across the product lifecycle.
Use these examples as inspiration for how product management AI agents can securely connect your tools, understand context, and deliver meaningful results.
1. Sprint planning agent
Helps product and engineering teams maintain visibility, alignment, and momentum throughout development cycles.
- Problem to solve: Sprints lose momentum when updates live across tools. Without a single view of blockers and progress, priorities slip and dependencies go unnoticed.
- What it does: Pulls open PRs, blocker tickets, failing tickets, design handoffs, and on-call incidents into one unified snapshot that highlights risks and surfaces dependencies early.
- The impact: Teams stay aligned and move faster with a consolidated view of progress that helps product managers make better tradeoffs and accelerate delivery.
2. Feature prioritization agent
Helps product leaders make confident, data-driven decisions about what to build next.
- Problem to solve: Feedback from customers, engineers, and executives is scattered across systems, making it difficult to compare inputs or measure impact.
- What it does: Synthesizes insights from customer calls, support tickets, and win-loss notes, then quantifies potential value and aligns recommendations to standardized metrics like activation and retention.
- The impact: Gives leaders a trusted view of what matters most to users and the business, bringing clarity and consistency to roadmap planning.
3. Competitor analysis agent
Helps product teams stay informed and proactive in a fast-moving market.
- Problem to solve: Competitive research is time-consuming and fragmented across shared folders, enablement decks, and sales notes.
- What it does: Aggregates insights from research reports, enablement materials, and internal discussions to surface differentiators, gaps, and emerging opportunities.
- The impact: Gives product managers and marketers a clear view of where they lead and where to invest next, turning scattered intelligence into actionable strategy.
4. Activation and adoption agent
Helps product and growth teams understand how users engage with new features and where they face friction.
- Problem to solve: Signals from analytics, surveys, and support tickets are fragmented, making it difficult to pinpoint why adoption lags or where onboarding breaks down.
- What it does: Analyzes behavior patterns, feedback, and support data to identify activation gaps and recommend improvements to onboarding flows, content, or in-app guidance.
- The impact: Gives teams the clarity to act quickly on engagement trends, improving activation rates and helping customers realize value sooner.
5. Product launch update agent
Helps teams keep product launches aligned, transparent, and on schedule.
- Problem to solve: After a release, teams spend hours gathering details and documentation to create updates for internal stakeholders and customers.
- What it does: Compiles recent feature releases into concise summaries with capability descriptions and guidance on customer messaging for easy sharing.
- The impact: Keeps launch communication consistent and timely, saving teams hours of manual work and ensuring key information reaches the right audiences faster.
6. Product usage metrics agent
Helps teams measure adoption, monitor engagement, and guide continuous improvement.
- Problem to solve: Usage data lives in different tools and dashboards, which makes it hard to see a unified view of how customers interact with the product.
- What it does: Consolidates usage patterns and KPIs across systems, summarizing metrics by feature, segment, or time period to uncover adoption trends and engagement gaps.
- The impact: Gives product leaders clear visibility into what’s working and where to iterate, helping teams make informed decisions based on real data.
Beyond AI agents: how Glean connects every part of product work
Product AI agents automate specific workflows. Together with Search and Assistant, they create a single, connected system that helps teams find knowledge and act on it.
Glean Search connects to every system product teams rely on — from Jira and Confluence to Figma, Drive, and Slack — so they can instantly find the right PRD, design file, or feedback thread without switching contexts.
Glean Assistant makes that knowledge conversational, allowing teams to ask questions like “What’s the latest feedback on the mobile release?” and receive permission-aware answers grounded in real company data.
Together, Search, Assistant, and Agents give product teams different ways to interact with the same trusted foundation — Glean’s Work AI platform. Each brings the company’s collective knowledge into focus, helping teams plan, build, and launch with speed and confidence.
Why product teams choose Glean
AI agents are only as effective as the data and context they’re built on. Glean’s Work AI platform unifies product knowledge, applies AI responsibly, and enables secure, scalable automation across every stage of the product lifecycle.
Enterprise-grade security: Glean enforces existing data permissions and keeps information access tightly controlled across integrated tools.
Fast time-to-value: Glean integrates seamlessly with the tools product teams already use — enabling quick setup and measurable impact within weeks.
Flexible AI infrastructure: Glean’s architecture supports a variety of enterprise AI models and workflows, making it adaptable to product organizations of any size or complexity.
Proven results at scale: In a Forrester TEI study, companies using Glean saw a 141% return on investment over three years, with millions saved through increased productivity and faster onboarding.
By turning disconnected data into a connected knowledge layer, Glean gives product leaders the clarity to make better decisions, ship higher-quality features, and continuously refine the customer experience.
Where great product management goes next
AI isn’t changing what great product management is about. It’s giving teams the clarity and connected context they’ve always needed to make faster, smarter decisions.
The teams that thrive in this next chapter will be the ones that see across the silos to connect the feedback, data, and knowledge that shape every release. Glean gives product leaders that foundation. By grounding decisions in trusted company context, teams can focus on building what matters.
With the right AI infrastructure in place, product work becomes what it was always meant to be: insight-driven, collaborative, and built on shared understanding.
Request a demo to learn how Glean helps product teams move faster, stay aligned, and build with confidence.
