Evaluating skill/agent built in Claude?

As PMs increasingly build AI-powered products and agents, a fundamental problem emerges: how do you know if your improvements actually work? This post highlights the evaluation bottleneck that plagues AI product development—moving beyond manual testing to rigorous, scalable measurement. The core challenge is that traditional software metrics don't transfer cleanly to AI; you can tweak prompts and context all day, but without a systematic evaluation framework, you're flying blind. Key takeaways: (1) AI product improvement requires structured testing methodologies similar to software QA, not just manual spot-checks, and (2) this evaluation gap is preventing teams from confidently iterating on their AI products. This matters now because as AI agents move from experimental to production, PMs who crack the evaluation problem will ship better products faster than those still relying on gut feel.

Why India's Product Managers are racing to learn the language of AI - The Economic Times

Product managers across India are urgently pivoting their skillsets toward AI literacy as the technology reshapes product strategy and competitive advantage. The shift reflects a broader recognition that PMs who lack AI fluency risk becoming obsolete in product decision-making, as AI moves from "nice-to-have" to core competency. Key takeaway: PMs need to develop AI fundamentals not as engineers, but as product strategists—understanding how to evaluate AI capabilities, define use cases, and manage AI-driven product decisions. A second critical insight is that this is a geographically accelerating trend, with emerging markets like India treating AI upskilling as table-stakes for career progression. This matters now because hiring managers are already filtering for AI-literate PMs, and the talent gap is widening—your career trajectory in 2026 increasingly depends on demonstrating concrete AI product knowledge, not general awareness.

Rovo AI in Confluence: Create faster, collaborate better, and know more with Rovo | Atlassian

Rovo represents a shift from generic AI assistants to purpose-built AI integrated directly into the tools PMs already use daily. The product tackles three PM pain points: accelerating documentation workflows (idea to draft in seconds), reducing information overload through intelligent search, and enabling faster collaboration without context-switching. For PMs specifically, this means less time managing Confluence pages and more time on strategy—critical when balancing rapid iteration with documentation debt. The enterprise-grade search accuracy matters because PMs rely on consistent access to product specs, roadmaps, and decision history; generic AI often hallucinates in these contexts. With AI becoming the default layer in workspace tools (as Google's recent announcements echo), PMs who leverage these integrations will measurably reduce team friction and accelerate decision velocity in 2026.

The AI-PM Co-intelligence Playbook

As AI becomes embedded in product workflows, PMs need a framework for leveraging AI as a thinking partner rather than a replacement—and "The AI-PM Co-intelligence Playbook" provides exactly that. The playbook likely walks through concrete ways to use AI for roadmap prioritization, user research synthesis, and stakeholder communication, giving PMs immediate tools to ship faster without losing strategic ownership. Key takeaways for modern PMs: AI excels at acceleration and pattern-finding but requires human judgment for trade-offs and vision, and teams that master this co-intelligence dynamic will outpace those treating AI as either magic or threat. This matters now because the PM role is rapidly evolving—staying ahead of AI adoption in your own toolkit is both a competitive advantage and a career insurance policy as the industry standardizes these capabilities.

I analyzed how the best PMs are using AI in 2026. Here's the stack that keeps showing up.

While most PMs default to ChatGPT, the highest-performing product managers are leveraging a specialized stack across their entire workflow—Perplexity for research, Claude for documentation, Granola for meeting intelligence, and purpose-built tools like Gamma for presentations and Amplitude AI for analytics. The key insight isn't just *which* tools exist, but *how* top PMs are architecting their workflows to eliminate busywork and shift time toward strategy, customer insights, and decision-making. This represents a fundamental shift in PM productivity: the differentiator is no longer domain knowledge or experience alone, but operational efficiency through AI leverage. Two critical takeaways for career growth: first, learning to compose multiple AI tools into workflows compounds productivity gains; second, the time saved fuels deeper strategic thinking—the actual value-add PMs bring. This matters now because the PM job market is increasingly bifurcating between tool-aware and tool-naive operators, and June 2026 is the inflection point where this becomes table stakes for competitive positioning.