TLDR Product Management 2026-09-29

Commercial payments, stablecoin backed cards and money movement — every new capability rides on the infrastructure beneath it. And that infrastructure has to hold up.

Marqeta gives the world's most innovative companies a modern card issuing platform built to scale globally, adapt fast, and stay dependable as your ambitions grow, with seamless API integration, multi-currency support, real-time fraud detection, and the reliability high-volume programs depend on.

Bring your product roadmap and we'll show you how far it can go. Talk to an expert →

Current AI tools often act as misleading software. Serious AI products must feature error checking, robust citations, no first-person language, and non-natural language interfaces when appropriate.

AI is pushing leaders back into hands-on work, but that shift may be temporary. As building gets cheaper, expert leadership becomes more important.

Organizations fail to deploy enterprise AI agents because they treat them like software rollouts rather than new hires needing onboarding and data prep. Clean data, workflow integration, and governance present far greater bottlenecks than model performance.

Google's search quality failures stem from product and organizational flaws rather than model limitations. By layering probabilistic AI features onto a deterministic search interface without updating UI promises or user feedback loops, companies risk eroding user trust through unverified AI outputs.

Pendo's Learning Lab has practical courses on AI agents, product judgment, and becoming AI-native. All courses are self-paced, free, and you'll finish with a badge worth sharing on LinkedIn. Explore the courses →

B2B AI vendors should publish deep, direct, and continuous competitive evaluations against named competitors to build buyer trust, as demonstrated by Gorgias open-sourcing a live evaluation harness across 18 vendors.

The AI building boom risks producing generic "zombie UI" if teams ignore intent and artistry. To counter this, teams must maintain a strong design POV, encode standards into production, refuse to settle for standard builds, and invent new interfaces with AI.

Traditional chat interfaces like Slack create an information bottleneck. It's essential to establish a shared context system to maintain aligned results and prevent compounding divergences when multiple users and AI agents collaborate.

Software accrues long-term value by creating dependencies that compound over time and increase switching costs. This process involves dependency formation across multiple categories, internal and external compounding such as network effects, and critical workflow multipliers that dictate overall switching risk and revenue capture.

Align strategy to work, human and agent, in one system. Connect data and flag drift. Learn more.

Delegating to AI leaves cognitive load and accountability with the user.

Axiom has added a feedback tool to its MCP server that enables AI agents to directly report roadblocks during workflows.