Methodology: Every two weeks we collect most relevant posts on LinkedIn for selected topics and create an overall summary only based on these posts. If you´re interested in the single posts behind, you can find them here: https://linktr.ee/thomasallgeyer. Have a great read!
Product Management & Strategy
Anchor strategies in customer problems and measurable outcomes, not revenue targets or activity volume
Compress strategy formation with clear choices, testable hypotheses, and a single source of truth for priorities
Prioritize bold, high-leverage bets and align teams on one governing metric to avoid incremental drift
AI in Product
Treat AI as augmentation for speed and scale while preserving human judgment and critical thinking
Define the AI Product Manager role around shipping AI features, evaluating AI-specific metrics, and managing model behavior and risk
Use rapid AI prototyping to accelerate discovery and concept validation while documenting data, IP, and compliance boundaries
Design & UX
Reduce UX debt early to protect product quality and unlock faster iteration later in the lifecycle
Pair design decisions with explicit problem framing and learning goals to avoid cosmetic “delight” misfires
Maintain user research loops so interface changes tie to validated needs rather than internal preferences
Data & Analytics
Operationalize one north-star metric per initiative and connect inputs to outcomes through explicit learning plans
Instrument AI features with domain-appropriate metrics beyond accuracy, including user trust, latency, and business impact
Treat metrics design as a product in itself, with pre-mortems, thresholds, and experiment guardrails
Engineering & Delivery
Leverage platforms and automation to shorten idea-to-release while keeping architecture simple and observable
Enable cross-functional flow with APIs, shared artifacts, and simulation to validate assumptions before build
Balance speed with resilience by setting crisp release criteria and clear ownership at component boundaries
Lean & Agile
Make assumptions explicit, test small, and scale only what the evidence supports
Separate product discovery from solution delivery to avoid prematurely locking scope
Use retros that examine decision quality, not just velocity or capacity utilization
Customer & Discovery
Start with pains and desired outcomes, not features, to keep discovery actionable and bias-aware
Validate with functional prototypes where possible to de-risk usability and desirability early
Close the loop from insights to roadmap changes so customer input translates into concrete trade-offs
GTM & Pricing
Balance growth initiatives with core product health, avoiding over-investment in any single lever
Sharpen positioning by linking jobs-to-be-done to differentiated capabilities customers can feel and measure
Build feedback channels from sales and success into product metrics to surface monetization opportunities sooner
Org & Leadership
Set 90-day leadership plans that clarify operating cadence, decision rights, and expectations for evidence standards
Move teams from firefighting to strategy by protecting focus time and publishing decision logs
Frame AI and automation as leverage for strategic thinking rather than headcount substitution
Notable Launches & Tools
OpenAI Instant Checkout enables direct purchases inside ChatGPT, reinforcing the need for complete, high-quality product data
Exhive is highlighted as an AI platform for product development, emphasizing collaborative speed from concept to delivery
Emerging guidance bundles for AI PMs consolidate core techniques, evaluation metrics, and toolchains into practical playbooks