Best of LinkedIn: Digital Products & Services CW 48/ 49

Set a north star and concentrate resources on a few outcome-driven bets

Retire reactive roadmaps. Tie priorities to customer value and business results

Treat the product operating model as the strategy’s execution system

Align architecture choices with the roadmap to unlock scale and speed

Use portfolio governance to balance exploration and exploitation

Frame AI as capability building. Pair use cases with measurable user and cost outcomes

Counter hype with design standards to protect clarity, ethics, and trust

Define AI-native roles, skills, and interfaces without loosening review rigor

Invest in high-quality data, evaluation, and human oversight for reliability

Apply AI to prototyping and research acceleration, not as a substitute for insight

Start from problem statements and evidence. Validate desirability before feasibility

Run interviews and lightweight tests to bridge interest and real demand

Make assumptions explicit and kill weak hypotheses early

Close the loop from insight to roadmap priority to shipped learning

Document decision trails to strengthen cross-functional alignment

Leverage convergent patterns for familiarity while differentiating at key moments

Prototype quickly, test often, and measure journey outcomes end to end

Respect human irrationality in flows to reduce friction and drop-offs

Build experimentation into delivery, not as an afterthought

Use instrumentation to inform iteration cadence and quality standards

Shift leadership focus from shipping features to delivering outcomes across journeys

Clarify roles for product, design, engineering, and marketing to reduce handoff loss

Anchor change in rituals. Backlogs, reviews, and retros that reinforce the model

Grow T-shaped skills and influence. Product managers communicate strategy with evidence

Resource product ops to standardize practices and lift team throughput

Treat platform and shared services as products with roadmaps and SLAs

Favor APIs, modularity, and governance that enable safe reuse

Balance speed with privacy, security, and compliance by design

Monitor performance and reliability as first-class product metrics

Align technical debt pay-down with value milestones in the roadmap

Codify playbooks for discovery, prioritization, and release management

Use lightweight templates and checklists to reduce variance across teams

Tie OKRs to product metrics and learning milestones

Establish portfolio cadence. Reviews that surface trade-offs and dependencies

Make telemetry and experiment readouts a standing agenda item

Define fewer, better KPIs that reflect user value and business impact

Ensure event hygiene and dashboards that inform weekly decisions

Link experiment results to roadmap adjustments and investment choices

View products as parts of broader customer ecosystems

Prioritize integrations that unlock trust and recurring usage over one-off exposure