Best of LinkedIn: Digital Products & Services CW 36/ 37

Practical techniques like agents, RAG, and fine-tuning were applied where they add clear value

AI-driven development workflows showed productivity gains for SaaS teams

Strategy guidance shifted from adding AI features to building for probabilistic outcomes and task success

AI accelerated research in domains like healthcare, while human judgment and oversight remained essential

Skills emphasis for AI PMs included lifecycle management, experimentation, GenAI and LLM fluency, data analytics, and AI-driven customer experience

Creative collaboration and continuous, human-centered co-creation emerged as patterns to speed innovation

Teams were reminded to balance speed with substance to protect quality

Leaders pushed to turn ambiguity into clarity through intentional, prescriptive actions in Product Ops

Linking product strategy to OKRs and KPIs was reinforced as a prerequisite for execution

Aligning product and platform roadmaps, supported by a clear Product Operating Model, remained essential

Simple status signals such as on track, at risk, off track were preferred over delivery-progress micrometrics

Clear metric definitions enabled better decisions and cross-team alignment

Models that bridge high-level strategies to concrete features helped close the planning-to-delivery gap

Stakeholder misalignment persisted as a core execution risk that Product Ops must surface and address

Financial thinking was encouraged in product work to secure funding and strategic influence

The Producer role in digital delivery was clarified to reduce confusion and handoff friction

Alibaba introduced Accio Agent for product research and market validation

Freeplay rolled out automated prompt optimization to improve AI models without manual re-engineering

Google DeepMind released URL Context to pull live data from URLs into products

Amplitude’s Product Benchmark Report provided actionable product and marketing insights

Multi-LLM capability surfaced as critical, illustrated by discussion of Microsoft’s shift to Anthropic from OpenAI

Evidence-based discovery was framed as investment protection, resonating strongly with engineering teams

Case work showed that disciplined discovery prevented waste and kept a business alive by targeting the right problem

Consultants were urged to price discovery work appropriately rather than giving it away

Time constraints remained the top barrier to effective discovery, prompting lighter, faster methods

Behavioral Economics informed product design choices to drive meaningful user outcomes

MVP pressure and half-baked work were flagged as anti-patterns to avoid

Teams doubled down on collaboration and clear purpose, supported by community formats and honest conversations

AI supported discovery and assumptions modeling, while responsibility for quality and ethics stayed with humans

Product development guidance reiterated the balance between velocity and rigor for durable results