Everything on Process Strategy

AI cannot be simply applied to existing businesses without first mapping and re-engineering the underlying processes. Most workflows contain undocumented exceptions and loops that only become visible through deep-dive interviews and system mining.

Impact: Prevents failed AI pilots by ensuring agents are built on accurate, comprehensive workflow data rather than idealized documentation.

— from Forward Deployed Engineering: AI Transformation Playbook · The Startup Ideas Podcast· Oct 01, 2026

Ephemeral chat plans are insufficient for long-term software maintenance; durable specifications are required to retain decision context across sessions and team members. This prevents the AI from re-interpreting requirements from scratch in every new session.

Impact: Reduces technical debt and rework by ensuring consistent interpretation of business requirements over the software lifecycle.

— from Intent-Driven Development: Strategic Context Engineering for AI · Engineering with AI· May 19, 2026

The Double Diamond framework (Discover, Define, Develop, Deliver) is critical for avoiding the trap of premature development. Most engineering failures stem from skipping the Discover and Define phases to jump straight to building.

Impact: Improves resource allocation by ensuring technical effort is spent on validated user needs, reducing waste and increasing the probability of product success.

— from Product-Minded Engineering in the AI Era · Tech Lead Journal· Mar 09, 2026

The traditional design process of long-term discovery and visioning is dead. AI speed forces a shift to 3-6 month directional prototypes that guide execution rather than dictate final aesthetics.

Impact: Teams that abandon rigid discovery phases will ship faster and adapt more effectively to non-deterministic AI capabilities.

— from AI Redefines Design Roles and Product Velocity · Lenny's Podcast: Product | Growth | Career· Mar 01, 2026

Spec-driven development suffers from decay, where documentation becomes stale and unrewarded. AI agents exacerbate this by confidently executing plans based on outdated specs without flagging drift.

Impact: Increases the risk of misaligned AI execution, necessitating automated spec maintenance systems.

— from AI Agent Efficiency and Market Attention Shifts · The Changelog: Software Development, Open Source· Feb 23, 2026