Instead of adding AI tools to existing workflows, Qualcomm is radically redesigning its marketing department. The new model places a foundational AI systems architecture at the core, with processes and people organized around it. This holistic approach aims for true transformation rather than incremental efficiency gains.
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To accelerate AI adoption, Block intentionally dismantled its siloed General Manager (GM) structure, which had given autonomy to units like Cash App. They centralized into a functional organization to drive engineering excellence, unify policies, and create a strong foundation for a company-wide AI transformation.
Don't just sprinkle AI features onto your existing product ('AI at the edge'). Transformative companies rethink workflows and shrink their old codebase, making the LLM a core part of the solution. This is about re-architecting the solution from the ground up, not just enhancing it.
The long-discussed alignment of sales and marketing is no longer optional; AI makes it mandatory. To effectively use AI insights for GTM, organizations must operate as a single, harmonious unit, possibly even merging the departments organizationally to ensure seamless, data-driven execution.
Using AI for incremental efficiency gains (10% thinking) is becoming table stakes. True competitive advantage lies in 10X thinking: using AI to fundamentally reimagine your business model, services, and market approach. Companies that only optimize will be outmaneuvered by those that transform.
Notion's CEO compares current AI adoption to swapping a water wheel for a steam engine but keeping the factory layout the same. The real gains will come from fundamentally rethinking workflows, meetings, and hierarchies to leverage AI that works 24/7, rather than just layering it onto existing processes.
View AI less as a tool for discrete tasks and more as the foundation for a central marketing hub. This system uses AI to create and maintain branded playbooks for all marketing activities, ensuring consistency and quality regardless of who is executing the work.
To maximize AI's impact, don't just find isolated use cases for content or demand gen teams. Instead, map a core process like a campaign workflow and apply AI to augment each stage, from strategy and creation to localization and measurement. AI is workflow-native, not function-native.
Shift from departments staffed with people to a single owner who directs AI agents, automations, and robotics to achieve outcomes. This structure maximizes leverage and efficiency, replacing the old model of "throwing bodies" at problems.
In the AI era, shift from silos like 'Demand Gen' to cross-functional pods focused on outcomes like 'Brand Relationship' or 'Product Delight.' This model, inspired by product development, aligns teams to solve specific customer problems and better integrates AI agents directly into core workflows.
The most successful companies are those that fundamentally re-architect their culture and workflows around AI. This goes beyond implementing tools; it involves a top-down mandate to prepare the entire organization for future, more powerful AI, as exemplified by AppLovin's aggressive adoption strategy.
