The primary barrier to AI adoption in large companies is not technological but organizational. Success depends on understanding the 'real' org chart—the informal network of influencers who control data and approve projects, which often differs from the official hierarchy.

The fundamental model of AI use is changing. It's moving from 'assisted' AI, which helps humans with their tasks, to 'agentic' AI, where autonomous systems perform tasks. This paradigm shift requires new methods for adoption, management, and measuring success, moving from 'seats' to 'tokens'.

Simply giving AI tools to existing departments like legal or finance yields limited productivity gains. The real unlock is to reimagine and optimize end-to-end, cross-functional processes (e.g., 'onboarding a new supplier'). This requires shifting accountability from departmental silos to process owners who can apply AI holistically.

The most significant gains from AI will not come from automating existing human tasks. Instead, value is unlocked by allowing AI agents to develop entirely new, non-human processes to achieve goals. This requires a shift from process mapping to goal-oriented process invention.

Adding AI tools to current processes yields only incremental efficiency. To achieve significant business impact, leaders must rebuild their entire go-to-market system—roles, workflows, and data flow—with AI at the core, not as an add-on.

The true power of AI is unlocked by adopting an "AI First" approach. This means completely redesigning workflows with AI at the core, rather than simply using AI to accelerate existing processes. This shifts employees' roles from performing tasks to managing the AI agents that do the work.

Unlike previous technologies that integrated into existing workflows, AI agents require us to fundamentally re-engineer our work processes to make them effective. Early adopters who adapt their operations to how agents "think" will gain compounding advantages over competitors.

Beyond a technical concept for coding agents, "harness engineering" provides a powerful mental model for enterprise AI adoption. It reframes the challenge from simply deploying models to redesigning the entire organizational system—processes, data access, and feedback loops—to create an environment where AI capabilities can truly succeed.

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.