To successfully automate complex workflows with AI, product teams must go beyond traditional discovery. A "forward-deployed PM" works on-site with customers, directly observing workflows and tweaking AI parameters like context windows and embeddings in real-time to achieve flawless automation.

In AI acquisitions, a startup's underlying technology is less important than its "workflow proximity." Atlassian's AI head advises buyers to assess how deeply a tool is integrated into a user's fundamental daily tasks. A tool central to a core workflow is far more valuable and defensible than a specialized, peripheral one.

The term "AI-native" is misleading. A successful platform's foundation is a robust sales workflow and complex data integration, which constitute about 70% of the system. The AI or Large Language Model component is a critical, but smaller, 30% layer on top of that operational core.

Rather than programming AI agents with a company's formal policies, a more powerful approach is to let them observe thousands of actual 'decision traces.' This allows the AI to discover the organization's emergent, de facto rules—how work *actually* gets done—creating a more accurate and effective world model for automation.

The defensibility of AI-native software will shift from systems of record (what happened) to 'context graphs' that capture the institutional memory of *why* a decision was made. This reasoning, currently lost in human heads or Slack, will become the key competitive advantage for AI agents.

Incumbent companies are slowed by the need to retrofit AI into existing processes and tribal knowledge. AI-native startups, however, can build their entire operational model around agent-based, prompt-driven workflows from day one, creating a structural advantage that is difficult for larger companies to copy.

The effectiveness of enterprise AI agents is limited not by data access, but by the absence of context for *why* decisions were made. 'Context graphs' aim to solve this by capturing 'decision traces'—exceptions, precedents, and overrides that currently live in Slack threads and employee's heads, creating a true source of truth for automation.

Counter to fears that foundation models will obsolete all apps, AI startups can build defensible businesses by embedding AI into unique workflows, owning the customer relationship, and creating network effects. This mirrors how top App Store apps succeeded despite Apple's platform dominance.

To build coordinated AI agent systems, firms must first extract siloed operational knowledge. This involves not just digitizing documents but systematically observing employee actions like browser clicks and phone calls to capture unwritten processes, turning this tacit knowledge into usable context for AI.