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.

A critical error in AI integration is automating existing, often clunky, processes. Instead, companies should use AI as an opportunity to fundamentally rethink and redesign workflows from the ground up to achieve the desired outcome in a more efficient and customer-centric way.

With AI, the "human-in-the-loop" is not a fixed role. Leaders must continuously optimize where team members intervene—whether for review, enhancement, or strategic input. A task requiring human oversight today may be fully automated tomorrow, demanding a dynamic approach to workflow design.

A successful AI strategy isn't about replacing humans but smart integration. Marketing leaders should have their teams audit all workflows and categorize them into three buckets: fully automated by AI (AI-driven), enhanced by AI tools (AI-assisted), or requiring human expertise (human-driven). This creates a practical roadmap for adoption.

Despite AI's capabilities, it lacks the full context necessary for nuanced business decisions. The most valuable work happens when people with diverse perspectives convene to solve problems, leveraging a collective understanding that AI cannot access. Technology should augment this, not replace it.

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.

The most effective way to build a powerful automation prompt is to interview a human expert, document their step-by-step process and decision criteria, and translate that knowledge directly into the AI's instructions. Don't invent; document and translate.

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.

AI excels at intermediate process steps but requires human guidance at the beginning (setting goals) and validation at the end. This 'middle-to-middle' function makes AI a powerful tool for augmenting human productivity, not a wholesale replacement for end-to-end human-led work.