Don't view AI as a tool to replace roles. Its power is in collapsing multi-day processes—like creating and QA-ing an advertorial—into minutes. The most valuable skill marketers can develop is learning to construct custom workflows by connecting various AI models via APIs to amplify their own output and speed.

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.

Companies can either augment existing processes with AI for incremental efficiency (e.g., co-pilots) or completely redesign workflows. While augmentation is common, the most transformative value and disruptive business models will emerge from a clean-sheet redesign of how work is done.

Drawing a parallel to the slow adoption of PCs in the 90s, Boris Churney argues companies won't see AI productivity boosts by simply layering it onto old workflows. The biggest benefits come from placing AI at the core of the business and redesigning processes around its capabilities, eliminating old bottlenecks entirely.

The historical adoption of electricity in factories shows that true productivity gains came from redesigning the factory floor, not simply replacing steam engines. Similarly, companies must fundamentally re-engineer processes around AI to unlock its transformative potential.

The greatest value of AI isn't just automating tasks within your current process. Leaders should use AI to fundamentally question the workflow itself, asking it to suggest entirely new, more efficient, and innovative ways to achieve business goals.

True productivity gains from AI will mirror the adoption of electricity. Early factories that just replaced steam engines with electric motors saw little benefit. The revolution happened when they completely redesigned the factory floor around the new technology. Similarly, companies must reimagine entire workflows around human-AI collaboration.

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.

Implementing AI effectively isn't about finding a magic prompt. It requires an R&D mindset: investing time to build proprietary systems. Expect a learning curve and failed experiments; the goal is building a long-term competitive edge, not an overnight fix.