For example, in customer service, AI agents can instantly surface relevant knowledge articles, suggest next-best actions or triage inquiries allowing human agents to spend more time resolving nuanced issues with empathy. In marketing, AI can generate campaign drafts or segment audiences, while humans refine messaging and creative direction. In software development, AI can write and test routine code, giving engineers more time to architect systems and solve complex problems. These redesigned workflows blend machine efficiency with human judgment, leading to better outcomes across the board.
To make this possible, leaders should consider creating a culture where AI is seen as a productivity partner, not a threat. That starts with transparency and trust. Employees need to understand how AI decisions are made, what agents are doing, and how their own roles are evolving.
But trust alone is not enough. Organizational readiness is often the limiting factor. According to a recent McKinsey survey, only 1% of organizations rate their generative AI initiatives as mature. Many remain stuck in pilot purgatory, where AI shows promise but fails to scale.
