tremendous progress in recent years by incorporating digital and analytics capabilities, and agentic AI builds on those improvements by automating and orchestrating tasks across teams and functions. Overcoming the persistent challenges of coordination across complex operational silos and workflows can allow organizations to achieve faster cycle times, as well as greater consistency and responsiveness at a scale no level of human coordination could match. Crucially, success calls for designing processes around agents—not bolting agents onto legacy processes. For example, rather than using agents to help customer service teams respond to complaints faster, leading organizations use agents to predict potential issues, trigger outreach before a customer calls, and resolve cases pre-emptively with personalized offers. The European insurer offers a clear view of what this looks like in practice. According to McKinsey analysis, in just 16 weeks, the company re-architected its commercial model around a connected network of agents working across the full customer journey. The improvements generated included the following: — Knowledge agents centralized over 1,000 policy and product documents, enabling frontline staff to retrieve accurate answers instantly. — Coaching agents introduced AI-driven call transcription and grading, automatically reviewing 95 percent of sales calls versus 3 percent previously. — Integration agents connected these capabilities into the existing CRM and agent portal—adhering to single-sign-on security policies and providing real-time performance dashboards. Together, these agentic systems shortened average call times by 25 percent, reduced manual cross-functional handoffs, and created a continuous feedback loop. As agents learned from each engagement, they continually refined next-best actions, message sequencing, and product pairing to stay aligned with evolving customer needs. Value creation with AI agents for end-to-end change depends, however, on matching the right agent to the right task: domain-specific agents that handle complex, contextual actions; generalist agents for tasks such as data synthesis or content generation; agents that check for errors; and orchestration agents that direct and synchronize the system as a whole. Humans have a crucial role in this effort. They can work closely with agents to supervise and verify, as well as manage issues that AI agents escalate to them. The most advanced organizations combine these human–agent collaborations into adaptive workflows that evolve with each iteration and customer signal. Agents for growth: Turning AI promise into impact 4