Staying Customer-Centric with Agentic AI: Insights from Forrester CX

Defining Agentic AI Use Cases

• 🎯 Agentic AI requires brands to clearly define their use cases and desired outcomes, moving beyond the hype.
• 💡 The focus should be on the value exchange for the customer, not just business benefits like productivity and efficiency.
• 🔑 Brands must understand that most consumers are not deeply familiar with AI and require clear explanations of its purpose and benefits.

Balancing Privacy and Personalization

• 🔒 Privacy personas help brands understand the nuances of consumer data sharing, moving beyond a simple "cares or doesn't care" dichotomy.
• 🤝 Conditional consumers will share data if incentivized, requiring a clear value proposition like loyalty programs.
• ⚠️ Skeptical protectionists are highly tech-savvy and averse to sharing data, necessitating alternative channels for support and information.
• 🚫 Making AI mandatory can alienate a significant portion of the customer base, potentially leading to increased frustration.

Navigating the Intermediary Phase of AI

• ✨ The current phase is an opportunity to build trust incrementally before full agentic AI adoption.
• 🗣️ Transparency about AI usage is crucial; consumers react negatively (deceived, angry, upset) when AI is used without disclosure.
• 🚀 Brands should focus on improving customer experiences with AI in confined, well-defined use cases to gradually acclimate consumers.

Rolling Out AI Experiments and Pilots

• 📈 Key Performance Indicators (KPIs) and leading signals are essential for determining readiness for broader rollout.
• 🛠️ Start with internal use cases to master AI capabilities before moving to customer-facing applications.
• 🧩 AI agent pilots should demonstrate expected behavior, accurate outputs, and up-to-date data before wider deployment.

Building Agile AI Teams and Governance

• 🤝 Cross-functional collaboration is vital, as no single department can manage AI development and deployment alone.
• 🏛️ A centralized AI governance council can prevent duplication of efforts, manage risks, and ensure accountability.
• 💡 This council forces critical thinking about use cases, KPIs, and the practical nuts and bolts of AI implementation, moving beyond the "because we can" mentality.