Build Smarter Automation with Practical Use Cases
Agentic AI buzz is all around us – but how do you cut through the hype to make it real? Marketers are excited by the opportunities of AI agents but struggle to find a starting point and gain momentum.
In this playbook, we’ll guide you on how to decide which AI agent use cases make sense for your brand. We’ll share potential pitfalls to avoid, tips for balancing human and AI involvement, and the data architecture requirements that enable success. You’ll walk away with information to explore agentic AI smartly so you can create more efficiency for your company and better experiences for your customers.
- Find the right use case. Simpler internal use cases can help build momentum without the risk of creating a poor customer-facing experience. Defining the problem and KPIs are key to success.
- Balance humans and automation. Companies must tread carefully when using AI agents for customer-facing interactions, and know when it’s time for a human to step in.
- Empower AI agents with data. AI agents need new levels of data architecture to succeed. They require foundational models, governance guidelines, rules for engagement, and more.
- Embrace the power of partners. Salesforce Agentforce and Merkle can help companies get to market quicker with prebuilt agents, established guardrails, and highly adaptable systems.
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