This workflow is designed for support teams seeking to leverage their existing knowledge base to accelerate ticket resolution and promote customer self-service. By automatically suggesting relevant articles, it helps both customers and agents quickly find solutions to common issues without extensive searching or research.
When a support ticket arrives, AI performs semantic analysis of the ticket content and searches the knowledge base for matching articles or solutions. High-confidence matches are either sent directly to customers (promoting self-service) or surfaced to support agents within their ticket interface, allowing them to quickly verify relevance and share with customers.
By implementing this automation, support teams typically see 30-40% of tickets resolved through knowledge base suggestions, reducing handle times and freeing agents to focus on more complex issues. The system can also track which suggestions lead to ticket resolution, providing valuable data on knowledge base effectiveness and opportunities for improvement.
You care about improving the entire lifecycle of buyer and customer journeys. And you want inspiration for areas where Ai may help.
You want inspiration for real-world ways to help people as you're building your Ai product.
You're focused on the Retention stage in your daily job and want to find ways for Ai to help you do more with less.
Does this AI agent workflow rely too much on AI and not enough on human know-how? Or the reverse? Is it missing steps or tools?
Note that this Ai workflow is presented as inspiration for what's possible. Adjust the amount, type and quality of the data inputs. Adjust how much or how little your human team mates (or you), AI and fully autonomous agents contribute.
And test it! Learn what works and what doesn't.
Don't forget! In the end, it's not just about efficiency. It's about delivering great experiences for your customers and customers-to-be.
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