
September 2023 Releases
As customer interactions grow in complexity, not every decision can—or should—be deterministic. Many require analysis, nuance, and judgment.
These kinds of decisions used to fall exclusively to humans, but with recent advances in generative AI, that’s no longer the case.
Enter Regal’s AI Decision Node—a new way to route contacts in journeys dynamically, based on the context of each interaction or other unstructured data.
The AI Decision Node is a GenAI-powered node in the Regal Journey Builder that evaluates unstructured content from messages or call summaries—or a field on the contact profile (like a form fill)—and intelligently routes each contact down the right path in a journey.
Instead of relying on rules-based logic trees or manual triage, it uses prompts and context to make fluid, adaptive decisions in real time.
This isn’t just smarter logic—it’s the difference between rule-based AI workflow automation automation and intelligent handling.
There are a few clear signals that an AI Decision Node could unlock serious gains for your contact center:
Signal 1: Your agents are overburdened, making repetitive triage decisions. In this case, you should offload to AI for efficiency, trackability, and consistency.
Signal 2: You have high personalization needs, where messages must be tailored based on context or tone.
Signal 3: You have bottlenecks in high urgency and/or high volume situations. AI can assess and act instantly, which improves resolution speed.
Let’s break it down with some examples:
The problem: Agents were spending over a minute after each call crafting and sending follow-up SMS messages. It wasn’t easy to standardize—and definitely not scalable.
The solution: The AI Decision Node is used to evaluate the purchase intent or key objection from the call summary, then select and send the appropriate personalized follow-up message.
The result: Lower after-call work (ACW), standardized yet personalized outreach, and an easier-to-evolve process.
The problem: Triage agents were a staffing bottleneck, slowing case resolution. They were reviewing every incoming SMS or email and deciding which team to reassign them to.
The solution: AI classified incoming email intent and routed tasks to the right teams. For certain intents like "File a Claim," an AI agent can be triggered automatically.
The result: More cases handled, faster resolution times, and less strain on triage headcount.
The problem: Initial outbound SMS campaigns to gauge customer interest in a call were causing agents to manually review messages, most of which were from uninterested customers, wasting time.
The solution: Use AI to assess sentiment in SMS replies. Call only positive sentiment leads and continue to nurture the rest via SMS.
The result: Fewer dials needed to reach the right lead, and higher agent productivity.
Customers using the AI Decision Node are already seeing wins:
Customer Win #1:
A leading home services company eliminated an expensive inbound triage team by automating intent detection and triage of inbound sms and emails using the AI decision node.
Customer Win #2:
A lead gen team at a final expense life insurance company reduced dials to connect by half by using the AI decision node to gauge customer sentiment first from SMS responses, and only dial those who expressed positive sentiment.
Customer Win #3
A claims team at a home insurance company automated 30% of an agent’s workload after a major disaster, ensuring rapid response while focusing human attention where it matters most.
Check out our demo video to see the AI Decision Node in action
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