The most important question in an AI project is not always, "Can we build it?" It is:
How will we know it delivered value?
Launching AI is an important first step. But the next step is proving that it actually improved the customer experience and delivered meaningful business outcomes. For me, trust in AI is built through data, disciplined measurement, and credible evidence that outcomes have improved.
That means having a clear measurement approach:
- Define the outcomes that matter
- Establish a credible baseline for comparison
- Look at a constellation of KPIs, not just one metric in isolation
- Connect the results back to the customer journey and business goals.
This is especially important for AI capabilities like Virtual Agents, Agent Copilot, predictive routing, or other automation use cases.
A single metric rarely tells the full story.
For example, containment may improve, but what happened to customer effort?
Handle time may decrease, but did quality stay consistent?
Agent productivity may increase, but did the experience feel better for the customer?
I am curious to hear from the Community:
Which KPIs would give you confidence that an AI capability, such as a Virtual Agent or Agent Copilot, is delivering real value?
That is the conversation I am excited to explore during our Education Day session at Genesys Xperience in Las Vegas on September 1:
From Data to Decisions: Using Insights and Journey Management to Optimize CX
#EducationDay #Xperience26
#AIConfiguration
#ConversationalAI(Bots,VirtualAgent,etc.)
#CommunityAnnouncements
#Reporting/Analytics------------------------------
Reinhard Beck
Genesys Education Consultant
Munich, Germany
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