Your Customers Expect More: Andrew Carothers on AI, Revenue and Digital CX
Returning to CX Insider, Cisco digital customer experience leader Andrew Carothers examines why AI investment should start with business problems, not enthusiasm for a tool. The discussion connects changing customer expectations to commercial outcomes, personalisation and the people needed to make automation useful.
The episode at a glance
- Digital CX must be accountable for commercial outcomes as well as experience measures.
- Andrew describes identifying around $1 million in recurring revenue that a customer process was leaving behind.
- AI can personalise interactions, but the value lies in redesigned journeys and changed ways of working.
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Four insights from Andrew Carothers’s episode
A customer-experience metric is not the end goal
Andrew revisits the need to connect digital CX to revenue, retention and measurable business outcomes. In the conversation he describes how scrutiny of returns has spread from CX programmes to technology procurement and AI investment more broadly. A dashboard that looks impressive means little if it cannot explain what changed for customers or the business.
Find the revenue hidden inside broken processes
A striking example in the interview is the identification of roughly $1 million in recurring revenue being missed by an existing process. Rather than merely optimising a step, the more consequential opportunity was to rethink how the process should work. For CX leaders, this shifts the question from “can we automate it?” to “why is the customer journey arranged this way?”
Personalisation needs a strategy, not just an algorithm
Andrew discusses Cisco’s efforts to apply AI within digital customer experience, including understanding customers and tailoring engagement. The lesson is to connect signals to a meaningful action: helping someone complete a task, anticipate a support need or find the next best service interaction.
Human work changes as automation improves
The episode explores why automation should reshape roles rather than simply be presented as a headcount exercise. Teams need to understand the limits of AI outputs, oversee decisions and own exceptions. A credible adoption plan combines technical capability with training, accountability and trust.
A practical takeaway
Audit one high-volume customer journey for lost revenue or repeat work. Record the current problem, select an AI-enabled intervention only where it helps, and measure the outcome in retention, completion or customer effort—not tool usage.
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