Dive Brief:
- CX leaders still have time to develop their AI strategies. Seven in 10 CX leaders say that AI is a business imperative, and 2 in 5 say it will be critical in two to three years, according to a survey of 1,000 CX leaders released last month by Genesys. Over a quarter say AI is already critical today
- The majority of smaller businesses, those with less than 1,000 employees, see the technology as a tool for financial gain. Two-thirds of smaller companies say they expect AI to improve financial performance, while only half of larger businesses or enterprise-scale companies say the same.
- Larger businesses are more likely to cite potential long-term benefits from AI with 54% of enterprise companies expecting AI to help them out-innovate the competition. By comparison, only 40% of small businesses anticipate the same benefit.
Dive Insight:
Companies are still exploring use cases for AI as the technology weaves its way into business environments.
Chatbots are the most common use case for AI, with 88% of respondents saying their companies are using the technology and 50% consider chatbots to be the most important use case for AI in CX. The majority of companies are also piloting AI for personalization or identifying customer journey pain points, the survey found.
Companies still need to put in the ground work before their AI investments can offer benefits, whether through automation or analysis. This starts with breaking down data silos, according to Julie Geller, principal research director at Info-Tech Research Group.
“Many organizations have customer information scattered across systems — CRM, marketing automation, support platforms, and more,” Geller told CX Dive in an email. “Investing in unified data platforms is critical because, for AI to deliver beyond the obvious, it needs access to data that is central to your organization, and managing it in one place makes the process achievable.”
Once unified data systems are in place, AI can start looking for customer behavioral problems that may not be immediately obvious, according to Geller. For instance, sentiment analysis can read between the lines of customer support calls.
“The key is layering AI models that understand emotion, action, and outcome simultaneously — like an orchestra that brings all elements of the customer’s experience together to identify where the harmony breaks,” Geller said.
More than 7 in 10 leaders expect AI to initiate all proactive customer service outreach in the future, according to the Genesys survey. This is another area where AI-powered analysis can help.
Proactive service goes beyond flagging issues or looking at NPS results, according to Geller. Companies need to look for patterns in usage data, sentiment changes, and engagement levels that indicate when a customer is struggling, then intervene at the right moment.
“The best practice is to use AI models that score risk factors in real time, cross-referencing them with past customer behavior,” Geller said. “It’s less about pushing generic ‘How can I help?’ messages and more about delivering predictive nudges that are personalized to what matters most for that specific customer at that specific time.”
