A Gartner survey has revealed that 77 percent of customer support leaders feel pressure from executives to deploy AI.
The statistic comes from its recent report entitled: “The Most Valuable AI Use Cases for Service and Support”.
That study also found that 75 percent of service leaders had received budget hikes to support AI initiatives in comparison to the previous year.
The investment will spur “the typical leader” into adding five new full-time-equivalent (FTE) roles over the next 12 months to help manage these AI projects.
As to what these AI projects may entail, Keith McIntosh, Sr. Principal, Research for Gartner Customer Service and Support, has identified several of the most effective use cases for AI in customer support.
“Service and support leaders are looking to AI for a wide variety of goals – efficiency, better CX, lead generation, and delivering other value back to the business.
The most impactful use cases are four-fold: those that enable assisted agents, empower customers through self-service, automate operational support, and introduce agentic AI across their stack.
Given this, let’s take a closer look across these four key arenas, digging into specific use cases across each.
1. Agent Enablement
So far, adoption of contact center AI assistance solutions has lagged expectations, as support leaders prioritize conversational automation.
However, use cases – such as auto-summarization, auto-replies, and desktop automation – are becoming increasingly mature.
Meanwhile, as contact centers invest in self-service, the easy contacts agents use to take a breather are disappearing. Instead, it’s tough call after tough call, and – in many cases – reps need more support.
As such, more contact center leaders will put the onus back onto agent enablement, focusing on lowering their cognitive load.
2. Low-Effort Self-Service
Self-service has gained a bad reputation over the years, and while generative AI (GenAI) has enabled superior knowledge retrieval, virtual agents continue to lag their human counterparts.
However, AI agents promise to move the needle, as they collaborate across systems to automate longer-tail resolution flows.
Indeed, Gartner has previously predicted that AI agents will autonomously resolve 80 percent of common customer service issues without human intervention by 2029.
Given this possibility, emphasis will remain on bolstering self-service applications and broader journey orchestration.
Such orchestration will be crucial as contact centers pivot from an approach of automating everything possible to considering where it is best for a human to engage, and where it is best for an AI.
3. Automating Operations Support
Beyond agents and customers, AI is supporting many other critical contact center stakeholders.
For instance, it’s empowering supervisors with new agent performance insights, knowledge management capabilities, and reporting solutions.
These reporting solutions are particularly exciting, as conventional contact center analytics tools collide with more powerful business intelligence (BI) applications. These will deliver insight into supervisor workflows and allow them to ask questions of their data.
Additionally, advancements in AI are making forecasting models and scheduling algorithms much more accessible for workforce planners still clinging to Erlang Calculators and spreadsheets.
4. Agentic AI
Agentic AI isn’t all about automating resolution flows. There are many other possible contact center applications.
For instance, AI agents can detect signals in cross-enterprise systems that suggest a customer is experiencing an issue and offer proactive support. That’s something some contact center teams are doing already.
Yet, many other possible applications could emerge over the next 12 months, as explored in the article: 10 Agentic AI Use Cases for Contact Centers.
More from Gartner’s Customer Support Analysts
“Organizations that prioritize these high-impact use cases will be best positioned to achieve operational excellence, deliver superior customer experiences, and stay ahead in the rapidly evolving AI landscape,” summarized McIntosh.
Yet, there are a lot of sub-use cases within the four arenas McIntosh highlighted, and unpacking all these will take up considerable time for customer support leaders and their collaborators in IT. Given this, here’s a broader overview of possible contact center AI applications.
Elsewhere, this isn’t the first time Gartner has exposed the building AI tension on CX leaders, with studies conducted by the research firm in July suggesting that many were “feeling pressure” from business executives to respond to ideas of implementing “limitless automation”.
Meanwhile, Gartner has also made recent predictions that by 2028, no Fortune 500 company will fully replace human customer service employees with AI.
