A customer opens a ticket via email on Monday. On Wednesday they call for an update. On Friday they use the website chat for a related issue. In three interactions across three different channels, they've provided the same context three times, and each time received a response as if it were the first. This is multichannel customer service in most organizations today: separate channels that don't talk to each other, and a customer who bears the burden of continuity alone.
AI is changing this logic, but not uniformly and not automatically.
51% of customers are ready for AI in service
In a survey of 4,879 customers conducted between January and February 2025, Gartner found that 51% would be willing to use a GenAI assistant to handle their service interactions on their own. It's the first time this type of willingness has crossed the majority threshold in Gartner research. On the supply side, an October 2025 survey of 265 service and customer support leaders found that 77% feel pressure from senior leadership to deploy AI and 75% have increasing budgets for AI initiatives.
The convergence between customer demand and executive pressure creates the conditions for accelerated AI adoption in multichannel customer service. But the pressure to act doesn't solve the underlying technical and organizational problem: channels must share data, context, and interaction history to produce a coherent experience.
The four most impactful AI use cases according to Gartner
In an October 2025 press release, Gartner identified the four most impactful AI use cases in customer service. The first is agent enablement: AI-powered assistance tools such as real-time conversation summaries, quick replies, customer data insights, and next-best-action recommendations. The second is low-effort self-service: intelligent virtual assistants and advanced search capabilities that allow customers to resolve issues independently. The third is operational support automation: AI in analytics, knowledge base content generation, and quality assurance. The fourth is agentic AI, which handles complex workflows and multi-step requests autonomously.
In multichannel contexts, these four use cases must operate across all channels with access to the same customer context. A virtual assistant in the chat needs to know what the customer wrote in an email three days earlier. A human agent answering the phone needs to see the entire history of digital interactions. The AI-powered knowledge base must be the same source regardless of which channel the customer accesses it through.
The multichannel knowledge management problem
Gartner identifies knowledge management as one of the main obstacles to multichannel coherence: many organizations have backlogs of knowledge base articles and unstructured content review processes. 58% of customer service leaders plan to reskill agents as knowledge management specialists to enable them to review and curate AI-generated content.
The issue isn't just technical. A multichannel knowledge base must be sufficiently up-to-date, accurate, and structured to allow both AI assistants and human agents to find the right answers quickly. A knowledge base fragmented by channel produces different answers to the same questions, inconsistencies that customers perceive as signs of internal disorganization.
Agentic AI redefines channel logic
Gartner describes the deepest shift not in optimizing individual channels, but in redefining the very logic of interaction. With agentic AI, the channel stops being the starting point and becomes a detail of orchestration. An AI agent can collect information from all channels, synthesize the complete customer context, and take actions that traverse different systems without the customer having to choose or manage the channel.
Gartner predicts that by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention. In this scenario, the distinction between chat, email, and phone is operational for the organization, but increasingly irrelevant for the customer. What matters is that the system has access to all the context and the ability to act across all the systems needed to resolve the issue.
Intelligent routing: who responds and through which channel
Gartner includes intelligent routing among the priority investments for customer service leaders in 2026. It's not just about routing requests to the right channel or agent: it's about matching in real time the type of problem, the customer's history, available skills, and the preferred channel to maximize the probability of first-contact resolution.
An intelligent routing system with access to the customer's multichannel history can recognize that a customer who has already contacted support twice for the same issue needs a senior agent, not a chatbot. It can recognize that an incoming email request contains urgency signals and route it to a channel with faster response times. And it can distribute the workload to prevent some agents from being overloaded while others sit idle. This real-time optimization is impossible without a unified view of data across all channels.
