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The Importance of AI in an Omnichannel CX Strategy – and How to Make It Work for Your Business

Implementing a strong Omnichannel Customer Service strategy is not just about being on every channel; it requires that all channels make up one seamless, unified brand experience, with customer service representatives having a complete, cross-channel view of every customer interaction at all times. This effective strategy is often easier said than done, as only 13% of surveyed customers believe that companies are reaching their full potential delivering on multiple platforms.

Many organizations turn to AI and other trendy technologies to solve this problem. The issue with this approach is that AI is only a tool – not a standalone solution. Organizations can’t use AI to compensate for a poor or nonexistent omnichannel customer experience strategy. By making AI a supporting part of a sound Omnichannel Strategy, organizations can ensure they’re not implementing AI just for the sake of having hot technology in their arsenal. When taking this approach, all the powers of AI can be unleashed, with all the ensuing benefits.

A common misstep that organizations make when using AI as part of their omnichannel strategy is focusing on only one side of the interaction. Customer-facing AI, like Chatbots, can positively enhance the customer experience, but that application of AI alone cannot create a frictionless experience across all channels. For that, organizations must look inward and apply AI in a way that helps their agents. Here are some ways that agent-facing AI can improve the customer experience across channels and help brands execute a true Omnichannel Strategy.

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Helping Agents During Digital Interactions

For interactions that occur over the phone, it can be risky to attempt to resolve any issues with Customer-facing AI. Prompting callers with automated numerical menus are common, but frustrating to customers. Because voice technology capabilities are often lacking when it comes to accents and lexicons, it can be difficult for a voice-based bot to understand spoken words and tone. Flaws can lead to miscommunication, prompting frustration or worse – undesirable outcomes.

There is an easier and far less risky option when it comes to implementing AI in the contact center. Organizations should look at bringing AI directly to agents to assist with social, chat, messaging and e-mail conversations. Here, the AI “listens in” on the conversation, determines the query topic and helps the agent provide answers more efficiently. These ‘agent assist’ applications can suggest responses for the agent, including articles, documents or web pages previously created about the issue at hand, and the agent can filter or choose responses best fit for the customer. Because the agent sees the recommended responses before they go to the customer, they can adjust them as needed, avoiding miscommunication and negative outcomes, staying in full control of the conversation.

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Facilitating Quicker Response Times and Building Resources

Agent-facing AI quickens response times both in the short- and long-term. It can generate simple responses for an agent instantly using Robotic Process Automation (RPA), such as account balances or transaction history. Not only does this benefit the customer, but it also frees up time for the agent to focus on more in-depth questions, rather than be bored with repeating the same mundane tasks over and over.

Long-term, for Agent-facing or Customer-facing AI to perform best, it needs a robust knowledge source from which to pull answers. Creating this resource may be the biggest challenge for organizations. Information is often scattered – in PDFs, on post-its, hidden in notebooks or filed on outdated servers – and organizing it all can be overwhelming.

By using Agent-facing AI, agents can help build this knowledge base in the course of their existing work. When there’s a question that AI cannot answer due to a lack of existing resources, the agent can flag the question for those responsible for the content, effectively closing the knowledge gaps and improving the organization’s coverage of customer questions. Once an answer or article is created and approved, the content will be loaded into the knowledge base so the next time an agent is prompted with a similar inquiry, an answer will be available. By building and maintaining a central resource of knowledge, both agents and agent-facing AI can find answers to serve customers on any channel more efficiently, while also eliminating silos.

Enabling Seamless Transition Between Channels

Besides providing information efficiently and quickening response times, AI assists agents by giving them insight into a customer’s previous interactions. Organizations should be tracking and logging all interactions that a customer has with a brand across channels – social, online, over the phone, etc. – and agent-facing AI can notify an agent if this is the first or fourth inquiry about a subject, preparing them to handle the situation appropriately. Virtual assistants can help the agent by extracting information about the customer from CRM solutions, allowing agents to approach interactions with more information. With this, a customer does not have to explain their background and previous interactions—which has been cited as a top source of customer frustration—with the organization, regardless of channel.

By using AI as part of a cohesive engagement strategy to promote seamless, effective interactions across channels, customers’ needs will be better met, and organizations will be able to offer a true omnichannel experience. The best and least risky place to start is with your contact center agents. Ensuring that insight into customer interactions across all channels and central knowledge bases are available to all agents will quicken response times and improve both customer satisfaction and agent experience.

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