Listing use cases for artificial intelligence (AI) is like describing how to use the internet — the AI implementation examples are nearly infinite, constrained only by the inventive minds behind the wheel. AI is being implemented in business practically everywhere, from personalizing sales outreach at scale, to writing code for your latest product feature, to powering your contact center knowledge base.
But when it comes to using AI to answer customer questions reliably, there are several critical aspects to consider for your knowledge management solution (KMS): the content being used, integration capabilities, and the approach you use to implement it all.
Below we share nine examples of how AI is being used to optimize the performance of contact centers teams, including Shelf customer success stories as proof.
AI Implementation Examples: 1
Find answers fast with AI-enabled pinpoint search
Delivering exceptional customer support in today’s dynamic landscape is a challenging feat. Customer questions and solutions are complex. The right answer to each problem can change frequently as product features and needs evolve. With traditional knowledge base searches, most contact center agents don’t find the right answer, causing them to search multiple times, across different sources including asking other agents for help.
AI-driven solutions are revolutionizing how support agents navigate company knowledge and expedite query resolutions. Conversational search functionality from Shelf uses AI and natural language processing (NLP) to locate exact keywords within any type of documentation. This ensures agents find pertinent information in their initial search, saving valuable time. They can get to the exact phrase and context of answers that were previously buried deep inside of large documents.
AI Implementation Examples: 2
Optimize self-service and chatbots with AI-powered recommendations
Consumer trends continue to underline the importance of offering self-service features, as buyers seek to answer their own questions online before engaging an agent for help. If your company lacks an AI-powered knowledge base, chatbots can quickly become a glorified contact form, preventing the type of satisfactory support that enables strong business growth.
But with AI algorithms and NLP, self-service tools can automatically recommend relevant articles based on customer search and browsing patterns. Shelf’s Self Service portals seamlessly integrate into communication channels and continuously learn from interactions, enhancing their ability to understand complex inquiries, anticipate customer needs, and deliver tailored answers promptly.
AI Implementation Examples: 3
Fast-track new agent onboarding with AI
New contact center agents can sometimes face a steep learning curve, which requires time before they can be productive. But AI-driven knowledge solutions have emerged as game-changing ways to expedite the onboarding process. By centralizing and integrating vast knowledge bases directly into agent workspaces, companies can swiftly ramp up and assimilate new team members.
AI can consolidate a large volume of content from various sources into a single platform, giving agents instant access to the most accurate answers and recommendations to solve customer problems. This knowledge can be integrated directly into live chats which not only accelerates the effectiveness of new agents but also enhances overall operational efficiency by significantly reducing handle times.
AI Implementation Examples: 4
Use AI to personalize customer preferences
If you’re a subscription-based business, your competitive advantage often hinges on your team’s ability to personalize each customer’s experience in a cost-effective way. By leveraging sophisticated AI-powered knowledge solutions like Shelf, you can use knowledge sources from within your company to create a holistic view of each customer’s profile, preferences, and needs.
For instance, with Shelf’s AI-powered suggestion engine, agents can harness decision trees that guide them through complex recommendations, ensuring that they navigate unique queries based on shared information and data about each customer. A continuous feedback loop facilitated by AI-powered systems keeps that knowledge fresh and relevant.
AI Implementation Examples: 5
Eliminate shadow knowledge using AI
Fragmented, disconnected knowledge sources can often lead to ‘shadow knowledge’, a term used to describe elusive, unstructured information that resides outside official channels. Shadow knowledge hinders operational efficiency and consistency in customer service because it takes agents longer to find and retrieve, and is kept siloed away from the rest of the team.
But AI-powered knowledge systems have emerged as a powerful solution to eliminate this problem. By centralizing information into a single, unified knowledge hub, you ensure easy access to accurate information, which builds trust in the KMS and will curb the need to rely on shadow knowledge.
AI Implementation Examples: 6
Streamline the collections process with AI
Most businesses want to have a smooth and consistent payment collection process that doesn’t alienate debtors. But traditional knowledge bases are prone to inaccuracies, outdated information, and struggle to keep up with changing customer activity and industry conditions.
But with AI powering their knowledge base, companies can significantly streamline their collections process. Integrated automation tools empower agents to swiftly access accurate information without toggling across disparate applications. Systems that use AI become comprehensive knowledge hubs, supporting managers with analytics, purpose-built content, and robust governance features. Arming your agents with accurate information helps reduce errors and hold times while optimizing the debtor experience.
AI Implementation Examples: 7
Use AI to optimize field service work
When your mission is to provide best-in-class technical support in the field, you need a remote team ready to solve a variety of requests — whether it’s to guide onsite techs through equipment manuals and blueprints, or to help them navigate repair instructions. In any case, your teams must be highly coordinated and informed.
Shelf, an AI-powered knowledge base, provides a user-friendly means of searching documentation regardless of the format. Instead of carrying heavy documents onsite, field technicians can rely on their remote counterparts — powered by Shelf’s AI functionality — to transform a simple blueprint scan into a fully-searchable digital document. Guided workflows featuring decision tree logic can help remote techs quickly pinpoint answers so that field technicians can resolve issues faster. AI-fueled features can also make how-to videos quickly accessible and searchable directly inside a single KMS.
AI Implementation Examples: 8
Automatically update content using AI
Maintaining knowledge bases across multiple libraries and platforms is a time-consuming process that requires extensive resources and often yields unsatisfactory results. AI can be used to convert lengthy documents into searchable knowledge articles automatically, which drastically reduces the burden of content maintenance.
Shelf’s AI-driven Knowledge Automation can consolidate lengthy technical information into bite-sized, canned responses that are easy to retrieve and share. Instead of going through the entire process of creating new articles each time knowledge needs to be entered, teams can upload documents into Shelf’s content publication workflow which instantly transforms each into searchable content.
AI Implementation Examples: 9
Provide rapid troubleshooting with AI-powered recommendations
When your business provides emergency support to customers, time is of the essence. Troubleshooting must happen as fast as possible. But when knowledge is spread out across platforms, agents have to perform multiple queries by hand before they come close to finding the answers they need (and it’s still not a guarantee). This traditional method stands in the way of providing the fast services consumers expect.
Shelf’s API seamlessly integrates with any CRM or ticketing system, providing AI-powered answers in the same screen that your reps work in every day. Instead of navigating through endless folders, answers get automatically presented in real-time directly inside the CRM screen, transforming the way your agents support your customers.
Whether it’s to reduce query handling time, speed up content maintenance work, or ensure all knowledge in your company is organized effectively, AI can help bring order and power to your contact center.
