What Is AI Knowledge?

AI knowledge is the collective data, patterns and rules that an artificial intelligence system has learnt from training on large datasets. This knowledge enables AI to process information, make decisions, predict needs and perform tasks based on learnt experiences.

AI knowledge provides data-driven insights to improve decision-making and operational efficiency in the contact centre, as well as field service. Many customer service leaders already see the value of AI. Our research shows that 79% of service organisations have already invested in AI technology and 95% of organisations using AI report cost and time savings.

Here, we look at how AI knowledge can transform your customer service.

AI knowledge is the information and insights generated or processed using artificial intelligence technologies.

When applied to customer service, AI knowledge refers to the application of artificial intelligence to enhance support interactions. This includes machine learning algorithms to understand customer questions and sentiments, providing accurate responses through AI chatbots, predicting customer needs and offering real-time assistance to customer service reps to improve efficiency and customer satisfaction.

AI knowledge enables companies to provide round-the-clock support, ensuring that customers receive immediate and consistent responses, whenever it’s convenient for them. By automating routine tasks and deflecting frequently asked questions to self-service, AI frees up customer service reps to focus on complex and high-value cases that require critical thinking and empathy. This can significantly improve operational efficiency and customer service experience.

AI knowledge significantly enhances knowledge management by automating the organisation, retrieval and application of information. It can analyse vast amounts of data to identify patterns and trends, categorise and tag content for easier access and provide personalised recommendations based on customer behaviour. It can also generate new knowledge articles, capturing institutional knowledge while also saving service reps valuable time.

Key areas of AI knowledge

Fundamentals of AI knowledge include:

• Machine learning (ML): This subset of AI uses algorithms to detect patterns in datasets and teach themselves to become more accurate over time. A machine learning app can help a customer service team reduce their churn rate by learning to predict which customers are most likely to stop using a product or service.
• Neural network: This advanced form of machine learning uses a network of interconnected nodes, called neurons, to process data in a way that mimics the human brain. Customer service reps can use a simple neural network to segment buyers into distinct groups and customise call scripts to match their interests or buying habits.
• Deep learning: This technique combines multiple layers of neural networks to provide deeper insights than a single neural network can provide. Customer service teams can use deep learning to analyse customer interactions for emotional responses to words, phrases or products.
• Natural language processing (NLP): This technology, based on deep learning models, helps computers understand the context of human words, sentences and vocal patterns. NLP can analyse transcripts of contact centre conversations and connect words and phrases that point to signs of customer happiness or discontent.
• Generative AI: This NLP technology can create content such as text, sounds and visuals. Generative AI apps typically use chatbot interfaces that let contact centre reps ask plain-language questions about information in knowledge bases, helping answer customer questions much faster.

Let's look at some real-world examples of how AI knowledge is optimising customer service operations across multiple industries:

Technology

A tech company depends on expert customer support reps to help customers diagnose and resolve complex technology issues. By implementing generative AI-powered chatbots, reps can query multiple data sources simultaneously and instantly find relevant answers for common and complex questions. This is one way this company has dramatically reduced the time required to resolve customer cases.

Energy

Support agents for enterprise companies, like this global energy company, deal with numerous customer service queries a day. To get up to speed quicker on call and chat transcripts for newly assigned support cases, this company has implemented AI tools such as conversation insights that deliver instant summaries of voice and digital transcripts, accelerating agents' ability to resolve issues.

Healthcare

This health insurance provider must frequently consolidate customer information, scattered across multiple data sources. By implementing AI tools like Unified Knowledge, they can integrate and centralise all their customer information, including information from third-party sources, making it simpler for agents to access. This company also uses tools like generative AI to produce customer emails automatically with answers to common questions, reducing response times to FAQs-style enquiries.

Here are some ways to build AI knowledge:

1. Training and workshops: Provide your customer service teams with AI-focused training programmes, workshops or online courses to help them to understand AI technologies and how to implement them effectively. Trailhead, Salesforce’s free online learning platform, is a great resource to get your team up to speed and stay on top of the latest advancements in AI.
2. Hands-on experience with AI tools: Encourage your team to use AI-powered customer service tools, such as chatbots, automated ticketing systems and AI customer service Agents to gain practical experience and familiarity. For example, they can use an Agentforce for Service to get specialised, always-on support to resolve cases faster — even at 2.00 a.m.
3. Collaboration with AI experts: Partner with data scientists or AI specialists to educate your team on the technical aspects of AI, helping them to understand how it can be applied to customer service experiences. Your team can even become certified AI specialists through Trailhead training.
4. Industry research and case studies: Regularly review industry case studies, white papers and research on AI in customer service to stay updated on the latest trends and successful implementations.
5. AI communities and forums: Join AI-focused communities, forums or networking groups where customer service professionals can share best practices, insights and learn from real-world applications of AI. Serviceblazer is a great community to connect with other service pros who are using AI to learn best practices. Get started for free today.
6. Experimentation and iteration: Start small by experimenting with AI solutions in customer service, track performance and iteratively improve based on customer feedback and outcomes.

These methods will help to set up your customer service teams to use AI technology effectively and enhance service delivery.

While there are many benefits to implementing AI to both your customers and business, it comes with its own set of challenges. Here are three challenges you may face in implementing AI, as well as how you can mitigate them:

1. Data quality and availability: AI systems rely heavily on large volumes of high-quality data for training and decision-making. Inconsistent, incomplete or biased data can lead to inaccurate or unreliable AI outcomes. To mitigate data quality and availability issues in AI implementation, ensure data preprocessing techniques like cleansing, normalisation and augmentation are applied to improve consistency and accuracy. Additionally, establish robust data pipelines and storage solutions to ensure continuous, scalable access to trusted AI.
2. Integration with existing systems: Integrating AI solutions with legacy systems can be technically complex and costly. Organisations can face difficulties in aligning AI technologies with their current infrastructure, requiring significant time and resources to ensure compatibility and seamless functionality. That’s why it’s so important to have the right customer service platform: Service Cloud. AI can be seamlessly integrated into Service Cloud to provide automated case classification, predictive analytics, chatbot support and real-time agent assistance for enhanced customer service efficiency. Salesforce solutions also provide data security and privacy so you don’t have to worry about protecting your organisation.
3. Experience and expertise gaps: Implementing AI often requires specialised skills in machine learning, data science and AI software development. Not having the right talent or training resources can lead to slower adoption and potential mismanagement of AI projects. Set your team up for success with Trailhead, Serviceblazer and Salesforce events: Agentforce World Tour, AI NOW Tour, Dreamforce and more.

While AI has made great strides in streamlining customer service operations, humans are still very much at the helm, providing empathy and making intelligent decisions behind the scenes. With the right training and tools in place, AI knowledge can help your customer service reps become more effective. Watch your CSAT soar.