What is artificial intelligence (AI)? A complete guide

The timeline for artificial intelligence is short, sweet, and rapid. In roughly 70 years, AI has gone from something that Alan Turing could only theorize to the driving force behind ChatGPT and other revolutionary technologies. Today, AI has evolved to be easy to set up and cost-effective with the right tools—and businesses that aren’t already using this technology are already behind the competition.

According to the Zendesk Customer Experience Trends Report 2024, 65 percent of CX leaders see AI as a strategic necessity, not a passing fad. That’s because artificial intelligence brings a wealth of benefits to consumers, teams, and businesses—and above all, it’s making customer service more human. In this guide, we cover artificial intelligence in detail and explain how you can use AI in customer service.

More in this guide:

• How does artificial intelligence work?>
• Weak AI vs. strong AI
• The rise of generative AI
• Types of AI by industry
• Benefits of AI
• CX use cases of artificial intelligence
• History of artificial intelligence
• Frequently asked questions
• Discover the most complete customer service solution for the AI era

How does artificial intelligence work?

AI works by using algorithms and computational models to analyze large datasets, learn patterns, and make decisions or predictions based on that data. Through machine learning, a subset of AI, these systems improve their performance over time by continuously learning from new information.

For example, Zendesk AI is trained on over 18 billion real customer interactions, so it automatically understands customer needs and the nuances of CX. This allows it to autonomously resolve complex customer requests, assist agents in resolving customer issues faster, and automate workflows.

There are several types of AI that you may encounter depending on the system you are interacting with:

• Natural language processing (NLP): NLP is a branch of AI that helps the technology understand, interpret, and respond to human language in a valuable way. This can help with processes like language translation and customer sentiment analysis.
• Machine learning (ML): ML helps computers learn from statistical methods to make data-based decisions and improve performance. This approach uses algorithms to learn but may need human intervention to correct errors. An example of ML is tailored product or article recommendations based on a customer’s search history.
• Deep learning (DL): Deep learning and machine learning are similar concepts, but you can think of DL as an upgraded version of ML. With DL, computers use neural networks to analyze data—and they can do so without human intervention. Examples of deep learning processes include self-driving cars or advanced robotics.
• Computer vision: This process enables AI to process visual information like images and videos. From there, the technology can extract meaningful information—facial recognition and object detection fall in this category.

It’s important to note that an AI system may have a combination of these, but you likely won’t see all of them in use simultaneously.

Weak AI vs. strong AI

Beyond the basic functions of AI, there are two subsets of artificial intelligence: Weak AI and strong AI.

Weak AI is artificial intelligence designed and trained for a specific task. Often called “narrow AI,” these systems operate under limited parameters and can’t perform tasks beyond their basic functions. Weak AI is featured in many technological aspects we encounter today, such as chatbots, Spotify song recommendations, and self-driving cars.

Strong AI is artificial intelligence that can understand, learn, and apply knowledge across tasks at a level comparable to humans. This is often referred to as “superintelligence” and is purely hypothetical. Researchers and scientists haven’t developed AI to this level, but to many, it’s a benchmark for future innovation to many.

The rise of generative AI

Generative AI is a branch of artificial intelligence that learns from existing information to generate new content. This can include stories, pictures, videos, music, software code, and more.

To illustrate further, let’s say a developer wanted to train their generative AI system to respond to customer requests more effectively. They would first “feed” the technology examples of successful customer interactions. This could occur in various situations, such as when support agents processed refund requests efficiently or successfully diffused an angry customer. The generative AI system would use that information to decipher patterns and structures—and from there, it could begin to operate autonomously.

In practice, training an AI system to handle customer requests is much more complex than that—for example, Zendesk AI is trained on 18 billion customer service interactions—but this shows how generative AI is changing our technological experience. It’s the system behind ChatGPT, DALL-E, and a wealth of other platforms that are writing articles, creating designs, automating workflows, and accelerating innovation. It’s shaking up entire industries and forcing businesses to adapt their models to include generative AI or fall by the wayside.

Types of AI by industry

Businesses are learning that AI can improve the customer experience. With this technology, teams are changing how they interact with customers and manage operations—here are a few examples of how different industries are achieving that.

Retail

In the retail industry, AI can enhance the customer experience with personalized communication and efficient support. For example, AI agents—an advanced form of chatbot—can answer customer questions in real-time, assist with product searches, direct users to helpful knowledge base articles, process returns, and more. With this technology, businesses can offer 24/7 support and delight customers at every turn.

Fintech

Fintech organizations can use artificial intelligence to improve operations and improve customer support. AI agents can help businesses deliver round-the-clock support and quickly resolve customer issues. It can also connect to backend systems to verify customer information, ensuring customers receive relevant and timely information tailored to their needs. Additionally, AI automates routine tasks such as checking in on the status of a transfer or activating a new bank card, which increases efficiency and allows human agents to focus on more complex customer interactions.

Healthcare

Artificial intelligence can help healthcare providers enhance patient care. AI agents can work directly with individuals to schedule appointments and facilitate medication refills. Patients can also be screened by AI agents to confidentially collect information up-front. They can then route them to the correct department, where a human agent can help determine the urgency of their request and whether they need to see a doctor right away.

Manufacturing

AI can help manufacturing businesses be more efficient in planning and production. Predictive analysis can alert the organization to any inefficiencies so they can reassess the production process or replace equipment. Additionally, AI agents can assist clients and business partners with real-time order updates, shipping estimates, and more.

Benefits of AI

Next, let’s examine some of AI’s top benefits. Here are a few ways this technology can positively impact your operations.

Improved customer experience

AI can make the customer experience (CX) faster and more personalized. Businesses can offer round-the-clock support in multiple languages through advanced bots like AI agents that can swiftly resolve complex customer concerns without agent assistance. These intelligent bots can also connect to your backend systems, such as a CRM or e-commerce platform, to offer personalized support. Zendesk AI agents are also trained on real customer interactions and your support data to respond like a human agent, acting as an extension of your brand’s identity.

Boosted team efficiency and reduced human error

AI can streamline repetitive and time-consuming tasks, reducing human error and enhancing efficiency across various use cases and organizations. For instance, workforce managers can use AI to accurately predict staffing needs and automatically schedule teams, ensuring optimal coverage.

CX managers can leverage AI to intelligently route tickets to the appropriate agent or department, streamlining workflows. The possibilities are endless, but the core benefit is automation that reduces the need for manual tasks, freeing teams to focus on more strategic activities, and ultimately evolving them into new roles—as managers, editors, and supervisors of AI.

Enhanced decision-making

Artificial intelligence can analyze vast amounts of data, identifying trends, patterns, and areas for improvement that human analysis might overlook, especially in CX. For instance, customer service managers can use AI analytics to monitor customer sentiment and proactively address potential churn. Additionally, AI can suggest the most common customer questions to automate, further enhancing efficiency and service quality.

Greater cost savings and scalability

By automating routine and repetitive tasks, AI helps small teams accomplish more without increasing costs. It can alert teams to any areas of improvement or inefficiencies—like a bottleneck in customer service leading to customer churn. For example, HelloSugar—a Brazilian wax and sugar salon—uses Zendesk to automate 66 percent of customer queries, saving them $14k per month in support costs.

Additionally, artificial intelligence can handle increased workloads without an increase in resources. For example, AI agents can support a drastic increase in customer demand without the need for an increase in human support agents. This can help businesses save year-round rather than hiring extra support agents for peak seasons.

CX use cases of artificial intelligence

Automate customer service

Artificial intelligence can provide fast, accurate, and personalized assistance to customers around the clock, boosting customer satisfaction (CSAT). For example, Zendesk AI agents are purpose-built for CX and can resolve complex customer questions and requests—all on their own.

AI agents also work well with human ones. When necessary, they can escalate requests to human agents, complete with all the gathered context, enabling quicker resolutions and enhanced service efficiency. For example, fashion retailer Motel Rocks uses Zendesk AI to respond to customers faster, allowing them to slash ticket volume by 50 percent and improve CSAT by 10 percent.

Assist agents

AI agents are only one example of how AI can lend your support team a helping hand. AI can summarize tickets, identifying a customer’s sentiment and intent, allowing agents to respond more quickly and accurately. It can also highlight similar tickets, showing how teammates have resolved similar issues, and adjust responses to match your brand’s tone, making them friendlier or more formal as needed.

Agent copilots are another tool that boosts support team efficiency. The Zendesk agent copilot guides agents through interactions, suggesting contextually relevant responses that agents can modify or use directly. It can also independently handle routine tasks, such as order cancellations, without agent intervention.

These tools seamlessly integrate AI with human expertise, creating a superior customer experience.

Automate workflows

AI can streamline and automate workflows, leading to increased efficiency. For example, customer service teams can use intelligent routing to send customer tickets to the right agent based on intent, sentiment, and language. This can reduce first reply time (FRT) by getting customers qualified help faster. UK premium department store retailer Liberty shows the benefits of this. The company uses Zendesk AI capabilities to route tickets and assist customers—leading to a 73 percent decrease in FRT.

Improve WFM

Workforce management (WFM) encompasses processes that CX managers use to ensure they have the right agents in the right channels at the right times while tracking daily performance. AI can enhance WFM by forecasting staffing needs and automatically generating schedules based on predicted demand, reducing staffing inefficiencies and overtime costs. This automation saves significant time and minimizes human error in shift planning and scheduling.

For example, bike-sharing company Tembici transformed its customer support operations by switching from manual cumbersome processes to adopting a streamlined workforce management solution powered by Zendesk. Since switching, they’ve improved agent efficiency by 75% and reduced resolution time by 80%.

Streamline QA

Customer service quality assurance (QA) involves systematically evaluating customer interactions, identifying areas for improvement, and providing effective coaching to enhance the overall customer experience. AI can streamline QA by analyzing interactions, saving managers and administrators valuable time.

For example, Zendesk QA uses AI to review all support interactions, both human and AI, automatically identifying knowledge gaps, flagging at-risk customers, and uncovering coaching and training opportunities. This approach ensures continuous improvement and a superior customer experience. Audiovisual (AV) rental company Rentman shows the power of Zendesk QA in action. The company uses it to get a full picture of customer conversations and agent performance—leading to CSAT rates of up to 96 percent.

History of artificial intelligence

Here are some key historical dates in the ongoing development of artificial intelligence:

• 1950: Alan Turing publishes “Computing Machinery and Intelligence,” hypothesizing that a machine can exhibit intelligent behavior indistinguishable from a human.
• 1956: John McCarthy introduces the term “artificial intelligence” at the Dartmouth Conference, ushering in AI as a field of study.
• 1960-80: Stanford University develops “The Stanford Cart,” an autonomous vehicle.
• 1997: IBM’s Deep Blue defeats Garry Kasparov, becoming the first computer to beat a human world chess champion.
• 2011: IBM’s Watson wins Jeopardy! against former champions Brad Rutter and Ken Jennings.
• 2016: Google’s DeepMind AI, AlphaGo, defeats world champion Go player Lee Sedol.
• 2020: ChatGPT and other large language models are released, demonstrating revolutionary capabilities in natural language processing.

While many researchers and events led us to this point in AI, those are some of the most important.

Frequently asked questions

Discover the most complete customer service solution for the AI era

Artificial intelligence is revolutionizing nearly every industry, and customer service is no exception. However, leveraging AI effectively requires selecting the right tools and systems to ensure high-quality AI-powered experiences.

In a world where poor customer experiences can cost you business, having AI specifically designed for CX is crucial. Zendesk AI is purpose-built for CX and human connection, drawing on billions of real interactions to understand the nuances of customer service automatically. It is deeply integrated into every aspect of our solution and works seamlessly alongside your human agents. Additionally, it’s easy to use and scale, allowing you to start from day one and supporting you as your needs grow.

Try us for free today to see how Zendesk AI can revolutionize your operations.