Artificial intelligence is reshaping customer service, giving forward-thinking companies a significant competitive edge. A recent study from marketing research firm Gitnux found that 85 percent of customer service interactions are now powered in part by AI and automation. What's more, 56 percent of contact center agents are using AI to assist with customer interactions, and 65 percent of agents believe that AI-driven support improves their productivity.
Indeed, customer service offers a strong foundation for AI implementation; however, even the most sophisticated AI tools will fall short without a well-defined AI strategy.
Many companies have already integrated AI into their support processes to take advantage of the productivity boost. However, people have different expectations depending on with whom or what they are speaking. They might look for empathy and deep understanding from a human, while expecting speed and availability from a computer or chatbot. When companies are open about their use of AI, customers feel more respected and are more likely to trust the interaction.
There are also ethical concerns. If companies conceal the use of AI, it can come across as deceptive or manipulative, especially in situations involving sensitive information or decisions. Customers should be given the choice to know with whom or what they're dealing so they can decide how to proceed, whether that's continuing the conversation or requesting a human agent.
All that said, there's a role for both AI and humans in the customer service mix, and together they can make a highly effective combination for solving customer needs for connection and enterprise needs for maximizing operational efficiency.
With its ability to process vast amounts of data at scale, AI can automate high-volume, repetitive tasks. Also, with its summarization capabilities, AI can empower agents to quickly recap customers' past transactions, logs, recurring issues, and even sentiment analysis from transcripts. It can then present the agent with potential courses of action based on similar cases and behavioral patterns. All of this information, which amounts to a powerful cheat sheet for every conversation, sets up the agent for success both in customer satisfaction and case handling time. That's huge.
Some additional practical ways by which AI can support live agents include the following:
- Agent reconnection, which helps maintain continuity by linking returning customers with the same agent with whom they previously interacted.
- Security verification to ensure that the system handles secure interactions through efficient, standardized checks.
- Language detection to instantly identify customers' spoken languages, ensuring they receive support in their preferred language.
Furthermore, AI can be a great tool for customer service managers to reveal aggregate trends in customer satisfaction and pain points that detect at-risk customers or flag service issues early. Operational metrics like response times and resolution rates from AI-scale analysis can help optimize workflows, automate task routing, and improve overall efficiency.
For AI to succeed, it must be embedded into existing workflows to support rather than disrupt agents. That means assisting with tasks like response suggestions and summaries, while keeping humans in control and ensuring agents are trained and comfortable with the tools.
Organizations also must proactively manage risks related to data privacy, security, and algorithmic bias. Since AI relies heavily on customer data, it's essential to comply with regulations, use anonymized data when possible, and regularly audit AI outputs to maintain fairness and trust.
Businesses don't need to implement AI across all operations at once. A gradual approach works best. By starting with three or four well-defined use cases and refining them over time, companies can build momentum and minimize risk. Even one successful pilot can validate the strategy. When implementing AI, companies should do the following:
- Establish clear policies for responsible AI use.
- Begin with a pilot program to assess whether the technology is suitable for specific use cases.
- Conduct thorough quality checks to ensure that chatbot responses are accurate and reliable.
- Clearly inform customers when they are interacting with a chatbot and include terms of use, such as clarifying that chatbots cannot enter into agreements.
Once processes are in place, addressing the human challenges of adoption becomes just as important. Agents might fear job displacement, and customers might become frustrated if AI systems are not transparent or effective. Overcoming this requires strong change management strategies, including clear communication, continuous training, intuitive interfaces, and feedback channels.
While AI offers significant productivity benefits, its irresponsible implementation can expose businesses to serious legal and reputational consequences. Poorly configured consumer-facing chatbots, for example, can lead to customer complaints and potentially trigger investigations by regulatory authorities, particularly in highly regulated industries.
To ensure successful AI adoption, mitigating these risks is crucial. A key first step is understanding the legal landscape within the country where the business operates.
Globally, governments are ramping up efforts to establish regulations that promote the safe, secure, and ethical use of AI. The number of countries with laws referencing AI grew from 25 in 2022 to 127 in 2023, according to a Stanford AI Index report. Even in jurisdictions without explicit AI regulations, authorities can still take action against businesses that cause harm through AI use.
The United States currently doesn't have a binding federal law regulating the development and use of AI. Utah was the first U.S. state to explicitly& regulate the use of AI chatbots through the Artificial Intelligence Policy Act in March 2024, with other states following suit.
AI acts as a force multiplier, improving speed, accuracy, and personalization, but human judgment completes the picture, especially in nuanced cases. In fact, there are scenarios where AI might never be suitable, such as high-stakes situations with medical emergency services or mental health crisis hotlines.
When thoughtfully integrated, AI enhances agent efficiency by providing real-time assistance, automating routine tasks, and offering personalized insights based on comprehensive customer data. Ultimately, balancing AI innovation with the human touch creates a customer service experience that is both efficient and genuinely satisfying.
David Fischer is chief sales officer of Luware.