Understanding the Intersection of Humans and AI in Customer Experience

Artificial Intelligence (AI) has been viewed as a massive change agent for customer experience (CX) delivered from contact centers. AI-driven analytics improve service delivery and customer satisfaction (CSAT) through insights that benefit agents, supervisors, and quality teams. In a recent webinar, two industry experts at NICE, arguably the leader in AI-powered CX, discussed AI’s practical applications and its complementary role alongside human contact center agents. Here’s a summary of the key points presented by senior product marketing manager Brooke Phillips and customer success leader Jared Norwood.

The Impact of AI on CX

AI has become indispensable for today’s CX leaders. According to Deloitte data, companies that deploy AI experience a 20 percent increase in customer retention and improved response times, accuracy, and personalization. McKinsey reports that companies with high AI adoption have 12 percent greater customer satisfaction than those with lower adoption. Moreover, 75 percent of CEOs consider generative AI a top priority, and they’re investing in it to increase profitability, according to KPMG.

Many organizations need help with assessments due to traditional methods that rely on small samples or subjective evaluations. Such methods give agents inadequate feedback. In addition to measuring CSAT, companies can use AI-based models focused on agent behaviors. AI-driven sentiment analysis provides a more accurate view of agent performance. It assesses the whole body of work and measures soft skills crucial for positive customer interactions. This leads to improved CSAT and net promoter (NPS) scores.

“We found that the top agents using AI had 256 percent more positive sentiment, shorter calls, less non-talk time, and less repeat contact. This is all because they’re performing soft skill behaviors that have been sifted through feedback. So, there’s a big difference between the agents using AI and those not,” said Phillips.

Republic Services, a waste disposal company with over 14 million customers and 40,000 employees, partnered with NICE to restructure its quality program by automating agent soft skill analysis using AI. The company saw a 30 percent reduction in repeat calls and improved agent efficiency, thanks to consistent analysis and targeted interventions. The company attributes these improvements to effective coaching, which helped agents handle calls better.

Improving Agent Summaries with AI

Contact center agents are responsible for providing detailed accounts of customer interactions, including why a customer contacted the company, what the outcome was, and what actions were taken. This information informs the next agent, ensuring continuity if the customer calls back. It also serves as a valuable data source for identifying trends and patterns.

Even today, agents still use sticky notes and other inefficient methods that could be faster and more accurate. This increases average handle time (AHT) or after-call work, resulting in poor agent satisfaction and summaries that need more context. Automated note-taking, driven by AI, offers a solution. By eliminating manual processes, companies can have accurate, reliable summaries that provide information to agents. An AI-generated summary captures the customer’s name, call intent, resolution, sentiment, and follow-up actions. According to a NICE study, contact centers can save up to $7 million annually by reducing after-call work.

“This has a lot of tangible benefits to the business. You’re able to reduce costs. You’re increasing accuracy. You have more consistent data-driven steps for the next agent in line. And you’re boosting your CX by providing more context about why a customer calls,” said Phillips. “This is one of the clearest examples of AI augmentation of the human experience. It’s not a replacement. AI is helping them do their jobs better.”

Personalizing Omnichannel CX

Consumers expect personalized and seamless omnichannel experiences in the digital age, as they engage with brands across multiple platforms. Understanding consumer behavior requires aligning data from these omnichannel interactions, which can be challenging.

“With the evolution of generative AI, brands can harness CX data and then create content that is truly customized for each market segment. So, they’re able to communicate the brand message to customers clearly and concisely,” said Norwood.

Examples of AI in Action

Phillips and Norwood shared real-world examples of NICE customers using AI to enhance agent performance, improve customer satisfaction, and gain other business benefits.

A large telecom company transitioned from traditional quality evaluations, which included only a few assessments per agent each month, to an AI-driven approach. The evaluations were initially paired with NPS to gauge performance, but this approach was subjective and inconsistent. In contrast, AI provided unbiased, reliable assessments. The company established sentiment and behavior scores benchmarks by leveraging NICE AI models. It discovered a strong correlation between high sentiment scores and positive interactions.

Another company, a global workforce organization, faced challenges managing quality and consistency during hyper growth. The company implemented NICE AI models to monitor agent performance. Agents were able to self-coach using personalized dashboards. This made the coaching process more focused, highlighting areas where agents need improvement.

“Without human intervention, based on the AI models, the agents can go through their coaching process. One of the net gains we saw from this was that the agents spoke up or became empowered and could have a dialogue with their leaders,” said Norwood.

A third company experienced recurring issues with password resets. These interactions took about 40 percent longer than average, impacting service level agreements and response times. Using AI-powered queries, the company pinpointed agents contributing to increased AHT. The analysis highlighted variations in agent performance for password resets. The data was then used to coach agents on effective strategies for handling interactions. AI also helped identify skills-related issues, providing insights for coaching and training and discrepancies affecting key performance indicators (KPIs).

The Takeaway

According to NICE speakers, the three best practices for strengthening CX with AI are aligning it with company goals, training agents on proven soft-skill behaviors and implementing automated note-taking. Improving agent engagement and providing them with the right tools also boosts CX. Additionally, training agents on soft skills positively impacts sentiment and CSAT. Automating agent note-taking saves time and money on every interaction, reduces AHT, and makes contact center agents happier overall.

There is a significant amount of fear and trepidation in the world of CX that AI will take people's jobs. To those people, I say AI won't take your job, but the agent that understands how to use AI will. In the webinar, NICE did an excellent job highlighting how AI is the latest tool for contact centers to help them do their jobs better. It's time to board the AI train.

Zeus Kerravala is the founder and principal analyst with ZK Research.

Read his other Network Computing articles here.

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