Artificial intelligence (AI) is one of today's most widely discussed technology topics. In fact, many of my conversations at Salesforce.com's Dreamforce user conference in September included AI as a discussion topic, which I documented in a related post. Despite a wealth of conversations, events, and research on the topic, buyers and even some technology providers are still confused by what AI really means.
Some use AI interchangeably with analytics; some use it to refer to automation; while others define it as machines that can make independent decisions. This article isn't meant to provide an academic definition of the term, but for the purposes of customer experience research at Aberdeen, we define AI as a set of technology capabilities that allow software to learn on its own (detecting trends and root-causes), provide recommendations to achieve desired goals, and take actions to also achieve predefined objectives.
When we discuss AI in most technology conversations, the most common capability is machine learning (ML), where a software program is designed to process vast volumes of data, typically through some human supervision to ensure the program learns correctly. If adjustments need to be made, data scientists overseeing the machine learning process can do so. Companies can combine these insights with predictive analytics to make predictions about the likelihood of certain events happening (e.g., customer retention). This information can then be used to prescribe employees (e.g., contact center agents) with guidance on specific activities to accomplish certain goals (e.g., prevent churn), again, by analyzing past data to determine the most common action that's associated with minimizing churn risk in a given context.
Some tools also enable AI systems to take action (e.g., schedule employees, send gift cards, etc.). Such use cases where AI handles certain activities must be closely monitored by humans to ensure the software works the way it should. When determining anomalies, humans can intervene to make adjustments. As such, AI enables companies to automate certain tasks, but automation isn't synonymous with AI.
Aberdeen's market intelligence allows us to monitor the top topics specific companies research to educate themselves to make educated technology purchase and renewal decisions. One of the sectors we closely monitor is contact centers. Recently, we analyzed the top contact center topics that Fortune 1000 organizations researched between March and September,
Research activity by the Fortune 1000 has been consistent across almost all topics. There are two notable exceptions: First, in late July, we saw a sudden surge across Fortune 1000 firms researching contact center solutions. While every company might have specific reasons for research on this topic, a quick news search provides the answer. In July, Google announced its Contact Center AI solution with a compelling demo where consumers interacted with a virtual agent to address specific needs. The announcement was widely covered in the technology and contact center circles and was so significant that it caused the sudden spike.
The second interesting finding from Figure 1 is the decrease in research activity related to private branch exchange (PBX). PBX has already become a key ingredient of business communications. As such, Fortune 1000 firms are not as active in researching PBX trends and best practices. Rather, they focus on emerging and key technologies that will help them differentiate their customer experiences.
While there is a lot of hype around AI, savvy users of the technology are more opportunistic in using the technology. We're far from the days where contact centers will be fully automated or operate without humans. However, forward-thinking companies have incorporated AI capabilities to enrich their self-service experiences. These industry leaders have successfully automated simple tasks that require minimal human decision making (e.g., copy-pasting content, creating a sales opportunity), and have already started reaping the rewards from their use of AI. In fact, Aberdeen's related research shows that companies using AI in their customer service outperform non-users across a variety of metrics.
Aberdeen's research goes beyond the returns observed by companies using AI. It also illustrates the building blocks that best-in-class users of AI establish to maximize their results. Our conversations with contact center solution providers help us understand which vendors provide the capabilities that help companies achieve maximum results and how buyers perceive each vendor.
An illustrative list of contact center solution providers with AI capabilities (including those that established a partnership with Google) is as follows (in alphabetical order): Aspect, BrightPattern, Calabrio, CallMiner, Five9, Genesys, Mitel, NICE, RingCentral, Salesforce Service Cloud, Talkdesk, and Verint.
Omer Minkara is a vice president and principal analyst at Aberdeen Research covering contact center and customer experience management.