The Role WFM Tools Play in a Bot-Infused World

Nathan Stearns, the Vice President of Workforce Engagement Strategy at NICE, explains the role that workforce management (WFM) tools play in an increasingly bot-infused world. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Customer self-service capabilities have been reducing workload in contact centers for decades. IVRs have been automating self-service since the 1970s. Improvements in search engines have given customers direct access to knowledge base systems to conduct their own troubleshooting and problem-solving. These and other tools have been running alongside the contact center workforce for years, creating a much-needed reprieve in headcount requirements.

More recently, chatbots and generative AI solutions have gained prominence, with growth accelerated by the COVID-19 pandemic. The pandemic years sped up the adoption of digital channels and increased the use of bots in contact centers. In fact,  according to a Gartner Press Release: “By 2027, 40 percent of all customer service issues will be fully resolved by unofficial third-party tools powered by GenAI, according to Gartner, Inc.” Eight in 10 consumers are more willing to do business with companies that offer self-service options, a survey by Simpler Media Group found, and 74 percent of chat interactions result in a first-contact resolution—higher than agent-assisted transactions.

Agents in contact centers are seeing the positives of AI in customer service, too. Chatbots are estimated to handle around 30 percent of the tasks currently completed by contact center staff. This frees up their day to focus on more complex interactions and boosts efficiency. According to Salesforce, 64 percent of agents with chatbots can spend most of their time solving complex problems, compared to 50 percent of agents without them.

The increase in chatbot usage—and the benefits contact centers realize as a result—have raised some key questions related to the role of workforce management (WFM) in a bot-infused world. Here’s what you need to keep in mind.

WFM Forecasting

Robust WFM systems specifically focus on workforce management—just the human element. That means most WFM tools capture the interaction history and workload of agents, excluding the interactions bots, IVR, or other self-service channels handled (if you’re one of the 58 percent of centers with their chatbot integrated into their scheduling/WFM tools). Forecasting data scrubs the self-service interactions to suggest an ideal headcount and schedule based solely on human needs.

If self-service success rates increase, then the historic workload of agents automatically decreases, and vice versa. No user intervention is required to make this happen, meaning you can rely on your WFM tools to give you appropriate scheduling forecasts no matter how effective your self-service channels currently are.

WFM Scheduling 

Because of how forecasting algorithms work, bot effectiveness has no direct bearing on how you schedule employees. Bot activity indirectly impacts employee schedules through the natural ebb and flow of customers using self-service. Those gradual changes will be apparent in the agent-based schedule history collected for forecasting and scheduling purposes.

Just as you wouldn’t look to your WFM system to plan employee schedules around IVR success rates, there’s no need for a WFM system to plan employee schedules around bot success rates. Instead, keep your attention on what matters at the point of data collection: the management of the workforce.

WFM Real-Time Adjustments

The ability of WFM forecast algorithms to self-adjust came about with the proliferation of IVRs in the 1980s. The algorithms are now designed to automatically scrub the queue history and remove outliers that unexpected changes in bot/IVR/self-service success rates may cause. They pick up on shifts in what the bots/IVR/self-service are (or are not) doing.

Ideally, WFM systems should generate a forecast per interval, not per day. This significantly improves the contact center’s ability to respond quickly to unique changes in bot/IVR/self-service success rates that may be time-of-day sensitive. Automated intraday re-forecast algorithms automatically adjust the employee forecast in near real time based on the self-service success of that particular time period.

Bots—and their ability to understand complex customer service scenarios—are improving at an unprecedented rate, and the market is nowhere near saturated. The good news is that WFM solutions on the market today are already set up to accommodate this increase in usage. There’s no need to schedule bots in WFM, which would result in the contact center having to license additional seats for the bots in WFM—an added, unnecessary expense. Rest assured, your WFM has it handled, no matter the impact your self-service channels have.