Workforce management has long been the operational backbone of the contact center. It helped organizations answer a fundamental question: do we have the right number of people available to meet customer demand?<\/p>\n
The formula was relatively straightforward: forecast volume, build schedules, manage staffing, and hit service levels. While those responsibilities remain critical, the expectations placed on customer experience organizations have changed dramatically.<\/p>\n
The challenge is no longer simply managing labor. It\u2019s coordinating a growing network of people, processes, technologies, and outcomes.
\nWorkforce Engagement Management expands beyond workforce forecasting and scheduling. Today’s CX leaders need to understand how decisions made in one area of the contact center affect performance in another.<\/p>\n
AI is making this possible by bringing together workforce, customer, and operational intelligence into a single decision-making framework. The result is something many organizations have never had before: an orchestration layer across the entire CX operation<\/strong>.<\/p>\n RingWEM is RingCentral’s AI-powered Workforce Engagement Management (WEM) platform designed to help contact centers coordinate the people, insights, and operational decisions that shape customer experience.<\/p>\n RingWEM brings together a suite of AI capabilities<\/a> across the three critical areas every contact center must manage effectively:<\/p>\n RingWEM helps eliminate many of the inefficiencies that slow decision-making, increase administrative effort, and limit operational agility, including:<\/p>\n RingWEM is designed to create a continuous flow of intelligence across the contact center. Each tool contributes a different layer of insight, helping CX teams understand what happened, why it happened, and what actions to take next.<\/p>\n Getting the correct agents scheduled at the right time is the first step.<\/p>\n AI Workforce Management<\/a> helps organizations align agent counts with customer demand through forecasting, scheduling, adherence monitoring, and intraday management. A WFM analyst can anticipate demand changes, adjust staffing levels proactively, and ensure the right skills are available when needed.<\/p>\n Rather than relying on static workforce plans, decisions can be guided by real operational conditions and emerging business needs.<\/p>\n Once agents are working, visibility into their performance is everything.<\/p>\n AI Quality Management<\/a> automatically evaluates 100% of customer interactions across voice and digital channels, eliminating the limitations of random call sampling. Supervisors gain a complete view of quality trends, compliance risks, and coaching opportunities without spending hours manually reviewing interactions.<\/p>\n Instead of reacting to issues weeks later, teams can identify performance gaps and provide targeted coaching with improvement opportunities to make a meaningful impact.<\/p>\n Understanding what was said is only part of the story.<\/p>\n Screen Recording adds context by showing exactly what agents were doing during customer interactions. Managers can see how employees navigate systems, search for information, complete workflows, and handle customer requests in real time.<\/p>\n The result is greater visibility into process bottlenecks, inefficient workflows, and system friction that may be affecting handle times, compliance, or overall service quality.<\/p>\n Surveys add a direct customer perspective.<\/p>\n By collecting feedback immediately after interactions, organizations gain insight into how customers perceive the experience. Satisfaction scores, effort ratings, and customer comments help validate whether internal quality standards are translating into positive outcomes.<\/p>\n Survey feedback also helps uncover blind spots that may not be visible through performance data alone.<\/p>\n While surveys capture feedback from a subset of customers, AI Interaction Analytics<\/a> analyzes conversations across the entire customer base. Patterns that might otherwise go unnoticed become visible, helping teams understand what customers are experiencing and what factors are shaping those experiences.<\/p>\n These insights provide the broader context needed to prioritize improvements and address issues before they become widespread problems.<\/p>\n Together, RingWEM tools create an integrated intelligence loop:<\/p>\n RingWEM’s capabilities can be used in many ways depending on an organization’s goals. To understand how RingWEM works in practice, let\u2019s consider a common scenario:<\/p>\n A retail contact center is preparing for a major holiday promotion that is expected to drive a significant increase in customer inquiries. CX leaders need to maintain service levels, supervisors need to ensure agents are prepared, and WFM analysts need to make sure the right staffing plan is in place.<\/em><\/p>\n Two weeks before a major holiday promotion, the contact center expects customer demand to increase significantly. Historically, similar campaigns have generated a 25% increase in call volume and a 40% increase in digital interactions.<\/p>\n A WFM analyst uses AI-powered agent forecasting to analyze historical interaction data and model multiple demand scenarios. The forecast predicts that daily contact volume will increase from 1,800 to 2,300 interactions, with the largest spikes occurring between 10 a.m. and 1 p.m. and again between 6 p.m. and 8 p.m.<\/p>\n The forecast reveals that the contact center will need an additional 5 agents to meet demand during peak periods. However, not all interactions require the same expertise.<\/p>\n Using skill-based agent scheduling, the WFM analyst builds schedules that ensure the right mix of voice agents, chat specialists, and multilingual agents is available throughout the day. Training sessions and coaching activities are automatically moved to lower-volume intervals or postponed entirely to maximize coverage. Rather than simply adding headcount, the workforce strategy aligns skills with anticipated demand.<\/p>\n By 9 a.m. on launch day, three agents scheduled for the morning shift have not checked in.<\/p>\n Using automated attendance monitoring and check-in, supervisors immediately see attendance exceptions from a centralized dashboard. At the same time, adherence tracking and reconciliation identifies another four agents who are spending more time in non-supporting states than scheduled. Without intervention, these gaps could significantly reduce efficiency during one of the busiest periods of the day.<\/p>\n At noon, customer demand exceeds the forecast by 12%.<\/p>\n The WFM analyst uses intraday planning and reporting with AI recommendations to compare forecasted, scheduled, and projected staffing levels. The system predicts that if no action is taken, service levels could fall below the organization’s 80\/20 target for voice within the next 45 minutes.<\/p>\n Instead of manually recalculating staffing requirements, the analyst receives AI-generated recommendations on where additional coverage is needed and when.<\/p>\n Using automated schedule adjustment plans, the analyst sends notifications to qualified off-shift agents through a mobile WFM app and identifies employees eligible for additional hours.<\/p>\n Within minutes, three agents accept extra shifts and log in remotely to begin working, helping to stabilize staffing before customer wait times begin to rise. Later in the afternoon, should demand drops below forecast, the same workflow is used to offer voluntary time off to overstaffed teams.<\/p>\n What previously required dozens of emails, messages, and phone calls can be coordinated in a few clicks by a WFM analyst.<\/p>\n Once support for the promotion ends, supervisors need to understand how agents performed under increased demand.<\/p>\n Using AI Quality Management, every interaction is automatically evaluated across voice and digital channels. Instead of reviewing a small sample of calls, a contact center supervisor has visibility into performance trends across the entire team.<\/p>\n The analysis reveals that agents consistently delivered strong customer service scores, but interactions involving international shipping through the promotion lasted 90 seconds longer than other inquiry types. Using screen recording, the supervisor discovered that many agents struggled to efficiently navigate the non-US order management workflow during high-volume periods.<\/p>\n Armed with this insight, the supervisor plans to deliver targeted coaching focused on workflow execution, handling international shipping inquiries, and reducing unnecessary hold time.<\/p>\n Performance data shows how agents performed. Customer feedback reveals how the experience was perceived.<\/p>\n In the days following the promotion, surveys show that overall customer satisfaction remained high despite the 25% increase in contact volume. However, customer comments frequently mention frustration around placing international orders and delivery expectations.<\/p>\n To understand the issue more deeply, CX leaders use AI-powered interaction analytics to analyze every customer conversation from the promotion period.<\/p>\n The analysis reveals that international inquiries generated the highest levels of customer frustration and accounted for a large percentage of repeat contacts.<\/p>\n These insights become actionable improvements for the next campaign:<\/p>\n With RingWEM and its suite of AI Workforce Engagement Management tools, the contact center successfully handled a 25% increase in customer demand while maintaining its service-level targets.<\/p>\n AI-powered forecasting helped the workforce team anticipate demand before it arrived. Skill-based scheduling ensured the right agents were available at the right time. Attendance monitoring and adherence tracking quickly surfaced agent concerns, while intraday AI recommendations and automated schedule adjustments enabled the contact center to respond before service levels were affected.<\/p>\n Agents also played a more active role by accepting extra shifts via mobile app, reducing administrative work for WFM analysts.<\/p>\n AI Quality Management and Screen Recording helped the contact center supervisor identify specific coaching opportunities by revealing where agents struggled during peak periods. Instead of relying on sampled interactions, coaching decisions were based on a complete view of agent performance.<\/p>\n At the same time, Surveys and AI Interaction Analytics provided a deeper understanding of the customer experience. Customer feedback and conversation analysis revealed recurring concerns around international ordering, giving CX leaders clear actions to improve future campaigns.<\/p>\n As customer expectations continue to rise, supervisors, quality managers, WFM analysts, and contact center leaders need more than visibility into individual metrics. They need AI that can connect workforce decisions, employee performance, customer insights, and operational outcomes in real time to go beyond WHAT is happening and focus on WHY.<\/p>\n With RingWEM and its suite of AI Workforce Engagement Management tools, every decision gains more context. Forecasts create staffing plans. Performance insights influence coaching priorities. Customer feedback shapes operational improvements.<\/p>\n As contact centers become more complex, AI will increasingly serve as the intelligence layer that keeps operations aligned, agile, and focused on outcomes.<\/p>\n See how RingWEM<\/a> helps contact centers move from disconnected workforce processes to coordinated, AI-powered CX operations.<\/p>\n Traditional workforce management focuses solely on forecasting and scheduling. RingWEM combines workforce management with AI-powered capabilities for performance management, customer insights, and operational optimization.<\/p>\n RingWEM uses AI to identify staffing risks early and recommend intraday management adjustments before service levels are affected. A contact center can then proactively staff up or down quickly by implementing an automated schedule adjustment plan (ASAP). Teams can also adapt to absences and different business conditions in real time with minimal effort.<\/p>\n RingWEM automates time-consuming tasks such as forecasting, scheduling, intraday management, adherence tracking, attendance monitoring, and time-off management. This frees analysts to focus on strategy instead of administration.<\/p>\n RingWEM provides real-time visibility into agent performance, quality, attendance, and adherence. It also eliminates the dependence on manual scorecard review with insights from 100% of agent interactions. Rather than focusing on individual scores, supervisors can identify overall performance trends and deliver more targeted coaching plans tailored for each individual agent.<\/p>\n Agents can view schedules, request time off, swap shifts, bid on schedules, and receive updates through a self-service portal and a mobile app. This improves flexibility while reducing administrative work.<\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":" Workforce management has long been the operational backbone of the contact center. It helped organizations answer a fundamental question: do we have the right number of people available to meet customer demand? The formula was relatively straightforward: forecast volume, build schedules, manage staffing, and hit service levels. While those responsibilities remain critical, the expectations placed …<\/p>\n","protected":false},"author":1145,"featured_media":61755,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[43331,43330],"tags":[],"class_list":["post-61754","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-innovation","category-trending"],"acf":[],"yoast_head":"\n<\/a>What is RingWEM?<\/h2>\n
\n
<\/a>What operational problems does RingWEM solve?<\/h2>\n
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<\/a>How do RingWEM tools work together?<\/h2>\n
AI Workforce Management (WFM)<\/h3>\n
AI Quality Management<\/h3>\n
Screen Recording<\/h3>\n
Surveys<\/h3>\n
AI Interaction Analytics<\/h2>\n
\nBy examining 100% of interactions, it uncovers sentiment trends, recurring issues, escalation drivers, customer intent, and retention risks.<\/p>\nAn integrated intelligence loop<\/h4>\n
\n
<\/a>How RingWEM coordinates CX teams in practice<\/h2>\n
Step 1: Forecast increased demand before it happens<\/h3>\n
Step 2: Build schedules based on skills, not just headcount<\/h3>\n
Step 3: Monitor attendance and adherence on launch day<\/h3>\n
Step 4: Respond to an unexpected volume spike<\/h3>\n
Step 5: Deploy staffing changes in minutes<\/h3>\n
Step 6: Identify coaching opportunities after the promotion<\/h3>\n
Step 7: Understand customer feedback and improve future planning<\/h3>\n
\n
The outcome<\/h3>\n
<\/a>The future of workforce engagement is AI-powered coordination<\/h2>\n
<\/a>FAQ<\/h2>\n
How is RingWEM different from traditional workforce management software?<\/h3>\n
How does RingWEM help organizations respond to unexpected changes in customer demand?<\/h3>\n
How does RingWEM help WFM analysts?<\/h3>\n
How does RingWEM help supervisors?<\/h3>\n
How does RingWEM improve workforce flexibility?<\/h3>\n