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AI is fast changing the way contact centres approach workforce management (WFM) – but not everywhere and all at once.
So where is AI making the biggest difference? We asked our panel for their best examples of where planning teams can make the most of this technology right now.
1. Helping Planners to Better Understand What’s Driving Service Level Risk
Most of the things we do every day in WFM aren’t new to the AI era. We’ve been forecasting, scheduling, managing intraday performance and supporting operational decision-making with technology for years. AI hasn’t invented any of those activities.
Where AI is making a genuine difference is in helping us do them better and much faster. Machine learning identifies patterns and relationships in demand that traditional forecasting approaches may miss, particularly when customer behaviour is changing.
AI provides agility when making decisions to respond to volatile demand. It can provide huge levels of insight that help planners understand what is driving service level risk and highlight emerging issues that might otherwise remain buried in the data, and LLMs allow stakeholders to use well-structured prompts to make the data come to life.
Contributed by: David Preece, WFM Delivery Lead, Route 101
2. Changing Scheduling From a Headcount Exercise Into a Skills-Matching Problem
AI is changing scheduling from a headcount exercise into a skills-matching problem. As self-service and AI agents absorb more routine interactions, human agents are increasingly left with conversations that require judgement, empathy, product knowledge, negotiation, or specialist expertise.
Having enough people on shift is therefore no longer sufficient if the wrong skills are available. AI can help predict the mix of skills required for each interval and build schedules around both demand and employee proficiency.
This is especially valuable in omnichannel operations where agents may work across voice, chat, email, and back-office tasks.
3. Revealing Avoidable Demand and Which Issues Took Longest to Resolve
A major opportunity is to connect WFM with the growing amount of intelligence AI can extract from customer conversations.
Traditional WFM knows that 10,000 contacts arrived; conversation intelligence can explain why they arrived, which issues took longest to resolve, where customers experienced friction, and which types of interactions required particular skills. That context can improve both short-term staffing and longer-term workforce planning.
It can also reveal avoidable demand: for example, a process failure or confusing communication may be generating thousands of unnecessary contacts. Instead of simply staffing for that demand, operations teams can work to remove its root cause.
This is where AI can make WFM more strategic – helping contact centres understand not only how many people they need, but what work should exist in the first place.
Contributed by: Tatiana Polyakova, COO, MiaRec
4. Gauging the Strain of an Interaction and Avoiding a Deluge of Harrowing Calls Landing on the Same Agent
You want to be able to prompt the AI to “Make the best schedule for the day, week, or month using the resources available”, and it will build it.
This is dependent on accurate and complete data, such as a scoring matrix for agents based on their experience, skills, and performance, so that each shift has the right mix available for predicted demand.
The large language model (LLM) instruction model depends on these foundations being in place. Scoring based on performance is market- and region-dependent, so granularity varies by geography and individual.
Doctors, for example, might require different metrics, since the variability of their cases means Average Handling Times might not be reliable.
Additionally, agents should get a fair mix of case types. AI can gauge the strain of an interaction, helping avoid a deluge of harrowing calls landing on the same agent, which could lead to increased sickness and absence.
AI is now also delivering automated quality management (AQM), providing auditors with a mix of different case types so they spend less time searching for specific call recordings.
Contributed by: Martin Taylor, Co-Founder and Deputy CEO, Content Guru
5. Giving Agents Genuine Autonomy Over Their Working Patterns
One of the most under-appreciated applications of AI in WFM is its impact on agent experience.
AI-powered self-service scheduling tools allow agents to request time off, swap shifts, and bid for preferred working patterns through intuitive interfaces, while AI guardrails ensure that every change sits within defined service level and skill requirements.
This gives agents genuine autonomy over their working patterns without creating operational risk for the contact centre.
When scheduling tools are embedded within the broader contact centre platform and aligned with real-time demand forecasts, the result is a workforce model that increases flexibility and engagement while maintaining consistency.
Organizations that have invested in this capability report improvements in both agent satisfaction scores and attrition rates.
6. Breaking Down the Traditional Separation Between WFM and Quality Management
AI is breaking down the traditional separation between WFM and Quality Management. By connecting workforce data with interaction analytics, including speech analytics, sentiment trends, and First Contact Resolution rates, AI can identify correlations between scheduling decisions and customer outcomes that were previously invisible.
For example, patterns in customer dissatisfaction may correlate with specific shift configurations, skill gaps, or periods of understaffing that historical WFM reporting would never surface.
This connected intelligence allows contact centre leaders to make workforce decisions that are grounded in customer experience outcomes, not just operational efficiency metrics.
The most forward-thinking organizations are already using this capability to build continuous improvement loops between their WFM, Quality, and CX functions.
Contributed by: Jonathan Kershaw, Head of AI and Contact Centre Experiences, Vonage
7. Empowering Planners to Ask “What Happens if Volume Rises by 20%?”
AI’s biggest impact on WFM isn’t a more accurate forecast. It’s turning planning from a periodic task into a continuous decision loop.
- First, WFM becomes conversational. Planners can ask “What happens if volume rises by 20%?” and get staffing scenarios in seconds, not hours.
- Second, forecasts get smarter. Beyond volume and handle time, AI can factor in conversation topics, intent, sentiment and repeat contacts to explain why demand changed, not just report that it did.
- Third, scheduling becomes optimization. AI can generate schedules that balance coverage, skills, labour rules, cost, fairness and burnout risk, while planners stay in control by reviewing and approving.
Finally, WFM must plan for digital labour too. The right question is no longer just “how many agents?” but which demand to eliminate, which to automate, and which truly needs a human. The future of WFM is orchestrating capacity, not just scheduling people.
Contributed by: Matthew Clare, VP, Product Marketing UJET
8. Allowing for Forecasting That Actually Learns
The shift I find most useful is forecasting that actually learns. Traditional WFM models rely on historical patterns and manual adjustments; AI spots demand signals (weather, marketing campaigns, product launches) that a spreadsheet misses and adjusts intraday rather than waiting for next week’s planning cycle.
Scheduling is the other big win. AI can balance agent preferences against forecast demand in seconds, and that matters because schedule satisfaction is one of the strongest predictors of attrition. Happier rosters, fewer sick days, better coverage.
Where I’d push your team to start: use AI to identify the gap between forecast and actual in real time, then let supervisors act on it the same day. That feedback loop is where Zoom Workforce Engagement Management adds the most value, closing the gap before the customer feels it.
Contributed by: Ben Neo, CX EMEA Lead, Zoom
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Are You Exploring These Use Cases in Your Contact Centre? Where Else Do You Think AI Is Changing WFM for the Better?
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Reviewed by: Jo Robinson
