3 Smart AI Moves to Improve WFM Success Today

There is a lot of noise in the contact center space about AI workforce management (WFM) and how it can help improve efficiency and customer experience (CX). However, in reality, few organizations have been able to move from AI pilots to embedded AI that drives measurable business outcomes. There are several reasons for this:

• First generation AI (ChatGPT, MSCopilot, …) aka Generative AI, wasn’t designed for the complexity of contact center workforce management.
• WFM analysts and contact center leaders don’t trust AI to produce accurate results.
• WFM analysts and planners fear AI will take over their jobs.

I recently presented at the Society for Workforce Planning Professionals (SWPP) annual conference, asking the question “Can AI and WFM Co-Exist?” And the answer is yes. However, AI needs to meet certain criteria to ensure measurable, impactful AI outcomes.

Why AI won’t replace contact center WFM planners

Legacy WFM solutions were designed for more simple, single channel, single location, limited hours call centers with predictable demand and planning cycles. Today’s modern contact center is much more complex.

• Demand shifts quickly across voice and digital channels (chat, email, messaging).
• Workforces are hybrid, multi-skill, and often split across sites and BPO partners.
• Customer issues are more complex, and expectations for speed and accuracy are higher.
• Agents expect more flexibility and control over their schedules.

While modern WFM technology and AI can automate many of the repetitive, rules-based tasks, experienced WFM planners and analysts are needed to address the unexpected, as well as sensitive, people-impacting decisions around contracts, exception handling, dispute resolution, coaching, and more. And there are often tradeoffs that need to be made between costs, service, agent burnout, fairness, and equity that require human judgement and empathy.

While AI might not replace the WFM Planner, it will impact their role and the activities they perform. Instead of building forecasts and schedules, WFM Planners might become “auditors,” spending a greater share of their time validating the results of the AI models.

Where AI can drive impactful outcomes in contact center WFM

So let’s let AI do what it does best, which is automate tasks that:

1. Remove manual, repetitive tasks — for both the supervisor and agent.
2. Improve decision quality by surfacing insights in near real-time.
3. Speed action within operational constraints with contextual, explainable recommendations.

Good candidates for AI automation include balance lookups, low-risk notifications, rule-safe routing, informational answers, adherence alerts, and intraday anomaly detection.

Tasks or workflows that require human review and should never be “set it and forget it” include special accommodations, fairness disputes, schedule rewrites after an aberration, policy exceptions, coaching, discipline, and other emotionally charged communications.

Three proven use cases for AI-powered workforce management

Following are three AI outcomes organizations are achieving by embedding AI into their WFM workflows:

• Improved supervisor capacity
• Consistent achievement of daily service goals
• Increased agent experience and retention

Improve supervisor capacity

Today, many supervisors spend an inordinate about of time chasing data, answering repetitive questions, reviewing and approving PTO, monitoring dashboards, and putting out fires/triaging service issues. By automating many of the routine, rules-based WFM processes, AI can help dramatically improve supervisor capacity for higher value activities, like scenario evaluation, guardrail design, exception handling, and coaching.

Here are a few examples of WFM workflows AI can automate:

Schedule change requests: Leveraging rules and guardrails already established in your WFM processes, AI can automatically check availability, vacation balances, and service levels to approve or reject schedule change requests without manager intervention/approval.

Anomaly detection: Instead of supervisors constantly monitoring multiple dashboards, AI can automatically surface anomalies and alert managers so they can take action faster.

Metrics that matter

To determine if AI is having a quantitative impact on supervisor time, you can compare before and after:

• The number of manual schedule changes made per week
• Supervisor ticket volume
• Time spent monitoring dashboards

Meet daily service goals

In today’s complex contact center environment, WFM Real-Time Analysts struggle to understand where anomalies are occurring that could impact service levels, and what the best action is to take to protect SLAs without overstaffing. Did you have an unexpectedly large number of call outs? Did volumes in the phone or SMS channel suddenly spike, perhaps do to a website issue that IT hasn’t alerted you to. Or has a new product launch increased handle times on sales calls?

AI is very good at analyzing data and summarizing what it sees. Organizations can leverage this capability to gain early insight into changes to plan and get an explanation of what is happening. AI can even present options to proactively adapt to intraday changes before service levels are impacted, albeit with a lot of human-led training and scenario building.

This training, by an experienced WFM Analyst, is critical because without it, responses could be directionally correct but not really actionable. For example, here are two possible AI responses:

• Weak: “Demand looks elevated this afternoon. Consider adjusting breaks or adding overtime.”
• Vetted and trusted: “Billing queue is projected 9 FTE short from 14:30—15:00. Moving 6 breaks by 15 minutes protects 80/20, keeps contract minimums intact, and avoids OT. Two agents need manual review.”

Metrics that matter:

Metrics to measure to determine impact and value of this AI use case include:

• Recommendation acceptance rate
• False positive rate
• SLA protection/achievement
• Recovery speed
• Reduction in OT and shrinkage spikes
• Adherence after intervention/adjustments

AI-Driven Outcomes

With real-time, AI-driven intraday insights and recommendations, WFM Real-Time Analysts and managers can quickly make adjustments to reduce under- and over-staffing, lower labor costs, and protect service levels, resulting in a more consistent customer experience across channels.

Increase EX and retain your best talent

Contact Centers notoriously have high attrition rates: 32% on average, with 60% agent turnover in some centers. In our recent contact center agent survey, The State of Agent Experience 2026, 70% of agents ranked schedule flexibility as their #1 or #2 job priority.

And for today’s agents, scheduling flexibility means more than just shift swaps and shift bidding. They want the ability to self-serve and receive instant, automated responses to their schedule change requests, without the typical, manual back and forth with their manager. Companies that are able to provide this level of flexibility are averaging a 24% reduction in attrition.

Ways leading contact centers are leveraging AI to empower agents include:

• Verint Agent Assist: With an AI, natural language interface agents can ask simple questions, like: “What is my schedule next week?” or “How much PTO do I have left?” to more complicated queries like, “What would be the best day next week for me to take off?” Agents can, in real time, make requests to change in-shift events, like breaks, lunches, and coaching, and get immediate responses, with the ability to escalate to a manager if needed.
• Mobile App: Similarly, modern WFM solutions offer a mobile app, with an AI, IVA-like interface that gives agents 24/7 access to schedule and performance data. They can make schedule change requests and get immediate responses after hours and on weekends.
• Verint TimeFlex Bot: Empower agents to make unlimited changes to their schedules, including changing shift start and end times, moving shifts to a different day, splitting shifts, and more. TimeFlex automatically calculates the impact of changes on overall service levels for every 15-minute increment. It allows agents to make changes while ensuring labor laws, business rules, and service levels are maintained.

Metrics that matter

Metrics to measure to ensure desired AI outcomes are achieved include:

• Agent self-service completion rate
• Service levels / speed to answer
• Agent attrition rates
• Absenteeism
• Schedule adherence

8 questions before you buy or roll out AI-powered workforce management

To determine if a workflow or task is a good candidate for AI automation, ask yourself the following questions:

1. What manual work disappears?
2. Which data feeds and latency matter?
3. What rules are non-negotiable?
4. Who owns the exception path?
5. How will users see why it answered that way?
6. What happens when the AI is wrong?
7. Which metric proves value?
8. Who tunes the system over time?

Recommendations for when and where to apply AI-Powered WFM

AI and WFM can co-exist, but only when AI is embedded directly into your WFM workflows, and the AI matches your use case criteria. When starting out on your WFM AI journey, we recommend you:

1. Use AI where the workflow is repetitive, connected, and time-sensitive.
2. Keep high-cost tradeoffs human-led until trust is proven in production.
3. Measure outcomes and tune decision rights over time.

Verint AI-powered workforce management capabilities

AI-powered WFM can help you adapt faster to the complex, continuously changing contact center environment to help protect the interests of your business, your agents, and your customers.

Verint Workforce Management is built on an open, AI-powered CX Automation Platform with the largest database of contact center operational and customer experience data in the industry. At its core, Verint Da Vinci AI combines the best generative AI, commercial, customer-provided, and proprietary AI models to create AI automations and specialized bots designed specifically for CX workflows. Bots continually train in the data hub to become more effective, and are embedded directly into workflows, putting AI at the fingertips of your managers and agents.

WFM-specific AI capabilities include:

• Exact Forecasting: Verint WFM automatically selects the best forecasting model for each queue/scenario.
• Agent Assist: Moves routine schedule reviews and approvals out of the supervisor queue, automatically handing agent requests for shift lookup, PTO, OT, and voluntary time off via policy-bound conversational AI.
• TimeFlex Bot: Empowers agents to make unlimited schedule changes without manager intervention.
• Predictive Actions (launching next week!): Intraday detection of KPI deviations that could impact end of day service levels; provides an explanation, predicts the impact, and recommends changes before SLAs break.

Stay tuned for more AI innovations in workforce management. To learn more about Verint Workforce Management and our AI outcomes, visit our Workforce Management page.