Workforce management (WFM) has been the backbone of contact center operations for decades. In a people-first business, it wasn’t glamorous, but it was essential. Yet the world WFM was built for is rapidly changing.
Enterprises today manage a blended workforce: humans, bots, and Artificial Intelligence (AI) copilots working side by side. Customers also expect fast, seamless resolution irrespective of who provides the solution. Meanwhile, Customer Experience (CX) leaders are battling high attrition, unpredictable demand, and growing pressure to reduce costs while improving experience.
All these developments are bringing WFM to an inflection point. With a focus on AI and agentic, AI-infused workforce orchestration is moving beyond spreadsheets and static forecasts to something more adaptive, predictive, and enterprise-wide.
The Chief Experience Officers (CXOs) also now need to pay attention, because this shift has the potential to reshape cost structures, agent engagement, and customer satisfaction all at once.
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From scheduling to orchestration
Traditional WFM was built for a human-only workforce, in an era where demand was mostly voice-based and relatively predictable. However, in the modern world, things are considerably more nuanced:
- Demand is omnichannel, and while multiple channels existed in the past multi-modal conversations and real-time channel switching means you cannot predict the capacity requirements by channels accurately
- Bots handle a growing share of interactions, but often need orchestration alongside humans
- Customer expectations fluctuate rapidly. A viral campaign, a weather event, or a system outage can suddenly spike volumes.
The focus has shifted. WFM is no longer just about building schedules. Rather, it is about orchestrating resources dynamically across humans and machines.
Consider the case of a retail bank: if bots start failing during a fraud scare, AI-driven WFM can reallocate agents from voice to chat within minutes. Traditional systems would have taken a day or more to respond.
Where legacy WFM falls short
The shortcomings in traditional WFM solutions are becoming increasingly apparent, including:
- Outdated / legacy forecasting algorithms do not account for real-world disruptions
- Rigid scheduling disregards agent wellness, fueling attrition
- Bots, which handle up to 40% of interactions in some enterprises, are rarely factored into planning
- Execution is siloed, focused only on contact centers rather than the whole enterprise
This results in predictable challenges that impact operational efficiency, customer and agent experience.
How AI-led WFM can remedy these gaps
AI doesn’t just patch over the limitations of legacy WFM, but it redefines the very foundation of how workforce planning works. Three shifts make this transformation stand out:
- Predictive and adaptive forecasting: Instead of relying on static averages, AI-led systems continuously ingest real-time signals. That includes interaction data across all channels, external triggers like weather events, product launches, or even social media activity. Forecasts can adjust intraday instead of being fixed the night before
- Agent-centric scheduling: Legacy systems optimized for efficiency, often at the expense of employee well-being. AI-WFM flips the script by using optimization algorithms and generative AI to reconcile business needs with individual preferences. It can build schedules that match peak service demand while still accommodating agent requests for flexibility, training, or wellness breaks. There is also an increased focus on self-service tools for agents, enabling them to make real-time schedule change requests. The payoff can be tangible: lower attrition, higher engagement, and an empowered workforce
- Human and bot integration: AI-WFM eliminates this divide between humans and bots by treating bots, digital workers, and AI copilots as part of the same capacity pool as humans. That means decisions about “who should handle what” are made dynamically. A bot struggling with authentication tasks could trigger AI-WFM to route more of those queries to human agents, while shifting simpler requests back to bots once the issue is resolved
Why CXOs should care
For CXOs, AI-WFM is a lever for business resilience and strategic advantage. Done right, it can impact costs, employee experience, and customer outcomes simultaneously:
- Cost efficiency with control: By balancing workloads across humans, bots, and digital workers, enterprises can reduce overstaffing during lulls while ensuring peak demand is covered without costly overtime. The ability to flex resources intraday means organizations spend less on “just-in-case” capacity
- Talent retention through better experiences: By respecting agent preferences, embed training during quiet periods, and prevent burnout from constant high-intensity work, enterprises can lower turnover costs and enable higher service quality through better retention
- Scalable CX quality at pace: Customers benefit when orchestration minimizes wait times, prevents unnecessary transfers, and ensures the right resource is assigned to their need the first time. This not only improves satisfaction scores but also builds resilience during crises, when volumes spike unexpectedly
- Enterprise-wide orchestration, not just contact centers: The same principles that optimize contact centers can extend to the back office, retail branches, and field operations
A bank could dynamically reassign staff between servicing digital queries and processing loan applications; a telecom could orchestrate field service appointments alongside contact center queues. AI-WFM becomes a cross-enterprise capability, not a siloed tool.
The next 2–3 years: How WFM can evolve further
If today’s AI-WFM feels like a leap, the next few years could be transformational. We could see:
- Autonomous orchestration across front- and back-office: Engines rebalancing demand and supply across all channels across front- and back-office with little human intervention
- Hyper-personalized schedules: Shifts tailored to each agent’s performance patterns, wellness, and personal commitments
- Enterprise “control towers”: Dashboards that provide a single view of every worker (human and digital) across the contact center, field service, retail outlets, and the back office. Leaders could finally orchestrate resources end-to-end, rather than managing silos in isolation
- Dynamic trade-offs: Simulations that help leaders shift priorities between cost, risk, and experience in real time
- Human + AI teaming as the norm: WFM pairing humans and copilots dynamically, deciding when each works solo and when they collaborate
The art of the possible is that WFM evolves from a back-office tool into the strategic nerve center of customer operations.
Who’s leading the charge
The vendor landscape is diverse, and each category is pushing the innovation frontier in different ways:
- Established leaders like NiCE and Verint are layering advanced AI into their WFM suites. These solutions are adding capabilities such as adaptive forecasting, AI-assisted intraday rebalancing, and automation that reduces the need for manual schedule changes Verint’s partnership with Parloa is especially notable, as it brings together AI staffing augmentation with traditional human scheduling, signaling a shift toward unified orchestration.
- Contact Center as a Service (CCaaS) platforms such as Genesys, Five9, and Talkdesk are embedding AI-WFM natively into their cloud stacks. The innovation here lies in integration: instead of managing workforce planning as a bolt-on, these vendors promise a single environment where routing, reporting, and scheduling all operate from the same AI engine. That creates faster feedback loops
- Specialists like Intradiem and Calabrio are pushing hard on intraday automation and employee experience. Their innovations focus less on forecasting and more on execution: automatically inserting training modules when call volumes dip, flexing breaks to match live conditions, and using AI to recommend micro-adjustments that improve agent well-being while still protecting service levels. These are practical, often overlooked innovations that directly impact retention
- AI-native startups are designing orchestration engines built for blended workforces from the ground up. Many of them are experimenting with optimization algorithms that continuously rebalance human, bot, and gig worker supply. Their edge is flexibility: because they aren’t tied to legacy architectures, they can model entirely new work patterns, such as dynamically assigning copilots to high-complexity tasks or pooling gig resources during seasonal spikes
For enterprises, the question is less about who has the most sophisticated AI and more about which innovation philosophy aligns with their strategy: the continuity of established players, the integration of CCaaS providers, the agent focus of specialists, or the radical flexibility of AI-native challengers.
The bottom line
AI-WFM is more than an incremental upgrade. It’s a strategic enabler for orchestrating the blended workforce. Enterprises that adopt it early will gain agility, reduce costs, and improve both employee and customer experience. Those that delay risk being locked into rigid schedules and operations that can’t keep pace with demand volatility.
The workforce of the future is not just human. It’s human and AI, orchestrated together, and WFM will be the system that decides how well that orchestra plays.
If you enjoyed reading this blog, check out Agentic AI: True Autonomy Or Task-based Hyperautomation? | Blog – Everest Group, which delves deeper into other topics regarding AI.
To discuss AI in more depth, reach out to reach out to Sharang Sharma ([email protected]) or David Rickard ([email protected]).
