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WFO 2026 Virtual Conference Session Recaps WFM and Performance Week

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The workforce management profession is undergoing one of the most significant transformations in its history. As organizations balance rising customer expectations, labor challenges, economic pressures, and the rapid adoption of artificial intelligence, workforce leaders are being asked to do more than forecast staffing needs and manage schedules. They are increasingly expected to shape business strategy, guide technology investments, develop talent, and help organizations navigate complex operational decisions.

A series of recent industry webcasts brought together thought leaders, practitioners, and technology providers to discuss the future of workforce management from multiple perspectives. The sessions explored how AI is reshaping contact center operations, how organizations can build more realistic business cases for automation, why granular capacity planning matters more than ever, and what leadership skills are required to succeed in this evolving environment.

Across these discussions, several common themes emerged. AI is proving to be a powerful enabler of productivity and scalability, but success depends on thoughtful implementation, strong governance, and continued human oversight. Workforce planning is shifting from static forecasting toward dynamic scenario modeling that helps leaders evaluate operational, financial, and customer experience trade-offs in real time. At the same time, the role of the WFM leader is expanding beyond analytics and staffing to include change management, employee development, cross-functional influence, and strategic decision support.

The following summaries capture the key insights, recommendations, and practical takeaways from these sessions. Together, they offer a roadmap for workforce leaders seeking to build more resilient operations, leverage AI responsibly, strengthen their teams, and prepare their organizations for the future of workforce management.

From Two Silos to One Workforce: Plan, Measure, Improve 

In this webcast, Andrea Matsuda, Head of Product Marketing for Workforce Management (WFM) at NiCE, and Aya Tzarfati, Senior Product Marketing Manager at NiCE, explored how organizations can successfully integrate AI agents into contact center operations. Their central message was that AI should be viewed not as a replacement for human employees, but as a growth enabler that expands capacity while increasing operational complexity. As AI and human agents increasingly work together, organizations must move away from managing these resources separately and adopt a unified workforce strategy.

Andrea emphasized that traditional workforce management remains critical in an AI-enabled environment. Rather than eliminating WFM practices, AI introduces new planning requirements, including forecasting AI token consumption, accounting for AI capacity alongside human staffing, and adapting schedules to prevent service disruptions. She demonstrated how a WFM copilot can automate routine administrative tasks, such as scheduling and forecasting support, while maintaining human oversight and approval for important decisions.

Aya focused on the evolving approach to performance management. Drawing on recent industry research, she highlighted that contact center leaders are increasingly prioritizing measurement frameworks that evaluate the combined performance of AI and human agents. Organizations are also investing in AI-assisted coaching to deliver more personalized employee development at scale. During the session, she demonstrated workflows where AI analyzes performance data, recommends individualized coaching actions, automatically drafts coaching plans, and delivers adaptive simulations tailored to each agent's development needs. Supervisors remain responsible for reviewing, refining, and approving recommendations, ensuring that coaching retains a human-centered approach.

A recurring theme throughout the discussion was the importance of managing AI-to-human handoffs effectively. Poorly designed transitions can force customers to repeat information, undergo duplicate verification processes, and experience greater frustration. To avoid these outcomes, organizations should preserve context throughout the customer journey and establish metrics that monitor both the quality and efficiency of AI-to-human interactions.

The presenters advocated for treating AI and human resources as components of a single workforce model. This means forecasting capacity jointly, incorporating AI token usage into operational planning, and establishing shared goals and performance measures that span the entire customer experience. Organizations should also use AI to support routine workflows and coaching activities while keeping humans accountable for final decisions and governance.

Several risks were discussed. These included unexpected AI token and billing overruns, sudden spikes in service demand, ineffective handoffs between AI and human agents, and poor data quality that can undermine AI recommendations. Participants also raised concerns about employee stress and attrition, noting that workforce strategies should focus on reducing cognitive load through smarter scheduling, targeted upskilling, and better workload management.

The webcast concluded with practical recommendations for organizations beginning their AI workforce transformation. Leaders should start by defining a unified workforce strategy that encompasses both AI and human agents. They should instrument handoffs using timeline, intent, and outcome metrics; ensure AI recommendations are transparent and auditable; and leverage AI-powered coaching and simulation tools to scale personalized development while maintaining supervisor control. Equally important is investing in strong data integration and governance practices so that AI recommendations are accurate and trustworthy. Finally, workforce management professionals and agents should be reskilled to focus on higher-value judgment, decision-making, and customer engagement activities as AI increasingly assumes routine operational tasks.

As a follow-up, Andrea noted that NiCE plans to publish a related blog post and promote it through company channels and LinkedIn, extending the conversation around unified workforce planning and performance management in the era of AI. 

3 Smart AI Moves to Improve WFM Success 

In this webcast, Florian Garnier, a workforce management expert at Verint, outlined three practical ways organizations can use AI to improve workforce management (WFM) outcomes. His focus was on increasing supervisor capacity, proactively protecting intraday service levels, and improving agent retention through greater scheduling flexibility.

A key theme throughout the session was the importance of applying AI selectively and pragmatically. Rather than attempting to automate every aspect of workforce management, organizations should focus on high-volume, repetitive tasks where AI can deliver immediate value. Examples include processing schedule change requests, managing employee notifications, and detecting intraday performance anomalies before they affect service levels. More sensitive and nuanced decisions, such as HR accommodations, employee coaching, and complex scheduling rotations, should remain under human leadership.

Florian demonstrated several AI-powered capabilities available through Verint and Calabrio platforms, including Agent Assist, Operations Assist, and TimeFlex. These tools are designed to reduce administrative burden, improve operational responsiveness, and give employees greater flexibility over their schedules. He also previewed an upcoming capability that will allow organizations to connect workforce management data with customer-selected large language models (LLMs), enabling more customized forecasting and planning.

The discussion emphasized the need to measure results rigorously. Organizations were encouraged to validate AI recommendations and track specific operational metrics, including reductions in manual schedule edits, lower ticket volumes, forecasting accuracy, absenteeism rates, and employee attrition. Participants were advised to challenge technology vendors to demonstrate clear business outcomes and return on investment rather than relying on broad AI promises.

Several common barriers to AI adoption were also discussed. These included selecting the wrong tools, lack of trust in AI-generated outputs, integration challenges, data privacy and sensitivity concerns, budget limitations, and unrealistic executive expectations for rapid ROI. Florian stressed that successful AI adoption requires strong AI literacy across the organization, executive alignment on objectives, and ongoing auditing of AI outputs to identify hallucinations, data quality issues, and process errors before they have operational impact.

The session highlighted several examples of measurable business value when AI is implemented thoughtfully. One business process outsourcing (BPO) organization reduced workforce management operational effort by 40%, while another organization achieved a 23% reduction in absenteeism through improved schedule flexibility. These outcomes demonstrate how AI can improve efficiency, strengthen intraday responsiveness, and give employees more control over their work schedules, all of which contribute to lower attrition and reduced labor costs.

The webcast concluded with a clear recommendation: start with targeted, well-defined AI use cases that address specific operational challenges, establish measurable success criteria, and continuously validate outcomes. By focusing on practical applications and maintaining appropriate human oversight, organizations can achieve meaningful workforce management improvements while building confidence in AI-driven decision making. 

From Capacity Planning to Decision Support: Multi-Location Planning and the Role of AI in a 2027 Business Case 

In this session, Mark Alpern, COO and Co-Founder and Karen Elliott, CEO and Co-Founder of Cinareo, provided guidance for contact center leaders preparing their 2027 operational and financial plans. The discussion centered on two critical topics: the importance of granular, site-level capacity planning and the need to incorporate AI realistically into workforce and cost models.

The presenters challenged the common practice of relying on blended, network-wide assumptions when evaluating operational changes. While aggregate planning can simplify decision-making, it often masks significant differences between locations, including variations in shrinkage, average handle time (AHT), attrition, learning curves, labor availability, and productivity. Through a detailed business case example, Mark and Karen demonstrated how projected savings from outsourcing, site consolidation, or geographic relocations can diminish substantially once site-specific factors are fully accounted for. Lower labor rates alone rarely tell the complete story. Additional costs associated with increased shrinkage, longer handle times, employee turnover, ramp-up periods, and one-time transition expenses can significantly reduce or even eliminate anticipated savings.

To improve planning accuracy, they recommended building workforce and capacity models at the site and interval level, then rolling those details into executive-level summaries. Equally important is establishing a continuous "inspect and adjust" process that regularly compares actual performance against forecasted assumptions and updates plans accordingly.

A significant portion of the session focused on the evolving role of AI in contact center operations. Rather than measuring success solely through containment rates or automation percentages, the presenters argued that organizations should focus on the fully loaded cost per successful resolution. This broader metric captures not only AI platform licensing and vendor costs, but also the operational impact of failed interactions, customer escalations, transfers to human agents, rework, and repeat contacts.

Mark and Karen cautioned against accepting vendor claims about AI containment rates at face value. They emphasized the importance of explicitly modeling customer opt-out behavior, AI-to-human handoffs, escalation rates, and resolution effectiveness. Since actual performance can vary significantly from vendor projections, organizations should develop optimistic, realistic, and pessimistic scenarios when building business cases. Early AI-driven savings should be treated conservatively and directed toward flexible cost areas, such as reducing overtime or overflow staffing, until long-term performance is validated.

The presenters also highlighted the value of AI as an augmentation tool rather than solely a replacement technology. AI assistants can accelerate onboarding, reduce learning curves for new hires, and improve the productivity of less-experienced agents, creating operational benefits even when containment rates fall short of expectations.

The session concluded with several practical recommendations for organizations developing 2027 plans:

• Build assumptions and forecasts at the site and interval level rather than relying on blended network averages.
• Model AI and human workforce assumptions together, including containment rates, handoffs, customer opt-outs, escalation volumes, and unit costs.
• Calculate fully loaded savings and transition costs before committing projected financial benefits.
• Use sensitivity analysis and scenario planning to identify operational, financial, and customer experience risks.
• Tailor business case outputs for executive stakeholders, emphasizing service risk for COOs, savings realization and timing for CFOs, workforce implications for CHROs, and customer impact for CX leaders.

The presenters also introduced a ten-question planning checklist designed to test the validity of workforce, location, and AI investment decisions. Participants were encouraged to apply this framework to all major 2027 planning initiatives, particularly those involving site moves, outsourcing strategies, or AI adoption.

A recurring caution throughout the discussion was the risk of making irreversible workforce reductions based on overly optimistic AI assumptions. Mark and Karen cited examples where organizations aggressively reduced staffing only to experience quality declines, customer dissatisfaction, and eventual rehiring costs. They also noted that AI economics remain dynamic. Rising technology costs, ongoing vendor fees, and customer preferences for human interaction can significantly affect long-term ROI. Survey data discussed during the session suggested that approximately 64% of customers still prefer access to a human agent in at least some service interactions, reinforcing the need for balanced workforce planning.

The overarching message was clear: organizations should adopt a conservative, evidence-based approach to AI planning. Human validation of AI outputs, rigorous data governance, legal and compliance reviews, and disciplined scenario analysis remain essential to building reliable business cases. Success depends not on maximizing AI deployment at any cost, but on understanding where automation creates measurable value while preserving operational flexibility, service quality, and customer trust.

When the Obvious Answer Is Not the Right One: Pressure Testing Capacity Planning 

In this webcast, Karen Elliott, CEO and Co-Founder of Scenario, and Mark Alpern, COO and Co-Founder of Cinareo stated how modern capacity planning can evolve from static spreadsheet-based forecasting into dynamic, scenario-driven decision support. Their session showcased Scenario, a decision-intelligence platform designed to help contact center leaders model complex operational decisions quickly and understand the trade-offs behind them.

The presenters began by positioning capacity planning as more than a staffing exercise. Effective planning requires balancing three competing priorities: customer experience, employee experience, and cost. Rather than relying on fixed assumptions and annual planning cycles, organizations need the ability to test multiple scenarios rapidly, understand the implications of each option, and make informed decisions based on real operational and financial impacts.

Karen walked attendees through the platform's configuration capabilities, demonstrating how organizations can model complex operating environments that include multiple sites, business units, support functions, currencies, and staffing structures. The platform supports detailed workforce assumptions such as operating hours, shrinkage categories, support roles, and financial models. Data can be imported through existing workforce management integrations or uploaded directly through Excel-based workflows, making adoption possible regardless of technical maturity. Once configured, the system provides detailed planning outputs with drill-down visibility by queue, location, day, and even 15-minute staffing intervals.

Mark then focused on the power of rapid scenario analysis. He demonstrated how planners can evaluate changes such as marketing campaigns, shifts in attrition rates, service-level target adjustments, AI-driven contact deflection, headcount reductions, or budget constraints within seconds. Rather than rebuilding forecasts manually, users can compare multiple scenarios side-by-side against a baseline plan and immediately see the impact on staffing requirements, service performance, and costs.

A key takeaway from the session was that seemingly obvious solutions often create unintended consequences. For example, lowering service-level targets, reducing staffing, or increasing AI automation may generate immediate savings in labor hours or full-time equivalents (FTEs), but those savings can come at the expense of longer average speed of answer (ASA), higher abandonment rates, diminished customer experiences, or increased employee stress. Scenario's value lies in helping organizations quantify these trade-offs before decisions are made, allowing leadership teams to evaluate risk alongside potential savings.

The presenters also highlighted the platform's financial modeling capabilities. Beyond staffing forecasts, Scenario calculates metrics such as hiring-ahead costs, recruitment and training investments, cost per interaction, and overall workforce expenses. These insights help planners build stronger business cases and support more productive discussions with executive stakeholders. Financial and operational impacts can be viewed simultaneously, enabling organizations to understand not only what a decision will save, but also how it may affect service quality and employee experience.

Throughout the demonstration, Karen and Mark emphasized the importance of continuous planning rather than annual forecasting exercises. By enabling organizations to test assumptions frequently and respond rapidly to changes in demand, attrition, budgets, or business strategy, scenario planning becomes an ongoing management discipline rather than a one-time event.

The session concluded with a clear message: effective capacity planning requires understanding the consequences of every decision, not simply choosing the option that appears most cost-effective on the surface. By combining multi-site, multi-currency workforce planning with robust scenario analysis, recruitment and training planning, multi-skill optimization, and interval-level forecasting, Cinareo helps organizations "plan quickly, plan easily, and plan often."

Ultimately, the platform is designed to support more informed decision-making across the leadership team. Whether addressing operational performance, financial targets, workforce strategy, or customer experience objectives, planners can provide COOs, CFOs, and CHROs with real-time visibility into the staffing and financial implications of their decisions, enabling faster and more confident business outcomes.

Verint Workforce Intelligence - 3 Features That Drive Business Outcomes 

During the session, Florian Garnier, Verint's Workforce Management go-to-market lead, demonstrated how Verint is evolving workforce management through a new layer of AI-powered assistants designed for employees, operations teams, and supervisors. Rather than focusing on traditional dashboards and reports, the discussion centered on how AI can help organizations make faster decisions, reduce administrative effort, and improve workforce outcomes.

Florian framed the conversation around three recently released or upcoming capabilities: Agent Assist, Operations Assist, and Supervisor Assist.

He first demonstrated Agent Assist, which became available earlier this year. The tool provides a conversational, self-service experience that allows agents to manage routine workforce requests without navigating multiple systems. Employees can request time off, view balances, explore scheduling options, and complete common workforce transactions using natural language in multiple languages. Behind the scenes, the solution integrates with systems such as Workday and ADP, enabling PTO balances and absence information to remain synchronized. The goal is to increase employee flexibility while reducing the administrative burden on workforce management teams.

The discussion then shifted to Operations Assist, released in June, which is aimed at workforce planners and operations leaders. Florian demonstrated how the tool consolidates operational information across skills and queues, enabling teams to monitor performance in real time while also looking ahead. Rather than simply reporting what has happened, Operations Assist predicts likely end-of-day service levels and recommends actions that can be taken to mitigate emerging risks. The emphasis was on helping organizations move from reactive workforce management to proactive decision-making.

Florian also previewed Supervisor Assist, which is expected to be released next month. The capability allows supervisors to ask workforce-related questions in natural language and receive immediate answers without manually searching through reports. Initial functionality will focus on people-related inquiries, with future enhancements bringing schedule data, adherence information, and additional workforce insights into the experience. The vision is to give supervisors faster access to information and shorten the path from insight to action.

One of the most forward-looking topics in the session was Verint's planned support for Model Context Protocol (MCP). Florian described an MCP connector that would allow workforce management data to be securely consumed by external large language models, opening the door to more advanced use cases such as event-aware forecasting, operational analysis, and AI-generated recommendations grounded in workforce data. While still on the roadmap, internal testing is already underway, and Verint is currently targeting availability in the Q2 2027 timeframe.

The conversation generated significant discussion around AI explainability and trust. Several attendees, including Vicki Herrell and Susan, emphasized that AI-generated recommendations will only be valuable if users can understand and verify how conclusions were reached. There was particular interest in postmortem analysis and service-level explanations that are supported by evidence rather than opaque AI outputs.

Florian acknowledged these concerns and agreed that transparency will be critical to adoption. He described auditing capabilities, historical adherence data, schedule histories, and future MCP-driven integrations as important building blocks for creating more accountable AI experiences. He also committed to providing additional demonstrations focused on operational postmortems and SLA explanations, where users can trace recommendations back to the underlying workforce data.

The overarching message from the session was that Verint sees the future of workforce management as moving beyond reporting and into intelligent assistance. Whether helping agents manage their schedules, assisting planners with intraday decisions, or enabling supervisors to quickly access workforce insights, the company is positioning AI as a tool for driving more informed actions. At the same time, customers made it clear that explainability, auditability, and trust must evolve alongside these capabilities if AI is to become an integral part of workforce management operations. 

Be the Best WFM Leader for Your Team 

In this webcast, Vicki Herrell, Executive Director of SWPP, moderated a discussion with workforce management leaders Bryan Hovasse of Point32, Mark Rhodes of Edward Jones, and industry veteran Marshall Lee. Together, they explored what it takes to be an effective WFM leader in today's rapidly evolving environment, focusing on building trust, increasing employee engagement, leading organizational change, and balancing people-centered leadership with operational results.

A central theme throughout the session was that successful WFM leadership begins with trust. The panelists emphasized that leaders who earn credibility are visible, approachable, and willing to work alongside their teams. Mark Rhodes and Bryan Hovasse shared examples of leading from the front by actively participating in day-to-day operations, demonstrating a willingness to understand the challenges their teams face firsthand. Trust is further strengthened when leaders are vulnerable, acknowledge mistakes, provide public recognition for successes, and reserve corrective feedback for private conversations. As Marshall Lee noted, employees are far more likely to support leaders who consistently demonstrate respect, authenticity, and accountability.

The discussion also focused heavily on employee engagement. Panelists stressed that engagement is not created through generic incentives but through understanding individual motivations and career aspirations. Strong leaders invest time in regular one-on-one conversations, provide meaningful feedback, and create stretch opportunities that help employees grow. Bryan Hovasse highlighted the importance of celebrating accomplishments and ensuring team members understand the impact of their work. Marshall Lee added that career development opportunities often become a stronger retention tool than compensation alone, particularly for high-performing employees seeking growth.

Another key topic was employee well-being and burnout prevention. The speakers acknowledged that many WFM teams operate under significant pressure, especially in lean organizations where individuals may become "one-person shops" responsible for multiple functions. Mark Rhodes and Marshall Lee emphasized the importance of setting clear expectations, protecting time off, and creating an environment where employees can disconnect and recharge. Sustainable performance, they argued, requires leaders to actively monitor workload and ensure that short-term business demands do not undermine long-term team health.

The panel also addressed the growing responsibility WFM leaders have as agents of organizational change. Workforce management professionals often serve as translators between operational teams, finance leaders, and executives. To be effective, leaders must clearly communicate the rationale behind forecasts, staffing recommendations, and modeling assumptions. Mark Rhodes and Bryan Hovasse encouraged attendees to use transparent methodologies, explain the mathematics behind workforce recommendations, and build internal advocates who can help champion data-driven decisions across the organization. Successful WFM leadership increasingly requires influence and storytelling skills in addition to technical expertise.

Artificial intelligence emerged as another important topic. The panel viewed AI as a powerful force multiplier rather than a replacement for workforce expertise. AI can accelerate routine tasks, improve productivity, and streamline analysis, but it should not be trusted without verification. Marshall Lee and Mark Rhodes stressed the importance of maintaining human oversight and validating AI-generated outputs using sound workforce management principles. They encouraged professionals to develop prompt-engineering skills and learn how to effectively leverage AI tools while retaining responsibility for final decisions and recommendations.

The panel also highlighted a common challenge across organizations: leadership development often lags behind technical development. Vicki Herrell noted that many strong individual contributors are promoted into leadership roles without receiving formal management training. To address this gap, organizations should invest in leadership development programs that teach coaching, communication, conflict resolution, and performance management skills alongside technical workforce management competencies.

Looking ahead, the speakers encouraged workforce professionals to invest in continuous learning. Formal credentials such as the Certified Workforce Planning Professional (CWPP) designation can strengthen both technical expertise and professional credibility. The panel also recommended building AI literacy, developing career-development plans for team members, and expanding professional networks through industry communities and LinkedIn connections.

The overall message was clear: exceptional WFM leadership is built on a combination of people skills, business acumen, and operational expertise. Leaders who foster trust, support employee growth, protect team well-being, communicate effectively across functions, and thoughtfully embrace new technologies will be best positioned to help their teams succeed. As the workforce management profession continues to evolve, the most successful leaders will be those who combine strong human leadership with a commitment to continuous learning and adaptation.