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WFO 2026 Virtual Conference Session Recaps

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The WFO 2026 Virtual Conference, presented by CrmXchange, QATC and SWPP, brought together industry analysts, solution providers, and contact center practitioners for six sessions on the future of quality and workforce optimization. A clear theme ran through every session: AI is reshaping how contact centers evaluate, coach, and measure performance, but it delivers the most value when paired with strong data foundations, thoughtful implementation, and human oversight. From automated quality management and AI-driven coaching to new approaches for measuring customer experience, the recaps below highlight the key insights and practical takeaways from each presentation.

Workforce Engagement in the Age of AI: Trends, Truths, and Best Practices

Hosted by Sheri Greenhaus, Managing Partner of CrmXchange, and Vicki Herrell, Executive Director of SWPP, this session featured Donna Fluss, President of DMG Consulting LLC, who shared expert insights into how artificial intelligence is transforming Workforce Optimization (WFO) and Workforce Engagement Management (WEM).

Donna challenged many common misconceptions about AI, emphasizing that it is designed to augment human capabilities rather than replace people. Audience polling reflected this view, with most attendees agreeing that workforce management remains a critical business function that AI is increasingly enhancing.

The discussion explored the core components of modern WFO and how AI can improve each one. Donna highlighted conversation analytics and Automated Quality Management (AQM) as foundational technologies and encouraged organizations to move from manual quality monitoring to AI-driven quality programs. She identified real-time agent guidance, post-interaction summarization, and AI-powered analytics as some of the most valuable applications delivering measurable benefits today.

A key theme was the importance of a strong technology foundation. Donna stressed the need for integration through APIs and MCP frameworks, centralized data strategies, human oversight, and carefully selected use cases. She encouraged organizations to focus on practical business outcomes rather than chasing the latest AI trends.

During Q&A, Donna recommended that companies begin with targeted AI pilots, particularly around interaction summarization and AQM, and build clear business cases with measurable ROI. She also emphasized agent training, self-service scheduling tools, and ongoing monitoring of AI-powered bots and automation. Integrating AQM with real-time guidance, she noted, creates continuous feedback loops that improve data quality, agent performance, and AI model effectiveness.

Donna cautioned against unrealistic expectations fueled by early generative AI hype, noting that poor implementation, inadequate pilots, and weak governance can reduce or eliminate expected returns. She also advised buyers to evaluate vendors carefully, as solution quality and AI capabilities vary significantly across providers.

Key takeaway: AI delivers the most value in WFO when it is built on a solid data and integration foundation, applied to well-chosen use cases, and governed with human oversight.

Manual QA to Automated Quality Management: Breaking Down Barriers to Adoption

This webcast featured Mallory Partain, Product Manager at Verint, who explored how organizations can successfully transition from manual quality assurance (QA) to automated quality management (QM).

Mallory addressed many of the common concerns that keep organizations from adopting automation. Using audience polls and real-world examples, she challenged misconceptions that AI will replace human evaluators, requires specialized expertise, or produces unreliable results. She showed how modern automated QM solutions use explainable scoring, prebuilt question libraries, form conversion tools, validation processes, and human oversight to improve efficiency while maintaining trust and accuracy.

A central theme was that automated QM is designed to augment, not replace, quality teams. By expanding coverage from the typical 1–3% of interactions reviewed manually to a much broader view of customer engagements, organizations gain deeper insights, more consistent evaluations, and greater operational visibility. Attendees agreed that explainability, calibration, and pre-deployment testing are essential for building confidence in AI-driven quality programs.

Mallory recommended a phased approach, noting that even partial automation can deliver meaningful results while teams validate outcomes, refine processes, and build internal support. She highlighted prebuilt question libraries, automated form validation and conversion, desktop analytics integration, and deployment flexibility across cloud, on-premises, and hybrid environments. Flexible pricing options, including per-agent and per-interaction models, were also discussed as ways to accommodate different budget and procurement requirements.

On measuring success, attendees were encouraged to establish baseline metrics before implementation and compare them with post-deployment results to demonstrate ROI. Customer examples illustrated gains in supervisor productivity, customer satisfaction, and compliance performance, along with faster onboarding of quality programs.

During Q&A, attendees asked about trust, accuracy, change management, and resource allocation. Mallory recommended ongoing testing, calibration, and AI-assisted reasoning to validate results, and stressed involving quality evaluators, IT teams, and internal champions early. Time saved through automation can then be redirected to higher-value work such as coaching, training, expanding channel coverage, and targeted audits.

Key takeaway: Automated QM lets organizations scale quality programs, improve operational performance, and generate insights that extend beyond the contact center. With the right implementation strategy, teams can accelerate time to value while keeping the human oversight needed for accuracy and trust.

Customer Experience Metrics and Capacity Planning Strategies at Principal Financial

This webcast featured Rick Kosiba of Real Numbers and Tony Graczyk of Principal Financial Group, who explored new approaches to improving customer experience metrics and contact center performance.

The discussion centered on Agent Contact Resolution (ACR), a practical, data-driven metric that measures whether an agent interaction prevents the customer from making a repeat contact within a defined window, typically 1, 3, or 7 days. Rick and Tony showed how ACR provides more actionable insight than traditional First Contact Resolution (FCR) and shared research showing a strong correlation between higher ACR scores and improved customer satisfaction.

The presenters explained how to calculate ACR using conversation IDs and caller phone numbers (ANI) to identify repeat contacts. They also demonstrated visualization techniques in tools such as Power BI, including agent performance scatter plots, callback timelines, and wrap-up code dashboards, to help leaders pinpoint opportunities for coaching, process improvement, and operational optimization.

One of the session's most compelling findings was that simply giving agents visibility into their ACR performance can drive meaningful behavior change. Real-world examples showed roughly a 16% improvement in ACR, along with higher customer satisfaction and fewer repeat contacts. Fewer callbacks improve the customer experience, lower operating costs, and free up capacity in the contact center.

Rick and Tony recommended choosing a callback window that aligns with business objectives; many organizations find three days most effective. They encouraged using ACR data to identify performance outliers, support targeted coaching, and uncover process improvements, and integrating ACR with QA programs, voice analytics, and operational reviews to create a continuous improvement engine. Attendees were invited to pilot ACR and analyze results by agent, queue, region, and contact type to understand the root causes of repeat contacts. The presenters also offered follow-up implementation support.

They noted that ACR may be less applicable in some B2B scenarios where multiple interactions are a normal part of the customer journey. As with any metric, results should be interpreted in the context of the business process to avoid penalizing legitimate multi-touch engagements.

Key takeaway: ACR helps organizations reduce repeat contacts, improve customer satisfaction, increase efficiency, and uncover systemic issues that create unnecessary customer effort. Combined with coaching, analytics, and quality management, it turns performance measurement into a strategic tool for continuous improvement.

How AI Closes the Quality Coaching Loop

This session featured Maya Girshan of NiCE, with additional insights from LeAnn Hopkins of NiCE, on how AI-powered quality management can transform coaching programs and drive continuous performance improvement.

Maya introduced a five-step framework for closing the quality coaching loop: Auto Score, Evaluation Summary, Coaching Recommendations, Practice, and Remeasure. Together, these capabilities let organizations move beyond QA programs that evaluate a small sample of interactions and use AI to assess 100% of customer conversations.

AI-driven auto-scoring provides full coverage and consistent evaluations. AI-generated summaries turn scores into actionable insights, highlighting strengths, improvement areas, and coaching priorities. Recommended coaching actions help supervisors focus on recurring patterns, while simulation and role-play let agents practice skills in a low-risk setting. Continuous remeasurement tracks and reinforces progress over time.

A key theme was the shift from manual, time-consuming quality processes to evidence-based coaching at scale. Maya shared customer results including a 65% increase in coaching activity, substantial reductions in coaching prep time, and an average savings of three to four hours per supervisor per week, freeing supervisors to spend more time developing agents.

The presenters emphasized that transparency and human oversight are critical. NiCE's approach includes plain-language scoring profiles, configurable thresholds, evidence-backed evaluations, and AI reasoning that explains how scores are generated. Human evaluators remain responsible for calibration, oversight, appeals, and final decisions. The message was clear: AI enhances human coaching; it doesn't replace human judgment.

LeAnn discussed product enhancements, including upgraded transcription models and the planned integration of screen-recording data into AI analysis, which will add context and improve scoring accuracy beyond transcript-only evaluations.

When attendees asked about trust, explainability, and accuracy, Maya recommended running AI scoring alongside human evaluations during initial deployment, calibrating results, refining profiles and thresholds, and expanding only once stakeholders are confident in the outcomes.

Key takeaway: AI-powered quality management lets organizations evolve from limited-sample QA to full-coverage, continuous coaching. Combined with human oversight, explainable scoring, and structured workflows, it can significantly increase coaching capacity, improve performance, and deliver measurable efficiencies.

Verint QM Intelligence: 3 Features That Speed Time to Value

In this session, Mallory Partain, Product Manager at Verint, highlighted three QM Intelligence capabilities designed to accelerate adoption of automated quality management: the Pre-Built Question Library, the Automated Form Converter, and the Form Validator. The presentation addressed common barriers, including concerns about AI replacing human evaluators, lack of trust in AI-generated results, and the effort required to move from manual to automated monitoring.

Pre-Built Question Library. Verint offers more than 60 pre-built evaluation questions organized into categories such as greeting, empathy, active listening, authentication, and professionalism. Customers can deploy a standard form immediately or build custom forms from proven questions, significantly reducing setup time and prompt-writing effort.

Automated Form Converter. This tool helps organizations migrate existing manual forms to automated scoring. It maps questions to library content, creates custom questions when necessary, flags questions that can't be automated, and requires users to review and approve every mapped question before publishing. Automated forms are limited to 25 questions, so longer forms may need to be split.

Form Validator. The validator lets organizations test automated forms against real customer interactions before deployment. Users can review scoring at both the question and interaction level, identify outliers, and see the AI's reasoning alongside transcript evidence. Validation results don't affect agent performance records. Attendees rated the validator the most valuable of the three features.

Recommended workflow:

1.      Convert existing manual forms using the Automated Form Converter.

2.      Review and approve all mapped or generated questions.

3.      Validate the form against representative customer interactions.

4.      Refine custom questions as needed and publish the final form.

Key takeaway: Together, the question library, converter, and validator help organizations move from manual to automated QM faster, reducing manual effort and improving supervisor productivity while building trust through explainable AI.

Best Practices in 60 Minutes: Building a More Effective QA Program

Quality assurance programs succeed when they are treated as tools for improvement rather than inspection. In this interactive panel, quality leaders Allison Diffley, Ammon Davis, Teri Figueroa, and Don Gandy shared practical strategies for coaching, scorecards, calibration, recognition, and technology that drive measurable improvement while maintaining consistency and fairness. Their common theme: effective QA focuses on clear expectations, objective standards, actionable feedback, and continuous improvement.

Coaching should be constructive and purpose-driven. The panel agreed that coaching works best when it starts with curiosity rather than correction. Allison encouraged leaders to ask questions before offering solutions to uncover root causes. Ammon emphasized role-play and practice, noting that agents learn more by rehearsing new behaviors than by simply hearing feedback. Teri highlighted grounding feedback in documented standards, and Don recommended focusing on one behavior at a time. Several panelists noted that having agents evaluate their own interactions increases engagement and accountability.

Design QA forms that drive action. The panel strongly favored simplicity. Ammon recommended limiting scorecards to roughly 10 to 15 questions and periodically removing items with consistently high pass rates. Teri stressed maintaining a centralized standards and definitions document for evaluator consistency. Don encouraged tailoring forms to specific interaction types, and Allison emphasized clear language and piloting changes before broad rollout. An emerging best practice was attaching remediation resources, such as job aids, training materials, or micro-learning, directly to specific questions so agents get immediate, targeted development when they miss an item.

Create fair, meaningful scoring models. Ammon described starting each evaluation at 100% and deducting points only for missed behaviors, so non-applicable questions don't hurt results. Teri supported "Not Applicable" responses that award full credit. Both she and Allison stressed piloting scoring changes to confirm that scores align with business expectations.

Calibration is essential for consistency. Teri recommended monthly calibration sessions focused on gray areas and emerging trends. Don noted that discussions should explore the reasoning behind decisions, not just the final score. Allison suggested framing calibration as an alignment exercise rather than an audit, and Ammon shared the practice of secondary reviews on all failing evaluations. The panel also recommended a formal appeals process, which gives agents a voice and helps surface gaps in standards or evaluator training.

Recognition should reinforce desired behaviors. Don highlighted recognizing agents who identify recurring customer issues, process breakdowns, or knowledge gaps. Allison encouraged publicly recognizing QA professionals who uncover systemic issues, and Teri recommended rewarding improvement as well as top performance. Panelists also noted the value of tailoring recognition to individual preferences, public or private.

Use technology and AI responsibly. Panelists agreed AI can improve efficiency and insight but cautioned against treating automated outputs as final. Don emphasized a human-in-the-loop approach, with AI as a starting point rather than the final authority. Allison recommended regularly validating AI-generated scores and transcripts, and Teri discussed using AI to consolidate data from multiple systems into executive reporting. Ammon suggested AI assistants that help auditors understand scoring rationale and locate supporting standards. The panel also stressed safeguarding customer information through security controls, access restrictions, and data governance.

Key takeaway: Successful QA programs are built on clarity, consistency, and continuous improvement: concise scorecards, documented standards, ongoing calibration, focused coaching, and data-driven priorities. Above all, QA shouldn't be a compliance exercise. It should be a framework for helping employees succeed, improving customer experiences, and driving business outcomes.