Contact centres have invested heavily in workforce management, quality assurance, and customer feedback programs.
Yet, Puzzel’s State of Contact Centres 2026 suggests those systems often operate with only 2% to 5% of customer interactions, leaving as much as 98% of interaction insight uncaptured.
This blind spot makes it difficult for organizations to uncover the repeat issues, emerging trends, and systemic friction points hidden across most customer interactions.
Megan Carrigan, SVP of Strategy & Innovation at Valtech, told CX Today that limited visibility into customer interactions prevents organizations from understanding the systemic issues affecting the customer journey.
“When only a fraction of conversations are analyzed, leaders are often reacting to isolated examples rather than understanding broader patterns across the customer journey.”
This limitation means organizations risk making staffing and CX decisions based on interactions that may not reflect the reality of the experience, as Puzzel further found that 85% of customer complaints go unreported.
Whilst manual tagging can also carry an error rate of at least 30%, Puzzel also found that 70% of contact center costs are linked to avoidable, repetitive inquiries, the very types of recurring issues that are difficult to identify through manual sampling alone.
As AI-powered interaction analytics becomes more widely adopted, workforce engagement management will be required to understand every interaction, not for greater visibility alone, but to use complete conversation data to identify operational friction and make more informed workforce and CX decisions.
From sampled conversations to complete visibility
Traditional quality management has relied on supervisors manually selecting a small sample of calls or transcripts for review.
While this approach can identify individual opportunities for coaching, it only provides a narrow view of performance that often fails to reveal broader operational issues that affect large numbers of customers.
Mark Hughes, Co-founder and CEO of Solidroad, told CX Today that effective coaching begins with complete visibility into customer interactions.
“You can’t coach what you can’t see, and you can’t fix a problem you don’t know you have,” he said.
When only a fraction of interactions are reviewed, organizations risk basing performance assessments on isolated examples, meaning feedback can appear inconsistent or arbitrary.
This means emerging issues will remain hidden within the majority of conversations that are never analyzed.
AI-powered quality assurance and conversational intelligence platforms are changing that model by enabling first-pass analysis across every customer interaction whilst automatically detecting sentiment shifts, repeat contact drivers, and compliance risks.
Hughes emphasized:
“QA becomes less about catching individual mistakes and more about spotting patterns you’d otherwise miss.”
To deliver meaningful improvements, organizations must ensure AI-generated insights are accurate, supported by human oversight, and translated into coaching and operational changes.
The Rise of the Blended Workforce
The foundation of WEM is changing as organizations deploy AI agents and assistants across customer service operations.
In fact, Gartner predicts that by 2029, agentic AI will autonomously resolve 80% of common customer service issues, shifting routine inquiries away from human agents and leaving employees to focus on high value interactions that require judgment and empathy.
David Karandish, CEO and Founder of Capacity, told CX Today must begin treating AI agents as an integral part of the workforce rather than as standalone technology.
“Organizations need to start managing AI agents as part of their existing workforce,” he explained.
However, as of late 2025, only 20% of organizations reported reducing agent headcount because of AI, as nearly 80% instead planned to move employees into new roles, placing greater emphasis on workforce orchestration.
For WEM platforms, this means extending visibility across the entire AI-assisted service operation so organizations can monitor how digital workers contribute to customer outcomes.
“AI agents should have scorecards, quality thresholds, and escalation rules, just as human team members do.” He also emphasizes that,” Karandish said.
“The escalation path is as important as the quality of the AI itself.”
As human and digital workforces become increasingly interconnected, WEM platforms will play a central role in ensuring both operate under consistent quality standards.
Why Scheduling is Becoming an AI Challenge
In regard to scheduling, the approach toward this has traditionally focused on forecasting customer demand and aligning agent availability to meet service levels as efficiently as possible.
Today, customer demands are shifting across channels, as real-time events can drastically influence contact volumes and AI changes which interactions reach human agents.
“Traditional workforce planning relied heavily on past data to predict future demand, but customer behavior is increasingly influenced by real-time events, new channels and changing expectations,” Carrigan noted.
When it comes to scheduling, relying on historical trends alone are no longer sufficient when customer behavior and AI-driven workflows are constantly evolving, as Puzzel reveals that contact centers now use an average of 3.9 platforms, while 94% of CX leaders say consolidating their technology stack is important.
When WEM systems and data are spread across multiple systems, organizations struggle to build an accurate picture of demand or translate insights into effective staffing decisions.
AI is able to respond to this fragmentation by making workforce planning more adaptive, including identifying changing demand patterns and continuously connecting interaction data with workforce decisions.
As WEM flexibility becomes a global standard, Puzzel found that 91% of respondents view AI copilots and agent-assist tools as essential over the next two years, while 85% believe AI will help shorten training and accelerate onboarding.
“AI lets organizations respond to changing conditions more dynamically,” she explained.
The inevitable growing share of routine customer interactions means workforce planners will increasingly need to account for how AI changes demand, volume, and reshapes the work that reaches human agents.
“The opportunity is not simply automating schedules,” Carrigan concludes.
“It is creating a more adaptive workforce strategy where technology handles complexity and employees are better positioned to focus on meaningful customer interactions.”
Measuring Workforce Engagement Beyond Efficiency
As workforce engagement management evolves, enterprise success will be measured by more than efficiency alone.
Whilst traditional metrics such as average handle time and schedule adherence will still have operational value, they provide only a partial view of performance.
Grace Putney, Director of Client Success at ICUC Social, explained to CX Today that the next generation of WEM platforms will need to prioritize outcome-based measures.
“Average handle time and interactions per agent… don’t tell you whether the customer’s issue was actually resolved or whether the interaction strengthened confidence and trust in the brand,” she said.
As organizations gain visibility into every customer interaction, WEM has an opportunity to connect workforce performance with the outcomes that matter most to both customers and organizations.
This addresses the hidden costs of unresolved customer issues, as Puzzel found that 70% of contact center costs are linked to avoidable, repetitive inquiries.
As recurring problems can generate unnecessary demand when they go undetected, organizations that analyze conversations at scale can identify the root causes before issues become larger operational challenges.
“The next generation of WEM platforms will be measured by outcome and resolution metrics,” she says.
“The platforms that stand out will focus on measures like resolution quality, sentiment change after an interaction, escalation accuracy, and consistency across human and AI-assisted conversations.”
Ultimately, the future of WEM will mean transforming conversation data into actionable intelligence that helps every part of the business better understand customer needs, improve service quality, and strengthen long-term customer relationships.
