The contact center industry is facing a customer experience (CX) breakdown that can no longer be dismissed.
The data tells a clear story:
- Forrester’s “Global Customer Experience Index (CX Index™) Rankings, 2025” shows that “in the US, for the second year in a row, 25% of brands’ customer experience rankings declined in 2025, compared to only 7% that improved. Additionally, in most US industries, CX quality declined across all three dimensions: effectiveness, ease, and emotion.”
- McKinsey finds that only 15% have fully integrated CX strategies.
- The American Customer Satisfaction Index (ACSI) notes that satisfaction levels have fallen across multiple sectors, especially in industries like cell phones, apparel, and postal services. These highlight the continued erosion of customer relationships despite significant corporate investment in experience improvement.
It is clear that this trend warrants serious attention.
So, what’s behind the CX stagnationand decline? In many cases, it’s not a lack of effort or funding. It’s a mismatch between intention and execution.
Organizations are investing heavily in AI, along with automation and data systems. But too often, those investments don’t translate into better outcomes because they’re not anchored in the realities of the human systems they’re meant to support.
So, what’s behind the CX stagnation and decline? In many cases...It's a mismatch between intention and execution.
When Investment Outpaces Alignment
Consider three persistent gaps:
1. AI and human preferences. 80% of organizations are projected to have deployed AI for CX (Gartner). But 55 (Genesys via No Jitter) to 75% (Five9) of customers prefer human support.
2. Data quality versus data ambition. Many firms continue investing in AI while struggling with data silos and inconsistencies that directly undermine AI effectiveness.
3. Trust deficits. Edelman’s “Edelman 2025 Trust Barometer” finds employer trust declining globally for the first time. Gallup’s 2025 data rein-forces this picture. Only 20% of U.S. employees strongly agree that they trust their organization’s leadership, down from 24% a few years ago.
In contact centers specifically, high attrition and employee burnout persist. The result is a fragmented workforce and a CX that mirrors the internal disconnect.
Root Causes That Demand Attention
Four structural problems lie at the heart of the issue:
1. Misaligned metrics. Organizations often focus on efficiency or productivity indicators like average handle time (AHT) or calls per hour while neglecting drivers like trust, leader routines and effectiveness, engagement, and employee satisfaction or wellbeing.
Even when these metrics are tracked, they often fail to reach the full range of stakeholders who need them, from frontline agents to supervisors, managers, and executives.
2. Disconnected data. Performance metrics, feedback, interaction data, and quality scores are often scattered across systems. This fragmentation makes it difficult for frontline leaders to make informed decisions and undermines trust across the organization.
3. Remote work disagreement. Agents largely feel hybrid work improves their ability to serve customers, but many managers disagree, according to industry accounts and studies. This misalignment and the isolation inherent in work-from-home (WFH) or hybrid operations contributes to friction and weakens collaborative culture.
4. Technology overload. Employees are fatigued by repeated waves of “transformative” technology that often create disruption without improvement. AI, in particular, is seen by many not as an enabler, but as a looming threat.
Why Traditional Approaches Are Falling Short
Despite significant investments in technology and transformation, as I noted in the beginning, CX continues to decline in many organizations.
The problem isn’t just the technology itself. It’s the assumptions about how it’s introduced and integrated.
...the humans who remain will handle the most sensitive and complex interactions and will be central to successful AI deployment.
Executives are understandably eager for breakthroughs. But in pursuing the next big solution - and AI clearly falls in that category - many overlook the underlying issues that have derailed earlier efforts. AI will struggle in environments where trust is low, data is fragmented, and employees feel excluded from the process.
Even industry forecasts reflect this uncertainty. In 2025, Gartner projected that agentic AI would resolve 80% of customer service issues by 2029. A few months later, it reported that half of organizations would abandon plans to reduce headcount due to AI-related implementation challenges.
While future outcomes remain unclear, one thing is certain: the humans who remain will handle the most sensitive and complex interactions and will be central to successful AI deployment.
Three Principles That Drive Results
The organizations that are gaining ground aren’t waiting for perfect conditions. They’re applying three proven principles to realign their approach:
1. Strategic engagement frameworks. These are structured, repeatable processes that foster shared accountability and participation across all roles. Performance reviews, coaching systems, team huddles, and quality assurance (QA) workflows are not just operational necessities. They’re opportunities to build connection, alignment, and recognition.
Key examples include:
- Agent-led ratings of support interactions. Agents anonymously rate coaching sessions via quick 1–5-star prompts, generating a real-time metric known as “Touch Quality.” This gives agents a voice and provides supervisors with feedback they can act on.
- Huddles with peer recognition and team metrics. Daily or weekly meetings that include highlights of top performers or recent wins build alignment and morale while reinforcing shared objectives.
- Supervisor coaching with built-in accountability. Documented follow-ups and agent feedback loops transform coaching into a mutual development process.
These practices support core human motivators: purpose, progress, and belonging.
- Gamification as an engagement amplifier. Game mechanics can elevate routine processes, whether by heightening awareness of KPIs, reinforcing team unity, or strengthening intrinsic and extrinsic motivation.
- One standout approach allows senior leaders to draft “fantasy” teams of agents from across the workforce. These leaders act as mentors and motivators, competing in weekly matchups not just on team performance, but on frontline leadership KPIs such as attrition, engagement, and a modified version of the “Touch Quality” metric.
- This model extends the reach of engagement frameworks, weaving accountability and connection deeper into the fabric of the organization.
2. Data access and transparency. Too many decisions are made without full visibility into performance. Addressing this starts with integrated systems that give teams real-time access to the data they need. When employees understand how their actions influence outcomes and can see benchmarks clearly, accountability and engagement improve.
In practice:
- Integrated dashboards. Systems consolidate data from disconnected tools, e.g., workforce management (WFM) platforms, QA systems, and CRM applications, into role-specific dashboards for agents, supervisors, and executives.
- Performance data as “AI fuel”. The real value of data transparency isn’t just visibility but how integrated systems organize and present information to drive smarter, more contextual AI outputs.
- When data from various systems is unified, aligned to business rules, and made accessible across roles, it not only improves human decision-making but empowers AI to deliver more targeted, relevant, and actionable guidance across coaching, QA, and CX analysis workflows.
- Supervisor efficiency. With immediate access to reports and KPIs, supervisors spend less time compiling data and more time on coaching and support.
Transparency not only improves coaching and feedback cycles, but it also ensures everyone sees where they stand and how they can improve.
3. Data quality as a cultural standard. Data excellence is not just a technical challenge; it’s a behavioral one. Leading organizations empower employees by connecting their inputs to visible, tangible outcomes.
For example, when agents accurately categorize issues, future calls are routed more effectively, reducing escalations and improving resolution rates. Agents who flag outdated knowledge base content help drive updates that reduce call volume and customer confusion.
On the supervisor side, coaching actions linked to specific behaviors, such as call control or tone management, can be tied to measurable improvements in key KPIs.
These kinds of feedback loops help transform routine documentation, tagging, and coaching into high-impact contributions that continuously improve both service quality and AI effectiveness.
The Real Value of AI Starts with Humans
AI can deliver significant value, but only when it’s guided by people who are informed, engaged, and equipped. The strongest AI outcomes come from organizations where frontline workers understand their role in shaping how technology functions.
When employees have access to strong engagement systems, clear performance data, and ongoing feedback, they participate in building smarter systems, not just feeding them.
There’s also a larger risk to consider. Research cited in a The Register opinion article, written by Steven J. Vaughan-Nichols, shows that AI models trained exclusively on their own outputs deteriorate over time. Without thoughtful human input, system quality erodes. Investing in people is more than a cultural choice. It’s a safeguard against long-term degradation and a driver of competitive advantage.
Where This is Headed
Top-performing organizations won’t frame technology and human wellbeing as opposing forces. They’ll use technology to amplify human strengths. As others fall behind, those that build from this foundation will surge ahead.
AI can deliver significant value, but only when it’s guided by people who are informed, engaged, and equipped.
The goal isn’t to trade off efficiency for empathy. It’s to move beyond short-term optimization and build for long-term resilience. Organizations that strengthen their human infrastructure today will be better equipped to lead as AI capabilities continue to evolve.