You’re probably thinking “oh great, another piece of AI content that I was holding my breath for.”
The AI acronym is like a song that’s stuck in your head, right?
It’s everywhere. Conference panels. Sales decks. Vendor emails. Board meetings. And now here we are again… AI in business process outsourcing (BPO).
AI has moved from a future concept to an active force reshaping customer experience and the BPO industry, not to mention the constant reminder of it in our everyday lives.
But while AI has accelerated rapidly, its real-world application inside call centers varies widely. Some tools deliver measurable gains in efficiency and quality. Others overpromise, underdeliver, or create new friction for customers and agents.
Many of us in the customer experience (CX) community are trying to navigate the fast-moving AI landscape by understanding where AI works, where human expertise remains essential, and how leading BPOs are integrating AI to elevate performance, not replace the people who power CX.
AI is influencing nearly every dimension of our lives, including service delivery, but I and other CX leaders call for the toning down of AI hyperbole. I find it interesting that the narrative has shifted from “AI replacing call centers,” to “AI enabling humans in call centers.”
That shift matters.
Let’s dive into this subject a bit deeper.
AI now plays a role in many aspects of customer service, but its impact is far from uniform. In the most effective environments, AI acts as an enabler: strengthening quality, improving speed, and helping frontline teams work smarter.
Many brands are far along in their AI journey while others are at the starting line. While most brands have similar CX objectives, not all do. Many are focused on enhancing customer experience, with an eye on costs, but not so much on contact deflection. While others are exclusively focused on deflecting, even avoiding contacts fueling dramatic cost reduction by any means necessary.
Contrary to the narrative, contact deflection started long before the recent AI revolution. Brands and BPOs have been implementing contact reduction strategies through advanced self-help portals, digital channels, and improved knowledge management for many years.
What AI does is accelerate what was already underway. It adds speed, scale, and pattern recognition to processes that were previously manual or partially automated.
Granted, AI can accelerate contact deflection, if utilized properly. But the most successful programs use AI to augment operations, not disrupt them.
So where exactly is AI making a difference in call centers today?
Automated QA, 100% call scoring, sentiment analysis, and root-cause insights.
Traditional QA models typically review only a small percentage (1–3%) of calls. AI-driven platforms can now analyze 100% of interactions, surfacing compliance risks, coaching opportunities, and emerging customer friction points in real time. That level of visibility changes how leaders manage performance.
Real-time prompts, knowledge surfacing, compliance reminders, and faster resolutions.
A 2023 study by Stanford and MIT found that generative AI tools increased customer support productivity by 14% on average, with improvements exceeding 30% among newer agents. That tells us something important: AI isn’t replacing agents — it’s compressing the learning curve.
Intelligent Self-Service
Smarter chatbots and virtual agents that reduce volume while maintaining customer satisfaction.
In structured environments, AI-powered bots can resolve 60–80% of routine inquiries (IBM). But resolution rates depend heavily on process design and data integrity, not just the tool itself.
AI-driven scheduling, occupancy management, and operational modeling.
Forecast accuracy improvements of even 10–20% can materially reduce overtime exposure and underutilization. In high-volume environments, those incremental gains translate into meaningful margin protection.
Automated detection that strengthens brand protection and regulatory adherence.
While automation now touches more workflows, AI remains most effective when paired with experienced frontline teams, seasoned leaders, and strong governance.
We cannot remove the human component in all facets of AI. In fact, we are now seeing more evidence of AI + humans working in concert to deliver better customer experiences and cost advantages.
There are persistent misconceptions about what AI means for the workforce. Despite bold predictions, AI has not eliminated the need for call center agents. Instead, it has shifted the nature of the work. And as AI technologies and platforms continue to evolve, we are finding impactful ways to utilize the power of AI in areas that make the most sense.
For example, the successful deployment of AI tools can reduce the need for human interaction in “simpler” workflows over time. However, this is not guaranteed as we have seen companies invest millions to accomplish this goal with mixed results.
While automation excels at handling structured, repetitive work, service interactions still require judgment, empathy, and adaptability. We will likely see continued utilization of AI in less complex tasks. This leads to a higher concentration of human interaction in more complex and technical workflows, which will require upskilling agents. Perhaps over time, AI may have a dramatic impact in highly complicated and emotional workflows as well.
Where AI excels today:
High-volume, repetitive tasks
Categorizing, scoring, summarizing
Identifying patterns and anomalies
Enhancing speed and accuracy
Where humans remain essential today:
Complex issue resolution
Emotional intelligence and empathy
Trust-building in regulated industries
Exception handling and escalation
Nuanced communication and negotiation
One unintended consequence of automation is that the human work becomes more complex. As AI absorbs routine inquiries, agents are increasingly left handling high-stakes, emotionally charged, or technically nuanced interactions.
The bar for human performance is rising — not falling.
According to IPSOS, 88% of consumers still prefer speaking with a human when dealing with complex service issues. That preference is not disappearing any time soon.
Across the global BPO landscape, AI maturity varies significantly. Some providers have embedded automation and analytics into daily operations, while others are still evaluating how these tools fit into their delivery models.
From our vantage point, we see a widening gap between AI-ready organizations and AI-marketing organizations. Some have deeply integrated analytics into QA, WFM, and coaching frameworks. Others are still leading with slide decks rather than measurable performance metrics.
Rather than focusing on specific technologies, leading BPOs tend to prioritize how AI supports service quality, workforce effectiveness, and long-term scalability. This creates meaningful differences in execution that aren’t always visible at the surface level.
Trends we see across our global supplier network:
Rapid adoption of agent-assist tools
Investments in proprietary automation frameworks
Expanded use of AI-driven QA and analytics
Growing demand for data science and CX engineering talent
Increased client expectations for measurable AI ROI
Clear gaps between “AI-ready” and “AI-marketing” vendors
AI is influencing how organizations think about global service models, but it has not replaced the need for geographic diversity. Instead, automation is increasingly used to enhance performance across onshore, nearshore, and offshore teams.
In practice, AI helps promote greater consistency, faster onboarding, and improved visibility across distributed operations. Location strategy still matters, but AI is becoming an important layer that strengthens delivery rather than redefining it.
AI adoption is not simply a technology decision. Organizations evaluating automation should consider operational readiness, customer expectations, risk tolerance, and long-term CX objectives.
Without clarity in these areas, even well-designed tools can fall short. A measured, intentional approach helps ensure AI investments align with service goals and organizational culture.
Before making decisions about automation, brands need clear visibility into:
Current process maturity
Data quality and accessibility
Customer expectations and service complexity
Change management readiness
Security and privacy obligations
Impact on employees and culture
How AI aligns with larger CX goals
Research from Deloitte suggests that most AI initiatives struggle to scale due to integration and change management challenges, not because the technology itself fails. That’s an important distinction. Technology adoption does not automatically equal operational transformation. Without this foundation, even the best AI tools fall short.
AI is not “free margin.” Upfront investments include licensing, integration engineering, data restructuring, governance design, and workforce retraining. While McKinsey reports that AI can reduce operating costs by 30–40% in optimized environments, ROI typically materializes gradually and only when execution discipline is present.
The better question for brands isn’t “Do you use AI?” It’s “Where does AI measurably improve quality, efficiency, or risk mitigation within your delivery model?” That conversation separates operational substance from marketing noise.
As AI becomes more embedded in service delivery, governance is becoming non-negotiable. Data privacy exposure, algorithmic bias, hallucination risks in generative systems, and expanding regulatory scrutiny require structured oversight.
This aligns with broader enterprise trends. McKinsey’s 2025 Global Survey on AI notes that organizations are increasingly redesigning workflows and elevating governance structures to capture real value from AI initiatives, not merely deploying tools in isolation.
For BPOs supporting financial services, healthcare, or insurance clients, this means implementing human override protocols, bias testing frameworks, audit trails, and transparent AI usage policies. Automation without governance creates operational risk, not advantage.
As AI advances, contact centers won’t disappear — they’ll evolve. We expect to see:
More intelligent agent-assist for quicker, more accurate resolutions
Expanding self-service capabilities across channels
Stronger predictive analytics feeding WFM and coaching
AI-driven personalization for improved CX outcomes
Increased use of synthetic datasets for training
Hybrid teams blending automation, frontline talent, and CX engineers
Interestingly, automation and workforce growth are not mutually exclusive. Major outsourcing markets continue to expand even as AI adoption increases. This reinforces what many of us in CX already know: AI augments capacity — it doesn’t automatically eliminate demand for skilled people.
And throughout all of this, high-performing BPOs will continue to differentiate themselves not by the tools they adopt, but by how they apply them.
CustomerServ approaches AI with a pragmatic lens shaped by decades of work inside the global BPO ecosystem. While innovation continues to accelerate, we believe the conversation around AI often runs ahead of operational reality.
AI can be a powerful enabler when applied thoughtfully. But outcomes are driven less by the technology itself and more by how it is implemented, governed, and integrated into day-to-day service delivery.
Tools alone do not improve customer experience — execution does.
Our role is not to advocate for AI adoption for its own sake. Instead, we focus on helping organizations understand where automation meaningfully supports performance, where human expertise remains critical, and how different BPO providers are applying AI in practice.
In many cases, the first step isn’t choosing a tool; it’s taking an honest snapshot of operational readiness. Are processes disciplined? Is the data reliable? Is governance structured? Is the workforce prepared? Is ROI clearly defined? Without alignment across these dimensions, even the most advanced AI platform will struggle to deliver sustainable impact.
AI can and will optimize the call center.
But the call center will always be a people business.