How AI is set to replace half of customer service jobs by 2030

Nearly half of customer service jobs could be automated by 2030

Forrester’s latest report outlines a major shift that’s impossible to ignore, AI will automate close to half (49%) of all customer service jobs by 2030. This is already happening. The next few years will radically alter how businesses structure their service operations. Traditional agent roles will decline, while new, more technical positions focused on AI management and customer experience optimization will grow in importance.

This transformation doesn’t mean customer service will become less human, it means it will become more strategic. People will move from answering questions to training, governing, and refining AI systems that do the heavy lifting. The purpose of the human workforce will shift from routine execution to driving value through judgment, creativity, and continuous improvement.

For leaders, this is not a call to cut costs, it’s a call to reimagine how your company connects with customers. AI is replacing tasks. The organizations that win will be those that see this change as an opportunity to repurpose their people. This requires early investment in AI fluency across all levels of the company. The value will come not from simply automating what exists today, but from redesigning what customer service can become.

According to Forrester’s modeling, a high-volume contact center that employs about 1,191 people today could operate with just over 500 employees within five years by automating 80% of its routine tasks. The potential for efficiency is huge, but the transition needs careful direction and strong leadership to avoid cultural pushback and workflow chaos. This is the time to lead.

AI already dominates routine customer inquiries

We’re already past the early phase of customer service automation. AI systems are now handling most front-line interactions in many organizations, and doing it well. Forrester’s report shows that AI is involved in 96% of inquiries at Anthropic, resolves 90% of passenger queries at Heathrow Airport, and manages between 68% and 80% of inquiries at digital-first companies like Rocket Money and TeamSystem. That’s not future potential, it’s present reality.

AI is moving beyond being a helpful tool. It’s becoming the primary engine that drives customer experience at scale. These systems don’t just automate, they make decisions, predict intent, and adapt in real time. That’s what separates AI-powered support from older technologies like chatbots or IVR systems. Those only boosted speed. Modern AI changes the very definition of service, how it works, how it’s measured, and what roles humans play within it.

C-suite leaders need to look at this as structural transformation. Bringing AI into the heart of your service model means redesigning workflows, retraining teams, and rethinking the metrics that define performance. Customer satisfaction, resolution quality, and brand perception will depend on your ability to blend human intelligence with machine efficiency.

Kate Leggett, VP & Principal Analyst at Forrester, said it best: AI doesn’t just make customer service more efficient, it reshapes it entirely. Executives who understand this will build service organizations that are faster, smarter, and more resilient than their competitors. The shift is happening now, and the opportunity is massive for those ready to move decisively.

Corporate leaders confirm the AI-driven transformation

Some of the clearest signals about where customer service is heading come from industry leaders already living through the shift. Verizon CEO Dan Schulman recently told Bloomberg—as cited by TheStreet—that AI is taking over a large portion of the company’s customer service operations. He pointed out that most inquiries, such as billing issues or technical troubleshooting, follow patterns that can be solved more quickly and accurately by AI systems.

Schulman’s comments underline a direct reality: automation is being deployed at scale. Verizon’s workforce changes confirm the magnitude of this shift. In late 2025, the company reduced its headcount from roughly 100,000 to around 87,000 after cutting more than 13,000 jobs. Reports suggest that some of those employees had helped train AI systems before being included in the layoffs. This demonstrates how rapidly implementation is progressing across major enterprises.

For C-suite executives, this is a strategic moment. The transition cannot simply focus on cost reduction or efficiency. Successful leaders will ensure that automation complements human expertise rather than replacing it entirely. AI should handle repetitive, high-volume requests, while human teams focus on the complex, sensitive, and emotionally charged interactions that drive long-term loyalty.

Schulman noted that Verizon’s future model is one where AI and human workers collaborate, but with fewer humans overall. This hybrid workforce will demand sharp clarity of roles, continuous reskilling, and clear performance metrics that tie technology outcomes to customer value. Leaders who define that structure early will shape stronger, more balanced service organizations.

Human roles are evolving toward oversight and complexity management

The Forrester report makes one thing clear, human roles in customer service aren’t disappearing; they’re changing direction. As AI systems handle more routine tasks, employees will move into positions focused on AI supervision, exception handling, and system optimization. The new value of human contribution will come from analysis, judgment, and the ability to manage edge cases where AI lacks full context.

The emerging service ecosystem will have two key layers of human involvement. The first will consist of front-line employees trained to oversee AI performance, monitoring accuracy, flagging errors, and making sure automated responses maintain quality and brand integrity. The second will include experienced professionals who address complex, technical, or relationship-driven issues. These are the interactions that demand empathy, high judgment, and advanced problem-solving.

For executives, this evolution requires a forward approach to talent development. Traditional scripting and procedural training will no longer be enough. Teams will need stronger analytical, interpersonal, and digital literacy skills to complement automated systems. This will also mean closer collaboration between service, IT, and data science departments to ensure AI performance aligns with operational goals.

Organizations that succeed in this transition will create service models where human intelligence and AI performance operate in sync. The clear advantage lies in using people to enhance technology rather than compete with it. Over time, these collaborative models will define modern customer service across industries, allowing companies to maintain quality and trust even as automation scales.

Emergence of new roles focused on AI governance and optimization

A major shift is underway inside customer service operations. As automation scales, entirely new roles are emerging around AI governance, data analysis, and system optimization. These are not traditional IT functions. Forrester identifies positions such as AI Agent Builders, CX Optimization Specialists, and Support Insights Leaders as pivotal in the next generation of service organizations.

These roles will be responsible for maintaining AI systems, detecting flaws or inaccurate outputs, and ensuring technology decisions align with customer experience objectives. They’ll also translate complex technical data into usable business insights. AI Agent Builders will combine business understanding with low-code development skills to design tools that meet operational goals. CX Optimization Specialists will monitor AI accuracy, identify hallucinations, and refine AI workflows for better reliability.

For executives, this development signals a new class of workforce priorities. The most successful companies will mix technical and business expertise to build agile teams capable of continuous improvement. Investments in skill development across analytics, governance, and automation will pay off fast in both performance and regulatory compliance.

The rise of these roles also means the traditional boundaries between IT, operations, and customer experience are disappearing. Leaders who establish clear governance frameworks early will gain more control over AI outputs and reduce the risk of misalignment between customer experience goals and automated execution. This is a structural transformation that demands both accountability and adaptability at every level of the organization.

High-volume B2C contact centers will experience the greatest workforce reductions

Forrester’s model shows that high-volume business-to-consumer (B2C) service centers are most vulnerable to automation-driven downsizing. These centers manage millions of customer inquiries every year, and most of those interactions follow predictable patterns. That makes them ideal for AI automation. In contrast, smaller B2B operations that handle more complex or collaborative issues will see less disruption and slower containment rates.

The projections are clear. A typical large-scale B2C contact center with 1,191 employees today could operate with around 504 within five years as AI containment reaches 80%. For B2B environments, containment is expected to reach only 70%, resulting in smaller workforce reductions. This will reshape cost structures, skillsets, and even vendor relationships across industries.

For senior executives, this is about determining the right balance between automation and human engagement. Over-automation can erode customer trust if quality control is not carefully managed. Leaders must take a measured approach, automating predictable workloads while maintaining human oversight for scenarios that require empathy, adaptability, or nuanced reasoning.

Decisions in the next two years will decide how ready large organizations are for the AI transformation. Those that start integrating oversight mechanisms, reskilling programs, and hybrid workflows now will manage the change with stability and confidence. The trend is inevitable, but how it’s managed will define whether it strengthens or weakens enterprise performance over time.

Workforce reskilling is critical but currently underprepared

Forrester’s research points to a clear capability gap. Companies are rapidly deploying automation, but few are adequately investing in reskilling the people who will be expected to manage and complement these systems. Only half of surveyed employees say their organizations have a structured curriculum for training in automation technologies. This shortfall poses a real risk to operational stability and customer experience quality.

Reskilling is a core requirement for a high-performing AI-driven workforce. Employees must learn to work alongside advanced systems, identify edge cases, and provide contextual insights AI cannot yet replicate. The organizations that invest early in digital literacy, analytical thinking, and technical collaboration will see fewer disruptions and stronger adaptability across their operations.

Executives should view reskilling as a strategic pillar. Sustaining service quality amid automation depends on a workforce prepared to handle more complex and judgment-based interactions. Companies that neglect this preparation will face higher turnover and an overreliance on external vendors for AI integration expertise. Both outcomes reduce agility.

Forrester points to IKEA as an example of proactive adaptation. The company retrained over 8,500 customer service representatives to become interior design advisers as AI absorbed routine inquiries. This move strengthened the customer experience while enhancing brand perception and internal mobility. The results demonstrate that when reskilling aligns with company vision, technology adoption becomes a growth opportunity instead of a workforce disruption.

Gradual transition strategies can mitigate disruption

Customer service organizations face a major transition, but not all change needs to be disruptive. Forrester recommends using natural attrition and targeted retraining to manage the shift instead of immediate large-scale layoffs. In many contact centers, turnover already averages about 60% annually. That rate can be leveraged to adjust staffing levels over time while minimizing the social and reputational risks associated with sudden reductions.

Managing this change requires discipline and foresight. Instead of cutting positions rapidly, companies should reinvest the savings from unfilled roles into development programs that build the next generation of customer experience professionals. Those professionals will be responsible for supervising AI systems, improving CX strategies, and ensuring technology performance aligns with brand promises.

For executives, a gradual approach creates operational stability. It also signals a long-term commitment to employees, which supports engagement and retention during transformation. A biomedical organization featured in the Forrester report used a skills intelligence platform to map transferable abilities between retail and customer service staff. Through this program, it cut projected layoffs from 28% to just 11%, showing that careful workforce planning can deliver both efficiency and continuity.

Effective transitions depend on aligning HR, operations, and AI strategy teams from the start. Leaders who maintain that alignment will find it easier to preserve corporate culture, retain essential knowledge, and develop stronger internal capabilities as automation scales. The transition to AI-driven structures is unavoidable, but how it’s executed will decide whether companies emerge more capable, or more fragmented, on the other side.

Traditional organizational structures must be reimagined for AI integration

Forrester’s findings make it clear: the conventional hierarchical model that defines most contact centers is incompatible with modern automation. These structures were designed for volume management and escalation hierarchies. As automation replaces many routine interactions, organizations will need to flatten their structures to become more collaborative, agile, and data-oriented.

This change affects how decisions are made, how teams are organized, and how responsibility is assigned. AI systems operate across departments, which often blurs accountability. Forrester warns that when AI becomes “everyone’s job,” it can quickly become no one’s responsibility. Executives are advised to implement frameworks such as RACI (Responsible, Accountable, Consulted, and Informed) to define who owns specific aspects of AI management and oversight. Without explicit governance, the quality and compliance of AI systems will degrade over time.

For C-suite leaders, this reorganization should not only address efficiency but also establish clear ownership of AI ethics, performance tracking, and optimization. Governance must be treated as a prime operational function, with dedicated budgets and measurable outcomes. The flatter structure should empower cross-functional teams to make faster decisions and continuously refine AI processes.

The transition to these new structures will demand a shift in leadership mindset. Middle management layers will likely shrink, and measurement systems must evolve beyond traditional efficiency metrics. Indicators such as customer value creation, retention, and revenue generation will become primary measures of AI-driven success. Leaders who implement this design early will gain adaptability, transparency, and stronger internal alignment, all critical as automation scales.

The transformation involves structural change

AI’s expansion in customer service is not simply about reducing headcount, it’s about redesigning how service delivery operates at every level. The focus is moving from repetitive execution toward strategic capability. While automation will remove many routine positions, it will also create demand for employees with skills in analysis, supervision, and cross-functional communication. That development is central to how organizations will balance technology and human intelligence going forward.

Forrester and other analysts emphasize that the future of work in customer service is not defined by loss but by transformation. Roles will evolve into new functions: automation supervisors, escalation specialists, and data-driven quality leads. These roles ensure systems function correctly and continuously improve. Businesses that treat this as a redesign, rather than a reduction, will manage the transition with fewer operational gaps and higher workforce engagement.

C-suite leaders must also prepare for the complexity of AI deployment. Over-ambitious cost cutting often backfires because automation projects tend to overestimate AI’s precision and underestimate the depth of human judgment required in certain tasks. When companies focus solely on efficiency, they typically face reinstalling human oversight a few years later at a higher cost.

Forecasts from Gartner and McKinsey indicate that up to 80% of basic customer interactions could be automated by 2030. However, more than 80% of organizations plan to redeploy rather than fully eliminate those roles. This confirms the long-term direction: AI reduces load but increases the demand for strategy, governance, and human oversight. Forward-thinking companies will focus equally on managing automation and advancing their human talent strategies.

Human-AI collaboration will define the future of customer service

The next phase of customer service will not be defined by humans or AI alone, it will depend on how effectively the two operate together. Forrester’s analysis and supporting forecasts from Gartner and McKinsey align on this point: automation will manage the bulk of repetitive and predictive tasks, while humans will handle interactions that require emotional intelligence, negotiation, and nuanced problem-solving. The endpoint is not replacement, it’s partnership designed for scale, accuracy, and customer satisfaction.

As automation systems mature, their value will come from how well they integrate into the broader service model. AI will handle the speed, capacity, and precision required for standard inquiries, freeing people to focus on high-value interactions such as escalations, service recovery, and strategic account management. For executives, this represents the evolution from fixed team structures to agile, hybrid service models. The ability to deploy talent dynamically, between human-led and AI-led functions, will define competitiveness.

Executives must take responsibility for guiding this collaboration. This means rethinking performance metrics to value human-AI synergy rather than output alone. It also involves investing in employee training that builds confidence in working with AI tools. Clarity and capability are essential for adoption; when employees understand their role within an AI-enhanced environment, productivity and morale both rise.

The companies that excel in this stage will be those that prepare for balance, where technology amplifies human potential, and people provide the insight and adaptability AI cannot yet achieve. By managing this relationship with precision, service leaders will build stronger, faster, and more resilient organizations capable of sustaining excellence as automation continues to advance.

The bottom line

AI’s impact on customer service is not a distant prospect, it’s the foundation of how modern service organizations will operate. The scale of change ahead is significant, but the opportunity is larger. Automation will handle most of what slows operations today, allowing people to focus on higher-level, customer-centric work that actually drives growth.

The real measure of leadership in this transition will be how effectively companies balance automation with human capability. Leaders who treat AI implementation as a workforce evolution, not just a cost reduction, will gain more adaptability, deeper insight, and stronger brand loyalty over time.

This next chapter isn’t about losing human value. It’s about unlocking it by combining human judgment with machine precision. The executives who move with purpose, clarity, and accountability will not only transform how service is delivered but also redefine what excellence in customer experience means in the AI era.