Research: Five ways AI is changing customer service teams

Key findings

• AI is reorganizing core CS operations: Nearly every team (≥95%) reported meaningful workflow changes. Triage, routing, translation, and categorization are increasingly automated. Hybrid human+AI systems are taking their place.
• Frontline work is changing to AI oversight: Humans now QA, monitor, and test AI outputs. When it comes to handling queries, they step in for nuance, rather than repetition.
• Structural change is widespread but uneven across companies: 83% reported new responsibilities or roles. Some built AI pods, while others retained traditional setups.
• Tier 1 headcount demand is falling: 28% saw hiring freezes, slowdowns, or natural attrition at Tier 1 level as AI Agents manage more requests and improve operational efficiency.
• Skill gaps are widening inside teams: Data literacy, QA, and cross-functional communication are all rising in value. For many companies, long-term role strategy is lagging behind.

Research methodology

For this study, we reviewed interviews from 2025 conducted by our research team, either with Fin customers or prospects. The selected interviews revolved around the individual experience of the participant, which gave a higher chance of information related to role changes to be present.

Data analysis

Of the 166 relevant interviews, more than 90% reported some sort of change either in their role, team, or processes due to implementing Fin, or a similar AI product. Only 13 participants reported no changes.

What we found

Across the dataset, here are the core themes that emerged.

1. Automation and AI integration replacing manual steps (94.58%)

Participants overwhelmingly described automation and AI integration creating hybrid human-machine workflows and removing large amounts of repetitive work. This highlights the disruptive and transformative power of AI in CS:

• Manual processes like ticket triage, routing, translations, and repetitive responses are now handled by Fin or other AI systems.
• Customer interactions were rerouted through new AI-driven flows – Fin, data connectors, and AI Agents/triage bots – changing how tickets are escalated and how content is refined.

2. Humans shift to oversight, AI handles execution (82.53%)

Roles have become more strategic and supervisory, while AI absorbed much of the execution work:

• Support agents and managers moved away from handling basic queries to managing AI performance, reviewing Fin tasks, and improving automation.
• New roles emerged, such as AI specialists, automation managers, and Fin owners. While managers now oversee Fin implementation and monitor AI adoption metrics.
• Some participants described career transitions (e.g. from customer care to AI systems strategist, or to product/ops roles).

This marks a clear transformation in how customer service agents work: moving away from directly resolving customer queries. Overall, AI made roles broader and more analytical, demanding less manual interaction and more responsibility for optimization, configuration, and strategy.

3. Reductions or slower growth due to efficiency gains (27.71%)

Headcount effects were mixed but mostly downwards or stabilized thanks to automation:

• Many participants mentioned reductions in Tier 1 or frontline support staff, as Fin could handle simpler requests.
• Some described headcount freezes or hiring slowdowns – teams avoided adding staff because AI improved efficiency.
• Others noted role reallocation rather than layoffs: staff moved to AI management or complex support tasks.
• Several mentioned explicit reduction targets, e.g. aiming to reduce headcount by 10–50%.

In short, Fin/AI is enabling companies to maintain or reduce staff while handling greater volumes of work.

4. New AI teams, flatter orgs, fewer escalation layers (6.02%)

Structural changes were smaller in volume but notable in nature:

• Specialised AI teams were formed (e.g. “LLM team” or automation functions).
• Some traditional hierarchies flattened as teams reorganised around workflows or customer complexity instead of regions.
• Roles were merged or redefined (e.g. CPO merging under customer org).
• Certain functions were eliminated – like dedicated escalation layers – because Fin could now route or solve these automatically.

Overall, team design is becoming more modular and data-driven, with AI-focused units and fewer siloed escalation paths.

5. Broader digital transformation and operational modernization (16.27%)

These reflect broader organizational and strategic shifts:

• Movement towards automation-first business models and digital self-service.
• Adoption of new vendor ecosystems and emphasis on data quality and AI analytics.
• Cultural changes – embracing experimentation, continuous improvement, and modernization.
• Several noted new collaboration patterns between data, ops, and customer success teams.

Essentially, “other” captures AI-driven modernisation across culture, tools, and strategy – going beyond support into how the organization operates and learns.

What these changes mean for CS leaders

Overall, a widespread transformation is occurring in how customer service agents and teams operate following AI Agent implementation. Roles are evolving, responsibilities are diversifying and collaboration across functions is becoming the norm. Given how pervasive these changes already are – and the continuous improvement of AI technology – it is likely that this transformation will become even more pronounced over time.

With this in mind, it’s important for CS leaders to ensure their team is ready for the future.

Customer service agents will need new skills to succeed in their evolving roles. While they are experts in customer interaction and company policy, their work now demands new competencies in data analysis, quality assurance/debugging, and cross-functional communication.

This evolution also forces a structural question. Our research shows some teams are reorganizing entirely around automation, while others retain traditional structures – and for many, it’s unclear whether the change is a deliberate plan aimed at specific outcomes or simply the result of experimentation.

To make the most of your transition, here’s some next steps to consider: