New research: Customer service team evolution

Our latest research shows that customer service teams are undergoing significant change as a result of implementing AI tools and broader technological shifts. Learn more about how they're evolving.

Customer Service

Published
Jan 5, 2026

New research: Customer service team evolution

Our latest research shows that customer service teams are undergoing significant change as a result of implementing AI tools and broader technological shifts. Learn more about how they’re evolving.

Emiliano Melchiorre

11 min

We analyzed 166 interviews with support leaders, managers, and frontline specialists to understand what changed once AI Agents like Fin became part of everyday work.

There’s anecdotal evidence that customer service teams are undergoing significant change, both as a result of companies implementing AI tools to support their work and due to broader technological shifts that are redefining their responsibilities. However, the scale and prevalence of these changes remain unclear.

Here’s what we gleaned from the data.

TL;DR: What’s changing

• 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

The goal of this research is to understand how many customer service teams have changed their roles, responsibilities and ways of working due to adopting AI agents, as well as understanding how these changes manifest within their organizations.

For this study, the data chosen consists of interviews conducted by the research team, either with Intercom customers or prospects. This data was chosen because the focus of the interviews revolved around the individual experience of the participant, which gives a higher chance of information related to role changes to be present.

The data was collected using Snowflake by pulling all interviews stored in gong conducted by a member of the research team from 01-01-2025 to 14-10-2025.

After the data was pulled, a python script was used to clean the conversation corpus for each conversation retrieved. Common English stopwords (e.g. “and”, “very”, “with”, etc.) were removed, as well as all the text associated with a speaker in the conversation that was not the interview participant(s). This was done to reduce the computational power required for the conversation coding, avoid API timeouts and reduce costs.

After the corpus was cleaned, the OpenAI API was employed, alongside a prompt, to code each conversation using closed codes defined in a closed codebook.

The codes used were:

• No role change mentioned: No explicit changes to roles, teams, or reporting lines are attributed to AI/Fin.

• Role responsibilities changed due to AI/Fin: Duties/ownership moved between humans and AI/Fin, or scope of a role changed because AI/Fin handles tasks.

• Team structure/reporting changed due to AI/Fin: Org/team boundaries, team charters, or reporting lines changed due to adopting AI/Fin.

• Headcount/hiring impacted due to AI/Fin: Hiring plans, headcount, staffing coverage, or shifts/rotations changed due to AI/Fin.

• Workflow/process changed due to AI/Fin: Steps, triage/escalations, routing, or playbooks changed because AI/Fin alters the process.

• Other organizational changes due to AI/Fin: Other changes inside the organization due to AI/Fin that don’t involve a change in responsibilities, team structure/reporting lines, headcount or workflow/processes changes.

Data analysis

166 conversations were retrieved. More than 90% of all conversations report some sort of change either in their role, team, or processes due to implementing Fin, or a similar AI product, with only 13 participants reporting no changes.

Fig 1: Types of changes due to Fin/AI agent implementation. Each conversation can have more than one type of change code associated with it (M = 2.35, Med = 2, Min = 1, Max = 4, N = 166).

More specifically, after implementing Fin or a similar AI product:

• 94.58% participants reported having their processes and workflows disrupted

• 82.53% participants reported seeing their role and responsibilities change

• 27.71% participants reported changes in company headcount or hiring

• 6.02% participants reported their team structure or reporting lines changing as a result

Additionally, 16.27% participants reported a change for a different reason from the ones highlighted above (“Other organizational changes due to AI/Fin”).

Sample representativeness

The sample is representative with a confidence level of 90% and a margin of error of ±6.4% (accounting for an overall unknown population size). The individual confidence intervals for each type of change are shown in the table below.

****************

Thematic analysis

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

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

Participants overwhelmingly describe automation and AI integration transforming support workflows. 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.

• Agents’ workflows shifted to revolve around monitoring or fine-tuning AI outputs instead of responding directly. For example, support inquiries now enter Fin first, with human review only if Fin can’t resolve the issue.

• 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.

• Testing, QA, and rollout processes also evolved: teams now iterate on AI behaviour and track Fin’s accuracy as part of their regular process.

In short, AI is embedded across every step of the customer service pipeline, creating hybrid human–machine workflows and removing a large amount of repetitive manual work.

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.

• Duties shifted between humans and AI – Fin now handles refunds, triage, simple customer messages, and even parts of the sales process.

• Some participants described career transitions (e.g. from customer care to AI systems strategist, or to product/ops roles).

[...]

Original source