PLAYBOOK ATLASPlaybook Atlas

Customer Service

AI in customer service: explore 33 applications.

Help support teams give grounded answers and complete the actions customers actually need.

Explore policy answers, case summaries, routing and service actions, from the incoming request through escalation and confirmed resolution.

Plan your first AI move See the 33 applications

Where the 33 applications sit

Explore the domains
  1. 12Customer Interaction Management
  2. 12Service Operations Optimization
  3. 9Customer Experience Enhancement
  4. 4Support Infrastructure
  5. 1Customer Relationship Management

01Why AI now

Why customer service is moving on AI, in three figures.

Each figure links to its source.

  1. Contact-center spending: $47B by 2028

    Gartner projects worldwide contact-center spending will reach $47 billion in 2028.

    Source: Gartner

  2. AI-powered care could automate 50–70% of contacts

    McKinsey says an AI-powered care ecosystem can automate about 65% of tasks and 50% to 70% of contacts.

    Source: McKinsey & Company

  3. 85% planned conversational GenAI trials in 2025

    A Gartner survey of 187 customer service and support leaders found that 85% planned to explore or pilot customer-facing conversational GenAI in 2025.

    Source: Gartner

How to evaluate a first project

Begin with one policy question or reversible service action. Test source accuracy, account identity, authorization and escalation using realistic tickets. Track whether the requested action completed and the customer’s issue was resolved; response speed and conversation volume alone do not establish service quality.

02Capability map

Where customer service puts AI to work, domain by domain.

Pick a domain to see its applications. Each one opens a full report.

Inside Customer Interaction Management

Applications in Customer Interaction Management

What the use cases ask AI to do

Share of the customer service use cases, by the kind of work the AI does.

  1. Reasoning

    Analyzes, predicts and decides: scoring, forecasting, classifying.

    62%

  2. Generation

    Creates: text, images, code and other content.

    35%

  3. Agentic

    Acts: plans, uses tools and completes multi-step tasks.

    3%

Under 1% each: perception and optimization.

03Workflow ideas

Ways AI could change the work, in customer service.

Atlas groups these applications by the changes they propose to a workflow. These preliminary classifications help you explore ideas; each application still needs a review of its sources and operating requirements.

Workflow categories in this catalog

Share of the 24 applications assigned a category.

How to evaluate an application

  1. Start with a bounded task

    Identify the inputs, the person responsible, and the decision or output the workflow must produce.

  2. Check the support for each method

    Look for source-backed details about the specific implementation, including its limits and human review.

  3. Measure the pilot

    Record the current baseline, agree on acceptance criteria, and test on representative cases before expanding.

Explore applications

04What gets built

The approaches customer service uses most, and when to skip them.

Knowing when not to use one matters as much as knowing when to.

  1. 01 · 8 applications

    Workflow Automation

    Use AI inside orchestrated workflows to automate multi-step business processes.

    Use it when

    • When you have repetitive, rule-heavy workflows that still require human judgment or document understanding.
    • When the process spans multiple systems (ERP, CRM, ticketing, email) and manual coordination is slow or error-prone.
    • When large volumes of semi-structured or unstructured data (emails, PDFs, forms) must be processed consistently.

    Skip it when

    • When the process is highly creative, exploratory, or non-repeatable, with no stable sequence of steps.
    • When decisions are extremely high-stakes (e.g., life-critical medical decisions, major legal judgments) and regulations require full human control.
    • When you lack reliable access to the necessary systems or data sources to complete the workflow end-to-end.
    Workflow Automation, in depth

  2. 02 · 6 applications

    Generative AI

    Models that learn data distributions to generate new text, images, code, and media.

    Use it when

    • When you need to generate natural language, code, or media that would be expensive or slow for humans to produce manually.
    • When tasks involve ambiguity, open-ended questions, or creative exploration rather than strict right-or-wrong answers.
    • When you can ground the model on reliable internal or external data sources to improve factuality.

    Skip it when

    • When the task requires strict determinism, formal guarantees, or regulatory-grade traceability (e.g., certain financial calculations, safety-critical control systems) without a robust verification layer.
    • When you lack access to high-quality, authoritative data or subject-matter expertise to validate outputs in a high-risk domain.
    • When a simple rules engine, search, or template-based system can solve the problem more reliably and cheaply.
    Generative AI, in depth

  3. 03 · 4 applications

    Conversational RAG

    Multi-turn RAG that grounds chat in documents while preserving conversation context.

    Use it when

    • When users need to ask follow-up questions and refine queries over multiple turns instead of one-shot Q&A.
    • When tasks involve navigating or exploring large document sets (manuals, policies, contracts, knowledge bases).
    • When personalization or user-specific context (role, preferences, history) materially changes the correct answer.

    Skip it when

    • When interactions are single-shot queries with no need for follow-up or memory (simple FAQ search).
    • When the knowledge required easily fits into a single prompt or system message without retrieval.
    • When strict, deterministic workflows or forms are more appropriate than open-ended conversation (e.g., tax filing forms).
    Conversational RAG, in depth

05The applications

33 customer service applications, each with a full report.

The widest first, by the number of use cases each covers. Every report shows what changed, how it works, who has done it and what can go wrong.

  1. Policy Answers and Verified Service ActionsHelp a customer get the right policy answer and, when permitted, complete one verified account change. Start with source-backed agent drafts or bounded self-service. For a service action, bind approval to current account state, execute through an enforcing API and confirm only what readback proves. Keep unresolved cases with a named owner.RAG (Retrieval-Augmented Generation)96 use cases
  2. Customer Service Sentiment Intelligence WorkflowsAI models analyze customer messages, tickets, and calls to detect sentiment, emotion, and urgency across every service interaction. These insights help teams prioritize at‑risk customers, tailor responses in real time, and surface systemic issues driving dissatisfaction. The result is higher CSAT, faster resolution, and reduced churn through data-driven customer care.Batch → RT15 use cases
  3. AI Customer Interaction OrchestrationAI Customer Interaction Orchestration centralizes and automates customer-service conversations across chat, messaging, and other digital channels. It uses conversational agents to resolve standard inquiries, guide complex cases, and adapt responses to each customer’s context and history. This improves customer satisfaction while reducing support costs and freeing human agents to focus on high‑value issues.Expert → AI13 use cases
  4. AI Support Ticket PrioritizationThis AI solution uses AI to automatically score, prioritize, and route customer service tickets across channels like email, chat, and helpdesk platforms. By intelligently triaging issues based on urgency, impact, and customer context, it ensures the right agent handles the right case at the right time, reducing response times and improving customer satisfaction while minimizing manual queue management.Batch → RT12 use cases
  5. AI-Accessible Customer SupportThis AI solution covers AI tools that make customer service channels more accessible, responsive, and consistent across help desks, IT support, and omnichannel CX platforms. These systems automate routine inquiries, surface the right knowledge instantly, and adapt interactions to users’ needs, improving resolution speed and service quality while reducing support costs.Expert → AI9 use cases
  6. AI Support Ticket OrchestrationAI Support Ticket Orchestration automatically classifies, routes, prioritizes, and updates customer service tickets across platforms like Zendesk. It ensures that each issue reaches the right agent with the right priority, reducing handling time, improving response and resolution SLAs, and boosting customer satisfaction while lowering operational overhead.Silo → Int9 use cases
All 33 applications, A to Z

Work with us

The catalog shows what is possible. A sprint decides what you should fund.

Reading every report still leaves the hardest question open: which move is worth funding first, here, given your constraints. That is the sprint: two weeks, fixed scope, and a candid answer either way.

Book a 30-minute callHow the sprint works

Two weeks Fixed scope A candid fit-or-no-fit answer

  1. 01

    Three ranked opportunities

    A decision-ready shortlist scored against value, feasibility, evidence, risk, and time to proof.

  2. 02

    One pilot-ready workflow

    A bounded operating design with actors, handoffs, data needs, controls, and success measures.

  3. 03

    A defensible 90-day plan

    Build-versus-buy guidance, ROI assumptions, risk treatment, sequencing, and executive decisions.

06Rules & standards

Rules and standards to check, with their scope and sources.

Selected US and EU references for these workflows. Applicability depends on jurisdiction, organization, data and intended use. The planning notes are Atlas editorial guidance; they are not official implementation-time estimates or an exhaustive list of obligations.

  1. TCPA · artificial or prerecorded voice calls

    Law or regulation

    United States · calls within TCPA scope

    The FCC treats AI-generated human voices as artificial or prerecorded voices under the TCPA. Consent requirements and exemptions depend on the call, recipient and purpose.

    FCC: Declaratory Ruling FCC 24-17

    Source checked

    Project planning note

    Classify each outbound calling workflow and retain the required consent and suppression records.

  2. EU AI Act · interaction and content transparency

    Law or regulation

    European Union · covered providers and deployers

    Article 50 assigns different transparency duties for AI interactions and certain generated or manipulated content. Applicable exceptions and the provider/deployer role matter; these duties are separate from high-risk classification.

    European Commission: Article 50 transparency guidelines

    Source checked

    Project planning note

    Check interaction disclosures for the actual channel, audience and provider/deployer role.

07The AI graveyard

One failure, and the lesson it paid for.

  1. DPD AI Chatbot Gone Rogue

    2024 · Source

    Viral PR disaster

    What went wrong

    AI chatbot convinced to criticize its own company and swear at customers. Lack of guardrails enabled brand-damaging outputs.

    The lesson

    Customer-facing AI needs robust output constraints and testing

Your next step

Find your first move in customer service.

Ask the workspace which move fits your company. Add your size, systems and constraints, and it works from these 33 applications to a plan your team can act on.

Plan your first AI move

Rather talk it through? Book a 30-minute call