PLAYBOOK ATLASPlaybook Atlas

Telecommunications

AI in telecommunications: explore 30 applications.

Connect network and customer signals to specific operational reviews and service actions.

Explore alarm analysis, capacity planning, fraud referrals and service restoration for telecom operations teams.

Plan your first AI move See the 30 applications

Where the 30 applications sit

Explore the domains
  1. 10Network Management
  2. 8Customer Management
  3. 7Fraud Prevention
  4. 7Infrastructure Maintenance
  5. 3Strategic Planning

01Why AI now

Why telecommunications is moving on AI, in three figures.

Each figure links to its source.

  1. Telcos: 6% of global AI spending by 2028

    IDC forecasts global AI spending of $632 billion in 2028, with telecommunications companies accounting for 6%.

    Source: IDC

  2. AI optimization: 40% fewer degraded sites

    Ericsson says AI-based closed-loop optimization produced a 40% reduction in degraded sites.

    Source: Ericsson

  3. Customer care: up to 50% could be automated

    McKinsey estimates that as much as 50% of customer-care activity could be automated with generative AI.

    Source: McKinsey & Company

How to evaluate a first project

Define the network segment, observation window and permitted action. Compare alarm or fraud triage with current rules using missed incidents and false positives. Evaluate planning and control separately, with maintenance windows, rollback and service-quality checks before expanding automated action.

02Capability map

Where telecommunications puts AI to work, domain by domain.

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

Inside Network Management

Applications in Network Management

What the use cases ask AI to do

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

  1. Reasoning

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

    75%

  2. Generation

    Creates: text, images, code and other content.

    25%

Under 1% each: perception, optimization and agentic.

03Workflow ideas

Ways AI could change the work, in telecommunications.

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 25 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 telecommunications uses most, and when to skip them.

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

  1. 01 · 5 applications

    Anomaly Detection TS

    Detects unusual patterns and events in time series data over time.

    Use it when

    • You need to detect unexpected changes or rare events in metrics or sensor data that evolve over time.
    • Historical labeled anomalies are scarce or incomplete, making fully supervised classification difficult.
    • Real-time or near-real-time monitoring is required to prevent or mitigate incidents (e.g., outages, safety risks, fraud).

    Skip it when

    • The data is not time-ordered or temporal context is irrelevant; use general anomaly detection on tabular data instead.
    • You have abundant labeled examples of specific failure modes and the goal is to classify them, making supervised classification more appropriate.
    • The process is highly non-repeatable or one-off (e.g., bespoke projects) with no stable notion of normal temporal behavior.
    Anomaly Detection TS, in depth

  2. 02 · 4 applications

    RAG-Standard

    LLM + retrieval layer that injects relevant documents into the prompt at query time.

    Use it when

    • You need an LLM to answer questions based on proprietary or private documents that are not part of its pretraining data.
    • Your knowledge changes frequently (e.g., policies, product docs, pricing) and you want updates to be reflected without re-training the model.
    • You have a large corpus of unstructured or semi-structured text (wikis, PDFs, tickets, emails, manuals) that users struggle to search effectively.

    Skip it when

    • Your task is primarily structured data querying or analytics (e.g., reporting over a relational database) where SQL or BI tools are more accurate and efficient.
    • You have very small, static knowledge (e.g., a short FAQ) that can fit directly into a prompt or be encoded as rules without a retrieval layer.
    • You require strict, deterministic behavior with no tolerance for probabilistic errors (e.g., certain financial calculations, safety-critical controls).
    RAG-Standard, in depth

  3. 03 · 4 applications

    Time-Series

    Model and forecast time-ordered data to capture trends, seasonality, and temporal dependencies.

    Use it when

    • You need to forecast future values of a metric that is naturally ordered in time (e.g., demand, load, traffic, sensor readings).
    • The target variable exhibits temporal dependencies such as trend, seasonality, or autocorrelation that can be exploited.
    • Decisions depend on how a process evolves over time rather than on static snapshots (e.g., capacity planning, staffing, pricing).

    Skip it when

    • The data has no meaningful temporal order or the order is arbitrary (e.g., shuffled survey responses without timestamps).
    • You only need to predict static attributes or cross-sectional outcomes at a single point in time (e.g., churn at signup) without temporal context.
    • Historical data is extremely sparse, short, or irregular, making it impossible to learn reliable temporal patterns.
    Time-Series, in depth

05The applications

30 telecommunications 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. Telecom Fraud Detection HubThis application area focuses on detecting and preventing fraudulent activity across telecommunications networks, services, and billing systems. It covers threats such as SIM swap and subscription fraud, account takeover, international revenue share fraud, roaming abuse, premium-rate scams, spoofed calls, and SMS phishing. The goal is to monitor massive volumes of call detail records, signaling events, billing data, device activity, and customer behavior in (near) real time to spot anomalies and suspicious patterns before losses accumulate. AI enhances traditional rules-based fraud management by learning normal behavior, adapting to evolving attack vectors, and prioritizing the riskiest events for action. Techniques like anomaly detection, graph analysis, and sequence modeling help identify subtle, cross-channel fraud schemes that static rules miss, while generative and analytical tools assist investigators with faster triage and explanation. This reduces revenue leakage, limits customer churn, and helps operators and partners meet regulatory and national-security expectations for securing communications infrastructure.Batch → RT23 use cases
  2. Telecom Market and Network Trend IntelligenceThis AI solution uses AI to detect, model, and forecast key trends across telecom customers, networks, and technologies such as 5G. By continuously analyzing churn drivers, traffic patterns, and emerging AI/5G use cases, it helps operators make data‑driven strategic bets, optimize investments, and stay ahead of market shifts. The result is higher revenue retention, smarter capex/opex allocation, and reduced risk in long‑term technology planning.Silo → Int6 use cases
  3. Telecom Network Operations Optimization HubThis AI solution focuses on using data-driven intelligence to optimize how telecom networks are planned, operated, and maintained end-to-end. It encompasses forecasting and preventing outages, tuning capacity and routing, automating incident detection and resolution, and streamlining support workflows that depend on complex network data and documentation. The core objective is to keep networks running with higher quality of service—fewer dropped calls, faster data speeds, and higher uptime—while reducing the manual effort and expertise traditionally required to manage large, heterogeneous telecom infrastructures. It matters because modern telecom networks generate massive volumes of telemetry, logs, and customer interaction data that are impossible for human teams to interpret in real time. By applying advanced analytics and learning techniques to this data, operators can shift from reactive firefighting to proactive and even autonomous operations. This reduces operating and capital expenditures, shortens planning and troubleshooting cycles, improves customer experience and retention, and creates a more scalable foundation for new services, from 5G slices to IoT connectivity and beyond.Time-Series Anomaly Detection6 use cases
  4. Telecom Predictive Condition MonitorThis AI solution applies advanced analytics, federated learning, and predictive modeling to continuously monitor telecom infrastructure, radio links, and enterprise networks for early signs of failure or congestion. By anticipating equipment issues and network degradations before they impact service, it enables proactive maintenance, optimizes NOC operations, and reduces unplanned downtime, truck rolls, and SLA penalties.React → Pred6 use cases
  5. RAN Energy Optimization HubThis application area focuses on reducing the power consumption of mobile radio access networks (RANs) by dynamically adapting how network resources are activated, configured, and utilized. Instead of running base stations, antennas, and supporting compute at near-constant power regardless of traffic, models learn traffic patterns, quality-of-service constraints, and hardware behavior to decide when and how to switch components, carriers, and capacity up or down. The goal is to minimize energy usage while maintaining agreed service levels for users and critical services. It matters because RAN is one of the largest contributors to mobile operators’ operating expenses and carbon footprint, especially with dense 5G and future 6G deployments. As networks become more heterogeneous and complex, manual or rule-based optimization is no longer sufficient. Data-driven optimization enables operators to cut OPEX, meet sustainability and Net Zero targets, and reduce infrastructure strain, all while safely handling variable demand, from zero-traffic periods to peak loads.React → Pred5 use cases
  6. Telecom AI Fraud IntelligenceThis AI solution uses AI to detect, analyze, and report telecom fraud across carriers in real time, sharing risk signals through interoperable APIs and policy-driven data frameworks. By orchestrating network-wide fraud insights, it reduces financial losses, improves compliance, and strengthens customer trust while lowering the manual burden on fraud operations teams.Batch → RT4 use cases
All 30 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 outbound AI-voice uses and confirm consent, exemptions and suppression handling before activation.

  2. GDPR · personal data and automated decisions

    Law or regulation

    European Union / EEA · processing within GDPR scope

    Personal-data processing needs an applicable lawful basis and safeguards. Article 9 adds conditions for special-category data; Article 22 addresses certain solely automated decisions with legal or similarly significant effects. Consent is not the only lawful basis.

    EUR-Lex: GDPR, Articles 3, 6, 9, 22 and 35

    Source checked

    Project planning note

    Review the lawful basis and safeguards for in-scope customer analytics and personal data in network-support tools.

Your next step

Find your first move in telecommunications.

Ask the workspace which move fits your company. Add your size, systems and constraints, and it works from these 30 applications to a plan your team can act on.

Plan your first AI move

Rather talk it through? Book a 30-minute call