In-house Telecommunications Spain Company newsroom

Telefónica

Predictive Maintenance · Security and Compliance

Traditional inspection, monitoring and protection of critical infrastructure required sending staff into the field, consuming significant time and money, slowing incident detection and increasing operational risk for assets such as power lines, pipelines and forested areas.

70% or 80% of the total cost of a mission
in a matter of hours
in a matter of minutes
over 95 per cent of the population

Reported impact

Business outcome

Telefónica reports faster remote inspection and monitoring, earlier anomaly detection, lower pilot travel effort and costs, safer operations, and improved real-time decision-making for critical infrastructure management.

  • Cost Reduction
  • Productivity Improvement
  • Risk Reduction

Solution

Telefónica has built a Drone as a Service operating model for critical infrastructure inspection that combines its drone service, Drone-in-a-Box technology, 5G network, edge computing, computer vision and artificial intelligence. At the ingest layer, drones stationed at the mission site capture high-definition, thermal and visual imagery from assets such as power lines, pipelines and forested areas while a remote pilot operates them from a control centre. The 5G network links the field location to the control centre and carries video and telemetry with licensed-spectrum connectivity and encryption. Rather than sending raw imagery to a central server, edge computing hosts the computer vision and AI workloads close to the network edge, where they detect anomalies, structural defects and intrusions in near real time. The system then filters and prioritises findings, sending conclusions and alerts to human operators for supervised decisions, regulatory compliance and faster response.

Data flow

At the mission site, drones and on-board cameras capture thermal and visual imagery from critical assets. The feeds travel over Telefónica's 5G network to edge computing resources near the network edge, where computer vision and AI analyse images in real time instead of sending them to a central server. The platform returns prioritised conclusions and alerts to remote pilots and operators in control centres for validation and response.

Solution architecture

6 components · 4 layers
  1. Ingest
    • drone service Provides the remotely operated drone capability that captures inspection imagery over long distances for critical infrastructure missions.
  2. Compute
    • edge computing Runs image analysis close to the network edge so processing happens in real time with minimal latency instead of at a central server.
    • computer vision Analyses thermal and visual images to identify anomalies, structural defects and security intrusions.
    • artificial intelligence Filters, prioritises and converts detected events into conclusions, alerts and recommendations for operators.
  3. Orchestration
    • Drone-in-a-Box technology Keeps the drone on site in standby mode so missions can launch within minutes without sending a pilot to the location.
  4. Uncategorized
    • 5G network Connects the remote control centre to the drone mission site and carries encrypted connectivity for flight operations.

Architecture clues

  • AI runs on-board or, more precisely, at the network edge to avoid overloading the drone device.
  • Human oversight remains part of the operating model; AI augments operators rather than replacing them.
  • Telefónica highlights licensed-spectrum 5G connectivity and encryption as a cybersecurity advantage over Wi-Fi or uncontrolled radio frequencies.
  • The article describes future scaling to fleets of drones managed from centralised control centres across different parts of the country.
  • The design supports BVLOS drone operations subject to AESA authorisations.

Evidence from the source

"Previously, inspecting critical infrastructure — such as a power line, a pipeline or a forested area — involved deploying staff, time and money."
"The 5G network enables a remote connection between the control centre and the location where the drone flight is to take place"
"The drone is not the star of the show; it is the entire ecosystem that makes it possible: 5G, edge computing, computer vision and artificial intelligence, all integrated."
"We don’t send images for someone to review; we send conclusions and alerts."
"We eliminate the pilot’s travel time, which in many cases accounts for 70% or 80% of the total cost of a mission."
"thanks to edge computing, data analysis does not have to travel to a central server; it takes place at the edge of the network, in real time and with minimal latency."
"with high-definition video and AI analysing the images captured by the camera as it flies."
Transparency Evidence & methodology 85/100 reference value · 94% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value85/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact15/25
    1 metric describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence94%
  • Source authority25/25
    The deployment is described by the adopting organization or a government source.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    7 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Telefónica

    Telefónica customer story

    First-party
    https://www.telefonica.com/en/communication-room/blog/drones-artificial-intelligence-critical-infrastructure-telefonica/
    Source type
    Customer-published story
    Published
    Jul 30, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Jul 30, 2026 · Telefónica · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.