AI in Telecom

–25% subscriber churn rate and +15% CLTV

The operator processed billions of data points from 40 million users with an AI platform to identify churn-risk behaviors with 85% accuracy. This enabled delivery of personalized promotions that increased customer lifetime value by 15%.

–22% in regulatory complaints and +14 NPS points

The company implemented precise transcription bots and speech analytics to audit the quality of remote sales and support. The system ensured employees follow rigorous compliance protocols, improving sales conversions by 7% and boosting transactional NPS.

–33% in information search time for 100,000 agents

Through the generative AI-based 'Ask AT&T' platform, more than 100,000 employees process 9 billion tokens daily to find immediate answers to complex customer issues during calls, generating millions in savings by reducing average handle time (AHT).

–60% in call center volume in just six months

The company developed an AI-based Customer Frustration Index (CFI) to monitor activations and billing. By applying self-healing to detected problems, it reduced frustration levels by 80% and ensured nearly 100% accuracy in digital payments.

FCR from 70% to 90% with 1 million monthly interactions

The company empowered agents with 'SuperAgent' and deployed TOBi, a conversational system handling over one million monthly interactions. By integrating autonomously with CRM and billing systems, they eliminated long wait times.

–80% in average resolution time for technical queries

The telco deployed natural language-trained AI agents to transform its mass contact center experience. This automation handles repetitive technical queries, saving analysts tens of hours per week of research.

Friction eliminated in rural technical support with Amazon Bedrock

This internet provider modernized operations by integrating an Amazon Bedrock chatbot that takes on multiple roles — from sales agent to helpdesk. It performs real-time network diagnostics and provides personalized responses, freeing humans for proactive sales.

–75% in monthly churn rate

The company deployed an AI solution to process 16 billion monthly call records and automatically generate 220 million personalized offers. This contextual approach transformed manual marketing into a retention engine that reduced churn by three-quarters in just three months.

+62% increase in self-service and –38% in human transfers

The 'Veronika' virtual assistant, built on Azure AI, scaled its user base from 1.3 to 2.2 million in six months. This advanced conversational AI resolves queries automatically, dramatically reducing the load on human contact centers.

99% improvement in customer journey testing time

Using IBM watsonx.ai, the operator went from 6.5 hours to under a minute for testing each new TOBi assistant journey using synthetic simulations. This accelerates deployment of new support features across multiple markets.

Direct churn reduction through proactive home resolution

Through data analytics platforms, the company integrates network telemetry to anticipate Wi-Fi or set-top-box issues in subscribers' homes. The support team identifies at-risk subscribers and resolves the incident before the customer files a complaint.

Massive scale to 10 million A-Dot subscribers in under 2 years

The 'A-Dot' assistant, trained with language models specific to the Korean market, delivers an experience informed by the user's routine and location. This allowed the operator to triple its active user base, using AI as an acquisition engine.

+85% increase in customer satisfaction (CSAT) and +40% in cross-selling

The operator deployed chatbots that reduced response time from two hours to under 30 seconds. Beyond improving satisfaction, they generated a 40% increase in cross-selling through proactive behavior-based product recommendations.

–40% reduction in customer abandonment through convergence

The operator uses AI models to identify mobile customers without home broadband and proactively offer them converged packages. The AI identifies the exact moment and the right offer, maximizing retention in its installed base.

+3% to +7% ARPU increase and –20% to –30% churn reduction

Using generative AI in the cloud to scale customer experiences, the company analyzes real-time usage patterns to offer the exact tariff plan or add-on service each customer needs, directly impacting revenues.

–15% in average handle time (AHT) for 22,000 employees

The company deployed an AI-based transformation for its 22,000 employees. Integration of agents in customer service has allowed complex queries to be resolved efficiently, increasing efficiency and improving NPS.

+7% direct increase in sales conversion

Deploying an AI-powered bot to assist remote salespeople in real time, the system transcribes conversations and offers assertive suggestions. This made commercial calls more precise, improving close rates.

3.8 TB of data processed daily without human intervention

The company implemented a cloud AIOps framework to manage massive network incidents, reducing analysis processes from days to hours. This architecture runs 500 GB simulations concurrently, proactively improving Net Promoter Score by preventing failures.

–80% in operating costs and 100% CapEx savings on drive tests

Replacing costly manual drive tests with AI that analyzes billions of mobile terminal reports, the company automated network monitoring, far exceeding the ROI of legacy geolocation tools.

+17% direct increase in spectral efficiency

To manage the massive complexity of its data, the telco deployed AI that accelerates network optimization processes by 60%. This ensures superior quality of experience by maximizing existing physical infrastructure without requiring additional spectrum.

–80% reduction in network outages for end users

By integrating predictive models into its Network Operations Center (NOC), the operator automated fault detection. The system cut the time to resolve outages by 50%, allowing issues to be mitigated before they massively affected subscribers.

+30% improvement in wireless network throughput

The company applied mathematical models and Transformer architectures (similar to ChatGPT) directly to its radio access network (RAN) layer. In live environments, the solution managed traffic and interference outperforming all traditional monitoring methods.

+24% increase in 5G downloads and –20% in latency

In collaboration with technology partners, the operator deployed a software-based radio access network with AI algorithms embedded in the physical layer. This implementation demonstrated that AI is essential for delivering on advanced 5G speed promises.

–75% savings in AI model energy consumption in the network

Using advanced compression techniques, the company reduced the size of its large language models by 80% without losing accuracy. This allowed AI to be embedded at the network edge, avoiding costly hardware purchases and minimizing carbon footprint.

+83% improvement in incident resolution time

Using a generative AI platform, the operator automated infrastructure log analysis, identifying root causes in under a minute (versus the previous hour), ensuring reliability for its 150 million users.

+25% improvement in spectral efficiency

The operator used AI algorithms to automate Remote Electrical Tilt (RET) adjustments on its antennas, moving from weekly to daily adjustments. This increased call handover success by 97%.

14% energy savings in 5G networks with MIMO Sleep Mode

The MIMO Sleep Mode solution predicts traffic patterns and automatically powers down antenna components during low-demand periods, then instantly wakes them as demand rises. This reduces carbon footprint and operational electricity costs simultaneously.

180,000 technical visits avoided in a single year

The operator implemented intelligent diagnostics to remotely test fiber (FTTH) routers. By identifying and resolving line faults without dispatching technicians, they saved millions in logistics and cut transportation emissions.

Level 4.0 autonomous network maturity score

The operator reached one of the highest levels in the industry by deploying AI for autonomous detection and resolution of IP network problems. The system manages demand spikes while ensuring continuity without human intervention.

–40% reduction in CAPEX for 5G site deployment

By automating network design, the time needed to plan sites was reduced from months to just two hours. AI models enabled high-performance infrastructure to be built at a fraction of the traditional cost.

90% improvement in engineer and support agent effectiveness

Using AI to synthesize incident histories, the operator reduced follow-up contacts by 20%. The AI provides immediate context, enabling accurate diagnostics from the very first interaction.

87% of corporate customers report positive ROI in just 1 year

In industrial deployments, the combination of AI-driven private networks at the edge enabled automation of predictive maintenance processes, reducing B2B customer operating costs by at least 11%.

–35% reduction in critical network incidents

Monitoring both the radio network and core with AI, algorithms detect performance anomalies before they cause failures, reducing overall network problems by up to 60%.

0.6-point improvement in network autonomy in twelve months

The Asian operator accelerated its progression from 3.2 to 3.8 in autonomous network scoring, demonstrating how AI models dramatically reduce manual intervention in field operations.

35,000 hours of work saved annually

Developed 12 applications to automate audit of sales and field services. These tools boosted employee productivity by 25%, achieving structural agility and reducing OPEX.

$50M annual OPEX savings — from 4 hours to 15 minutes per proposal

The company used AI to reduce complex sales proposal preparation time from 4 hours to just 15 minutes, freeing its commercial force to focus exclusively on closing contracts.

10x faster incident response time

The operator deployed AI to automatically classify and resolve more than 200,000 system reports. Processing 10,000 monthly reports, autonomous scalability massively reduced human error.

+40% improvement in operational efficiency with unified analytics

By unifying data from multiple business systems, the company allowed non-technical users to make AI-based decisions without depending on data engineers, streamlining administrative tasks at the corporate level.

$20M in direct savings by unifying multi-brand support

The company unified support for all its subsidiary brands under a conversational AI suite. Deflecting calls to intelligent self-service channels generated eight-figure savings in a single fiscal year.

–70% reduction in CDR-to-bill gap

Continuous analysis of event streams with generative AI allowed operators to detect micro-revenue leaks, ensuring every complex 5G service is billed correctly in near real time.

+35% productivity and –65% time on routine tasks

Deploying copilots for the corporate workforce allowed staff to spend less than half the usual time on office processes, focusing their capabilities on solving profitable problems.

+60% improvement in the detection-to-billing resolution cycle

The AI translates technical fraud findings into actionable business language, making financial analysts 40% more productive at identifying and correcting revenue gaps quickly.

34.9% revenue growth in AI infrastructure (AIDC)

Facing commoditization of mobile services, the operator pivoted to providing sovereign AI infrastructure for corporate customers. Its AI-optimized data centers grow at double digits annually.

$200M annual savings modernizing legacy systems with AI

Consolidating systems and applying AI to automate internal processes — from staff ticket resolution to device supply chain — the operator transformed its fixed cost structure.

–30% in network log storage costs

The operator implemented a data infrastructure that reduced information ingestion time to 24 hours. This allowed them to use AI to monitor vibration and temperature patterns in physical equipment, detecting failures before critical breakdowns and outages.

$450,000 annual training savings and +127% in sales KPIs

Using algorithms to identify knowledge gaps in 30,000 frontline employees, the company delivered hyper-personalized microlearning. In 60 days, baseline knowledge rose 8% and key sales performance indicators jumped 127%.

300 million euros in value creation with 150 AI assistants

The multinational deployed over 150 generative AI-based assistants now used by 80% of its workforce. They automated everything from drafting job offers to B2B management, allowing teams to focus exclusively on innovation.

£230,000 saved per corporate tender processed

Using AI to automate response generation for mega-tenders, the company compressed what used to be a year of human work into just a few days, eliminating the need to hire external administration firms.

48.9% EBITDA margin — record high in the region

Through an aggressive data analytics-driven transformation program, the operator restructured its costs and accelerated migration to converged services. This algorithmic efficiency allowed them to operate with superior profitability versus much larger competitors.

+40% improvement in productivity detecting revenue leaks and fraud

Leveraging Amazon SageMaker, this operator automated detection of complex fraud schemes (SIM boxing, wangiri). The AI spots the anomaly within days — not weeks — and drafts the alert in business language to immediately stop revenue bleeding.