Episode-XXV: Satisfaction is All You Need

Authors: Ian Hood, Fatih E. NAR, Ranny Haiby, Robert Shaw, Rimma Iontel, Hanen Garcia, Volker Tegtmeyer

Satisfaction Is All You Need

Introduction

If the telecommunications industry had a relationship status with customer satisfaction, it would be “It’s complicated.” While operators once measured success by how many bars you had on your phone, today’s kingmaker is Net Promoter Score (NPS), which determines whether customers recommend your service or just recommend therapy to your customer service agents.

The telecom landscape is caught in a perfect storm; expanding networks, systems getting more complex with each mobile network generation (xG headed to >=6G) implementation, diverse gritty consumption plans, and competitors who keep promising the impossible (unlimited truly unlimited data, anyone?). Traditional approaches to these challenges are about as effective as trying to fix network congestion by politely asking customers to stop using TikTok.

Throughout 2024 and now, fast-paced 2025, we have witnessed telecom’s AI journey evolve from small individual projects to large-scale AI adoptions that are key to strategic business directions. The industry finally understands that AI isn’t just for impressive presentation decks; it’s part of the critical infrastructure, needed to understand operations better, anticipate customer needs, and transform those infamous “please stay on the line, your call is important to us” systems into services and interactions which customers might enjoy using while trusting it, or even better, never feel the need to call in at the first place.

This article explores the architectural patterns and practical implementation strategies reshaping how service providers embrace the value of AI. It is based on our field experiences and recent observations and discussions with customers at big industry events such as AWS ReInvent-24 and MWC 2025. Having implemented numerous telecom AI use-cases through the open-experiments initiative, we’ve watched the industry mature from theoretical “wouldn’t it be nice if” conversations to pragmatic implementations that balance innovation with the rigorous demands of networks that simply cannot go down.

The focus has shifted to solutions that deliver tangible value, measured not by how many GPUs you’ve bought (and keep in a warehouse as you have no place nor power to use them with), but by the positive impact via meaningful AI implementations to improve customer experience.

In this blog, we’ll look back at the AI adoption journey and then walk through a proposed five-levels pragmatic achievement plan that provides a practical roadmap for telecom operators at any stage of their AI journey. We’ll examine how each phase builds upon the previous one, from leveraging existing search infrastructure to sophisticated dynamic context assembly, and evolving to autonomous networks with ai agents. We’ll also explore integration with existing operational systems, security architectures, deployment approaches, and the critical but often overlooked organizational changes and skills necessary for success. Throughout, we’ll stay focused on telecom-specific considerations that address the unique challenges the telecom industry is facing.

@ The end of the day, “Satisfaction” really is all your customers need…

The AI Adoption Journey

The telecommunications industry’s adoption of AI has followed an evolutionary path that parallels the industry’s digital transformation journey. This evolution can be understood through three distinct stages, each representing a shift in approach, capability, and value creation.

Stage-1 [The Outsourcing Era]: The Age of Dependency

During this initial stage, service providers were almost entirely dependent on external AI services (for example, Contact Center AI (CCAI) software as a service, hosted on cloud) within narrowly defined domains, such as customer care. This was resulting in limitations, such as:

• Limited API Access: Operations were restricted to cloud-based API access of proprietary hosted models.
• Minimal Control: Providers had little influence over model behavior or customization.
• Cost Inefficiencies: High per-token pricing models created unpredictable expenses that scaled poorly.
• Innovation Constraints: Dependency on external roadmaps limited telecom-specific innovation.

This stage resembled the frustrating customer experience of calling technical support only to be met with scripted responses that failed to address unique problems. Telcos were paying premium prices for generic AI solutions that weren’t tailored to their specialized needs.

Stage-2 [The Independence Movement]: Breaking Free from Vendor Lock-in

The second stage marked telecommunications providers’ push toward greater autonomy:

• Open-Source Adoption: Introduction of powerful, adaptable open-source models like LLaMA, Mistral, and DeepSeek.
• Internal Capability Building: Development of in-house expertise for model fine-tuning and deployment.
• Cost Structure Transformation: Shift from variable per-token costs to infrastructure investment.
• Telecom-Specific Customization: Ability to tailor models for industry-specific terminology and workflows.

This period mirrored telecommunications operators’ historical realization that customizing their own network solutions often delivered better results than out of box vendor offerings. The independence gained during this phase provided greater control and customization potential, but came with the challenge to build up expertise to deal with significant technical complexity.

Stage-3 [The Practical Reality Check] : Balancing Innovation with Operational Realities

The current stage reflects a maturation in approach, focusing on sustainable AI adoption :

• Hybrid Deployment Models: Strategic balancing of self-hosted and cloud-hosted models based on use-case requirements.
• Operational Integration: Seamless incorporation into existing enterprise workflows and systems.
• Performance Optimization: Fine-tuning of deployment architectures to maximize value while controlling costs.
• Governance Framework Development: Implementation of controls that address the regulatory and security requirements unique to service providers.

Today’s approach acknowledges that effective AI deployment in telecommunications requires thoughtful architecture that addresses the industry’s unique challenges:

• Stringent regulatory constraints across multiple jurisdictions.
• Extraordinary data sensitivity including customer and network information.
• Integration with mission-critical systems where failures trigger C-suite escalations.
• Need for continuous operation in environments that cannot tolerate downtime.

[Open vs. Proprietary]: The “Know What’s Inside”

The debate between open-source and proprietary foundation models represents one of the most consequential decisions that telecommunications providers are facing. This choice parallels the fundamental difference between cooking from scratch versus consuming a prepackaged meal, where one offers complete transparency and control, while the other offers convenience at the cost of true knowledge and healthy businesses.

In the telecommunications context, this debate is particularly significant because of the industry’s strict regulatory requirements and the critical nature of its infrastructure. The choice between open and proprietary approaches fundamentally affects everything from operational control to compliance reporting:

Table-1 Open vs Close AI Model Comparison

The engineering teams within telecommunications organizations often advocate for open-source approaches, valuing visibility and control over the “engines” powering their business systems. Meanwhile, finance departments frequently question the substantial up-front infrastructure investments required, particularly when considering the rapid evolution of hardware requirements. This tension creates a push-pull dynamic that must be carefully managed through; Total Cost of Ownership (TCO) calculations & Return of Investment (ROI) plans and staged implementation approaches.

The industry consensus increasingly recognizes that a strategic hybrid approach delivers optimal value. Critical workloads involving customer data or network operations benefit from on-premises deployments using open-source models, ensuring maximum control, compliance, and customization. Meanwhile, general-purpose tasks with lower sensitivity such as generating initial drafts of corporate communications or processing non-sensitive documentation remain suitable candidates for API-based approaches that minimize operational overhead.

This balanced approach allows telecommunications providers to maintain control where it matters most while leveraging the convenience of cloud services where appropriate. The key is making these decisions strategically rather than arbitrarily, with clear governance frameworks determining which path is followed for each use-case.

Pragmatic Achievement Plan

A key insight from successful telecommunications AI deployments is the value of a progressive achievement strategy. Rather than attempting an immediate comprehensive transformation (which has the success rate of a network upgrade during peak hours), leading service providers are adopting different levels based on their needs and capabilities at a given time in their applied AI journey.

Level-1: Leverage What You Already Have

Most telecommunications operators already maintain extensive document repositories and search systems, often filled with documentation no one can find when they actually need it. The initial phase enhances these existing investments by connecting traditional Term Frequency-Inverse Document Frequency (TF-IDF) search capabilities with foundation models through Retrieval-Augmented Generation (RAG).

Figure-1 Term Frequency (TF) Based Search — RAG

BM25 (Best Matching 25), the algorithm powering most modern enterprise search platforms, like Elasticsearch, is the perfect bridge technology for telecom RAG implementations. For telecom operators, this means their existing search infrastructure can be connected to AI models with minimal modification, leveraging search technology they’ve already deployed.

What makes this implementation practical is its ability to work with existing infrastructure that most operators have already invested in. A telecom provider can typically implement this approach in a matter of weeks with lower computational requirements than embedding-based approaches, delivering immediate value while minimizing upfront investment. When a network engineer queries “What troubleshooting steps should I take for intermittent connectivity on Vendor-X 5G RAN equipment?” the BM25 algorithm identifies the most relevant technical documents, which are then fed into a foundation model that synthesizes a coherent answer.

Common Use-Cases:

• Technical support knowledge retrieval.
• Network troubleshooting documentation access.
• Policy and procedure reference.
• Equipment installation and maintenance guidance.
• Historical incident review and analysis.

This approach delivers significant improvements for many telecom use-cases, particularly technical support and documentation search, while working within the constraints of keyword-based retrieval. It’s the equivalent of teaching your existing search system to not just find documents, but to extract and combine the exact information needed from them.

Implementation Roadmap:

1. Inventory existing search infrastructure.
2. Configure RAG pipeline with BM25 retrieval.
3. Integrate with foundation model API.
4. Test and tune with domain-specific prompts.

Level-2: Bring Models In-House

As usage grows and regulatory requirements demand greater control (privacy officers tend to get nervous about customer data leaving the building), telecommunications companies implement on-premises model deployment. This transition marks a significant shift from consuming AI as a service to treating foundation models as part of a core infrastructure.

Figure-2 AI-n-Action Architecture

The architecture above represents a production-grade on-premises deployment that balances flexibility with operational control. Each component serves a specific purpose in the AI value delivery pipeline:

Foundational Model Servers provide the core reasoning capabilities, with multiple models deployed to handle different workloads:

• LLaMA-3 (8B/70B): Open-source models that balance performance with reasonable hardware requirements.
• Mistral (7B/8x7B): Efficient models with strong reasoning capabilities for technical content with mixture of expert (MoE) deployment options.
• DeepSeek-R1 (70B/671B): Specialized models for complex telecom reasoning tasks with optimized MoE.

Retrieval Systems enhance model outputs with telecom-specific knowledge:

• Elasticsearch: Leveraging existing BM25-powered search infrastructure.
• Quadrant/PGVector: Vector databases that enable semantic search capabilities as implementations mature.

Orchestration Services manage the flow of requests and optimize performance:

• Request Router: Directs queries to appropriate models based on complexity and priority.
• Context Assembly: Gathers relevant information before model invocation.
• Caching & Optimization: Reduces computational load through response storage and prompt optimization.

In the AI inference community, there is notable consensus around vLLM as the preferred serving technology for on-premises deployments. Its PagedAttention mechanism provides significant memory efficiency and throughput advantages over alternatives, making it particularly well-suited for high-demand environments typical in telecommunications, where “system overload” isn’t just a technical term but a daily operational state.

The vLLM advantage comes from its innovative approach to attention computation, the most memory-intensive part of transformer models. Rather than pre-allocating full-sized memory blocks, PagedAttention dynamically manages memory in smaller chunks (pages), significantly reducing GPU memory requirements while maintaining throughput. For telecom operators, this means serving up to 4x more concurrent requests with the same hardware compared to traditional serving frameworks.

vLLM’s capabilities are especially powerful when combined with modern model architectures designed for computational efficiency:

• Mixture of Experts (MoE) Optimization: vLLM efficiently handles MoE model architectures, where selective activation of expert pathways already reduces computation requirements. The combination creates a multiplicative efficiency effect, with only the necessary expert parameters loaded into the optimized memory pages, dramatically increasing inference throughput for telecom workloads.
• Selective Attention Mechanisms: For models featuring conditional computation of attention heads, vLLM’s architecture provides additional efficiencies by only allocating memory resources to the actively engaged attention components. This is particularly valuable for telecom-specific tasks where attention can be selectively focused on relevant aspects of network data or customer communications.
• Quantization Integration: vLLM supports various quantization schemes, further extending the efficiency gains. Telecom operators can deploy quantized models (8-bit, 4-bit) through vLLM, enabling deployment on less powerful hardware while maintaining service quality through optimized memory usage.

Figure-3 Impact of MoE with vLLM use

This architecture (powered with vLLM for inference) supports critical telecom-specific requirements that cloud APIs cannot deliver:

• Data Sovereignty: Customer and network data never leaves controlled environments.
• Customization: Models can be fine-tuned for telecom terminology and workflows.
• Latency Control: Predictable performance without dependency on external network conditions.
• Cost Predictability: Fixed infrastructure costs rather than unpredictable per-token pricing.

Implementation Considerations:

• Hardware sizing based on expected inference volume.
• Model selection appropriate for specific telecom use-cases.
• High-availability design for critical workloads.
• Monitoring and observability for performance optimization.

For network operations teams, this translates to AI assistants that can analyze network traces, interpret alarm patterns, summarize relevant key performance indicators (KPIs), and generate configuration snippets without sending potentially sensitive operational data to external providers.

Level-3: Enhancing The Retrieval

As usage scales and more complex use-cases emerge (like deciphering what customers actually mean when they say “my internet is slow”), providers enhance retrieval capabilities with embedding-based approaches. This evolution marks the shift from lexical matching to semantic understanding, the difference between finding documents containing specific words and finding documents expressing relevant concepts.

Figure-4 Enterprise Knowledge Systems Flow

The embedding-based retrieval architecture introduces two parallel processing pipelines that transform both documents and queries into high-dimensional vector representations:

Document Processing Pipeline:

• Document Ingestion: Captures content from multiple sources including technical documentation, customer interactions, network logs, and equipment manuals.
• Chunking: Intelligently segments documents into digestible pieces optimized for retrieval (typically 512–1024 tokens per chunk).
• Embedding Generation: Transforms each text chunk into a dense vector representation (typically 768–1536 dimensions) that captures semantic meaning.
• Vector Storage: Indexes these embeddings in specialized vector databases optimized for similarity search.

Query Processing Pipeline:

• User Query: Captures natural language questions from technical staff or customer service representatives.
• Query Embedding Generation: Transforms the query into the same vector space as the documents.
• Hybrid Search: Combines traditional keyword search with vector similarity to leverage the strengths of both approaches.
• Context Assembly: Constructs a coherent package of the most relevant information for the foundation model.

Implementation Considerations:

• Text Embedding Model Selection: E5, BGE, and GTE provide strong performance for telecom content without licensing constraints.
• Vector Database Selection: Planning adequate storage based on data capacity and needed surrounding facilities/capabilities.
• Retrieval Latency: Optimizing for responsive user experience.
• Hybrid Retrieval Weighting: Balancing vector and keyword approaches.

This approach significantly enhances the quality of information retrieval, particularly for complex technical queries and nuanced customer support scenarios that term-based approaches struggle with. For telecom operators, embedding-based retrieval delivers several critical advantages:

• Intent Recognition: The system understands that a customer reporting “spotty coverage at home” and another reporting “calls keep dropping in my living room” are describing the same fundamental issue, despite using different terminology.
• Cross-Domain Connections: Technical documentation often exists in silos (radio access network, core network, customer premises equipment), but embedding models can identify relevant connections across these domains.
• Multilingual Support: Many telecom operators support multiple languages, and embedding models can find relevant information across language boundaries.
• Handling Telecom Jargon: The industry’s alphabet soup of acronyms and technical terms (MIMO, OFDMA, QAM, etc.) becomes less problematic as embeddings capture the relationships between these specialized terms.

While the implementation requires more sophisticated infrastructure than term-based approaches, the technology has matured significantly. Open-source embedding models have democratized access to high-quality embeddings without requiring specialized AI expertise. Modern vector databases provide scalable, production-ready platforms for storing and querying these embeddings with millisecond-level response times.

The result is a telecom knowledge system that feels almost prescient, anticipating the information engineers and customer support representatives’ needs before they even finish formulating their questions.

Level-4: Get Smart About Context

The most advanced implementations incorporate intelligent context selection based on query analysis. This phase represents the evolution from “retrieve whatever seems relevant” towards “strategically assemble exactly what’s needed,” a critical distinction for complex telecom environments where context requirements vary dramatically by query type.

Figure-5 Evolving the Context Build-Up

Dynamic Context Assembly introduces a sophisticated pipeline that transforms how information is prepared for foundation models:

Query Analysis: Examines the user’s question to determine its characteristics, including:

• Complexity level (simple fact vs. multi-step reasoning).
• Domain categories (network, customer service, billing, etc.).
• Technical depth required (executive overview vs. engineer-level detail).
• Time sensitivity (historical context vs. current state only).

Intent Classification: Categorizes the query into specific intent types, such as:

• Troubleshooting (“Why is this cell site experiencing packet loss?”).
• Configuration (“What settings should I use for this network element?”).
• Explanation (“How does carrier aggregation work?”).
• Comparative (“What’s the difference between NSA and SA 5G deployments?”).
• Procedural (“What steps should I follow to upgrade this component?”).

Knowledge Source Selection: Identifies the optimal information sources to answer the specific query type:

• Technical documentation for equipment specifications.
• MELT Observability Data (Metrics, Events, Logs, Traces) for operational troubleshooting.
• Customer interaction history for service issues.
• Regulatory guidelines for compliance questions.
• Training materials for conceptual explanations.

Multi-Source Context Assembly: Strategically combines information from different sources to create a comprehensive context package:

• Prioritizes most relevant sources based on intent classification.
• Balances technical depth with query requirements.
• Incorporates both historical context and current state when needed.
• Structures information to facilitate the reasoning path needed for the answer.

Context Window Optimization Techniques:

• Structured summarization reduces document size while preserving key information.
• Multi-hop retrieval breaks complex queries into sequential sub-queries.
• Adaptive prompting adjusts model instructions based on query classification.
• Information density prioritization maximizes relevance per token.

This approach maximizes the value of retrieval systems by dynamically assembling the most relevant context based on query characteristics, significantly improving response quality for complex telecom scenarios.

For telecom operators, dynamic context assembly delivers transformative capabilities:

• Optimized Context Windows: Foundation models have finite context windows (typically 8K-32K tokens). Dynamic assembly ensures this limited space contains only the most relevant information, rather than filling it with marginally useful content.
• Problem Isolation: For troubleshooting queries, the system can automatically gather related alarms, configuration changes, and similar past incidents, mimicking how expert network engineers diagnose issues.
• Cross-Domain Reasoning: For questions spanning multiple domains (e.g., “How will this network change impact our business customers?”), the system assembles context from technical, business, and customer systems.
• Adaptive Detail Levels: Responses adjust automatically to the user’s role and expertise, providing concise executive summaries for leadership and detailed technical explanations for engineers.

The implementation of dynamic context assembly represents a significant maturation in AI architecture, moving from static, one-size-fits-all approaches to intelligent, query-aware systems that reflect how human experts actually solve complex telco problems.

Level-5: Agentic Approach (The Evolution to Autonomous Networks)

Building on the sophisticated context assembly capabilities developed in Level-4, leading telecommunications providers are now implementing a true agentic approach that represents the next frontier in AI architecture.

This evolution marks the transition from assistive AI systems to autonomous agents that can take independent action within carefully defined operational boundaries.

The transition (Reactive -> Proactive) to agentic systems in telecommunications follows a natural progression:

• Level 1–3: AI assists humans by retrieving and presenting information.
• Level 4: AI assembles context intelligently based on query intent.
• Level 5: AI agents take initiative, executing tasks autonomously while maintaining human oversight.

Level-5 solutions and architectures are in active design phase across multiple communities & industry domains (including telco). One of the enablers for Level-5 approach is Model Context Protocol (MCP), a standardized client-server (North-South) framework that defines how AI components exchange information across telecom systems. MCP emerged from the need to handle diverse information sources while maintaining operational reliability.

Table-2 Evolving the Context Build-Up

The MCP framework delivers the foundation of agentic AI transformation, by providing:

• Standardized Interfaces: Common protocols for agents to interact with operational systems.
• Context Coherence: Maintenance of operational context across multiple interactions.
• Intent Communication: Clear expression of goals and constraints between agents.
• Audit Trails: Comprehensive documentation of agent actions and reasoning.

Without the standardized context management that MCP provides, agentic approaches would lack the coherence and visibility required for mission-critical telecommunications environments.

Building on MCP foundation, Agent Communication Protocol (ACP) as a complementary extension to MCP, aimed at facilitating multi-agent (East-West) communication within autonomous networks. ACP aims to standardize how agents “talk” to each other, acting as a universal connector that removes interoperability barriers between AI agents.

Over time ACP is expected to evolve into a standalone standard optimized for robust agent interactions, but in the interim this approach allows operators to introduce multi-agent collaboration without overhauling their established architectures.

For telcos, { MCP + ACP } infused solution architecture may (if they are picked up by AI & App-Dev communities) unlock new possibilities, such as:

• Distributed Network Management: Coordinated clusters of AI agents can manage different network domains (RAN, core, transport, etc.) in unison. Through ACP, these distributed agents would discover each other and delegate tasks dynamically, exchanging state information horizontally. This E-W agent communication would enable a decentralized yet coherent control of the network, improving scalability and resilience.
• Real-Time Problem Resolution: When incidents or anomalies arise, agents can collaborate in real time to diagnose and resolve issues. For example, a diagnostic agent that detects a fault can directly notify a planning agent via ACP to generate a remediation plan, while an execution agent prepares to apply fixes, all in a matter of seconds. Such immediate agent-to-agent dialogue accelerates root-cause analysis and remediation action, minimizing downtime and mitigating customer impact.
• Adaptive AI Decision-Making: By communicating their observations and recommendations to each other, AI agents can collectively adjust strategies on the fly. ACP-facilitated dialogues allow an agent to share insights (e.g. a sudden traffic pattern change or a predicted capacity issue) with peer agents managing related functions. The group of agents can then adapt decisions in real time; balancing load, reconfiguring routes, or tweaking policies in order to optimize overall network performance under evolving conditions. This collaborative decision loop makes the autonomous network more responsive and context-aware than any single agent acting alone.

Integration with Telecom Machinery

The true value of Agentic AI in telecommunications isn’t found in isolated data science experiments or impressive demos that never see production. Rather, it emerges when AI integrates with the nervous system of telecom operations in microservices architecture fashion (similar to 3GPP Service Based Architecture Approach).

Figure-6 High Level Telecom Agentic Framework

Telecommunications agentic systems operate with specialized agent types:

Diagnostic Agents:

• Continuously monitor network health indicators.
• Identify anomalies before they impact service.
• Correlate events across network domains.
• Formulate hypotheses about root causes.

Planning Agents:

• Develop resolution strategies for identified issues.
• Generate maintenance and optimization plans.
• Evaluate alternative approaches based on impact.
• Create step-by-step implementation procedures.

Execution Agents:

• Implement approved configuration changes.
• Execute routine maintenance procedures.
• Perform service adjustments within defined parameters.
• Coordinate with orchestration systems.

Validation Agents:

• Verify the effectiveness of implemented changes.
• Confirm service restoration or improvement.
• Document outcomes and lessons learned.
• Update knowledge bases for future reference.

The orchestration layer serves as the central nervous system for agentic operations, managing:

• Task Decomposition: Breaking complex telecommunications operations into manageable subtasks.
• Workflow Management: Coordinating agent activities across operational domains.
• Resource Management: Ensuring efficient use of network and computational resources.
• Quality Assurance (QA) Management: Gathering and processing results from agent activities.

This layer would leverage MCP to maintain operational context across agent interactions, ensuring coherent operations even in complex telecommunications environments.

Unlike experimental AI systems, telecommunications agentic frameworks need to incorporate robust control mechanisms:

• Safety Guardrails: Preventing actions that could impact critical services.
• Oversight Management: Providing transparent visibility and intervention capabilities.
• Compliance Framework: Ensuring all agent actions adhere to regulatory requirements.

These controls are essential in telecommunications environments where service disruptions can have significant economic and public safety impacts.

“We’re finally moving from asking our AI systems questions to having meaningful conversations about network strategy while the AI handles the operational hassle.”

Regulatory Compliance & Security

Telecommunications providers operate in one of the most heavily regulated industries on the planet, where data breaches aren’t just PR nightmares, they’re existential threats. This regulatory reality requires telecom AI implementations to incorporate security and compliance by design, not as afterthoughts. Key security components are; Data Protection, Access Control and Compliance.

Figure-7 Telco Data Processing Environment (DPE)

Data Protection: The First Line of Defense

Telecom operators handle treasure troves of sensitive data, from personal data, to location information that tracks your daily commute, to the metadata of your communications. Protecting this information requires a comprehensive approach:

• Data Classification Frameworks: Systems that automatically categorize information into sensitivity tiers, much like nuclear materials are classified but with fewer hazmat suits. AI can help create standardized classification systems that increase compliance rates significantly.
• Anonymization Pipelines: Sophisticated processing flows that strip personal identifiers while preserving analytical value. Properly designed anonymization can maintain most of the data’s analytical utility while substantially reducing privacy risk exposure.
• Tokenization Services: Replacing sensitive values with non-sensitive equivalents that maintain referential integrity. Advanced implementations use differential privacy techniques that mathematically guarantee privacy protections.

Implementation Approaches:

• Automated Personally Identifiable Information (PII) detection and classification.
• Content-aware filtering pipelines.
• Privacy-preserving analytics frameworks.
• Differential privacy mechanisms.

Access Control: Because “Admin Access for Everyone” Isn’t a Strategy

The principle of least privilege becomes even more critical when AI systems could potentially access vast datasets:

• Role-Based Access Control: Limiting model usage by job function, ensuring customer service representatives can’t suddenly access network configuration tools (no matter how tempting when that one customer keeps calling).
• Attribute-Based Access: Fine-grained permissions based on user attributes, data characteristics, and environmental factors. “Your system may be able to ask ‘Who are you? What are you trying to access? Where are you? When is this happening? And how suspicious is all of this together?’ before granting access.”.
• Context-Based Access: Dynamic permissions based on usage scenario, with stricter controls for unusual access patterns: “Your system could automatically escalate authentication requirements when it detects unusual query patterns. If a normal user suddenly starts requesting bulk customer data at 3 AM, that’s going to trigger additional verification steps :-).”.

Framework Components:

• Identity and access management integration.
• Attribute-based access control policies.
• Anomalous behavior detection.
• Escalating authentication mechanisms.

Compliance Infrastructure: Proving You Did the Right Thing

In telecommunications, it’s not enough to follow regulations, you must prove (!) you’ve followed them:

• Comprehensive Audit Logging: Immutable records of all data access and model interactions. Several Tier-1 operators are implementing blockchain-based audit trails for non-repudiation of AI system actions.
• Usage Pattern Monitoring: Continuous analysis of system usage to detect potential misuse or anomalies. Monitoring systems can identify compromised accounts making unusual query patterns, preventing unauthorized access to sensitive customer data.
• Automated Compliance Reporting: Streamlined generation of regulatory documentation. Automation can significantly reduce the person-hours required for compliance reporting.

Framework Elements:

• Immutable audit logging infrastructure.
• Real-time usage monitoring.
• Automated report generation.
• Compliance dashboard creation.

The telecommunications industry has learned, sometimes through painful experience, that security measures that are too cumbersome will be circumvented by well-intentioned employees just trying to get their jobs done. The most successful implementations balance robust protections with usability. Telecommunications providers operating across multiple jurisdictions face particular challenges:

• Geo-Fencing of Models and Data: Ensuring that certain data never crosses borders where different regulatory regimes apply.
• Jurisdiction-Specific Training: Models trained only on data permissible for use in each regulatory environment.
• Harmonized Compliance Framework: Creating a security architecture that satisfies the strictest requirements across all operating regions.

Cross-Jurisdictional Implementation Approaches:

• Compliant data segregation architecture.
• Regional model deployment strategies.
• Harmonized global compliance frameworks.
• Jurisdiction-specific security controls.

As data sovereignty concerns continue to intensify globally, telecommunications providers are building flexibility into their security architectures to accommodate rapidly evolving regulations, proving that in the telecom world, compliance isn’t just a legal requirement but a competitive differentiator.

“You shall measure both security metrics and friction metrics. If authentication takes too long or blocks legitimate work, you haven’t actually improved security, you’ve just created incentives for workarounds.”

Deployment Architectures

Telecommunications providers must consider their network topology, latency requirements, and data sovereignty needs when designing AI architectures:

Figure-8 Centralized vs Distributed Architecture

Centralized AI Architecture

Characteristics: Consolidated GPU resources in core data centers.

Advantages:

• Efficient resource utilization and sharing across business units.
• Simplified model governance and version control.
• Reduced infrastructure management overhead.
• Better economies of scale for high-end hardware.

Disadvantages:

• Potential latency challenges for edge-sensitive applications.
• Single point of failure risk.
• Bandwidth consumption for data backhaul.

Key Considerations:

• Hardware consolidation and sharing approaches.
• Model governance and versioning systems.
• Centralized monitoring and management.
• High-availability design patterns.
• Best For: Enterprise-wide knowledge systems, complex analytics requiring significant computational resources.

Distributed AI Architecture

Characteristics: Model deployment across regional data centers or network edge.

Advantages:

• Reduced latency for time-sensitive applications.
• Improved data sovereignty and compliance.
• Enhanced resilience through geographic distribution.
• Local processing reduces backhaul bandwidth requirements.

Disadvantages:

• More complex management and synchronization.
• Potentially higher total infrastructure costs.
• Deployment constraints for larger models at the edge.

Key Considerations:

• Model distribution and synchronization methods.
• Edge hardware selection and sizing.
• Network connectivity requirements.
• Local data storage and processing capabilities.

Best For: Time-sensitive operations, high data throughput/volume, local language processing, applications with strict data residency requirements.

Figure-9 Hybrid Architecture

Hybrid Architecture

Characteristics: Tiered approach with workload-based distribution.

Advantages:

• Optimized resource allocation based on application needs.
• Balances performance and cost considerations.
• Graceful degradation options during connectivity issues.

Implementation Pattern: Small models at the edge for routine tasks, larger models in regional centers for complex reasoning, with intelligent routing based on query complexity.

Key Design Elements:

• Workload classification methodology.
• Query routing intelligence.
• Model placement strategy.
• Cross-tier communication protocols.

Industry Direction:

The telecommunications industry is increasingly gravitating toward hybrid architectures that combine the efficiency benefits of centralized infrastructure with the latency and sovereignty advantages of distributed deployment. This approach allows operators to optimize both performance and cost by strategically placing AI capabilities where they deliver the most value for specific workloads.

Decision Framework:

When determining the optimal architecture for your telecommunications environment, consider these key factors:

• Latency requirements for critical use-cases.
• Geographic distribution of your network.
• Data sovereignty regulations in operating regions.
• Scale and complexity of AI models required.
• Existing infrastructure capabilities and investments.
• Technical expertise available for deployment and maintenance.

Infrastructure Considerations

When planning AI infrastructure for telecommunications environments, architects should consider several critical factors that influence technology selection and deployment strategy:

Flexibility and Scaling Principles

Rather than fixed resource recommendations, telecom operators should adopt a flexible resource planning framework based on:

Workload Elasticity Model: Assess peak-to-average ratio of AI workloads to determine how much elasticity is required.

• High-volatility workloads (e.g., customer support during outages) benefit from cloud bursting capabilities.
• Predictable workloads (e.g., network analytics) can be optimized for cost with dedicated infrastructure.

Scaling Dimension Assessment: Determine whether your primary scaling challenge is:

• Inference Throughput: Number of concurrent requests (favors horizontal scaling).
• Model Size: Complexity of models being served (favors vertical scaling).
• Context Length: Maximum token processing requirements (favors memory-optimized systems).

Deployment Place Strategy: Consider containerized deployment units that allow seamless movement between:

• On-premises data centers.
• Public cloud environments.
• Hybrid combinations based on workload characteristics.

Figure-10 Flexibility vs Scalability

Serving Blueprint

The choice of model serving technology should be guided by workload-specific requirements rather than general industry trends:

Stateless vs. Stateful Serving:

• Stateless serving is simpler but requires full context with each request.
• Stateful serving maintains conversation context but introduces session management complexity.

Process Optimization:

• Consider dynamic processing capabilities for throughput-sensitive applications.
• Implement priority queuing for latency-sensitive operations like network fault management.

Infrastructure Abstraction Layer:

• Implement a serving abstraction layer that allows models to be deployed across different hardware types.
• Enables graceful migration as infrastructure evolves without application-level changes.

Figure-11 Model Serving Blueprint

Infrastructure Security and Compliance Considerations

Telecommunications infrastructure demands specialized security measures:

Data Residency Controls:

• Infrastructure tagging and placement policies to enforce geographic constraints.
• Data flow maps to ensure compliance with telecommunications regulations.

Isolation Requirements:

• Role-based isolation levels (multi-tenant vs. dedicated) based on data sensitivity.
• Network segmentation aligned with telecom data classification policies.

Infrastructure Audit Capabilities:

• Comprehensive activity logging with immutable audit trails.
• Blockchain-based verification for non-repudiation of AI system actions.

This approach creates a security framework that balances protection with operational efficiency, ensuring regulatory compliance while maintaining system usability.

Figure-12 Security & Compliance Framework

Technology Selection Framework

Rather than prescribing specific technologies, telecom architects should evaluate options against a consistent framework:

Technology Maturity Assessment:

• Production readiness in telecommunications environments.
• Community support and development momentum.
• Enterprise support options and SLAs.

Operational Integration Capabilities:

• Compatibility with existing monitoring and management systems.
• Support for telecom-specific observability requirements.
• Integration with existing CI/CD, GitOps, and MLOps pipelines.

Performance Characteristics Alignment:

• Match technology characteristics to workload profiles.
• Consider both peak performance and consistent performance under load.

This approach provides a robust framework for telecommunications providers to make infrastructure decisions without being constrained by specific hardware recommendations that may quickly become outdated or may not align with their specific operational constraints.

Real-World Examples

Looking at implementations from the open-experiments Telco-AIX repository (Link), several use-cases demonstrate the practical value of AI in telecommunications:

Revenue Assurance and Fraud Management

The Revenue Assurance implementation provides AI-assisted fraud detection for telecom transactions. By analyzing patterns in transactions such as unusual data usage, suspicious calling patterns, or abnormal location data, the system helps identify potentially fraudulent activities in real-time.

The architecture follows a modern MLOps pattern using containerized pipelines for data processing, model training, evaluation, and deployment. This approach enables continuous improvement of the fraud detection system as new patterns emerge.

Service Assurance and Customer Experience

This use-case employs transformer neural networks to correlate network performance metrics with Net Promoter Score (NPS) predictions. This implementation helps operators understand the relationship between technical metrics and customer satisfaction, enabling proactive management of service quality to maintain positive customer experiences.

The system analyzes metrics like latency, jitter, throughput, packet loss, and protocol data to predict customer satisfaction levels, providing actionable insights for network operations teams.

5G Network Operations

The 5G Network Operations implementation predicts fault occurrence rates in 5G radio networks based on various network KPIs. The model analyzes metrics like cell availability, throughput, latency, packet loss rates, and other technical parameters to forecast potential issues before they affect service.

Using a REST API interface, this system integrates with existing network management tools to provide predictive insights that help operators maintain service quality.

Network Security Operations

The SecOps implementation leverages AI to identify critical security features that correlate with attack surfaces. The system analyzes metrics like disk attacks, protocol anomalies, firewall events, authentication failures, and other security indicators to generate security intelligence.

This approach helps telecommunications providers focus their security efforts on the most vulnerable areas of their infrastructure, enhancing overall network protection.

Root Cause Analysis with LLMs

Using a model chain approach, this project demonstrates how foundation models can be integrated into network troubleshooting workflows. The system processes network logs and metrics, detects anomalies, and leverages LLMs to provide detailed root cause analysis and remediation suggestions.

This implementation is particularly valuable for complex networking environments where traditional rule-based approaches struggle to identify the underlying causes of service disruptions.

Skills Transformation for AI-Driven Telecom

The transition to AI-enabled telecommunications infrastructure requires a significant transformation of organizational capabilities and talent. At MWC 2025, leading operators highlighted that successful implementations depend not only on technology but on purposeful evolution of team structures and skills.

Traditional telco org-structure typically operates with clear separation between network engineering, IT, and business functions. AI implementations demand a new operational model:

Table-3 Traditional Skill Silo vs AI-Driven

This transition requires both upskilling existing talent and strategic hiring of specialists who bridge domains. Telecommunications providers are building competency in four key domains:

Foundation Model Operations

• Model deployment and serving expertise.
• Prompt engineering for telecom-specific use-cases.
• Parameter-efficient fine-tuning techniques.
• Inference optimization for telecom environments.

Telecommunications Data Engineering

• Network telemetry processing at scale.
• Real-time streaming architectures.
• Multimodal data integration (network data, customer data, service metrics).
• Telecom-specific data privacy implementation.

AI Integration Engineering

• API design for AI service integration with OSS/BSS.
• Telecom workflow orchestration with AI components.
• Hybrid deployment patterns across cloud and on-premises.
• AI observability instrumentation.

AI Governance and Compliance

• Model performance monitoring specific to telecom KPIs.
• Regulatory compliance for AI in critical infrastructure.
• Responsible AI implementation for customer-facing services.
• AI risk management frameworks for telecom applications.

Organizational Transformation Patterns

The most successful telecom operators are implementing specific organizational changes to support AI adoption:

AI Center of Excellence (CoE) Model

Leading operators establish dedicated AI Centers of Excellence that:

• Develop technical standards and best practices specific to telecom AI.
• Create reusable components and reference architectures.
• Provide internal consulting for business units.
• Oversee knowledge transfer and capability building.

Embedded AI Teams

Beyond centralized expertise, successful implementations embed AI capabilities directly into operational teams:

• Network operations teams with dedicated ML engineers.
• Customer experience teams with conversational AI specialists.
• Service assurance groups with anomaly detection experts.

Dual-Track Career Paths

Progressive telecom organizations are establishing new career frameworks that recognize deep AI expertise while valuing telecommunications domain knowledge:

• Technical track for AI specialists with telecommunications focus.
• Domain expert track with AI implementation capabilities.
• Leadership tracks combining business, technical, and transformational skills.

Skills Development Approaches

Telecommunications providers are employing multiple strategies to build needed capabilities:

• Strategic Hiring: Recruiting data scientists and ML engineers with experience in distributed systems and real-time processing.
• Partnership Ecosystems: Collaborating with specialized AI firms and academia to accelerate capability development.
• Internal Academies: Creating structured learning paths for existing employees, often with telecommunications-specific use-cases.
• Rotation Programs: Temporary assignment of network engineers to data science teams and vice versa to build cross-functional understanding.

The skills transformation journey faces several challenges specific to telecommunications:

Table-4 Challenges vs Mitigation Strategies

Leading telecommunications providers measure their skills transformation progress through:

• Capability Maturity Models: Formal assessment of AI competencies across the organization.
• Project Autonomy Metrics: Tracking the decreasing dependence on external vendors for AI implementations.
• Time-to-Production: Measuring improvement in deployment velocity for new AI use-cases.
• Cross-Functional Collaboration: Assessing the effectiveness of interactions between network, IT, and data science teams.

The organizations demonstrating the most advanced AI implementations at MWC 2025 had all invested significantly in skills transformation for at least 24 months prior to achieving production-scale results, highlighting that technical architecture alone is insufficient without corresponding organizational evolution.

Closure

The telecommunications industry has crossed a threshold in AI adoption, moving from speculative pilots to production implementations. The evidence from MWC 2025 and the growing interest and adoption of Telco-AIX repository confirms that operators are now focused on implementation speed rather than theoretical potential.

Our five-level achievement framework approach from leveraging existing search infrastructure to implementing agentic systems, provides a pragmatic roadmap with immediate value at each stage. The emergence of Level-5 agentic systems represents a paradigm shift from reactive to proactive operations, with the Model Context Protocol (MCP) providing the essential foundation for these autonomous operations.

The democratization of AI technologies has created perfect timing for telecom adoption. Open-source models now rival proprietary alternatives, vector databases have reached production maturity, and technologies like vLLM have dramatically reduced infrastructure requirements. The business impact extends beyond efficiency:

• Personalized, contextually aware customer experiences.
• Enhanced network reliability through predictive maintenance.
• Accelerated service delivery through automated configuration.
• New revenue streams through AI-powered enterprise services.
• Optimized capital expenditures through targeted network investments.

Most significantly, AI is helping telecommunications providers refocus on their core mission of reliable, high-quality connectivity by reducing operational complexity. Future developments will likely include:

• Edge AI optimization for ultra-low latency applications.
• Deeper integration between network automation and AI.
• Multi-modal approaches incorporating diverse telemetry data.
• Telecom-specific foundation models trained on industry data.
• Standardized AI interfaces for operational systems.
• Increasingly autonomous network operations across organizational boundaries.

In the Telecom Industry, scale adoption always follows cost reduction (the Jevons Paradox in action). As AI deployments continue to see significant price cuts, we’re poised to witness AI expand into ever wider domains with increasingly transformative impacts across the telecom landscape.

References

1. Telco-AIX : https://github.com/open-experiments/Telco-AIX
2. For Sustainability & AI please visit our following article; Episode XXII: AI Accelerators’ Performance vs Sustainability.
3. For a comprehensive foundation on the data engineering that powers these AI systems, see our previous article “Episode-XXIII TrueAI4Telco”.
4. Model Context Protocol (MCP): https://modelcontextprotocol.io/introduction
5. Agent Communication Protocol (ACP) : https://docs.beeai.dev/acp/alpha/introduction
6. vLLM: https://docs.vllm.ai/en/latest/