Growth Opportunities for Telcos' Enterprise GenAI Solutions, 2025-2030

Large Language Models to Drive Transformational Growth and Enable AI-Driven Enterprise Products and Services

Artificial intelligence subset generative AI (GenAI) refers to technologies capable of creating new content, such as text, images, code, audio, and video, by learning from existing data and models. For telecommunications service providers (telcos), GenAI presents transformative opportunities beyond network optimization and customer support. By deploying proprietary large language models (LLMs) and integrating AI-driven services, telcos can unlock new B2B revenue streams and position themselves as strategic partners in enterprise digital transformation.

While most telcos have initiated AI, their maturity levels vary widely-from proof-of-concept stages to large-scale implementation across multiple use cases. However, a major barrier remains: the lack of unified, real-time enterprise data architectures hampers model training and limits the effectiveness of GenAI solutions. A well-defined AI strategy and roadmap, along with improved data readiness, are essential for realizing AI's full potential.

This report examines the current landscape of telco-led GenAI solutions for enterprise clients; benchmarks the most important telcos in North America, Europe, and Asia-Pacific; analyzes emerging trends and market dynamics; and highlights the key enablers and challenges shaping growth. It also identifies strategic opportunities for telcos to develop industry-specific AI and data management offerings.

Scope of Analysis

• AI refers to technologies that emulate human intelligence and assist decision-making with self-learning capabilities in the generative AI in telecom market.
• GenAI is a subset of AI in the enterprise AI solutions market. The platforms generate new content, such as text, images, code language, audio, or video, by learning from existing data and models in the AI in telecommunication market.
• Telecommunications service providers (telcos) can use GenAI for network operations and customer service in the North America telco AI adoption market, as well as to deploy their own large language models (LLMs) and create new revenue streams in the B2B segment. Their ability to work closely with enterprises to build solutions and integrate AI-based tools and services make them important participants in the ecosystem of the generative AI in telecom market.
• Most telcos have started implementing AI technology in the enterprise AI solutions market, but they are at different stages of maturity-from proofs of concept to deployment of multiple AI use cases at scale in the AI in telecommunication market. A clear strategy and roadmap articulation are crucial in the AI adoption journey. Few telcos have architectures that support integrated enterprise data pools, including data from real-time sources, indicating low data readiness to support AI applications in the North America telco AI adoption market. This results in difficulty training AI models for GenAI applications and ineffective AI outcomes.
• This report provides a perspective on telcos' current GenAI offerings to enterprise customers in the generative AI in telecom market, trends in their evolution, and drivers and restraints impacting market growth. It also offers telcos opportunities to explore industry-specific AI and data management solutions in the enterprise AI solutions market.

The Impact of the Top 3 Strategic Imperatives on Telcos' Enterprise GenAI Solutions

Innovative Business Models

• Why: Maturing natural language processing (NLP) and computer vision technologies deliver more predictable outcomes, enabling telcos in the generative AI in telecom market to embed them into new B2B offerings. Traditional revenue streams are eroding, pushing telcos to seek growth through new business models. GenAI provides a strategic opportunity for new models, such as AI-as-a-service and virtualized solutions, that unlock recurring revenue in the enterprise AI solutions market. These models allow telcos to more effectively monetize data, 5G, and edge assets.
• Frost Perspective: Enterprises will increasingly demand scalable, secure AI solutions because of a lack of in-house expertise in the AI in telecommunication market. Telcos, with their reach and end-to-end capability, can become trusted AI partners. New business models are essential to stay competitive and relevant in the North America telco AI adoption market. This shift supports long-term growth while reinforcing telcos' role in industry-specific transformation.

Disruptive Technologies

• Why: Telcos are shifting from consuming third-party AI to developing telecom-specific LLMs using proprietary data in the generative AI in telecom market. These specialized LLMs enable domain-specific capabilities, such as automated service workflows, network optimization, and predictive analytics, which can drive high-value B2B offerings in the enterprise AI solutions market. By building their models, telcos reduce dependency on hyperscalers, enhance data control, and position themselves as key enablers of B2B customers' digital transformation in the AI in telecommunication market.
• Frost Perspective: The most advanced telcos are developing GenAI use cases tailored for enterprise needs, ranging from agentic AI to industry-specific solutions in the North America telco AI adoption market. Over the next 5 years, telcos will be better positioned to scale these offerings securely and responsibly as they consolidate and commercialize frameworks. Moreover, GenAI capabilities will integrate deeply with the telco infrastructure, enabling telcos to act as trusted digital transformation partners in the generative AI in telecom market.

Competitive Intensity

• Why: Many leading telcos have invested in AI centers of excellence (CoEs), are adopting advanced self-service analytics, and are actively modernizing their cloud-based data infrastructure to integrate data from disparate sources in the enterprise AI solutions market. Progress varies, however, depending on strategic priorities and the maturity level of companies and markets in the AI in telecommunication market.
• Frost Perspective: Over the next 5 years, telcos that have wisely invested in CoEs, self-service analytics, and data infrastructure will shift from foundational enablement to scaled innovation. These investments will result in faster time to market for AI-driven products and services in the North America telco AI adoption market. AI CoEs will evolve from experimental hubs to engines of B2B solution co-development. Self-service analytics and cloud-data platforms will integrate with 5G and edge, enabling intelligent services at scale in the generative AI in telecom market.

Growth Restraints

• The success of AI and ML algorithms in the enterprise AI solutions market depends on the quality of the data available in the enterprise. Clean and standardized data enable AI/ML technologies to deliver value and positive business outcomes in the AI in telecommunication market. Accessing clean and usable datasets is challenging for most telcos that are adopting AI in the North America telco AI adoption market.
• There is a high risk that GenAI applications will respond with errors and hallucinations. Inaccurate or fabricated information can compromise companies' decision-making. It is necessary to carefully evaluate data sources and workflows, formulate strategies, and integrate existing development tools with AI in the generative AI in telecom market.
• Legacy systems operate in silos, and few telcos have an architecture that supports an integrated enterprise data pool, including real-time data in formats that AI can use. AI-based use cases in the enterprise AI solutions market will leverage multiple technologies, requiring complex system integration capabilities. Therefore, telcos must overcome system integration issues to run AI tools efficiently in the AI in telecommunication market.
• Regulatory and ethical issues, such as privacy considerations that restrict access to data before anonymization, intellectual property issues, a lack of algorithm transparency, algorithm biases, and job security concerns, will hinder the AI market's growth in the North America telco AI adoption market.

Growth Drivers

• With declining revenue growth from core services, telcos in the generative AI in telecom market must increase their offerings and create differentiation to remain relevant in a competitive market. AI technologies enable telcos to support new business opportunities by offering digital services in the enterprise AI solutions market.
• Digital infrastructure's ability to generate, process, and store large volumes of unstructured data makes it easier for enterprises to implement AI solutions. The ubiquity of cloud computing, the rapid expansion of wireless communication networks, and the increasing reliability of low-cost sensors have removed some technical barriers that enterprises face in deploying AI solutions. This allows them to quickly implement these solutions with lower AI-related hardware and information technology (IT) infrastructure costs in the AI in telecommunication market.
• With advancements in AI and ML algorithms and LLMs, AI solutions in the North America telco AI adoption market will offer more predictable outcomes, resulting in automation and higher efficiency.
• The availability of pre-trained models and low-code and open-source AI tools will remove some technical barriers and support faster adoption of AI solutions across businesses of all sizes in the generative AI in telecom market.