Skip to content


Generative AI Governance Frameworks for Telecommunications Leaders

Altman Solon is the largest global strategy consulting firm exclusively working in the TMT sectors. Our recent work sheds light on generative artificial intelligence (AI) in the telecommunications industry. This report focuses on governance in generative AI as an essential feature in generative AI product innovation.

The telecommunications sector has a unique opportunity to leverage generative AI tools, from customer service chatbots to automated network planning and optimization. To understand how the telco sector sees generative AI, Altman Solon partnered with Amazon Web Services (AWS) and surveyed over 100 senior business leaders at Tier 1 communication service providers (CSP) in North America, Latin America, Asia Pacific, and Western Europe. The survey findings were enhanced through interviews with 25 telco executives, consistently highlighting generative AI governance as an area that needs improvement within their companies. Through these conversations and researching industry case studies, Altman Solon outlined a structure – people, processes, and technology—necessary for implementing robust generative AI governance.

Governance risks with generative AI

"I don't believe we've made the necessary investments in terms of guardrails, responsible AI, transparency, and other governance areas to unlock some of these use cases... If you don't have good measurability of where you can test and experiment and get quality data back on whether it worked, you're not going to make the right decisions."

Director of AI Product Management, Tier 1 Wireless Communication Service Provider, U.S.

The importance of strong generative AI governance, which encompasses the rules governing the development, usage, and application of large language models (LLMs), cannot be overstated. Recent, high-profile instances of generative AI misuse, such as application bias replicationleakage of confidential corporate data, and rogue customer service chatbots insulting customers, serve as stark reminders of the real-world implications of weak governance. They underscore the critical need for robust governance guidelines when developing and using generative AI tools.

Meanwhile, global AI regulations are underway, and various approaches are being implemented in different parts of the world. The E.U.'s Ethics Guidelines for Trustworthy AI, for example, favor a risk-based approach that prioritizes potential harm. In contrast, Canada has a series of guidelines that lay out fundamental principles around generative AI but lack rules around implementation. These different approaches have significant implications for how companies shape their generative AI compliance and implementation.

Telecommunications companies have an opportunity to be at the forefront of these evolving regulations, establishing themselves as leaders and partnering with global institutions. Telefónica, an early adopter of AI governance policy, has done just that. The Spanish multinational has released public statements on ethics and AI governance frameworks and embedded "Responsible AI Champions" within its business units. Telefónica has also embarked on a joint initiative with UNESCO to develop and implement ethical AI guidelines.

Generative AI governance requires evolving existing AI and data governance

"Data governance and AI governance are two peas in a pod. They are different, yet they complement each other. A strong data governance is a stepping-stone to a mature AI governance."

Senior Director, AI Product Management North American Communication Service Provider

Generative AI governance should be seen as an evolution of existing data and AI governance policies. Data governance—which covers data security, quality, access, and repository—informs generative AI governance. AI governance—which covers ethical controls, model repositories, governing boards, and risk assessment—influences generative AI-specific governance. Communication service providers with mature data governance, including robust data security measures and methodical data organization and cataloging, will be able to deploy generative AI governance better.

Generative AI Governance Taxonomy

Nonetheless, our interviews with telco executives revealed that generative AI governance is a near-universal pain point, even among early adopters of generative AI. Respondents believe current governance practices don't address the specificities of generative AI, including ethics in generative AI, verification of generative AI content, and preventing biases in model development. Indeed, sound generative AI governance is necessary for effective generative AI tool development and implementation.

People, process, and technology: The winning combination for generative AI governance strategy

"To handle generative AI governance, we've realized that it's not a matter of simply adding more people. It requires a combination of tooling, automation, and the right people at the right time."

Head of Data & AI Business Partnering- Digital & Global, European CSP

Building solid generative AI governance requires developing expertise in three key areas:

  • People: Form teams of generative AI experts and establish a center of excellence. Initially, a centralized approach is common, but as capabilities grow, a "hub-and-spoke" model emerges, with the generative AI center of excellence as the hub and experts in business units as spokes. Mature companies often have independent ethics committees for oversight and collaborate with external entities for responsible AI development.

  • Processes: Implement formal frameworks, risk assessments, accuracy metrics, and protocols for third-party models. Those in the early stages of gen AI adoption rely on manual processes, but mature organizations blend programmatic governance and manual reviews.

  • Technology: Utilize tools for model management, performance monitoring, compliance, and auditing. Generative AI requires advanced verification tools and software to explain model outputs and assess ethics and bias.

    Generative AI Governance Comparison: Select characteristics

Generative AI governance: The starting point for generative AI leadership

Generative AI-powered tools can be leveraged across business functions within telcos, from customer-facing chatbots to network planning and optimization. Strong governance is a necessary starting point for CSPs striving to lead in generative AI. For successful governance of these tools, companies should consider the following tactical, foundational steps:

  • Documenting and applying generative AI governance principles, with thoughtful consideration for people, processes, and technology impacted by the deployment and utilization of generative AI
  • Formalizing risk assessments for generative AI use cases hand-in-hand with implementation and deployment choices
  • Structuring generative AI governance teams alongside existing AI and data governance teams
  • Ensuring data governance and AI governance policies are harmonized
  • Creating and formalizing a repository for generative AI models
  • Evaluating verification software tools and platforms to support maturing generative AI governance efforts

CSPs are keen to adopt and scale generative AI tools for internal and customer-facing use cases. However, they must balance innovation with managing regulatory compliance and upholding ethical practices. CSPs are realizing that effective governance safeguards against reputational risks and enhances service reliability and customer trust, positioning the company for competitive advantage in a rapidly evolving digital landscape.

Altman Solon helps leading telecommunications organizations navigate the adoption and integration of generative AI solutions. Through proprietary insights from over 100 senior business leaders at Tier 1 communication service providers in the U.S., Western Europe, and Asia Pacific, this report offers insight into generative AI adoption patterns, relevant use cases, and top concerns regarding generative AI in the telecommunications sector. 

Submit the form to receive our in-depth report on managing generative AI in telecommunications.


Leadership & Oversight

Daniel Torras


Priya Mehra


Elisabeth Sum