  Telecom networks are becoming increasingly complex, with diversified
deployment scenarios, multi-standards, and multi-vendor support. The intricate
nature of the telecom network ecosystem presents challenges to effectively
manage, operate, and optimize networks. To address these hurdles, Artificial
Intelligence (AI) has been widely adopted to solve different tasks in telecom
networks. However, these conventional AI models are often designed for specific
tasks, rely on extensive and costly-to-collect labeled data that require
specialized telecom expertise for development and maintenance. The AI models
usually fail to generalize and support diverse deployment scenarios and
applications. In contrast, Foundation Models (FMs) show effective
generalization capabilities in various domains in language, vision, and
decision-making tasks. FMs can be trained on multiple data modalities generated
from the telecom ecosystem and leverage specialized domain knowledge. Moreover,
FMs can be fine-tuned to solve numerous specialized tasks with minimal
task-specific labeled data and, in some instances, are able to leverage context
to solve previously unseen problems. At the dawn of 6G, this paper investigates
the potential opportunities of using FMs to shape the future of telecom
technologies and standards. In particular, the paper outlines a conceptual
process for developing Telecom FMs (TFMs) and discusses emerging opportunities
for orchestrating specialized TFMs for network configuration, operation, and
maintenance. Finally, the paper discusses the limitations and challenges of
developing and deploying TFMs.
