Expert Systems, Neural Networks, Machine Learning, and Artificial Intelligence have been part of the telecom roadmap, products, services, and innovation for over four decades. So, when GenAI burst onto the scene, the industry took the implications and potential opportunities in its stride.
However, the research and innovation landscape has fundamentally altered with the vendor community playing a central role in how we move forward. Things are changing at such a fast pace that leaders require new skills to manage the business, the threats, and place well-reasoned bets that could separate them from the competition or conversely, if poorly managed, sink the prospects.
AI is permeating every aspect of the wireless industry value chain from chipset design to devices, from applications to edge computing, from RAN to core, from network operations to customer service, from marketing initiatives to churn management, from employee education to supply-chain management, etc.
But how do you go about managing the wide disparate AI initiatives? Which ones do you rely on your partners vs. take on yourself? How much should you invest and how fast? What frameworks do you have in place to course correct? Which initiatives require industry collaboration vs. solo sojourn? One has to keep in mind that all these initiatives are driven by financial interests so what’s good for some might end up being disastrous for others.
In this paper, we take a look at AI in Telecom landscape to see where industry is investing, what kind of results are we seeing, and what can we learn from early experiences? What’s working in the short-term and what long-term shifts are in play? How will the money flow change and grow in the coming years? How far are we from closed-loop automation? What does the regional competition look like? How will regulations play a role in shaping AI in Telecom?
We don’t have answers to all these questions but enough data points to start formulating viewpoints on emerging areas of consensus and disruption.