AI holds vast potential for telecom, but conversations can be watered down discussing endless possibilities of some undefined future vision. This hinders the ability to execute with focus and discipline in the near term.
In a rush to feel like progress is being made, partnerships may be signed without strong direction. When the dust settles, will these alliances actually benefit both parties or will we discover the AI company has become stronger and better positioned?
In our discussions with operators, they are not concerned with some far-off AI nirvana. Rather, they want to understand how AI can help them now, how to get started and what to expect.
A recurring set of themes tend to come up in these meetings. Here, we’ve rounded up the eight essential questions we believe any telco should ask itself about understanding AI’s role in its telecom network operations.
How do you define AI?
AI means different things to different people. Yet, it is fraught with misconceptions and unrealistic expectations. Defining it means understanding different AI models and their applicability to the right applications. AI is not new, but generative AI represents a new development. At a fundamental level, any AI discussion must be rooted in what kind of AI to apply to what application with an understanding of what is expected to be gained.
Is there a difference between automation and autonomy?
Automation and autonomy have different meanings in the context of telco networks. While automation involves executing predefined tasks, autonomy means AI systems learn, adapt, and make decisions independently. Initially, telcos will use AI for insights only, eventually evolving to AI-driven actions within networks as trust is developed. For example, an autonomous AI system in network management can predict potential service disruptions and implement preventive measures without human intervention.
What expectations do you have of AI?
If you believe AI will be a magic wand akin to the early hype of cloud or metaverse, you are not ready for AI. But if you understand AI is simply a tool to augment operations and serve grander automation ambitions, AI might be right for your organization. In fact, AI can enhance each step of the Monitoring, Analyzing, Decision Making, and Action Orchestration (MADA) model, moving from human-driven processes to AI-driven sophistication. For instance, AI can analyze network performance data to identify patterns that might indicate impending issues, enabling proactive maintenance and reducing downtime.
What data do you use to make decisions within your telecom business?
It’s not always clear how decisions are made. Is it gut instinct, personal experience or hard data? AI can only thrive on data and before it can be successfully deployed, a solid foundation in data collection, management and digitalization must be established. That means all data in the organization must be accurate, actionable and organized with availability across the business.
Does telco AI have specific requirements?
Yes, unlike general language Gen AI models, AI in telco must be precise, reliable and able to understand the specialized context of mission critical telco networks. Of course, this is true for all industries looking to transform core operations and processes. For instance, AI applications in network optimization need to make real-time decisions, ensuring uninterrupted service and optimal performance. Errors can have significant, far-reaching impacts on network integrity. AI “hallucinations” could bring down a network. Bad data could result in bad outcomes. Preparing to implement AI means recognizing required governance and guardrails to avoid unforeseen and unwanted outcomes. When the first AI-driven debacle happens, the media will be quick to pounce. There is no room for error.
For all the talk of telco AI, are there any actual examples of it already being used to manage network issues, enhance efficiency or improve customer experience?
AI approaches have long been used in complex analytics like customer churn or retail location strategies.
Most recently, telcos have started to implement parts of the MADA model described above for autonomous monitoring and analytics. Next, Gen AI will introduce rapid adoption of spot solutions to increase the efficiency of any process requiring understanding and reference to large documentation sources.
The most demanding element of telco AI integration critical to autonomous networks is the “Decision Making” component of MADA. In other words, injecting AI into closed loop operations to replace expert intervention in the moment and make autonomous decisions.
While this is beginning to happen, telecom remains highly manual, requiring any application of AI be implemented as a power tool in the existing automation transformation to autonomous operations.
Ultimately, telcos must embark on a journey of digitalization and automation. Without universal automation controllers and network observability in place, it will be difficult to evolve from the “insights” stage of AI to the “action” stage.
What are the most common AI misconceptions?
We are seeing an overestimation of capabilities like those seen in generative AI models. For example, while AI can automate certain customer service interactions, it can only replace the nuanced understanding and empathy of human customer service representatives if it can be trained on nuanced cultural and domain specific data.
Beyond data quality, there is a misconception that writing a great AI model will solve most challenges. While AI models are important, they are no less critical than the processes built around them. Success hinges on the right model deployment and lifecycle management, effective data governance, scale, MLOps and the right strategy to introduce AI into production.
How do I get started on my telco organization’s AI journey?
First steps include establishing a data platform and undertaking digitalization initiatives. For AI to be effective, it needs access to high-quality, relevant data. A unified data platform is vital for consolidating information from diverse sources, such as network traffic, customer usage patterns, and service performance metrics. Digitalizing this data ensures that AI algorithms have access to current, comprehensive, and accurate information. For example, digitalizing customer interaction data enables AI systems to provide personalized customer service solutions, improving efficiency and customer satisfaction.
Telco AI represents a journey of transformation that involves integrating it into the operational fabric of organizations and the industry itself. In these early days, stakeholders must understand its realistic applications and prep a foundation for early successful implementations.
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