The telecom industry is undergoing a monumental transformation. The rise of new technologies such as 5G, cloud computing, and the Internet of Things (IoT) is putting pressure on telecom operators to find new ways to improve the performance of their networks, reduce costs and provide better customer service. Cost pressures especially are incentivizing telecoms to find new ways to implement automation and more efficient processes to help optimize operations and employee productivity.
Generative AI (gen AI) is emerging as a transformative technology that can help telecom operators meet these challenges. It’s a type of artificial intelligence that can learn, predict outcomes, problem-solve and more.
Gen AI is well-suited to help tackle complex problems in the telecom industry, such as:
- Optimizing network performance by generating recommendations for beneficial network configuration changes
- Developing new ideas and delivering insights to power revenue streams, such as identifying new marketing opportunities and campaigns that effectively reach and convert customers
- Identifying and resolving network problems quickly and efficiently by generating hypotheses about the root cause of problems and suggesting solutions
- Automating the design and deployment of new networks, such as creating what-if scenarios for the design and deployment of new and expanded use cases in the network
- Predicting future network outages, spikes in traffic or other problems by analyzing data from historical events and deploying seasonality models
Challenges to implementing gen AI
However, there are also some challenges that telecom operators must address to fully benefit from gen AI, which include the following:
- Data silos: There is a proliferation of data silos in the telecom industry. Gen AI solutions require large amounts of data to train and operate, which is challenging for large telecoms with siloed data systems and limited access to customer data. No AI solution can work without an integrated data platform.
- Legacy systems: Many large telecoms have legacy systems not designed to be compatible with gen AI solutions. These systems can make it difficult and expensive to integrate gen AI solutions.
- Risk aversion: Some large telecoms are risk-averse and reluctant to adopt new technologies that have yet to be proven.
- Cost: Gen AI solutions can be expensive to develop and deploy, creating barriers for telecoms already facing financial challenges.
- Security: Ensuring the organization’s data inside the gen AI platform remains safe is crucial to telecom companies.
- Regulation: Gen AI solutions raise new regulatory concerns, such as the potential for bias and discrimination. Large telecoms need to be aware of these concerns and take steps to mitigate them.
- Skills: A need for more skilled workers with the knowledge and experience to use gen AI effectively makes it difficult for large telecoms to find the people they need to implement and maintain these solutions.
- Culture: Some large telecoms have a culture that is resistant to change. As a result, it can be a challenge for them to adopt disruptive technologies.
AI requires an effective data strategy
Snowflake enables telecoms to break down their data, applications and organizational silos with the Telecom Data Cloud. The Snowflake approach has always been to bring the application/processing to the data rather than the other way around. And now, with gen AI and large language models (LLMs), it’s no different. At our recent Snowflake Summit, we announced Snowpark Container Services (in private preview), which enables developers to effortlessly register and deploy containerized data apps using secure, Snowflake-managed infrastructure with configurable hardware options, such as accelerated computing with NVIDIA GPUs. This additional flexibility drastically expands the scope of AI/ML and app workloads that can be brought directly to the data.
Additionally, running LLMs privately inside your Snowflake account means you are always in full control of your data. Telecoms can use the existing workforce to leverage the LLM capabilities in the Snowflake ecosystem and deploy the whole solution in a cost-effective, consumption-based pricing model.
One key aspect often overlooked in this transformation (and all the hype around gen AI) is the human element. Striking the right balance between automation and retaining the existing workforce is crucial. It’s about raising the productivity of every employee. Investing in retraining and upskilling employees whose tasks have been automated ensures they can take on higher-value roles. This approach helps foster a positive company culture and provides a smooth transition for your workforce.
Gen AI provides a tremendous opportunity for telecoms to improve their business. We have officially entered “the second half of the chessboard,” meaning that every digital innovation will likely result in a dramatic technological shift from this point onward. As the technology grows in leaps and bounds, we can expect to see more large telecoms adopt gen AI capabilities.
Want to learn more? Join us for the Bringing Generative AI to the Data series, designed for both executives and developers looking for an introduction to the power of LLMs and other generative AI on data. Watch now.