Markus Perrson, Global Industry Director – Telecommunications, IFS
As 5G rolls out, telco Communications Service Providers (CSPs) are being squeezed from all angles. Now, connectivity is a commodity: the UN has even declared it a basic human right. Selling airtime alone cannot pay the bills – and the pressure shows.
In August 2023, T-Mobile US announced it will cut 5,000 jobs (7%) in the US. Similarly, AT&T has cut some 45,000 jobs in the last two years, whilst Verizon has lost 18,000 roles.
As network traffic volumes increase, so too does the burden and cost, of storing and managing Big Data. To compete, operators must look to rapid automation, optimization and business diversification. In this blog, I’ll explore how deploying Artificial Intelligence (AI) can help operators reduce costs, while also transforming the data burden into a monetizable revenue opportunity. I’ll also look at some of the ways AI is driving automation within the telecommunications sector.
The data dilemma: data literacy and harmonization
The telecommunications industry is transforming, driven by the rapid explosion of usable data and AI-enabled analysis to identify new revenue streams. But to fully harness the power of AI and automation, individuals and organizations must develop data literacy and data harmonization.
In a world where personal data, preferences, and biases are being used to influence behaviors, beliefs, and decisions, data literacy ensures telecommunications organizations can efficiently collect, structure, and gather insights from their operations.
AI can process Big Data quickly and accurately. But there’s a problem. Most telecom operators have multiple versions of provisioning, billing and customer data systems, making it impossible for an AI engine to understand. That’s why IFS Cloud, for example, our AI-enabled enterprise software solution, provides CSPs with a single platform and master data set spanning all assets and services business-wide. Here, data harmonization – standardizing and integrating data from different sources to ensure that it is consistent and accurate – underpins a single version of the truth enterprise-wide.
It’s interesting that research commissioned by Nokia found that whilst 87% of operators surveyed have started to implement AI into their network operations, legacy systems with proprietary interfaces mean operators cannot access the high-quality data sets needed to integrate AI at pace. Only 6% believed they were successfully applying AI and machine learning algorithms to achieve ‘zero touch’ network operations.
5G networks: solving the challenges of slicing and mobile edge with AI
Network slicing is the ability to create multiple virtual networks on top of a shared physical infrastructure, each with its own characteristics and service level agreements (SLAs). Mobile edge computing is the ability to deploy applications and services at the edge of the network, closer to the end users, to reduce latency and improve performance. Both technologies enable operators to offer more flexible, scalable, and efficient services to different types of customers, such as enterprises, consumers, or public sector entities.
To use these approaches effectively, operators must understand different customer segment needs and the associated costs. AI can play a crucial role by helping telecom operators analyze their network data to inform decision-making and strategy. AI can help operators to:
– Analyze usage patterns, preferences and pain points to identify customer segments and industries that could benefit.
– Discover use cases such as enhanced mobile broadband, massive IoT, ultra-reliable low-latency communication, smart cities, smart manufacturing, autonomous driving, etc.
– Evaluate the feasibility and profitability of each use case.
– Design, optimize and manage the network slices and mobile edge applications required.
Wider AI applications within telecommunications
One of the obvious benefits of AI in telecommunications is its ability to process vast amounts of data quickly and accurately, providing insights into customer behavior and preferences that would have been impossible to obtain through traditional means. For example, AI can rapidly analyze call records and network traffic patterns to identify trends and predict future demand. This information can then be used to optimize network capacity and improve service quality.
Another benefit of AI in telecommunications is its ability to automate routine tasks, freeing employees to focus on more complex work that requires human judgment and creativity. For instance, AI can automate customer service inquiries by providing customers with personalized responses based on their previous interactions with the company. This can improve customer satisfaction while reducing the workload on human agents.
Resource scheduling is another AI application that telecom companies can utilize. The AI-driven IFS Planning and Scheduling Optimization module within IFS Cloud is an example of industry-leading AI-based software that is already used extensively for real-time resource scheduling and optimization.
Creating new revenues: B2B networks and industrial use cases
The exciting business innovation capabilities, particularly with B2B and industrial mobile networks, enabled by the sector’s access to AI are already starting to appear. For example, Telecoms.com reports Ericsson has built a dedicated 5G SA network capable of powering data gathering connected robots, livestock monitoring and agricultural automation. 5G data connectivity will connect farms, allowing robots to gather phenotyping data with stereoscopic cameras. Network coverage spans local crop and livestock farms and parts of the city of Ames.
Alongside leading ERP, EAM, FSM and ESM functionality in IFS Cloud, the recent acquisition of Industrial AI software company Falkonry, Inc. adds AI-generated anomaly detection capabilities to the IFS roadmap. The self-learning solution continuously monitors large volumes of data for assets, machines, systems, and industrial processes to discover and analyze unusual behavior and causes of failures.
Conclusion
Technologies such as network slicing and mobile edge computing are enablers of new business models and opportunities for telecom operators. By using AI-powered solutions such as IFS Cloud to discover potential use cases, operators can differentiate themselves from their competitors and offer more personalized, customized, and optimized services. Data harmonization supports this by ensuring that operators have access to high-quality, consistent, and comprehensive network data that can feed their AI algorithms. Together, data harmonization and AI-enabled enterprise software such as IFS Cloud are helping operators transform their networks into agile platforms and analyze data for innovation and value creation.
