According to Omdia (owned by Informa), service providers face intense pressure to transform their IT systems, operations, and processes. Telco AI investment is expected to ramp up in 2021, but there is not as much clarity as there should be around best practices and business outcomes.
Omdia’s research indicates that early 80% of service providers see the use of AI and analytics, when it comes to the automation of network activities, as an “important” or “very important” IT project for 2021. Nearly 60% of them are planning to increase investment in AI tools.
Top AI use cases are expected to include network fault prediction and prevention, automation of end-to-end life-cycle management, and the management of network slicing. AI will also support a variety of non-network use cases, including using AI to support new
business models such as contextual offer management as well as automating and personalizing customer engagement and delivering customer insights.
Let’s take automation as an example. As networks become more complex and services more difficult to manage (e.g. 5G core networks, edge computing and network slicing), Omdia emphasized that automation was becoming critical. Automation of service fulfillment and assurance and creating highly prized “closed loops” – where the need for human intervention is minimal – are usually seen as some of the main drivers for AI investment, as a way to improve operational efficiencies.
It is often said that it is crucial to consider the potential ROI (Return On Investment) before initiating an automation project. ROI is certainly a good starting point for sorting out “must have” from the “nice to have” automation project, whether looking at it from the perspective of five-year cost savings, annual operating cost, time to value, or some other indicator. However, measuring automation outcomes is more complex than it may at first seem. It is of course useful to directly compare operations costs before and after adoption of an AI-based solution, but it is not the full story. It is also important to consider the business outcomes that require prioritization. These can include improving the accuracy of a process, increasing consistency and predictability, including ensuring compliance with specific SLAs, delivering greater reliability, boosting productivity, or reducing turnaround times. The list is extensive, but to make a success of an automation project it is important not to lose sight of the end goal, and to identify those KPIs (Key Performance Indicators) which specifically support the business outcomes an organization is seeking to achieve.
An AI-driven automation also needs to be sustainable. It’s not just about having the capabilities in place to address incidents as they occur. A process automation also needs to continue to be relevant even when a network/IT element is upgraded, or a vendor swapped out.
Visibility is also essential, because to improve anything you need to be able to measure it. But how does a service provider know if they are automating more successfully than their peers? There are plenty of sources of AI-linked training and support, as well as best practice guidance and models provided by industry bodies like the TM Forum. But there is not as yet a commonly agreed methodology to assess automation in the telco space. Some vendors have internal measures, such as internal process automation indexes, but this is not the same as having an industry-wide measure.
“Cloud-native and distributed cloud architectures and the growing importance of the network edge are adding to the complexity. AI is increasingly needed because existing operations are too reactive and rely heavily on human operators to execute functions,” said Kris Szaniawski, Omdia’s practice leader of service provider transformation.
“In current stressful circumstances, service providers that provide a fragmented customer experience will be quickly punished,” warned Szaniawski, who noted that progress toward enabling omnichannel customer engagement “has not always been as advanced as it should be.”
The Omdia report concluded by by suggesting service providers should make “targeted use of AI to better orchestrate customer journeys, as well as invest in well integrated central data repositories and robust data management capabilities.”
- Network optimization
- Preventive maintenance
- Virtual Assistants
- Robotic process automation (RPA)
In these areas, AI has already begun to deliver tangible business results. AI applications in the telecommunications industry are increasingly helping CSPs manage, optimize and maintain not only infrastructure, but also customer support operations. Network optimization, predictive maintenance, virtual assistants and RPA are all examples of use cases where AI has impacted the telecom industry, delivering enhanced CX and added value for enterprises.
As Big Data tools and applications become more available and sophisticated, AI can be expected to continue to accelerate growth in this highly competitive space.