According to studies by BCG, Artificial Intelligence (AI) should be central to the transformation of Telcos, as it assists in enhancing performance in both the short and long term (Banerjee et al., 2020). We discuss the challenges Telcos currently face and how AI is vital to mitigating the effects of these challenges:
The Covid-19 pandemic and the regulations imposed during this time, such as working from home, have pushed Telecommunication services providers customers to equip their homes as their new office environment. This trend has stuck and continued past 2020 as office attendance is still at a low in 2022, explains Makarchuk (2022).
According to Makarchuk (2022), what customers want from their Telecoms service providers is reliable, high speed data connectivity, with flexible pricing options, a high level of personalisation and access to relevant additional services, such as teleconferencing and VPN access. Due to alternating demand trends, Telcos need to shift their focus to improving their Customers Experience (CX). This can be done by providing customers with offerings personalised to their needs (Banerjee et al., 2020). This can be achieved more easily and quickly through the use of machine learning and Artificial Intelligence (AI) (Makarchuk, 2022).
Banerjee et al. (2020) explain that AI can assist Telcos in the following ways:
- Provide hyper-personalised engagement with consumers
- Configure bundles according to customer needs such as combining teleconferencing, VPN and productivity measuring applications
- Provide predictive analytics on demand and network load
Since the beginning of the year, the plight of the telecoms industry has been the disruption in the supply chains of labour and equipment (Makarchuk, 2022). The constraints placed on the supply chains from manufacturing locations that were previously relied upon have resulted in longer lead times to clients (Bornstein, 2020). This has made it more difficult for Telcos to keep up with the higher demand of consumers and has negatively impacted serviceability and, ultimately, revenue (Bornstein, 2020).
Simultaneously, huge market players have planned to start the highly-anticipated rollout of 5G networks, as well as the upgrading of relevant infrastructure (Makarchuk, 2022).
Many Telcos are experiencing delays in the process of expanding and upgrading to 5G networks because of issues such as; a reduced capacity of employees, regulatory delays, or restricted access to the office (Banerjee et al., 2020).
With risks in service delivery and unreliable equipment suppliers, AI can be used within the Telecoms industry to manage the manufacturing process, provide diagnostics on terminal devices and automate the procurement process in order to hasten upgrades, orders and maintenance of infrastructure (Makarchuk, 2022).
AI has proven to be vital in optimising both fiber and 5G deployment and network planning through the use of automation even before the pandemic (Banerjee et al., 2020). It has been particularly useful in allowing Telecoms service providers to switch from a “site-centric” rollout plan to one that places customer value at its centre (Banerjee et al., 2020). Banerjee et al. (2020) explain that AI has the ability to identify primary sites for rollout based on a variety of factors, such as financial data and customer experience, while also taking into account vendor delays and timelines for regulatory approval.
Customer Support Services:
The influx of customer support requests throughout the pandemic has made it difficult for Telcos to maintain the same level of support as before the pandemic hit (Makarchuk, 2022). Banerjee et al. (2020) explains that shortages in staff, limited site access, and deficient component supplies are making it challenging for Telecoms service providers to provide efficient and effective customer service and maintenance. This is further exacerbated by the pressure placed on Telcos for fault resolution due to overloaded networks (Banerjee et al., 2020).
Employing AI customer support tools can result in the effective reduction of support requests that reach and require the assistance of support staff as a large portion of customer requests can be attended to by AI tools, reducing Telco’s overheads (Makarchuk, 2022). Employing an effective AI-based customer service strategy can result in a 20% to 40% deflection rate in customer calls, which in turn can lead to a decrease of 10% to 20% in the overall call centre costs (Banerjee et al., 2020).
According to Banerjee et al. (2020), employing an AI-based customer support service involves four processes:
- Self-service menus, utilising chatbots and search technology across all channels.
- Digital interactions with human agents to allow for a seamless transition from human to bot interaction.
- Deflecting voice calls through the use of text or AI voice bots.
- Utilising voice channels to deal with more complex requests or to offer premium services using human agents.
Business Operational Efficiency:
The pandemic has brought to light a need to ensure that business processes are crisis-proof, especially functions relating to customer experience (CX), service delivery, and sales (Banerjee et al., 2020). Improved flexibility and agility (Banerjee et al., 2020) will allow Telcos to increase their recovery time from disruptions, such as an economic or health crisis.
A popular AI-powered tool that Telcos are employing is Robotic Process Automation (RPA). The purpose of employing RPA is to improve operational efficiency and reduce delays (Makarchuk, 2022). Automating a Telco’s business processes through RPA allows for efficient and accurate “repetitive and rule-based operations” (Marr, 2019).
Another important avenue is utilising AI-based tools for decision-making (Makarchuk, 2022). These tools consistently monitor business operational parameters and highlight opportunities to improve these processes (Makarchuk, 2022). Banerjee et al. (2020) predicts that the collective use of AI to scale can result in an average revenue increase of up to 10% for the average Telecoms company and reduce operational costs by up to 20%.
Considering the vast amounts of customer data available to Telcos, AI and machine learning can allow valuable, actionable insights to be drawn from this data to assist in making business decisions (Marr, 2019). These insights allow companies within the Telecoms industry to extract data to result in improved issue resolution, more efficient daily business operations, and better customer service (Marr, 2019).
According to Makarchuk (2022), the success of Telcos who embark on a journey of digitalisation will unavoidably rely on their capacity to utilise AI technology early on in the process and the development of supporting software. He further states that embedding AI tools into Telco’s key processes and customer services will give these Telecoms companies a competitive advantage (Makarchuk, 2022). Banerjee et al. (2020) explains that Telcos need to prioritize opportunities to embed AI technologies where it will create the most value.
Banerjee, S., Candelon, F., Lorenzo, R., Cacouros, A. and Harguil, S., 2020. Transforming Telcos with Artificial Intelligence. [online] BCG Global. Available at: <https://www.bcg.com/publications/2020/transforming-telecommunications-companies-with-artificial-intelligence>
Bornstein, C., 2020. Supply Chain Challenges for the New Normal. [online] ISE. Available at: <https://www.isemag.com/cande-netdev-ops-gis-open-source-networks/article/14267203/supply-chain-challenges-for-the-new-normal>
Makarchuk, R., 2022. How to Make the Most of AI in Telecommunications. [online] Intellias. Available at: <https://intellias.com/ai-in-telecommunications/>
Marr, B., 2019. The Amazing Ways Telecom Companies Use Artificial Intelligence And Machine Learning. [online] Forbes. Available at: <https://www.forbes.com/sites/bernardmarr/2019/09/02/the-amazing-ways-telecom-companies-use-artificial-intelligence-and-machine-learning/?sh=435a15f94cf6>