AI and the telecom industry

While the telecom industry is mature and has a built-in infrastructure, rapid change is happening alongside the huge impact brought by Covid-19, including the rollout of 5G and the increasing adoption of IoT. These changes also bring increased competition from new technology-driven entrants, and there is growing regulatory pressure.

Telecom service providers are large and multifaceted organizations, so it isn’t uncommon for the business to operate in silos and use separate solutions to manage workers and contractors in the field and equipment and supplies, whether owned, rented or leased. Combining asset management, work management and mobile field enablement seamlessly from a single software package allows some transparency.

With customers at the heart of what a telecom provider does, service is the key touch point and main source of friction. Telecoms, by their nature, often only come into mind when changes are being made or when something goes wrong. With customer expectations at an all-time high, getting it right the first time has never been more important.

Customers want to be empowered to report issues, book their own appointments, and track service crew location and work progress. Customers want short service windows, so it is crucial to automate scheduling so that the right crew member or contractor is dispatched. The crew will need access to the account, history and job details and will need to have the right parts and tools to give them everything they need to get the job done.

Asset downtime is not an option for a telecom service provider because the costs go far beyond the lost usage. Service must be focused on prevention-focused uptime and performance, not a reactionary break-fix model. Remote monitoring capabilities enable any potential issues to be detected or predicted and then addressed.

The expansion of connected assets and equipment via the IoT, AI and ML technologies is key to a telecom company implementing these automatic, predictive processes. This will prevent downtime, improve profitability and create a competitive advantage. A major challenge facing telecom providers is the network infrastructure they manage, and the assets they have responsibility for can cover a large geographic area.

Location intelligence is also fundamental in keeping up with customer expectations and giving them a competitive edge. Telecom service providers are turning to location and GIS to help with their complete digital transformation by leveraging location services and maps in new IoT products and services while supporting traditional lines of business.

With organizations’ increasing migration toward cloud-based solutions, an optimized, integrated solution will lead to happier customers, faster service and increased profits. But with many organizations still deeply entrenched in legacy systems, these can be complex to integrate with some cloud-based technologies.

Organizations need to realize that the work done by those in the field is the first line of contact with customers, so they need to support workers with the best, most straightforward solution. Ideally, this should be a single solution that combines all scheduling, service and equipment tools with industry-focused IoT and AI/ML support.

The role of AI

AI has helped the telecom sector redefine customer experience, bringing forth new opportunities but also complicating business models. AI applications in the telecommunications industry help CSPs build self-optimizing networks to improve customer satisfaction and prevent outages. Since AI can help networks adapt and reconfigure according to customer needs, they can provide consistent service more proactively.

AI also makes use of advanced algorithms to detect and predict any network anomalies. In the context of cybersecurity, this means giving businesses the ability to detect a cyberattack in advance. Additionally, these technologies considerably reduce response time, allowing telecom businesses to thwart the threat before it exploits internal information systems.

AI and machine learning have enabled telecoms to extract valuable business insights. Since telecoms have a massive amount of data, AI can use it to make efficient and effective decisions through customer segmentation, predicting the lifetime value of a consumer and making purchase recommendations.

Cloud, 5G and AI, cognitive engagement with consumer insights have made it possible to answer a wide variety of questions, all in the customer’s language. However, in the future, as businesses get comfortable turning customer insights on to machines, human customer-service agents might become a thing of the past, allowing customers to engage with an intelligent-agent avatar.

AI is also predicted to leap from only dealing with insights to predicting consumer behavior and impacting business decisions. This should lower costs and enhance customer experience, increasing their lifetime value. With intelligence-powered data, reliable insights and manual expertise, there may be no limit to what AI can help us achieve.