I have been engaged in the telecommunications (”telco”) industry for over 25 years, and it is so refreshing to see the positive energy moving into this sector, as a result of applying generative AI (”gen-AI”) technologies. Gen-AI can easily analyze different types of data such as: text, images, code, voice and even create new content on demand, enabling personalization, and new customer or employee engagement experiences.
For years, growth and profitability has been a challenge requiring the telco industry to diversify and offer more professional services and seek out higher margin offerings.
Gen-AI trials are being launched, it seems everywhere, from AT&T, SK Telecom, and Vodafone. Even Bell Canada, a tier one Canadian company, according to Bell’s Director of IT Delivery, Neel Mehta is betting big on the connection between solid data foundations and an effective AI strategy. “We see data as the new oil, and AI as the refinery,” according to Gupta. Today, Bell is using a combination of generative and predictive AI to service its customers. One of Bell’s use cases is using AI to predict environmental events, such as snowstorms or heavy rains, which may cause network outages. This early risk foresight improves resourcing and workforce communication. The costs for servicing major outages of teleco providers is a heavy burden, as Rogers Communications found out last year, when more than 12 million users of its’ cable internet and cellular networks, including those of subsidiary brands Rogers Wireless, Fido, Cityfone, and Chatr were severely impacted. Using AI methods to analyze all router risk impacts and getting workforces ready for major outages is a major productivity improvement opportunity for the telco carriers.
According to IBM’s Institute for Business Value, 60% of executives will be piloting gen-AI solutions by January, 2025. What is important is that rather than taking many months to launch a pilot, gen-AI pilots can be deployed in a few weeks.
The telco industry is plagued with outdated operating practices and processes, so gen-AI is providing new profitability possibilities. In some cases, returns on incremental margins can increase 3 to 4% points in two years, and as much as 8 to 10% points in five years, by enhancing customer revenue through improved customer life cycle management and by reducing operating costs.
The industry has so much technology debt in legacy systems, that finding new pathways with gen-AI must feel like a breath of fresh. A number of use cases that can be bring more value to telco leaders include:
1.) In sales operations, capturing all the sales documentation on how to sell different products and services and pricing model information (essentially absorb all the knowing sources) can be absorbed into a powerful knowledge gen-AI engine. Other innovative sales solutions include: using a chat bot to answer any question that a sales manager or sales rep may have on a sales funnel, or bringing together real-time knowledge from the full customer experience value chain and sales practices. This may seem like a daunting opportunity, but we finally have a new suite of technology innovations where gen-AI combined with AI search and knowledge management methods will absolutely drive a winning combination to fuel increased profitability.
2.) In customer service operations, increasing agent productivity, and developing AI chatbots to improve agent support, provide promising return on investments, and are estimated to secure a 15-20% productivity impact. Another useful use case is to transcribe all voice or written client interactions into summarizations, further creating a smarter customer service knowledge center. A Latin American telco recently reported that they increased their call center agent productivity by 25% and enhanced both their customer experience, and agent skills by leveraging gen-AI recommendations.
3.) In marketing operations, gen-AI can identify new sales leads from customer calls and then target using customer micro-segments directly from the call insights. AI enables hyper-personalization, deeper customer insights, and faster content generation. A unique AI model could be developed that extracts household details, using customized marketing messages and even include customer preferences, based on cognitive learning style from persona intelligence methods, i.e.: succinct messaging, to authority messages, to storytelling messages. McKinsey recently reported that a European telco had increased their customer conversion rates for marketing campaigns by 40%, while reducing costs by using gen-AI to personalize content.
4.) In network operations, gen-AI can optimize technology configurations, enhance labor productivity, improve inventory and network planning. One large telco has accelerated their network mapping by analyzing and structuring data about network components, including specifications and maintenance information, from supplier contracts. This can enable the telco to more accurately assess component compatibility, maintenance requirements, improve operational planning and optimize capital.
What questions should Telco Board of Directors and C-Suite leaders ask to advance their AI leadership capabilities?
1.) Do we have a clearly defined AI transformation strategy and what are the cost savings opportunities to unlock unprecedented growth and new value?
2.) How good is our data and how easily accessible is it to take advantage of gen-AI?
3.) How skilled are our leaders to design, develop and sustain a gen-AI environment?
4.) Do we have the right technical infrastructure to apply gen-AI successfully?
5.) How are we ethically applying gen-AI? and
6.) How are we ensuring that we are managing our sustainability goals, given the high demands of AI on energy and water?
In summary, the telco players that are innovating and experimenting with early generative AI use cases will no doubt grow faster and capture a more significant share of the nearly $100 billion in incremental value that McKinsey is predicting. That is in addition to the $140 billion to $180 billion in productivity gains that gen-AI will create in the telco industry of what could be unlocked by traditional AI. Keeping a vigilant eye on the AI ethical impacts of sustainability and energy consumption levels is a key risk factor that the industry also must grapple with.
