GenAI isn’t the Prophesied Job Killer. What Does This Mean for Telcos?

GenAI’s certainly the buzzword of the decade, fed by OpenAI’s rapid growth and the promise of eventual artificial general intelligence (AGI). But although use cases for improving personal productivity have emerged, enterprise applications remain elusive. 

Salesforce’s Super Bowl 59 ad is perhaps the most apt example of this gap; the ad showed genAI assisting Hollywood actor Matthew McConaughey in avoiding a dining disaster (that most people could solve themselves, by the way). What does it say when a technology pioneer such as Salesforce fails to demonstrate a compelling case for enterprise adoption?

Of course, we don’t discount genAI’s potential for enterprise transformation. However, to leverage the technology effectively, it’s essential to realize what it can—and cannot—realistically achieve. At present, it’s becoming increasingly clear that the promise of wide-scale job automation is yet to be realized, compelling enterprises to pursue more modest ambitions.

In this article, we explore how telcos—and enterprises in general—can integrate genAI with their broader digital transformation strategy by exploring more realistic use cases around task automation and augmenting human workers. 

The Semblance of GenAI doom

The narrative surrounding genAI has undergone a significant shift from the long-prophesied promise of transformative innovation to being overshadowed by the looming fear of widespread job displacement. However, this backlash is premature at best and, at worst, unwarranted altogether. Industries at large, including telcos, remain pragmatic in their approach, experimenting with genAI’s potential for task automation without expecting job elimination as a probable outcome. In the context of telecommunications, this may mean genAI solutions will assume the form of simple autonomous network activities, according to Dr. Khalid Basit, COO at Telecom Intelligent Automation Council (TIAC). This can be summarized as telcos moving towards modest and achievable goals, which will be elaborated further below. 

Substantiating the Take

At present, genAI (in the form of GPT and copilots) is primarily adopted by individuals, rather than being deployed through company-wide programs across organizations. Telcos are venturing into organizational deployments with caution. Additionally, as genAI’s potential for job replacement remains unclear, telcos have set more modest goals, targeting enhancements that improve productivity and service delivery. 

• Incorporating genAI with intelligent robotic process automation (iRPA).  Intelligent RPA is widely adopted by enterprises to automate both back and front-office tasks. Telcos are leveraging genAI both by:
  
  
  1. Using iRPA solutions with incorporated genAI (such as in the form of copilots). These platforms include genAI features that accelerate process discovery and automatically design process automation workflows.
     
     
  2. Incorporating genAI independently within automation workflows. For example, genAI chatbots may rely on RPA in the backend to access and update data in the CRM or ERP for straightforward self-service tasks.
     
     
• Prioritizing the front-end. For now, telcos are exploring genAI use cases in the front office, for enhancing CX and automating customer service. GenAI-powered chatbots, trained on an enterprise’s internal knowledge base and previous customer interactions, can be deployed to enhance self-service experiences. 
  
  Dr. Basit also highlighted an overlooked opportunity with high potential—empowering humans with agent-facing genAI chatbots. These agent-facing genAI assistants analyze customer interactions and provide tailored recommendations to the contact center agent. Thus, while resolutions are still human-led, genAI personalizes the experience, improving first call resolution rates and reducing average handling times.
  
  
• Back-end optimization shows potential but has limited exploration. We asked Dr. Basit, about genAI adoption and ambitions amongst telcos. He explained how telcos are yet to explore genAI deployments on the network side, exercising caution until its reliability is more established. 
  
  While genAI has evolved in its natural language processing powers, hallucinations, considerations around managing vagueness, and gaps in process planning discourage its deployment in the network. 
  
  
• Adoption curve for telcos. TM Forum’s “AI in Practice” benchmark report revealed that many telcos began their AI journey around 2019-2020, with genAI becoming a key focus recently. The primary focus has been on specific task automation use cases rather than broad job elimination, with deployments centered on areas like energy saving, churn prediction, and customer experience enhancement.
  
  For instance, telcos are utilizing AI for zero-touch network optimization and operation, and simulating user behaviour to proactively address network issues. AI-powered chatbots and virtual assistants are streamlining customer service, making life easier for clients. Furthermore, AI is automating business processes to effectively use data, including automating quoting, ordering, provisioning, monitoring service performance, and troubleshooting.
  
  A leading example of this is Telus’s development of Fuel iX, which is integrated with the company’s own genAI platform. The motivation for developing it was to control issues regarding data training, data integrity, security, and, in the case of autonomous genAI, possible hallucinations. This has given way to an increase in code development and has improved other metrics like the average customer service handling time. Thus, we see a real-life example of a Telco using AI to augment human capabilities rather than replace them.

What’s next for telcos? The evolving scope of genAI

The short answer to the question of ‘What’s next?’ is in essence that telcos must utilize genAI as a tool for enhancing the existing capabilities of current technologies and teams, and not as a replacement for human workers. Reiterating this idea might help dispel the aforementioned fears regarding genAI’s use in the industry.

And how exactly does one go about doing that?

• Use case selection. The first line of action is a careful assessment of the needs of the enterprise and the identification of areas where genAI can prove to be an asset. Due to the nature of the industry and its significant impact on people’s lives – caution must be exercised when using genAI for transactional tasks and for making decisions – demanding a more selective approach here.
  
  Examples of genAI priority areas would be tasks that are repetitive, time-consuming, or data-intensive. The next step would be incorporating the insights to structure focal areas like Customer Relationship Management (CRM) and Business Support Systems (BSS). GenAI integration does not stop there and can also include complementary genAI technologies and practices for broader process automation initiatives. These include investing in the training and upskilling of programs and teams. Another priority for enterprises that use genAI, and especially telcos is their relationship with customers about genAI usage.
  
  
• Maintaining trust with customers. This includes transparency around data collection, governance and security, to address consumer fears around how their data is stored and used. Moreover, in front office interactions, it’s important to let consumers choose from themselves between AI and human-led interactions. The goal is to build trust wherever possible and be proactively mindful of any areas where distrust could arise.
  
  
• Strategic Considerations. This brings us to a discussion revolving around the strategic considerations for AI adoption within organizations. So, it essentially gives rise to a need for moving beyond a specific technology to address the broader need for an AI framework. Within the framework could be the identification of the different types of AI like agentic AI (understanding customer intent and initiating actions), intelligent AI (pattern recognition for automated actions), and data analytics (pattern analysis for decision-making).
  
  A key point for this identification is that data quality through different channels significantly impacts the reliability of AI outputs. This further emphasizes the importance of the previously discussed data governance. Hence, the level of governance required varies depending on the AI deployment, with autonomous systems needing effective measures in place to minimize unwanted instances like genAI hallucinations, and to mitigate them in case they do occur. 
  
  
• Sustainable Scaling. Dr Basit suggests that Centres of Excellence (CoE) are the ideal way to scale intelligent automation.  Tim Olsen, Founder of EASi AI, terms it essentially as the “HR for digital workers”. This allows one to test and develop clear AI strategies, establish ethical guidelines, and collaborate with relevant resources to bridge internal AI talent gaps.
  
  
• Establishing an AI CoE is crucial to drive adaptation to the technology, but it should be seen as an evolving entity, starting small and growing. This center should focus on identifying use cases, testing genAI technologies, and aligning AI initiatives with business objectives. AI should be treated essentially as a business unit with a specific mindset, and this business sponsorship is essential to the alignment between AI development and business needs.

Conclusion

One can conclude that the painting of genAI as a job killer in the telco industry has now been replaced by a more sensible understanding of its potential. Telcos around the world are embracing genAI as a tool for innovation and efficiency, rather than a replacement for teams. Prime examples would be task automation and customer experience enhancement.

Keeping in mind there is still a lot to explore, the focus on telcos’ side should be on data security, transparency with clients, and taking ethical implications into account. The goal is to augment human capabilities and productivity which will ultimately drive the industry towards operational excellence. And while telcos navigate the AI sandbox, they must remain focused on the strategic deployment of their services to ultimately deliver greater value to their customers.

In a nutshell, telcos must embrace genAI for efficiency, not for job elimination!