Pioneering progress: Telecommunication's trailblazing GenAI adoption
Innovative thinking in the telecom industry is driving the integration of AI applications
Ever since the launch of interactive chatbot ChatGPT in 2022, many companies have been engaged in a generative AI (GenAI) “Space Race”, rushing to implement applications of this groundbreaking tool. The benefits of GenAI for telecommunications providers include increased revenue, cost savings and more personalised interactions with customers, but there are a number of challenges to consider as well.
When exploring where GenAI might benefit an organisation, it’s wise to seek input from an experienced technology partner who can help evaluate and recommend options that will deliver the maximum ROI.
| Expert insight: | “AI can reduce costs and increase revenue but only with the right core IT systems and data. It’s critical to address the business change in governance and business controls.” Paul Jevons, Director, expert in tech-enabled transformation. |
| By the numbers: | Financial impact of GenAI in the technology, media and telecommunications (TMT) space New GenAI use cases estimated to generate $380-$690 Billion Annually*: - Tech: $240-$460 billion annually - Media: $80-130 billion annually - Telecom: $60-100 billion annually 52% of TMT companies are already using AI (ahead of other industries) Early adopters of AI could see a profit margin boost from 3%-15%** |
** Data source
Applications of AI that can enable rapid growth and efficiency gains
Beyond the now ubiquitous customer service-enhancing chatbots, there are many other applications of AI that can enable rapid growth and efficiency gains in the technology, media and telecommunications industries.
In a recent article, McKinsey illustrated the expected total impact of GenAI on various business functions and the total expected revenue generated by GenAI by industry. It’s interesting to note that the TMT industries are expected to generate some of the highest percentages of total industry revenue (tech: 4.8%-9.3%, media and entertainment: 1.8%-3.1%, telecommunications: 2.3-3.7%)
The areas where GenAI is expected to provide efficiencies and cost savings include supply chain optimisation, process automation and risk management. It will likely drive improvements in the areas of sales and marketing as well. Sales teams will benefit from uses such as targeted product suggestions for potential customers, and recommendations to help human sales representatives. In marketing, GenAI can be employed to automatically generate campaigns that include personalised messages to customers or potential customers. This personalisation has been shown to lead to higher campaign response rates, and increased customer satisfaction.
With so many possibilities, it can be difficult to determine where this new technology can have the greatest impact.
Charting a profitable path
Organisational goals, resources, culture and stakeholder appetite for testing the unknown are all factors to be considered when evaluating where to channel investments. Being an early adopter of emerging technology comes with the competitive advantage of being among the first to realise the rewards of innovation. Unfortunately, it also presents the risks inherent in piloting untested tools. An understanding of both the challenges and the opportunities of AI adoption by TMT companies can guide initial conversations.
| Challenge | Opportunity |
|---|---|
| Limited talent pool: Finding experienced advisors to advise on and implement solutions is key, as is hiring and retaining talent with the right skills. | Freedom to innovate: Creating an organisation that celebrates technical innovation can attract & retain top experts with ground-breaking ideas and skills. |
| Customer resistance/fear: Customers are concerned about data privacy and sometimes misinformed about the abilities of AI, so transparency and communication are important. | Customer centricity: AI-powered analysis of customer data enables a more personalised service and a clearer, smoother customer journey. |
| Employee displacement fears: “Streamlining” and “automating processes” makes employees concerned about being replaced, so transparency, emphasis on human oversight of systems and presenting reskilling options can help. | Technology as a partner, not a competitor: When tedious workflows are automated and repetitive tasks eliminated, employees are free to focus on more impactful and engaging tasks. This aids both employee acquisition and retention. |
| Inadequate/siloed data: All applications of AI require vast amounts of accessible, shareable data, so it’s imperative to ensure that datasets are clean and not fragmented | Predictive maintenance: Intelligent systems can monitor their own health and access past data to determine the optimal time for maintenance, before an issue occurs. |
| Regulatory complexity: Governments are still deciding where and how to regulate this new technology, so keeping up with new and changing regulations requires constant attention. | Fraud prevention: Analysis of interactions can identify anomalies and automatically block access to bad actors. |
Key success factors
With so many considerations to keep in mind, taking the first step can be the most daunting. Although many factors will influence the ultimate performance of an organisation’s AI transformation, there are some best practices to note:
Partner with experts: Open a dialogue between digital experts and business leaders. This will ensure that the solutions align with business needs and industry standards, while also mitigating the risks of integrating new technologies.
Don’t lose sight of the human factor: At each step of the process, and through implementation, human evaluation and oversight is necessary to make adjustments and determine success.
Select the tool you need, not the whole toolbox: It may be tempting to try to throw every exciting potential solution at a problem, but taking the time to review needs and select a manageable set of solutions, in a phased approach, can help ensure effective investment.
When you're think finished, you’re not finished: Ongoing monitoring of data clarity, data analysis results, overall performance and adherence to goals is crucial to fine-tuning applications and creating sustainable success.
| Nearform case study: | Helping a telecom giant prevent outages by introducing end-to-end automation |
|---|---|
| Issue: | A huge telecom company needed a solution to stop recurring major service outages that disrupted business during key times. |
| Nearform created: | Automated dashboard creation that sped up monitoring and alerting procedures and eliminated human error. This included: - Response planning: ensuring issues were assigned to the correct people. - Minimising customer downtime: catching incidents before they reach customers. - First response: delegating support to appropriate teams. - Proactive SRE: adapting metrics to pre-empt technical errors. |
| Impact: | In only six weeks, Nearform created robust processes to safeguard our client against major outages: - Automated deployments in under 30 minutes for all 37 of their services - Decreased response cost - Easier scalability - Monitoring of all systems to catch issues before they are in production |
| Nearform expert insight: | “We developed dashboards that enabled the industry standard observability our client needed, giving them the resilience they require to protect them against the loss of revenue from major outages.” Luca Lanziani, Head of DevOps and Platform Engineering at Nearform |
Telecom companies leading the way
Although consumer-facing applications of artificial intelligence appear to be only in their relative infancy, there are already many established ways to further leverage it in the TMT space.
Innovative thinking in the telecom industry is driving the integration of AI applications, and projections show that the global AI in telecommunications market size is growing. In 2020 it was $12B, and by 2027 it is expected to reach $15B, a CAGR of 43%.
This growth is due to the many ways that AI-enabled applications leverage the vast amounts of data gathered and shared in the telecom industry to address some of the most difficult challenges.
In addition to predictive maintenance of equipment and fraud and unauthorised network access prevention mentioned above, there are several telecom-specific applications that are driving adoption. These include network optimisation, Robotic Process Automation (RPA) to streamline back-office operations and deep data analysis of diverse datasets that is raising the average revenue per user (ARPU) and driving subscriber growth.
The best time to start exploring the power of GenAI was yesterday. The next best time is now.
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