“AI will transform every industry, and it’s critical that we all understand how it works and how to use it responsibly.” – Andrew Ng
As one of the world’s leading telecommunication companies (a.k.a. telcos), when British Telecom implemented an AI network threat system to handle over 4,000 security threats daily, it marked a milestone of AI scaling. The volume of threats meant that human detection and remediation were practically impossible, making AI the only cost-effective solution.
Telcos increasingly turn to AI to optimize their operations and improve their services. However, several risks are associated with scaling AI, either from external issues, including regulatory challenges, or internal infrastructure and integration challenges. We will be better able to handle these benefits and hurdles by considering them prior to launching AI implementation projects rather than being surprised by them after launch.
Risks for implementing AI have been a shifting landscape. With regulations from multiple jurisdictions aiming to protect consumer privacy while not overly restricting AI services, telcos have multiple considerations to mitigate the potential negative impact of AI systems. Here are seven critical considerations for applying AI to telco challenges.
1. Violating Regulations
One of the most significant risks of scaling AI in telecommunications is violating regulations. Telcos must comply with various regulations, such as the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and the ISO/IEC 27032 standard. Failure to comply with these regulations can result in severe penalties, legal liabilities, and damage in the hyper-competitive marketplace. T-Mobile recently experienced significant pushback from customers regarding their AI implementation of monitoring text messages for “offensive” content.
2. Costs vs Benefits or ROI
Another risk of scaling AI in telecommunications is the costs vs benefits or return on investment (ROI). Implementing AI can be expensive, requiring significant investments in talent, infrastructure, and technology. Telcos must carefully evaluate the costs and benefits of AI and ensure that the ROI is positive. One of the first departments to feel the brunt of AI has been customer support. Now that natural language engines have reached a high level of capability, telcos are reducing customer support staff to maximize their corporate R.O.I. British Telecom has stated that AI will replace at least 10,000 jobs. (Source: https://economymiddleeast.com/news/telecom-ai/)
3. Integration with Existing Systems
Integrating AI with existing systems can also be challenging. Telcos have to ensure that their AI systems can integrate with their legacy systems, such as billing, provisioning, and network management. Since the legacy systems are often built around mainframe architectures without the flexibilty for AI integration, this is a major roadblock for some companies.
4. Scaling Issues
As with any new application, going from the test phase to the network-wide roll-out presents potentially unforeseen issues. However, the main problem with AI is that it is designed to operate without human interference, making it harder to control once launched. Ideally, when rolling out your following AI implementation, you will want to have control systems in place to mitigate these scaling issues early. The results can be business-critical if the issue gets too far into the system too quickly.
5. Customer Privacy Concerns
AI in telecommunications also raises customer privacy concerns, especially related to monitoring customer communications. Telcos must ensure that their AI systems protect customer data and privacy. They must also be transparent about using customer data and obtain customer consent when necessary. Failure to protect customer privacy can result in legal liabilities, reputational damage, and customer churn.
6. Proper Level of Human Control
Finally, AI in telecommunications requires the proper level of human control. Telcos should make certain that their AI systems are transparent, explainable, and auditable. Building in some level of human oversight can ensure that their AI systems
AI is here to stay and impact more business processes across virtually every industry. Telcos have aggressively adopted AI systems to enhance consumer-facing applications and internal network operations. However, companies have to address the challenges of implementation risks prior to rollouts. Mitigating the risks by working with experienced AI risk consultants and risk control applications will prevent regulatory infractions, reduce consumer privacy issues, minimize the need for human oversight, and help integration with legacy systems. Ultimately, confronting AI risks will ensure AI serves telcos and their clients synergistically.