As the Fourth Industrial Revolution rolls in, this time driven by artificial intelligence (AI), we are witnessing the distinction among the physical, digital and biological worlds slowly fade away.
AI has become the dominant force behind many products that are now indispensable for modern life. Think voice-activated assistants like Siri, Facebook’s face recognition algorithm, personalized recommendations from Netflix and GPS systems that get you to your destination via the best possible route.
How AI Has Transformed Telecom
AI has helped the telecom sector redefine customer experience, bringing forth new opportunities but also complicating business models. Here are some of the ways that AI has contributed to the telecom industry:
1. Improved Network Optimization
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.
2. Fraud Detection
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 cutting-edge technologies considerably reduce response time, allowing telecom businesses to thwart the threat before it exploits internal information systems.
Many organizations store clients’ financial information away from the network so that user information is not compromised in case of a cyberattack. In fact, 61% of enterprises say that it is impossible to detect breaching attempts without AI technologies, whereas 48% say that the allocation of their annual budget toward AI in cybersecurity has increased by an average of 29% in 2020.
3. Predictive Analytics
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.
4. Robotic Process Automation
In a survey conducted by Deloitte in 2017, 40% of companies stated that they expected their businesses to transform through “cognitive computing systems.” Considering the massive volumes of data that telecom industries deal with, there is a chance of human error at each step. However, by automating these processes through robotic process automation, not only is human error minimized, but mechanical and rule-based procedures can also be followed more efficiently.
Better Technology Brings Bigger Risks
While there are many benefits to AI, it is not without its challenges. According to a study conducted by Intouch International, 67% of American internet users are advocating for strict internet privacy laws. Many are afraid that online privacy is now a losing game since personal information has become the domain of AI-powered apps.
For example, using bank and financial apps has become commonplace, but do you know if your credit or debit card information is secure in cyberspace? We’ve all gotten used to providing personal information when signing up for new digital services, but what if the service isn’t dedicated to protecting our info?
Even the best information security programs are not 100% guaranteed to work. Besides, hackers use increasingly sophisticated tools to breach security and access user information that they can later exploit for nefarious uses.
This has led to hundreds of cyberattacks over the past few years. In 2018, a security breach at Facebook compromised the personal information of 50 million users. Similarly, earlier this year, Twitter announced that accounts of more than 130 high-profile users, including those of Kanye West and Joe Biden, were hacked.
This is proof of how big the issue of cybersecurity really is. Conventional technology isn’t advanced enough to keep up with new developments, nor is it efficient enough to keep track of digital footprints.
Data-Sharing Policies To Ensure User Privacy
This increasing threat to private data has mandated governments and technicians to think about regulating control and consent over shared information. In recent years, developments have made digital environments more secure.
One such venture is the SPECIAL (Scalable Policy-aware Linked Data Architecture For Privacy, Transparency and Compliance) project. It develops technologies allowing data controllers and subjects to mediate consent and interact in user-friendly ways.
Similarly, in late 2019, Congress passed the Consumer Data Privacy legislation to determine how companies like Google, Facebook, Amazon and Apple collect and use data. In fact, on May 7, 2020, the Covid-19 Consumer Data Protection Act passed a bill on how businesses would use data collected through IoT to predict insights during the pandemic. However, privacy rules ensure that consent is granted for location tracking, and all data will be deleted once the crisis is over.
While all these measures are well and good, the policymakers have managed to regulate only specific sectors or types of information. There is a growing need for a singular, comprehensive federal law that standardizes the collection and use of personal information. Until that happens, exploiters will continue to take advantage of the loopholes in existing laws, and user information will be at risk
The Future Of AI In Telecom
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 over 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.
Take, for example, the fact that the oil giant Shell uses machine learning to detect unsafe actions and alert concerned authorities to ensure a safe, healthy workspace in real time. Similar applications of AI in the telecom industry are imminent.
With intelligence-powered data, reliable insights and manual expertise, there may be no limit to what AI can help us achieve.