Three critical challenges for the telecommunications industry today are the exploding demand for bandwidth, the difficulty in finding skilled workers, and the limitations of existing networks and infrastructure.
AI is one of many technologies that can help business leaders meet these challenges. On its own, AI can do very little. When it’s integrated with data-science tools, machine learning (ML) applications, cloud platforms and edge computing, it can bring new life to a brand, its employees and its customers.
We’ll explore the role of AI in the telecom industry, practical applications, telecom AI solutions, and strategies for adoption to help telecom companies adapt to the changing landscape.
AI is the overarching set of technologies that enables machines to think and act much like humans. Its exact definition changes constantly as fields emerge and become sufficiently well-defined enough to become parts of products like AI-enabled smartphones or services like mobile hyperscale computing.
The principal virtue of AI is that it can parse enormous data sets far faster than any human. Within those data sets, it can spot trends and predict outcomes. AI has already begun to accelerate digital transformation and enable strategic change across industries.
For example, manufacturers have used it to predict demand for specific products, then streamlined their supply chains to create, maintain and expand production and gain an advantage over their competitors.
In the telecommunications sector, AI has been used to inform network build strategies, optimize antenna placements and tower heights, analyze customer data and usage trends, and of course to facilitate a number of customer-service functions. There are many more opportunities to deploy AI in the telecom sector, if network operators and ecosystem partners take the time to consider how data can help evolve their business and industry.
AI when integrated with data-science tools, machine learning applications, cloud platforms, and edge computing, can bring new life to a brand, its employees, and its customers.
Customers meet telecom brands in a variety of places, from social media to their monthly bills. A customer pleased with their experience needs little help from the brand. Things are going as planned, and the connection between them is genuine and authentic, so they’re engaged with the brand enough to buy.
If that changes, the brand must work overtime to re-engage and retain the customer. This involves understanding the problems they face and working to resolve them as quickly as possible without alienating the customer. Time is money in a very real sense when these situations arise.
Chatbots can handle many recurring interactions, and most customers are accustomed to chatbots and won’t balk if they encounter one. But more-complex problems require human or human-like interaction. This is where combining generative AI with conversational search, retrieval-augmented generation (RAG) and other technologies can save the day.
Call-center employees can benefit from access to these technologies as well. Tasked with handling the most challenging customer problems, they need up-to-the-minute information that scripts, training, and preparation may not provide. Here again, conversational search and RAG can almost instantly find and share the information and context they need to solve this customer’s problem. These technologies also enable employees to get information they need regarding policies from HR, finance, and other internal teams.
The challenge for business leaders is understanding which tasks machines should handle, which ones require human interaction, and which ones require automation with a human touch. With that context and insight, the task pivots to ensuring that each problem goes through the channel that’s best equipped to solve it and preserve the customer’s relationship with the brand.
Optimizing and maintaining networks involves caring for both the carrier’s physical infrastructure and their technology estate. A bad scenario for physical infrastructure could involve sending crews into rough terrain during foul weather to keep critical services working. These situations can put the carrier’s vehicles, equipment and people in harm’s way.
Predictive maintenance is an excellent way to avoid these situations. IoT sensors and edge computing capabilities in a base station can push data to a cloud platform, where the operator then leverages AI to compare data and predict when the devices might fail based on their actual use. Overlay that information with weather forecasts, and it’s not hard to see that updating devices just before winter begins might be the way to avoid sending crews into a blizzard to bring a base station back online. From there, it’s a planning exercise to ensure that you have enough of the right parts on hand as winter approaches.
Similar thinking works for the tech side of the network. AI can scan data from your tech estate to detect faults, begin to conduct root cause analysis, and suggest fixes for problems in code. It’s then up to a human to decide whether to implement those changes. Working with AI in telecommunications may be a new step for some employees, so it’s important to consider data science upskilling when updating your systems and incorporating AI.
Leveraging AI in product development is no longer optional. Consumers today already expect companies to use AI-generated insight to improve the products, services and experiences that matter most to them. The key here is to leverage AI to stay focused on what the user needs.
Begin by using an AI-enabled customer data platform (CDP) to map the customer journey in real time. The CDP reveals why people do things and documents what they actually do, thus informing ideas for new products. Digital twins of customer groups, meanwhile, allow operators to test multiple scenarios to predict customer responses rather than gather expensive focus groups or conduct internal surveys. Initial hypotheses can be generated by humans, but a strong AI solution will then be able to generate new ones after the first few rounds of tests.
It can be tempting to use the discovery phase to understand every possible issue and use case for customers. Instead, use discovery to reach a decision, then begin to build the product and use AI to parse user data for future product improvements.
Keeping a focus on personalized customer service will help harness AI for new product development and expansion.
…the overarching idea should be to balance the best of what humans can bring to your transformation with the best of what machines have to offer.
The growing use of mobile devices and the growing number of channels through which people communicate expands the vulnerabilities available to cybercriminals. They’re already using AI to increase the speed, volume and sophistication of their attacks. The only way to counter their efforts is to use AI to increase the sophistication of your defenses.
The risks of these attacks include reputational damage, revenue loss and possible financial penalties for failing to comply with laws and regulations.
Mobile devices transmit a vast amount of personal data that becomes vulnerable at many points as it travels between devices. The need for AI-driven robust privacy measures to protect your data, devices, people, and infrastructure are not negotiable.
AI-enabled tools can scan your network for anomalous or potentially malicious activity, then alert incident teams, and even suggest remediation. Agents can be authorized to undertake defensive countermeasures as soon as an incident has been detected.
The growing use of mobile devices and the growing number of channels through which people communicate expands the vulnerabilities available to cybercriminals. They’re already using AI to increase the speed, volume and sophistication of their attacks. The only way to counter their efforts is to use AI to increase the sophistication of your defenses.
The risks of these attacks include reputational damage, revenue loss and possible financial penalties for failing to comply with laws and regulations.
Mobile devices transmit a vast amount of personal data that becomes vulnerable at many points as it travels between devices. The need for AI-driven robust privacy measures to protect your data, devices, people, and infrastructure are not negotiable.
AI-enabled tools can scan your network for anomalous or potentially malicious activity, then alert incident teams, and even suggest remediation. Agents can be authorized to undertake defensive countermeasures as soon as an incident has been detected.
Leveraging AI in product development is no longer optional. Consumers today already expect companies to use AI-generated insight to improve the products, services, and experiences that matter most to them.
AI in telecommunications transformation requires major changes, new collaborations and significant upskilling. As you face these issues, the overarching idea should be to balance the best of what humans can bring to your transformation with the best of what machines have to offer.
You’ll need to upskill people across the company, not just at the bottom. Of course you’ll need people to improve their technology skills, but it’s important to also look at data science soft skills for everyone involved.
Unless you have a significant internal AI capability, you’ll need to partner with AI vendors and hyperscale cloud partners. These teams can help you build new apps, integrate workloads, and translate your business goals into reality.
The future of AI in the telecom industry is bright. Everything is changing, leaving a large opening for AI to step in and revolutionize processes. Transforming any business is a huge undertaking, but telcos are even more complex than most. Trends that began to accelerate just a few years ago are now moving faster than ever with no signs of slowing. The relevant issues change constantly, and the implications of technology for your company and your legacy tech stack change with them.
At the same time, leaders who focus on balancing human skills with machine capabilities, who leverage the speed with which AI can work, and who understand the additional adjacent technologies that AI requires will have an easier journey.
No matter where you are in your AI-enabled transformation journey, Sand Technologies has the resources and professionals through Sand Academy to provide clear, common-sense guidance and the technical expertise with AI in telecom needed to provide a better experience for telecom customers.
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