Artificial intelligence (AI) and machine learning (ML) have long been touted as major disruptors in the business landscape, and ones which have seen increasing penetration over the last decade.
A recent survey by Gartner reveals that one-third of organizations are now applying AI across multiple business units, with more than half (54%) of AI projects now making it from pilot to production—a long way from the pioneering days of the early AI/ML business revolution. We’re now operating in a business landscape where 80% of executives believe automation can be applied to any business decision.
AI in telecoms is a particularly powerful opportunity, as this data-rich industry leverages its inherent advantage through continued modernization and digital transformation. The truth is that AI and machine learning in telecom have already demonstrated remarkable promise—something we’ve seen in Neural Technologies’ own experience of helping enhance risk resilience and optimize performance. According to one report, the global market for AI in telecoms is expected to grow at a remarkable rate of 47.3% CAGR between 2022-2026.
In this landscape of accelerating adoption of machine learning and AI in telecommunications, communication service providers (CSPs) can continue to build business advantages through a modern, data-driven outlook.
How data automation can drive modernization
There’s no shortage of data in the telecommunications business. Every second sees millions of calls, texts, digital messages, and customer purchasing events feeding into the huge data wealth of the industry. What’s often been more challenging has been turning that data into actionable insight that can improve operations. That’s where AI and ML modernization can come in.
“Although telecom operators produce huge amounts of data, the recognition of how information needs to be captured rather than just gathering raw data is not understood,” noted Dr George Bolt, Head of Analytics and Product Manager for Optimus RP (Revenue Protection) and Optimus DI (Data Integration) at Neural Technologies, in a recent interview.
“If the end objective of wanting to produce increasingly more effective business solutions driven by quality data are set clearly, then this can be the driving factor for how sources of data should be managed.”
These innovative technologies are having a profound impact on all areas of the telco ecosystem, but one which is particularly powerful in the area of operations. Field and service operations account for an average 60% to 70% of most telco’s operating budgets according to analysis by McKinsey, providing fertile ground for AI and ML solutions to deliver rapid return on investment. Leveraging a data-driven approach which combines historical data alongside demographic, income, and search trend data can help CSPs forecast retail staffing needs with 80% accuracy according to McKinsey’s analysis.
Pandemic workforce shifts, and isolation requirements, mean detailed, data-driven staffing processes have been hugely important for many CSPs, particularly in areas like retail customer experience and service. McKinsey research highlights that smart scheduling with AI/ML solutions allowed one CSP with over 10,000 retail employees to realize improvements in cost savings, service levels, and sales.
In the United States, AT&T—the largest telecommunications company by market capitalization—has established AI/ML capabilities in the heart of its business strategy. Chief Data Officer, Andy Markus, recently referred to the company vision to “truly integrate data and AI into the core fabric of how we run the business.”
AT&T has focused on the data infrastructure at the heart of this opportunity—a vital step for an organization that carries 543.7 petabytes of data across its global network. This approach to standardization means data scientists and decision makers with access to reliable datasets can be confident in the data that they’re leveraging, and overcome some legacy challenges around pioneering but often siloed AI/ML solutions.
Decision making is critical
The AT&T approach shows how fundamental ML and AI are to future business decision making in telecoms, and the vital need to ensure that decision making is both accurate, and timely.
“The pace is continually accelerating as technology becomes more proficient at solving complex problems at the scale of AT&T and the demands of the business and our customers increase,” Markus noted in the recent interview.
AT&T is not alone in these challenges, as CSPs around the globe seek to embrace increasingly mature AI and ML technologies to modernize business strategies. Automation and optimization is taking place across this environment, leveraging AI and ML solutions like those offered in Neural Technologies’ suite of products.
At a fundamental level, this means smart data integration and mediation, backed by sophisticated automated solutions that allow CSPs to integrate and rapidly garner insight data of any type, and from both legacy and new data sources. This kind of flexibility is a critical part of being a future-proof operation.
Resilient and protected revenue systems are also a vital part of this transformation, as companies look to ensure that growing data volumes don’t mean growing threat surfaces that expose both revenue and customers to fraud.
“For fraud, revenue assurance, credit risk management, our Optimus RP (revenue protection) platform utilizes ActivML for a range of business problems….This increases the capability of revenue assurance to look across multiple data sources rather than one at time using traditional techniques,” said Dr George Bolt.
Credit risk management is one such area, with AI and ML solutions able to rapidly assess and understand customers, providing dynamic credit limits that reduce exposure to bad debt, but also improves customer experience through more responsive, personalized thresholds and credit offerings. That’s what AI and ML should do—not only reduce risk and optimize efforts within a business, but deliver tangible, outward-facing benefits for customers.
AI and ML are such powerful platform technologies, that a list of their potential impacts in telecom could run the entire length of the ecosystem—from customer experience and revenue protection, right through to predictive maintenance to keep networks running. How telecom companies adopt these technologies could have a major influence on how competitive they remain in future, but one thing we know from our own experience, is that those that do embrace this change are on a path to unlock significant business benefits.
