Will AI fix what ails your telco?

Telecoms.com periodically invites expert third parties to share their views on the industry’s most pressing issues. In this piece Brian Murray, Head of Product Marketing, Anritsu Service Assurance, assesses the role of AI in the telecoms world.

Guest author

May 13, 2024

5 Min Read

In today's artificial intelligence-driven (AI) world, it's easy to be swept up in the allure of efficiency, cost savings, and the promise of innovation made possible by AI technologies. But as we've learned from past experiences, simply adopting new technology isn't a magic fix. To truly reap the benefits of technology such as AI, telcos need to understand the challenges the business faces, define what success looks like, and ensure that the technology deployed aligns with the operator's business goals.

View technology through the lens of the past

Before taking a leap into an AI future, visit past technologies to determine whether the promises you pinned your hopes on came to fruition. For instance, the past decade was all about big data and the potential value of large data sets. Although vendors were pushing big data strategies, in reality, they were selling Hadoop-based or proprietary storage solutions. 

From the vendor's perspective, the value of big data came second to the technology. However, many telco providers invested heavily in big data, counting on the promised value, only to learn a very costly lesson. This harsh lesson is as true today as it was then: Technology doesn't automatically equate to value.

The AI promise: A reality or an illusion?

Using virtually all communication channels available, AI providers claim that this technology will solve virtually anything, from boosting productivity to increasing business performance and everything in between. Add to that the recent rise of ChatGPT, which appears to have emboldened the market into believing that generative AI (GenAI) has unlimited potential and can fix whatever problems the communication industry faces. While AI promises excellent value, due diligence is required before investing.

Since custom value doesn't come out of the box, operators need to take a step back and consider that the AI must first understand the business, its processes, data, and technologies. However, areas where AI can add nearly immediate value are in:

  • Automating mundane or repetitive tasks.

  • Analysing extensive data sets to gain insights.

  • Handling predictive analytics.

  • Providing personalised recommendations.

  • Driving innovative support.

This reality check is imperative in recognising AI's current limitations, including:

  • Complex decision-making: AI lacks the inherent human understanding and emotional intelligence needed for complex decision-making scenarios, especially those involving ethical considerations or understanding human emotions and social norms.

  • Creativity and innovation: While AI can support innovation, the spark of creativity largely remains a human trait. AI can process data but doesn't possess the ability to think abstractly or creatively.

  • Building relationships: Establishing and maintaining deep and meaningful client, partner, or other stakeholder relationships is a distinctly human ability that AI simply can't replicate.

  • Strategy formulation: Formulating long-term strategies requires a deep understanding of many complex factors, such as human behaviour and social and economic trends, which AI may not fully comprehend.

  • Understanding and interpreting context: AI can struggle with understanding and interpreting nuanced human communication and context, which can be crucial in negotiations, conflict resolution, or other sensitive business areas.

Although all industries face the same high-level obstacles when it comes to AI, the telecom sector faces unique challenges.

Harnessing AI's true potential

Across all industries, AI is often presented as a cure-all solution. However, history cautions us against falling for the marketing hype without rigorous validation. This vigilance becomes even more critical for the telecom industry. 

Whether you want AI to improve the customer experience, reduce costs, or automate processes, technology history and experience cautions that the reality will differ from the marketing hype. When making the decision to invest in AI or not, telcos need to ask AI vendors difficult questions, including:

  • How can the vendor validate claims made about their AI's capabilities?

  • What inputs are required to gain maximum return on investment (ROI) from the AI solution?

  • How does the AI vendor define and measure AI's success for the specific and unique requirements of a telco's environment?

  • Can the vendor justify an ROI timeframe?

  • Given the rapidly changing field of AI, how is their AI product positioned to evolve?

The answers to these questions will help decide whether AI will resolve the telco's challenges, provide solid information on how to gauge the success of the AI investment and determine whether the technology aligns with the operator's business goals.

Answers to these questions, as well as prioritising objectives using methods like the MoSCoW Prioritisation Technique (MoSCoW Analysis), the RICE (Reach, Impact, Confidence, and Effort), or the Kano model, will help the telco determine if onboarding AI is the right decision.

For instance, MoSCoW focuses on what matters most to both the customer and stakeholders. Standing for Must Have, Should Have, Could Have, and Won't Have (don't need), MoSCoW, prioritises the telco's need for the technology in each of the four categories. For example, suppose the AI component of a vendor's offering falls outside the operator's list of Must Haves or Should Haves. This sends a clear message that the value of that AI solution has, at best, yet to be verified or is dubious.

To harness AI's potential, telcos must ask vendors hard questions and prioritise objectives. To avoid the costly mistakes of previous technology investments, the journey from potential to actual AI value is contingent upon a thorough understanding of the problem(s) the operator is trying to resolve, a clear articulation of what success looks like, and a relentless focus on aligning AI deployments with solid business objectives. 

Service providers on the cusp of investing in AI need to ensure that their strategy is rooted in discernment, perform due diligence, and relentlessly pursue tangible value. Only then will AI provide the value needed to fix what ails the operator's business.


Brian Murray is the Head of Product Marketing at Anritsu Service Assurance. He has over 12 years’ experience in the telecom industry and has worked for some very recognisable companies such as The NOW Factory, IBM, and Mobileum, where his many roles spanned both product and marketing. His passion lies in creatively communicating the real value of his product suite to customers while matching it with their needs through a deep understanding of their unique pain points. His dedication to this philosophy has been instrumental in building lasting relationships with customers and positioning Anritsu Service Assurance as a leader in its domain.

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