From Self-Driving Cars to Self-Driving Networks: Rise of AI in Telecom
Unless you have been hiding under a rock, the odds are that you have heard about the fascinating world of AI and the transformational impact it is going to have on our lives. Certain parts of the euphoria remind us of the dot-com bubble from the ’90s — when the technology hype hit its peak. There seems to be intense excitement at all levels, from CEOs and analysts to grassroots developers; all are expecting AI to not only dramatically change our lives but the world. Well, we really can’t blame them, given all the excitement created by the billions of dollars being invested in this domain. With all the stars perfectly aligned, perhaps it is best to “peel the onion” a little bit in hopes of gaining a better understanding of what AI really is, why now and why all the hype, and how can it dramatically change the Telecom Industry landscape, in particular.
What is Artificial Intelligence and why the hype now?
AI is essentially humans teaching machines how to learn and mimic their intellect.
Quite like a toddler- who learns by experience, constantly absorbing information, decoding it and understanding patterns — we feed machines huge amounts of data that create their own algorithms and constantly tweak them to meet their objectives. Over time the program gets smarter and very human-like, i.e., less artificial and more intelligent.
How is AI different from Predictive Analytics and Big Data? Are there financial benefits of investing in all three?
Often, people inquire what is the difference between Big Data, Predictive Analytics, and Machine Learning. They want to have the ability to differentiate between the three to ensure adequate allocation of resources and to set right expectations with all the stakeholders. .
Big Data: Collection of “data sets” which are large, complex, and difficult to process using on-hand database management tools.
Predictive Analytics: Practice of extracting information from existing data sets in order to determine patterns and accurately predict future outcomes, trends, and behaviour.
Should we bet on AI and Robotic Process Automation (RPA)? Everyone seems to be deploying them, should we invest in them now?
What are the telecom specific use cases of AI? What are the financial benefits?
The telecom industry is at a crossroads. No other industry is driven as much by bits and bytes while at the same time faces disintermediating challenges for the same reasons. Business models are changing, customer expectations and behaviour are constantly evolving, legacy economics and paradigms are becoming untenable. This is why 2014 was the first year when the total global revenue of telcos had marginally negative growth. From then on, every investor relations call is bombarded with questions on increasing revenue, reducing OPEX and CAPEX.
Here are some ways AI could help alleviate the pain:
Between the three telcos in Canada, the collective annual capital spend is ~$10BN to grow and sustain their respective networks. Using this run rate would peg the capital spending over the next decade to be at ~$100BN, even when not taking into consideration a spike of an estimated 40 percent due to an increased spend on 5G and fibre deployments.
We estimate at least 10–15 percent ($10BN) of the capital envelope can be reduced using cognitive technologies holistically across the process.
Security and Fraud: Detect fraudulent activity in credit-card transactions or identifying web traffic to the website and customers, looking to exploit network and infrastructure vulnerabilities.
AI algorithms can help combine historic patterns, psychographic analysis and behaviour (plus “look-alike” patterns) with ongoing real-time engagement to provide relevant, targeted, contextual experiences for consumers. The outcome will be upselling recommendations and offers, helping improve the conversion rate of offers, and enabling incremental wallet share. The same algorithms could be potentially leveraged to predict subscribers willingness to pay a particular price for a product, estimate product price elasticities, and quantify sales leads likelihood to close.
How do we tackle AI as an organization? How much investment is required?
It is best to use a good framework to get the ball rolling organization-wide. Some frameworks that we have used in the past include:
We understand that AI can be intimidating and complicated, which is why we encourage you to engage us for a better understanding of the foundational capabilities, needed to get an AI program up and running. From building use cases, decision-making paradigms, economic impact analysis, choosing governance model and platforms to be deployed, we have gone through the process a few times. Every business is unique; we look forward to learning more about your specific needs to determine how we can accelerate your digital transformation.
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