Contributed by Don Alusha, Senior Analyst, ABI Research.
Communications Service Providers (CSPs) are making good progress to implement Artificial Intelligence (AI) and Machine Learning (ML) as they seek to modernize their networks and diversify their revenue streams. At present, AI and ML technologies are seeing increased adoption on a per use-case, per domain basis – a natural development given the heterogeneous technology systems that characterizes the industry. For example, the bulk of existing commercial activities fall under customer management, intelligent user interfaces, sales and marketing. Increasingly, the question for CSPs is how to kickstart a transition towards networks that are more automated to achieve operational efficiency and rapid service onboarding.
Narrow AI may not be conducive to the creation of a new ‘intelligent’ ecosystem. That is, an interconnected environment with uniform business connections and centralized sponsorship underpinned by standardized data formats and AI telco standards. On the one hand, AI opens new opportunities from operational efficiency and top-line growth perspectives. On the other hand, the industry may have to step out of the ‘bounded rationality’ phenomenon highlighted in ABI Research’s studies. End to end harmonized AI platforms are required to drive data sharing and universal cognitive solutions across multiple teams and/or platforms.
A Far-Reaching AI strategy
Implementation of AI/ML in telecoms is characterized by an evolutionary singular strategy; namely, one tweaked and fine-tuned in line with specific use cases typically associated with their own solutions partners, dedicated departments or roadmaps. For example, customer services reduce human involvement by using AI in chatbot functionality. To that end, technology providers are establishing an adoption roadmap wherein use case specific actions taken now yield short term desired outcomes, but also have an effect that radiates out for years to come towards a wider AI stratagem. Almost all network equipment vendors (NEPs) have announced a roadmap to set up a global AI accelerator to drive AI adoption in telecoms. NEPs are using narrow AI use cases in several different product lines, including radio equipment.
At the other end of the spectrum, CSPs and partners are seeking to understand where it makes sense to use AI, understand the business value, and manage it on a per domain basis. Bounded rationality in a nutshell. A digitally empowered telco mandates varying degrees of integration, interoperability, data sharing and openness; a feat that may run counter to the narrow, use-case specific AI diffusion of today. That said, harmonization of a narrow approach and one that promotes a holistic strategy is certainly not easy and something beyond the power of any CSP, even Tier-1 providers.
The example of hyperscale cloud providers further drives that point home. Amazon, Facebook, and Google have designed far-reaching AI strategies, but they did not do that by taking specific narrow domains and somehow transforming them. The industry at large must overcome divisions on two strands to fully capitalize on benefits of AI. One, digital in the form of BSS/OSS assets, networks, business modeling algorithms and anything in between these domains. Two, strategic in the form of new ethical frameworks, governance systems and, the most valued asset of all, the necessary human capital that is fundamental to fully capturing the utility of AI/ML.
AI and Human Capital
AI adoption in telecoms poses a fundamental question: how can adopters, be it CSPs or vendors, change the structure of current systems and processes to produce more of what is desirable and less of that which is undesirable? An effective strategy would be to look for leverage points – places in the “old” operations where a small change can lead to a large shift in behaviour. In addition, a key aspect of AI/ML at a broad level is that, ultimately, an organisation’s only sustainable competitive advantage lies in its workforce. More specifically, what its employees know and how they apply AI input to business requirements is a key consideration that warrants a “continue to grow over time” mindset. Solution providers should foster this mindset by introducing reskilling programmes, embedded in a culture of lifelong learning from the outset. Naturally, this change will not happen overnight, even in the most progressive organisations. But it is vital that the industry plant the seeds of AI today, in order to create value tomorrow.
CSPs, vendors and technology multinationals must tackle multiple AI facets, one of which is organisational. Workforce mindset, which is the common set of shared beliefs and values underpinning employee behaviour, is one of the key defining attributes of a company, and the hardest construct to influence. CSPs and vendors must embrace a long-lasting organisational change if a top down AI strategy with a global organisational reach is to succeed. One way of addressing this is by easing the workforce into a hybrid mode of operation that incorporates AI expertise in stages, starting with (small) activities that are best suited for AI. Further, market players that eventually aim for a global AI strategy should seek adequate sponsorship, unwavering commitment and direct governance from one or more C-level executives.
Complexity may well be the hallmark of the industry for the next decade or two. Virtualisation, edge computing, 5G, IoT will create and will require networks that cannot be managed with today’s processes. The demand therefore will be for levels of human capital that embrace radical technologies such as AI/ML to manage that complexity and drive sustained growth. CSPs and vendors that get that right, the market entities that understand how to mobilize and apply the human capital, and the partners that produce or facilitate it will be the big winners. When assessing AI and ML as technologies, decision makers should consider the implications of embracing it, but should not believe that AI by itself is the sole answer to commercial success. For vendors and CSPs to succeed, they must create the right culture and environment to integrate AI into their DNA thereby creating a workable “human plus AI” model. Kung Fu Posture
ABI Research is a Research Partner for FutureNet World Virtual 2021. Join them on the 20-21 April 2021.
Implementation of AI/ML in telecoms is characterized by an evolutionary singular strategy; namely, one tweaked and fine-tuned in line with specific use cases typically associated with their own solutions partners, dedicated departments or roadmaps. For example, customer services reduce human involvement by using AI in chatbot functionality. To that end, technology providers are establishing an adoption roadmap wherein use case specific actions taken now yield short term desired outcomes, but also have an effect that radiates out for years to come towards a wider AI stratagem. Almost all network equipment vendors (NEPs) have announced a roadmap to set up a global AI accelerator to drive AI adoption in telecoms. NEPs are using narrow AI use cases in several different product lines, including radio equipment.
