Advances in artificial intelligence (AI) have captured the attention of both businesses and consumers like never before. The emergence of generative AI has also made the technology more accessible to everyday consumers, allowing them to interact with new AI tools in natural language like conversational dialogues.
A report by IDC has found that two-thirds of Asia-Pacific organisations are exploring potential use cases or are already investing in generative AI technologies in 2023. While AI becomes increasingly ubiquitous and embedded into our everyday life, businesses are scrambling to adapt to changing consumer expectations, which have been emboldened by the taste of the unparalleled convenience AI offers.
As these consumer expectations evolve, so too do business priorities. According to a Juniper Networks survey, over 90% of organisations in APAC have already embraced AI deployment for a variety of use cases aimed at improving operations and helping these businesses to meet ever-increasing consumer demands.
With AI being used in a variety of ways, it is also crucial for leaders to identify and focus on which business areas will reap the most benefits for the organisation. The survey found that 55% of leaders are automating their networking and cloud functions using AI, over other business areas such as sales, finance, and customer service. Even among companies yet to embrace AI-powered solutions, 23% of leaders in APAC identified networking as the most promising function for reaping the benefits from implementing AI.
It is evident that AI in networking is and will continue to be a top priority for enterprises as they optimise their processes and improve performance. However, getting enterprises organised enough to incorporate AI solutions into their business operations is a behemothian task.
AI for networking presents both bountiful opportunities and novel challenges
Harnessing AI’s full value requires businesses to reimagine whole workforce organisation strategies – considering factors ranging from technicalities to ethics, for instance – and make decisive strategic moves towards cultivating a culture supporting AI implementation.
Countries like Singapore have introduced initiatives like its Model AI Governance Framework and software tool kits. These resources help organisations navigate key ethical and governance issues when implementing AI solutions, fostering a culture that builds on transparency and trust in this technology.
In addition to ethical considerations, organisations adopting AI technologies like machine learning (ML) for improved productivity and operational efficiency must understand the technology’s unique requirements. ML systems and large-language models generate substantial data traffic, surpassing the needs of most other network applications. To thrive in this modern landscape, substantial computational power and a robust network are essential to support these advanced AI/ML models. Coupled with emerging explainable AI (XAI) frameworks, which allow humans to better understand the outputs of AI systems, advanced AI/ML models will also be able gain further trust and acceptance from users through greater verifiability.
When successfully integrated, AI for networking can help predict user experiences, dynamically adjust bandwidth, self-correct for maximum uptime, perform rapid event correlation and root cause analysis, and deploy virtual network assistants. AI-driven networks are designed to autonomously troubleshoot and resolve network issues, making the value of AI in simplifying the management of wired, wireless, and wide area networks abundantly clear.
This is especially important in APAC, where the proliferation of devices and escalating volume of data have made IT infrastructures and networks increasingly complex to manage, while budgets continue to tighten against the backdrop of a slowing global economy.
An experience-first approach is revolutionising enterprise networks
While AI for networking presents some of today’s most complex technological and operational challenges, the payoff of tackling them promises to completely disrupt industries with new levels of insight and automation within their networks. Instead of relying on reactive strategies and addressing network issues after they arise, AI presents the opportunity for a paradigm shift towards a more experience-focused proactive and predictive approach.
Furthermore, with access to the right type and volume of data sets, enterprises with AI-native networking can anticipate network congestion, identify potential service disruptions, and forecast customer demands. This level of automation and insight, paired with an experience-first approach, revolutionises the very foundation of enterprise networks and the way businesses operate.
Since the resurgence of AI in the last few years, enterprise networking has never been the same. In APAC, where AI spending is projected to reach US$78 billion by 2027 as per IDC’s Worldwide Artificial Intelligence Spending Guide, enterprises here have limitless opportunities to revolutionise their operations and leverage their networks to deliver exceptional service levels and experiences to end users, applications, and devices for years to come.
