Predictions for AI-driven enterprises in 2022
By Lee Ming Kai, Head of Systems Engineering, APAC, Juniper Networks
Over the past year, enterprises have taken drastic measures to adopt better technology and innovation to support new business priorities and work processes. Such technologies include significant upgrades to network, security and automation, all of which are pertinent to support a growing hybrid work environment. Google’s survey with Economist Impact revealed that 53% of employees in APAC feel that employee productivity will be a significant benefit in a hybrid work model, and this is only the tip of the iceberg – confidence in hybrid working will grow in time as we continue to reap and witness the benefits it can bring to both the employer and employee.
However, to support a seamless hybrid work environment, significant investments to infrastructure must be made. According to latest data provided by IDC in September 2021, Asia Pacific is expected to increase its total spending on ICT by 9.3%. In particular, investments in Artificial Intelligence (AI) show the fastest growth of 30.1% in 2021; to reach $25 billion by 2025, globally.
The trend towards AI adoption is also one of the major focuses for APAC enterprises when upgrading their ICT infrastructure. Juniper Networks’
AI research paper in 2021 revealed that some 99% of APAC respondents feel their organization would benefit from embedding AI into their daily operations, products and services. In fact, some 42% of respondents reported that 50% or more of their operational decisions are currently assisted by AI decisioning, or will be soon, compared with only 23% of respondents in North America.
AI-driven assistants will largely take over the troubleshooting process in networks
AI, natural language processing (NLP) and natural language understanding
(NLU) are replacing charts, pies and dashboards. The days of staring at charts will go to the wayside when organization decision makers can just type in a question and get an answer, or have issues flagged and in some cases fixed on their own – known as self-driving. Enterprises are going to see a trend around AI-driven assistance replacing dashboards and changing the way IT teams troubleshoot, essentially eliminating the “swivel chair” interface.
Full stack AIOps will be the key AI theme of 2022 enterprise networking
Fueled by increasingly complex networks and distributed workloads, artificial intelligence for IT operations (AIOps) has throttled its way to the top theme of the coming year. Expect to see enterprises invest in four key areas where AIOps make the biggest impact: user experience, operations experience, DevOps/application experience and location services. Furthermore, organizations will also increasingly turn to AIOps to boost cybersecurity and immediately identify and mitigate potential issues before they occur.
As remote and hybrid work environments continue to be the norm, enterprise-grade networking and security will move into the home networking space
2022 will further cement the home as a staple enterprise micro-branch, driving enterprise IT teams to take an even sharper look at their network edge. To ensure end-to-end network visibility, reliability and security, we can expect enterprise-grade networking solutions to begin permeating remote and hybrid workforces. Many companies will take a hybrid approach, moving from legacy security solutions to a client-to-cloud Secure Access Service Edge (SASE) approach, moving their most high-risk remote worker to SASE, an architecture that brings together networking and security, providing direct, secure access to applications as they move to the cloud.
AI assistants that can manage and troubleshoot networks on par with human domain experts, will be promoted to a member of the IT team in 2022
In the enterprise, AI, machine learning and AIOps ultimately have the potential to become as trusted a source as the most experienced IT domain expert. While we’re not there yet, in the coming year we can expect AI assistants and conversational interfaces to take on a more serious and trusted role in the enterprise. At present, AI conversational interfaces can answer up to 70% of support tickets with the same effectiveness as a domain expert. As network complexity and distributed workloads increase, AIOps and virtual AI assistants will become viewed as an essential member of IT teams. Further, as cloud services continue to scale to provide unlimited, cost-effective processing and storage, both enterprises and technology providers will be empowered to adopt AI assistants across various support teams – feeding in the volume and quality of data necessary to train AI technologies to increase their accuracy.
Longformer and Few-Shot machine learning algorithms will move conversational interfaces closer to passing the Turing test
Within the next 10 years, AI technology in many industries will begin to reach a level of accuracy that is on par with human expertise. In the enterprise, today’s AIOps-powered conversational interfaces are just a few years away from reaching 90% accuracy, a major achievement akin to what IBM Watson accomplished when it competed on Jeopardy. This means that we are progressing toward AI technologies that can answer questions and manage technical issues on par with an IT domain expert. Like the average human employee, AI can learn and improve over time to ultimately become indistinguishable from a human domain expert.
The line between networking and security will continue to blur
Networking experts used to speak one language and security experts another, but today, they must be bilingual more than ever, especially on the edge with architectures like SASE. Companies that were traditionally security companies are getting into networking and vice versa – solutions must be integrated. Every step of the way, security needs to be tied in with routers, switches and access points to make decisions and enforce them across the whole connected fabric.
Undoubtedly, AI brings about tons of benefits for all kinds of enterprises, large or small. From chatbots in customer service, to discovering new areas of operational efficiencies, and intelligent networking solutions that facilitate seamless transition between devices, these use cases define what it means to work in an experience-first era. To succeed in the modern workplace, enterprises and business leaders should strongly consider implementing AI-driven technologies into their work processes and daily operations. This prepares them for a digital-first business landscape and ultimately moves them up the value chain.