Juniper Networks powering the wave of AI takeup among APAC enterprises

EITN has a chat with Lee Ming Kai, Vice President, Systems Engineering, APAC, Juniper Networks, about the role networks play in AI takeup.

EITN: What APAC businesses can do to upscale their AI capabilities in the absence of adequate talent?

Lee Ming Kai: There are several things that businesses can do to help ease the transition into using AI in the face of a talent crunch. Skilled employees provide irreplaceable contributions to the AI rollout, given the level of change that AI will bring about to daily operations. Businesses should build an AI strategy on their unique and clearly defined organizational challenges, ensuring that the AI implementation process can be achieved on a step-by-step basis. The uptake of an incremental approach will build a strong basis to allow for the successful and well-integrated adoption of AI as a solution and partner to businesses.

EITN: What is the role that Juniper plays in helping APAC businesses upscale their AI capabilities?

Lee Ming Kai: Juniper strives to deliver a simplified experience for those who run networks and those who depend on them, transforming how people connect, work, and live. As Juniper continues to produce the latest and greatest hardware, our AI and software capabilities gain more features and functionality to solve some of the biggest challenges for IT professionals.

For example, Juniper’s latest EX4100 series of enterprise-grade wired access switches is optimized for simplicity and scale with native cloud-based operations to deliver rich telemetry to the Mist cloud for device management with advanced client-level insights and visibility in real-time, minimizing the risk of downtime to enable great end-user experiences.

With many enterprises struggling to ensure accurate placement and orientation of wireless Access Points (APs) during WLAN deployments, Juniper’s Wireless Assurance cloud service simplifies wireless network deployments by auto-orienting Wi-Fi 6 and 6E access points on the map via the Juniper Mist dashboard.

With Marvis, driven by Juniper’s Mist AI, the industry’s first and only virtual network assistant offers enhanced device information and network visibility. While other vendors may require overlay sensors, Marvis clients (like the new Windows client and already available Android client) get valuable client-side data with no additional hardware or software. This allows for even better user experiences, as finding and remediating network issues for Windows users will be even quicker. All support tickets can run through the assistant, so Marvis is constantly learning and getting better, eliminating over 90% of user-generated wireless trouble tickets in some instances.

With Juniper’s software innovation using predictability, programmability, automation, and insights, businesses can build agile networks that transcend customer expectations – providing AI-driven solutions for sectors from education to healthcare to secure banking.

Furthermore, Juniper’s MIST AI comes with a set of rich APIs that allow our partners and customers to integrate our platform with their own applications or workflows to build out new AI-driven use cases. Having a mature and live AI system to work with is often invaluable in the upskilling of AI capabilities for a lot of our customers.

Lee Ming Kai: A first example would be our partnership with the Australian Rail Track Corporation. Australia’s Inland Rail was working on a huge infrastructure project to ease congestion with growing freight demands and connect producers to markets. Supporting the design and construction of the 1,700-kilometer rail alignment is an AI-driven enterprise network from Juniper.

Juniper’s Mist AI has not only enhanced connectivity across the campus but has also allowed Otemon Gakuin to automate its network and solve its scalability issues.

By leveraging Juniper’s Wireless Access Points that work in conjunction with Juniper Mist Cloud and Mist AI, staff were able to connect to remote sites and securely connect IoT devices by tunneling back to their data centers in Brisbane where additional security controls were in place. All in all, the user experience was enhanced through simplified IT operations by leveraging Juniper’s Mist AI, and Marvis Virtual Network Assistant, with a conversational interface and prescriptive actions. From mapping the geography and engineering the rail line to project management, marketing, and legal, an exceptional network experience was delivered.

In Japan, Otemon Gakuin University rolled out Juniper’s AI-driven solutions across its network which has since significantly reduced the operational load for its IT team. The school has not only increased general operational efficiency but also significantly improved Wi-Fi connection stability, resulting in a decrease in inquiries to IT support. It was critical to maintaining the stable operations and security of the university’s network in order to provide an environment where students can concentrate on their studies and research.

Juniper’s Mist AI has not only enhanced connectivity across the campus but has also allowed Otemon Gakuin to automate its network and solve its scalability issues.

Over in the US, the City of Philadelphia was looking to scale its citywide network to support city operations and constituent services. After deploying Juniper’s Wireless Access Points, driven by Mist AI, all 30,000 of its employees now experience a highly stable and efficient network regardless of where they were working and which device they were connecting from.

With Juniper Mist Edge, staff working from home could connect seamlessly to their applications and data as if they were sitting in their offices. With Juniper’s AI-driven solutions, the City of Philadelphia has laid the foundation for a more efficient, innovative city.

EITN: What role does Juniper play in building organization and employee trust in AI capabilities, and when integrating AI into their workstyles?

Lee Ming Kai: Juniper is providing network solutions by leveraging cloud AI in networks for organizations to dramatically cut support tickets, which frees up IT teams from the drudgery of tactical issues, allowing them to focus on improving end-user experiences. Nearly everyone we surveyed (97%) reported that employee satisfaction has increased since implementing AI to take over some operational tasks.

Nearly 1 in 3 (30%) AI/ML leaders believe cloud services/applications to be the most critical component in allowing remote or hybrid work models to continue. Based on an Ipsos survey for the World Economic Forum, two-thirds of employees around the world want flexible work options when COVID-19 is over, with almost a third saying that they will consider quitting if they were forced to go back to the office full-time. Given how flexible work arrangement is critical to keeping the best talent, we need to make sure they are well supported, including the best IT support.

The IT industry will become more specialised and more valuable as AI takes over repetitive tasks to augment existing IT teams, opening up time and opportunities to pursue more strategic and high-level activities. Working alongside AI will usher in an emphasis on skills and flexibility. Practitioners won’t have to understand the nuts and bolts of algorithm tuning or data modeling, but they will have to know how to harness the insights that AI provides.

EITN: In your opinion what is the level of adoption of AI? In which area specifically do you see take-up of AI, and which areas is take-up lacking?

Lee Ming Kai: Within Asia Pacific (APAC), 92% of respondents reported that their organization already utilizes AI-powered solutions to automate or aid decision making, as compared to 2021’s report, where only 42% were reported to have done the same. The significant increase in overall AI implementation rates across APAC organizations is largely attributed to the benefits that AI can bring, with 52% of respondents agreeing that it will “assist in reducing risk and increasing quality” at work.

This year’s survey conducted by Juniper found that 63% of companies say they are at least most of the way to their planned AI adoption goals. More company leaders than ever (27% in 2022 versus 11% in 2021) are looking to deploy “fully” enabled AI use cases with widespread adoption.

Among the 656 respondents reporting that they are utilising AI-powered solutions, the business area receiving the most AI automation was networking and cloud at 55%. This is followed by operations (45%), information technology (44%), and sales and marketing (39%). At the lower end of the scale, human only 28% reported using AI for human resources, 25% for legal, risk, and compliance functions, and 24% for manufacturing which came in last place.

AI/ML leaders in APAC indicate the top risks from inadequate oversight of AI as accelerated hacking or AI terrorism (44%) and privacy (41%). It is no surprise that AI/ML leaders see cybersecurity (29%) and cloud availability (24%) as the most critical components of AI adoption.

Leaders recognize the key areas that are prime for a reduction in size because of increased AI adoption, namely in operations (44%), sales and marketing (41%), and networking (38%).

EITN: Why do companies need to involve their employees in implementing governance protocols that actually work for their enterprises?

Lee Ming Kai: Clear communication is a necessary and integral part of any change implementation – especially one that rolls out pervasive changes in ways of working such as that affected by AI. With the introduction of AI, employees will need to interact with new and highly capable non-human actors in their daily work. As such, involving employees throughout governance implementation will both reduce resistance and allow early feedback. With all AI implementations, cross-functional and executive involvement is critical to ensure proper AI governance (monitoring and mitigating reputational, operational, and financial risks associated with AI).

AI governance must be the business’ top focus – especially if they want to realize the full potential of AI in their organizations. A company’s board members, customers, and regulators will have many questions about their use of AI and data, from how it’s developed to how it’s governed – it’s important to involve them in implementing government protocols to mitigate current and future risks.