New Delhi: In an attempt to integrate legal work in courts with artificial intelligence, Bar Council of Delhi (BCD) has opened a one-of-its-kind e-research library at the Rouse Avenue courts. Inaugurated on July 5 by law minister Kapil Mishra, the library has various software to assist lawyers in their legal work. With initial funding of Rs 20 lakh, BCD functionaries told TOI that they are also planning the expansion of the library to be accessed from anywhere.Named after former BCD chairman BS Sherawat, the library boasts an integrated system, including the legal research platform SCC Online, the legal research online database Manupatra, and an AI platform, Lucio, along with several e-books on law across 15 desktops.Advocate Neeraj, president of Central Delhi Bar Court Association, told TOI, “The vision behind this initiative is to help law practitioners in their research. Lawyers are the officers of the honourable court who assist the judicial officer to reach a verdict in cases. This library will help lawyers in their legal work. Keeping that in mind, considering a request by our association, BCD provided us with funds and resources.”The library, which runs from 9:30 am to 5:30 pm, aims to develop a mechanism with the help of the evolution of technology to allow access from anywhere in the country. “We are thinking along those lines too. It will be good if a lawyer needs some research on some law point and can access the AI tools from anywhere; she will be able to upgrade herself immediately to assist the court and present her case more efficiently,” added Neeraj.Staffed with one technical person and a superintendent, the facility will incur around Rs 1 lakh per month to remain functional.With pendency in Delhi district courts now running over 15.3 lakh cases, AI tools can help law practitioners as well as the courts. Advocate Vikas Tripathi, vice-president of Central Delhi Court Bar Association, said, “Imagine AI tools which can give you relevant references, cite related judgments, and even prepare a case if provided with proper inputs. The AI tools have immense potential.”In July 2024, ‘Adalat AI’ was inaugurated in Delhi’s district courts. This AI-driven speech recognition software is designed to assist court stenographers in transcribing witness examinations and orders dictated by judges to applications designed to streamline workflow. This tool automates many processes. A judicial officer has to log in, press a few buttons, and speak out their observations, which are automatically transcribed, including the legal language. The order is automatically prepared.The then Delhi High Court Chief Justice, now SC Judge Manmohan, said, “The biggest problem I see judges facing is that there is a large demand for stenographers, but there’s not a large pool available. I think this app will solve that problem to a large extent. It will ensure that a large pool of stenographers will become available for other purposes.” At present, the application is being used in at least eight states, including Kerala, Karnataka, Andhra Pradesh, Delhi, Bihar, Odisha, Haryana and Punjab.

• Private data centers: 29.5%
• Traditional public cloud: 35.4%
• GPU as a service specialists: 18.5%
• Edge compute: 16.6%

“There is little variation from training to inference, but the general pattern is workloads are concentrated a bit in traditional public cloud and then hyperscalers have significant presence in private data centers,” McGillicuddy explained. “There is emerging interest around deploying AI workloads at the corporate edge and edge compute environments as well, which allows them to have workloads residing closer to edge data in the enterprise, which helps them combat latency issues and things like that. The big key takeaway here is that the typical enterprise is going to need to make sure that its data center network is ready to support AI workloads.”

AI networking challenges

The popularity of AI doesn’t remove some of the business and technical concerns that the technology brings to enterprise leaders.

According to the EMA survey, business concerns include security risk (39%), cost/budget (33%), rapid technology evolution (33%), and networking team skills gaps (29%). Respondents also indicated several concerns around both data center networking issues and WAN issues. Concerns related to data center networking included:

• Integration between AI network and legacy networks: 43%
• Bandwidth demand: 41%
• Coordinating traffic flows of synchronized AI workloads: 38%
• Latency: 36%

WAN issues respondents shared included:

• Complexity of workload distribution across sites: 42%
• Latency between workloads and data at WAN edge: 39%
• Complexity of traffic prioritization: 36%
• Network congestion: 33%

“It’s really not cheap to make your network AI ready,” McGillicuddy stated. “You might need to invest in a lot of new switches and you might need to upgrade your WAN or switch vendors. You might need to make some changes to your underlay around what kind of connectivity your AI traffic is going over.”

Enterprise leaders intend to invest in infrastructure to support their AI workloads and strategies. According to EMA, planned infrastructure investments include high-speed Ethernet (800 GbE) for 75% of respondents, hyperconverged infrastructure for 56% of those polled, and SmartNICs/DPUs for 45% of surveyed network professionals.