AI Research

Lessons Learned From CDW’s AI Research Report

Published

1 day ago

July 8, 2025

1. Solve Problems Instead of Deploying New Tools for Their Own Sake

Organizations may feel pressured to try something just because of hype. You should resist the urge. Instead, be clear about a problem that needs to be solved and how AI would fit in.

There may be some easy deployments to start with through capabilities or features that exist in solutions that your organization is already using, such as within a productivity software suite or an electronic health records system.

Another problem area could be any repetitive administrative tasks that would benefit from automation. One of the reasons that ambient listening tools have held consistent interest is because organizations want to reduce clinician burden and mitigate burnout. How can health systems reduce “pajama time” for clinicians so that they can repair patient relationships?

2. Amid Regulatory Uncertainty, Have a Solid AI Governance Structure

As algorithms improve and regulatory responses remain in flux, healthcare organizations need to have agility and stability in their own AI governance structure. And with requirements that can vary state by state, a multidisciplinary approach is crucial to keep up with changes.

Create the proper work groups with the right representation of stakeholders to ask the right questions around potential use cases, the end-user experience, recognizing and mitigating risk, ethical concerns, algorithmic bias, compliance, and data quality.

Infrastructure considerations also need to be factored in. How ready is your organization to adopt more AI solutions? Do your teams have the right skill sets? Have you secured your environment? Are there any on-premises considerations versus workloads that should move to the cloud? Organizations will need to build out landing zones and may have different strategies when it comes to how they are using their compute and storage.

EXPLORE: How should healthcare organizations navigate artificial intelligence evaluation and implementation?

3. Keep Data Security and Privacy at the Forefront

Data governance goes hand in hand with AI governance, as most AI-powered solutions require high-quality data, which is table stakes at this point. This also requires strategies around how to protect that data.

There is also a need to have more transparency in some of the solutions that are out there so organizations can adequately assess whether a solution is going to meet regulatory requirements. Transparency is key, as real danger exists if an AI solution gets a prediction wrong or poor data is used. A one-size-fits-all approach to AI in healthcare is just not possible, and there will likely still be a need for human discernment or a human in the loop to ensure outcomes are not causing harm.

This article is part of HealthTech’s MonITor blog series.

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AI Research

E-research library with AI tools to assist lawyers | Delhi News

Published

41 minutes ago

July 9, 2025

The Editors

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.

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AI Research

Enterprises will strengthen networks to take on AI, survey finds

Published

1 hour ago

July 9, 2025

Denise Dubie

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.

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AI Research

Amazon Web Services builds heat exchanger to cool Nvidia GPUs for AI

Published

1 hour ago

July 9, 2025

Jordan Novet

The letters AI, which stands for “artificial intelligence,” stand at the Amazon Web Services booth at the Hannover Messe industrial trade fair in Hannover, Germany, on March 31, 2025.

Julian Stratenschulte | Picture Alliance | Getty Images

Amazon said Wednesday that its cloud division has developed hardware to cool down next-generation Nvidia graphics processing units that are used for artificial intelligence workloads.

Nvidia’s GPUs, which have powered the generative AI boom, require massive amounts of energy. That means companies using the processors need additional equipment to cool them down.

Amazon considered erecting data centers that could accommodate widespread liquid cooling to make the most of these power-hungry Nvidia GPUs. But that process would have taken too long, and commercially available equipment wouldn’t have worked, Dave Brown, vice president of compute and machine learning services at Amazon Web Services, said in a video posted to YouTube.

“They would take up too much data center floor space or increase water usage substantially,” Brown said. “And while some of these solutions could work for lower volumes at other providers, they simply wouldn’t be enough liquid-cooling capacity to support our scale.”

Rather, Amazon engineers conceived of the In-Row Heat Exchanger, or IRHX, that can be plugged into existing and new data centers. More traditional air cooling was sufficient for previous generations of Nvidia chips.

Customers can now access the AWS service as computing instances that go by the name P6e, Brown wrote in a blog post. The new systems accompany Nvidia’s design for dense computing power. Nvidia’s GB200 NVL72 packs a single rack with 72 Nvidia Blackwell GPUs that are wired together to train and run large AI models.

Computing clusters based on Nvidia’s GB200 NVL72 have previously been available through Microsoft or CoreWeave. AWS is the world’s largest supplier of cloud infrastructure.

Amazon has rolled out its own infrastructure hardware in the past. The company has custom chips for general-purpose computing and for AI, and designed its own storage servers and networking routers. In running homegrown hardware, Amazon depends less on third-party suppliers, which can benefit the company’s bottom line. In the first quarter, AWS delivered the widest operating margin since at least 2014, and the unit is responsible for most of Amazon’s net income.

Microsoft, the second largest cloud provider, has followed Amazon’s lead and made strides in chip development. In 2023, the company designed its own systems called Sidekicks to cool the Maia AI chips it developed.

WATCH: AWS announces latest CPU chip, will deliver record networking speed

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