Archive 08.05.2025

Brown University researchers have developed an artificial intelligence model that can generate movement in robots and animated figures in much the same way that AI models like ChatGPT generate text.

A research team from AMOLF in Amsterdam has created a soft robot that walks, hops, and swims—all without a brain, electronics, or AI. Just soft tubes, air, and some clever physics.

You’re expected to support next-gen AI on infrastructure that was never designed for it. 

In highly regulated industries like government, financial services, and healthcare,  IT teams face growing pressure to drive innovation while staying compliant.  

But traditional systems can’t keep up with  the speed, scale, or complexity that generative and agentic AI demand. 

The result is a widening gap between business expectations and what existing infrastructure can support.  

This creates a two-fold challenge: 

1. Ensuring continuous security and compliance across production systems.
   
2. Delivering AI fast enough to meet business needs, without blowing through time, cost, or resources.  

And for most teams, starting from scratch with open source tooling isn’t realistic.  

What’s needed is a composable, integrated AI foundation that works across environments, simplifies operations, and gives IT teams the tools and shortcuts to deliver outcomes fast, without compromising security or governance.

Why AI stalls in high-security environments

AI initiatives often lose momentum — or never reach production — due to barriers like:

• Procurement and integration delays: Acquiring and deploying new tools takes too long, stalling adoption and impact.
  
• Disconnected infrastructure: Disparate and siloed systems block access to the right data and make it hard to design a scalable, resilient AI stack.
  
• Operational complexity: IT teams spend more time maintaining tools than enabling data science.
  
• Rigid security and compliance demands: Every new component requires vetting, testing, and regulatory approval when AI demands quick iteration.
  
• Poor observability and blind spots: Limited visibility leads to rework in the short term and audit delays down the line. 

These challenges are deeply interconnected. Patchwork fixes won’t address them.

Delivering outcomes at scale requires a platform that simplifies infrastructure, embeds security and governance,  and gives IT teams the tools and control they need to move fast, without compromising trust. 

Validated by Nutanix. Built for regulated environments.

IT leaders need more than a patchwork of tools; they need a secure, scalable platform that works right out of the box.

That’s exactly what we’ve built with Nutanix.

We’re proud to be recognized as Nutanix’s 2025 Americas Technology Alliance Partner of the Year — a reflection of our shared focus on simplifying agentic AI for highly regulated, on-prem environments.

This recognition highlights the strength of our joint solution: a fully validated, enterprise-grade AI platform that runs securely on Nutanix infrastructure. It enables rapid deployment, built-in governance, and full-stack integration, helping teams accelerate adoption without compromising compliance or control.

 Simplifying agentic AI delivery on-prem

Current tools and infrastructure weren’t built for the speed, scale, or security that AI demands — especially in highly regulated, on-prem environments.

DataRobot and Nutanix remove the friction with a fully integrated platform that simplifies setup, secures access to the latest models, and embeds observability and governance from the start.

Together, we offer a validated AI stack purpose-built for high-security on-prem and air-gapped environments. This end-to-end solution gives IT leaders the speed, and compliance needed to deliver AI value within budget, without sacrificing control. 

It’s fast to set up, easy to manage, and built to support secure, governed AI from development through deployment. 

Nutanix: Build a scalable, secure foundation

• Setup and scale securely: Deploy Nutanix Enterprise AI on CNCF-certified Kubernetes — scalable, cost-efficient, and ready for high-security workloads.
  
• Simplify access to the latest models: Deploy open-source LLMs, including NVIDIA NIM, with streamlined  model selection and management.
  
• Manage and protect every endpoint: Stand up resilient endpoints with built-in security to test and run models safely.
  

DataRobot: Deliver business-ready agentic AI apps

• Build enterprise-grade AI apps: Use prebuilt templates or create custom apps for high-security use cases within a flexible, production-ready development environment.
  
• Ensure trust and compliance: Apply built-in guardrails and generate compliance documentation to support audit readiness.
  
• Deploy and govern at scale: Move apps to production with continuous monitoring and full governance oversight.

DataRobot delivers mission-critical AI apps for federal agencies

Federal agencies can’t afford disruption. That’s why DataRobot delivers mission-critical AI apps designed to integrate with existing systems and deploy where your data lives, so you can move fast, stay compliant, and drive mission outcomes without overhauling your infrastructure.

Our AI applications tackle a wide range of critical use cases, including:

• Budget insight and contract optimization: Identify underutilized funds and model future resource needs to drive smarter, faster budget decisions.
  
• Fraud, waste, and abuse surveillance: Detect anomalies early with continuous data monitoring and automated compliance alerts.
  
• Talent management and force readiness: Optimize recruitment, retention, and training investments to maintain a mission-ready workforce.
  
• Operational Efficiency and Predictive Maintenance: Streamline procurement and predict equipment failures to boost fleet availability and lower costs.

Secure on-prem AI without compromise

For IT leaders in regulated industries, AI success often means tough trade-offs: speed vs. control, innovation vs. compliance, usability vs. security, budget overruns vs. mission outcomes. 

In sectors like public, healthcare, and financial services, the challenge is meeting every requirement — without slowing down progress.

To move from AI ambition to real results, ask yourself:

• Is your infrastructure fast, secure, and flexible enough to adapt?
  
• Are security and compliance controls built in and simple to use?
  
• Do your tools and apps enable teams to deliver on business needs?

This is the new operational baseline for secure AI delivery in highly regulated environments, and it’s exactly what Nutanix and DataRobot make possible.

With this platform, you can deploy AI securely, efficiently, and without compromise — turning ambition into measurable outcomes.

Book a demo with a DataRobot expert and explore what’s possible for leveraging the latest AI advances in your on-prem data environment.

The post Simplifying secure on-prem AI with Nutanix and DataRobot appeared first on DataRobot.

It's easy to take joint mobility for granted. Without thinking, it's simple enough to turn the pages of a book or bend to stretch out a sore muscle. Designers don't have the same luxury. When building a joint, be it for a robot or wrist brace, designers seek customizability across all degrees of freedom but are often restricted by their versatility to adapt to different use contexts.

A team of researchers has unveiled a cutting-edge Amphibious Robotic Dog capable of roving across both land and water with remarkable efficiency.

An edible robot leverages a combination of biodegradable fuel and surface tension to zip around the water's surface, creating a safe -- and nutritious -- alternative to environmental monitoring devices made from artificial polymers and electronics.

What makes people think an AI system is creative? New research shows that it depends on how much they see of the creative act. The findings have implications for how we research and design creative AI systems, and they also raise fundamental questions about how we perceive creativity in other people.

An edible robot made by EPFL scientists leverages a combination of biodegradable fuel and surface tension to zip around the water's surface, creating a safe and nutritious alternative to environmental monitoring devices made from artificial polymers and electronics.

A team of researchers has unveiled a cutting-edge Amphibious Robotic Dog capable of roving across both land and water with remarkable efficiency. The study, published in Bioinspiration and Biometrics, was inspired by mammals' ability to move through water as well as on land.

A human clearing junk out of an attic can often guess the contents of a box simply by picking it up and giving it a shake, without the need to see what's inside. Researchers from MIT, Amazon Robotics, and the University of British Columbia have taught robots to do something similar.

MIT engineers are getting in on the robotic ping pong game with a powerful, lightweight design that returns shots with high-speed precision.

How does the air flow around an airplane? How does the blood move through our veins? And how can pollutant emissions in combustion processes be minimized?

Artificial Intelligence In Business Application
5 Examples Of AI In Business Intelligence Applications

In the current scenario for ai in business application, data became crucial for every industry around the globe, and you know that 90% of the available information is generated in seconds leveraging intelligence Artificial Intelligence tools and it is impossible to gather such massive data from the human workforce.

AI based business intelligence applications are now a trend across the world. Organizations across various industries are implementing numerous applications of AI in business management and renovating their operational and functional flows to achieve their objectives in this digital space.

AI’s most significant technology Machine Learning (ML) is also disrupting the business intelligence mobile app development industry. To evaluate performance and match the progress with the targets, identify customer needs, predict customer preferences, and so on, organizations are increasingly investing in AI and ML-based business intelligence software applications and measuring their performance progress with ease.

Hence, the use of AI in business intelligence applications development is increasing since over the past few years. Today, in this article, we would like to walk you through:

• Practical examples of Artificial Intelligence in business intelligence applications that keep organizations competitive
• Applications Of AI In Business Management
• Reasons Behind Why Businesses Need AI-Powered BI Systems

Herein, we have discussed a few major business intelligence application development vendors like SAP, Siemens, GE, and other leading BI app developers that are designing and developing AI based business intelligence applications for businesses across various industries.

Practical Examples Of AI In Business Intelligence Applications

Here are the top use cases of AI-based business intelligence applications –

1. SAP’s HANA Platform

SAP is a Germany-based leading custom software development company. SAP platform consists of different models in their gallery, but as per our requirement, we are going to discuss SAP’s cloud platform HANA.

Most organizations use this software to manage databases of information they have gathered. To be more clear or precise, it will duplicate & ingests structured data like customer satisfaction from an app, relational databases, and many other sources.

You can install this HANA platform in different ways; one is by running on-premise via a company server or using a cloud source. The platform will collect information with the help of access points like financial transactions, equipment at production plants, desktop computers & mobile, and sensors across various business verticals.

If your salesperson is using a tablet or Smartphone to document purchase orders, and the data will be collected from those recorded transactions, which can be examined and analyzed by HANA platform to know customer or user problems and choices.

Walmart is one of the top retail chain stores (11,000) in the world and the USA, which doesn’t require any introduction. It is using the HANA platform to record & process its high-volume transactions that happen in 10 seconds.

2. Avanade’s Deep Analytics Platform

Avanade is the USA’s top IT consulting and services company that is engaged in providing artificial intelligence, business analytics, digital transformation, cloud migration, application development services, workplace management, and other enterprise-centric digital services and solutions.

Avanade is a company that is developed by two IT giants Accenture & Microsoft that has the capability to utilize Cortana intelligence and remaining solutions for data-based insights & predictive analytics.

Pacific Specialty, an insurance company that knocked on the doors of Avanade to develop a deep analytics platform with the focus to provide more information to its staff regarding the business. The insurance firm’s primary aim is to use policy & customer data to enhance team and company growth.

When you can understand your policyholders’ interests, trends, and behavior with the help of analytics, the company can give good advice about existing and new products that are available to the company.

Once, the company concluded that the coming future would be filled up with smart technologies where machines will do the maximum work that can be done by human resources. According to the study conducted by Avanade states that organizations can raise their revenue by 33% with the usage of smart technologies.

And they also revealed that it is going to create new job roles for professionals and many more benefits to users. It is also not precisely mentioned which professionals are going to be changed with the adoption of advanced smart technologies.

3. Apptus’ AI-powered eSales Platform

It is one of the best business applications for artificial intelligence. Apptus is a AI applications development company that is using AI for automation, streamlining operations, and enhancing the efficiency of processes.

Apptus has developed an AI-powered business intelligence platform- eSales for e-commerce and retail companies. This revolutionary application makes use of ML, NLP, predictive analytics, and deep learning like AI technologies and assists companies in automatically processing customers’ data and maximizing sales.

Apptus’ eSales platform identifies and analyzes data related to customers’ search and purchasing behavior and helps e-commerce service providers to display or send personalized product recommendations automatically. It will increase customer loyalty and optimizes sales value.

E-commerce companies are exploring 100% sales benefits using this platform. Based on search patterns, this AI & ML powered BI solution automates demand predictive tasks and creates conversions with ease.

Still, the technology is in the adoption stage, Cloudera Founder & CTO, said that deep learning is very good at anomaly detection and prediction. He also said it is getting simple for deep learning networks to comprehend what information is exactly authentic. And he also says, you cannot teach the platform what to work on, just provide a chunk of data from which it will sort out what it requires.

4. Siemens’ AI-based Reporting and Analytic Platform

 Siemens is using its ML technology to monitor and validate how its industry machinery equipment is working. The company launched MindSphere, an open industry cloud platform in beta.

The primary focus of this cloud platform is to monitor machine performance and detect defects for service requirements with the help of machine tools & drive train analytics.

This AI-powered BI application is being used by many industries to keep an eye on machinery and measure key performance metrics. Such prediction into devices will help companies make informed decisions about anticipatory maintenance & also be used to manage their equipment efficiently so that they can have a long lifespan.

When you compare Predix with MindSphere, the Siemens platform can work efficiently on every machine and plant regardless of the manufacturing industry. The core intention of the platform is to help plant operators to increase the uptime of their equipment and makes maintenance more competent by predicting when there is a possibility of machinery breakdown.

By using these types of platforms, industrial plants are seeing a reduction in maintenance costs. Siemens will provide a box whenever you opt for MindSphere, which you can attach to the machines, and it will collect the information related to the performance of the equipment by which the engineer can take action.

5. GE (General Electronics)

The latest technologies are taking a major part in the newest advancement in various industries. The usage of sensors increasing in physical equipment like vehicles, equipment spaces, machinery, and production plants, and these can be automated & analyzed by artificial intelligence.

When it comes to IoT, it is not about just consumer gadgets, oil rigs, commercial trucks, cargo ships, and trains can be automated or digitalized, examined, and predicted through networks.

Industries like aviation and oil & gas are using GE’s Predix operating system to know the historical performance data of the equipment by using the advantage of the industrial apps, which can be used to identify different types of operational outcomes like when there is a possibility of machinery failure.

If you think GE’s operating system is only for automating primary operations, then you have mistaken because it can process a large amount of information and prepare a forecast report within seconds.

The oil & Gas industry is using Accenture’s intelligent pipeline solution to examine pipelines that are a million miles across the globe. It gathers information from the pipelines & external sources for the safety and proper use of the resources.

When it comes to the airline industry, they are using an app called Aircraft Landing Gear that is built on Predix. The app helps airline engineering crews to check for how many days it will be in service before a flight is placed into the service. The app will prepare a schedule depending on the information that helps to minimize unexpected or unplanned equipment issues & flight delays.

For instance, this AI-based BI solution maximizes the performance of the equipment. After Pitney developed an automated solution on top of Predix, it raised its machinery yield by 20%.

These are a few examples of AI-enabled business intelligence applications. The above-listed BI applications developers are making use of revolutionary AI and ML technologies and tools and creating top-notch BI solutions for companies across all sizes.

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Now, let’s take a look at the best use cases of AI in Business applications.

Applications Of AI In Business Management

The integration of AI, ML, predictive analytics, and deep learning technologies into BI applications will help organizations identify and predict market trends, customer behavior, and optimize overall performance progress.

Custom AI app development companies are assisting enterprises in building futuristic AI based business intelligence applications and playing a major in digitizing data collection, processing, and analyzing operations.

Let’s move on to how can AI empowers Business Intelligence Applications or ai application in business or artificial intelligence in business management.

• Integration of Artificial Intelligence in business application optimizes features and functionalities of BI
• AI and ML in BI applications make data storing and processing faster and help organizations derive valuable insights into customers’ data
• AI development companies are adding intelligent tech capabilities to BI applications to define and intelligently process raw information
• Artificial intelligence in business intelligence, along with ML, and deep learning algorithms, smoothly segregates input data and simplifies complex data analysis processes
• A blend of AI and Natural Language Processing (NLP) makes BI software solutions better understand voice commands and perform data analysis tasks accurately as defined or required by the organization

These are a few benefits of AI Business Intelligence applications. Operational efficiency, productivity, analytics and insights, quick decision-making, business scalability, virtualization and visualization, and cost savings are all key advantages of using AI in business intelligence applications development.

Hire top AI applications development companies and get your BI solution developed with a sort of enterprise-friendly features and functionalities.

Reasons Behind Why Businesses Need AI-Powered BI Systems

The explosion of new big data sources, such as mobile, tablets, and the Internet of Things (IoT) devices will no longer undermine businesses.

They need increasingly practical experiences. This prompts AI-driven BI frameworks that will dramatically change business data into simple, precise, real-time narratives and reports.

BI Apps Delivers Data Insights– AI in business applications for deriving insights is gaining popularity. Big data growth in the market makes it difficult to make strategic decisions within the deadline. In recent years, Artificial Intelligence has increased BI systems to provide dashboards that provide alerts and business insights to key decision-makers.

AI in Business Applications Fills Resource Gaps – There is a shortage of experts with data analytical skills worldwide, and the well-developed country, USA also has a shortage of 1.5 million (approx.) data analysts. Therefore, it is very important to hire data experts in each department of a company to complete the given tasks.

Preventing Data Overload – Data is growing at an unimaginable rate these days and can easily choke off the business activities of organizations. This is where AI-powered BI tools come in, when a company has data bursting its BI platform from different sources.

It aids to analyze all the information and provides customized insights. Therefore, investing in AI-based BI software can help organizations break down data into maintainable insights

 

Final Words

We conclude that the need for Artificial intelligence in business application development is high. AI-enabled BI applications development will have a bright scope in the years ahead.

It is the right time for businesses across industries to invest in AI business intelligence software. Companies can reduce resource overheads, better manage enterprise data operations, build sales strategies, well-handle leads, and get 100% returns on investment using BI solutions.

USM is the best AI development company in the USA and India. Our seasoned AI-based BI applications developers create outstanding app development strategies and develop full-fledged automation solutions that scale up your operations.

Are you looking to integrate AI solutions to get a high ROI?

Let us connect, Our AI expert team will guide you on the right path.

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The growing use of smart home devices is undermining the privacy and safety of domestic workers. New research reveals how surveillance technologies reinforce a sense of constant monitoring and control by domestic workers' employers, increasing their vulnerability and impacting their mental wellbeing.

A research team develops disinfection robot combining physical wiping and UV-C sterilization.