AiThority Insights: Weekly Trends Shaping the AI Industry for the Week Ending March 21st
Welcome to this week’s AiThority’s roundup! We’re excited to share the latest AI and technology breakthroughs.This week, we explore everything from the deployment of AI infrastructure to exciting advancements in purpose-built Artificial Intelligence. Additionally, read more about the integration of AI and cloud computing, as well as how Large Language Models are shaping product development.
Explore the frontiers of AI technology, one headline at a time.
Microsoft VP Lori Borg Joins Netrio Board of Directors
Netrio has appointed Lori Borg, a Microsoft executive, to its Board of Directors to drive national expansion in the managed service provider (MSP) market. Her expertise in strategy, partnerships, and market growth will help strengthen Netrio’s go-to-market approach and customer experience. Borg’s leadership comes at a crucial time, following Netrio’s integration with SUCCESS and PCA. With her background in technology and mergers, she aims to accelerate Netrio’s growth and establish it as a leading national MSP. Netrio now serves small- to mid-market enterprises, offering IT infrastructure, cybersecurity, cloud services, and more to enhance efficiency and reduce costs.
IBM Taps NVIDIA AI Data Platform Technologies to Accelerate AI at Scale
IBM is expanding its AI and storage capabilities through a new collaboration with NVIDIA. The partnership introduces content-aware storage (CAS) for unstructured data, deeper watsonx integrations, and enhanced AI consulting services. IBM will use NVIDIA’s AI Data Platform to improve AI performance, increase accessibility, and optimize compute-intensive workloads across hybrid cloud environments. The integration of IBM’s storage with NVIDIA AI aims to streamline data access, enabling faster AI inference and reasoning. These innovations will help enterprises scale AI applications, enhance governance, and accelerate digital transformation while addressing AI’s technical and cost challenges.
Hitachi Vantara Introduces Hitachi iQ M Series, a Modular Design with Hybrid Cloud Data Orchestration for GenAI and Industry-Specific Workloads
Hitachi Vantara has launched the Hitachi iQ M Series, an AI-ready infrastructure solution that combines scalable storage, NVIDIA-accelerated computing, and flexible file system options. Designed for cost efficiency and adaptability, it allows businesses to scale compute and storage independently, optimizing AI workloads. The M Series integrates the NVIDIA AI Data Platform to enable real-time AI insights and enhance enterprise AI applications. Additionally, Hitachi Vantara has partnered with Hammerspace to improve data orchestration, ensuring seamless access to distributed data. The Hitachi iQ portfolio offers end-to-end AI solutions, helping businesses streamline operations, maximize AI potential, and drive data-driven innovation.
Deloitte Unveils Zora AI, Agentic AI for Tomorrow’s Workforce
Deloitte‘s Zora AI™ is an AI-powered platform that automates complex business processes using intelligent agents. Built on NVIDIA AI, it enhances efficiency in finance, HR, supply chain, sales, and customer service. HPE is leveraging Zora AI for Finance to automate operations and improve decision-making, expecting a 50% reduction in reporting time. Deloitte uses it internally to cut costs by 25% and boost productivity by 40%. Available via cloud subscription, Zora AI integrates seamlessly with existing systems. It offers secure, transparent AI-driven insights, helping businesses optimize workflows, enhance decision-making, and drive innovation in the era of autonomous enterprises.
Cisco to Deliver Secure AI Infrastructure with NVIDIA
Cisco and NVIDIA have launched the Secure AI Factory to simplify and secure enterprise AI adoption. This architecture integrates security at all levels, from applications to infrastructure, using solutions like Cisco AI Defense and Hybrid Mesh Firewall. The collaboration offers flexible, scalable AI infrastructure, leveraging Cisco’s networking and NVIDIA’s AI expertise. It supports ready-to-deploy and customizable options, ensuring security and operational simplicity. Solutions will be available by late 2025, with many components already accessible.
Oracle and NVIDIA Collaborate to Help Enterprises Accelerate Agentic AI Inference
Oracle and NVIDIA have integrated AI tools to simplify enterprise AI adoption. Oracle Cloud Infrastructure (OCI) now offers over 160 AI tools and 100+ NVIDIA NIM microservices, enabling faster AI application development. The partnership enhances AI vector search in Oracle Database 23ai and introduces no-code AI Blueprints for quick deployments. NVIDIA’s GPUs accelerate AI workloads, and OCI provides scalable AI infrastructure. This collaboration ensures enterprises can deploy AI quickly, securely, and efficiently across various environments.
Accenture Expands AI Refinery and Launches New Industry Agent Solutions to Accelerate Agentic AI Adoption
Accenture has launched an AI agent builder on its AI Refinery platform, enabling business users to create and customize AI agents without coding. Built on NVIDIA AI Enterprise, the tool enhances agility by allowing rapid adaptation to business needs. Accenture aims to develop 100 industry-specific AI agent solutions by year-end, benefiting sectors like telecom, finance, and insurance. Clients like ESPN, HPE, and the UN are already leveraging these AI-driven solutions for enhanced efficiency and engagement.
Hewlett Packard Enterprise Introduces New Enterprise AI solutions with NVIDIA to Accelerate Time to Value for Generative, Agentic and Physical AI
Hewlett Packard Enterprise and NVIDIA have introduced new enterprise AI solutions under NVIDIA AI Computing by HPE, enhancing performance, security, and power efficiency for AI training, tuning, and inferencing. HPE Private Cloud AI now integrates with the NVIDIA AI Data Platform, offering a turnkey private cloud for AI-driven business actions. New HPE AI servers, featuring NVIDIA Blackwell technology, support large-scale AI workloads. Additionally, HPE unveiled GPU optimization tools, security enhancements, and modular liquid-cooled data centers to meet growing AI demands.
ServiceNow and NVIDIA Advance Agentic AI to Redefine Enterprise Intelligence
ServiceNow and NVIDIA are expanding their partnership to optimize AI agent deployment with new evaluation tools and integration of NVIDIA Llama Nemotron models. These tools help enterprises assess AI agent performance, ensuring accuracy, transparency, and efficiency before deployment. The integration of Llama Nemotron models enhances AI agents with advanced reasoning, enabling smarter automation and decision-making. ServiceNow’s AI Agent Orchestrator, acting as a central AI control hub, ensures seamless coordination across business processes. This collaboration marks a significant step in delivering enterprise-ready AI solutions that drive efficiency and business transformation.
Weekly Roundup: Expert Views on AI Trends
AiThority Interview With Mamoun Benkirane, CEO and Co-Founder of MarketLeap
AIThority’s Weekly Voice: From the Expert
Mamoun Benkirane, CEO and Co-Founder of MarketLeap, shares insights on AI’s role in D2C purchase cycles and product developments at MarketLeap. After recognizing the challenges e-commerce sellers face, he created MarketLeap to simplify global expansion using AI. With new funding, the platform will enhance AI-driven automation in pricing, marketing, and inventory management. AI is vital for D2C sellers to analyze data and manage operations. Benkirane also sees future AI advancements in specific workflows and industries, with AI assistants improving search and retrieval rather than replacing entire workflows.
Don’t Miss These Must-Read Articles of the Week
The Future of Enterprise AI: Turning Data Overload into Actionable Intelligence
Enterprise data is growing rapidly, creating a challenge of “data overload” with unstructured information scattered across systems. AI-driven platforms like Mindbreeze, Coveo, and Elastic are tackling this by offering advanced search, semantic understanding, and real-time insights. These systems use contextual search and knowledge graphs to turn fragmented data into actionable intelligence, helping employees find relevant information efficiently. With security and compliance features, AI solutions support multi-deployment models and integration with collaboration tools. Future developments include generative AI for summaries and predictive insights, transforming enterprise knowledge management into a proactive, user-centric process.
How Small, Specialized Language Models Can Outperform the AI Giants
While large language models (LLMs) like ChatGPT offer broad capabilities, they often lack the precision needed for specialized tasks. Small language models (SLMs), trained on focused datasets, provide more accurate, efficient, and cost-effective solutions for domain-specific needs. SLMs excel in areas like customer service, where repetitive tasks require precision. To leverage SLMs, businesses should define clear use cases, pilot the models in specific workflows, and measure success before scaling. A hybrid approach combining both SLMs and LLMs can optimize AI investments, offering targeted support and enhancing operational efficiency. Small models are proving to have a big impact in business.
Combining AI with Predictive Analytics for Fraud Detection and Risk Management
Fraud detection is crucial in today’s digital world, with businesses facing growing challenges from cyber threats and increasing digital transactions. AI, machine learning (ML), and predictive analytics enhance fraud detection by analyzing large data sets in real-time, identifying patterns, and adapting to new fraud tactics. These technologies improve speed, accuracy, and reduce false positives. While challenges like data quality exist, AI-driven systems enable proactive fraud prevention, optimizing resources and minimizing losses for businesses in the financial sector.
AI Quote Of The Week by AiThority
AI is reshaping how we think about content categorization at scale, and nowhere is this more complex than in areas like CSAM detection. Traditionally, these laws were built to protect real human victims, but now that AI is generating hyperrealistic digital subjects, it raises tough legal and ethical questions. If there’s no real human harm, does the law still apply? And beyond CSAM, this challenge extends to other categories—deepfakes, AI-generated violence, synthetic revenge porn. When the “people” involved aren’t real, does that change our response or how it should be categorized?
These aren’t just tech problems—they’re social ones. As AI-generated content keeps pushing boundaries, lawmakers and enforcement agencies need to move just as fast to keep up. The line between what’s legal, ethical, and enforceable is getting blurry, and we need a global conversation to define where it should be drawn. – TK KEANINI, CTO at DNSFilter
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