Targeting AI

Generative AI and agentic AI tools are only as good as the problem that they are used to solve. In some cases, using generic AI tools can help with non-specific issues. However, Raj Shukla, of enterprise AI platform vendor Symphony AI, says the future of AI technology will focus on vertical applications and open models. In this Targeting AI episode from AI Business, he emphasizes that open source models provide flexibility and the ability to fine-tune for specific use cases. Featuring: Raj Shukla, CTO, Symphony AI In this episode, we cover: Symphony's AI mission of bringing AI technology to legacy industries that may struggle with adoption. A vertical approach combines predictive, generative and agentic AI to address specific challenges. The move in vertical areas from a traditional rule-based approach to a more dynamic, non-deterministic tool. AI applications in these verticals can significantly improve operational efficiencies and strategic decision-making. To learn more about vertical AI applications, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news. To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech. References: Small Language Models Gaining Ground at Enterprises Vertical AI agents explained: The future of enterprise tech AI21 releases open source tiny language model

President Donald Trump signed an executive order last week that looks to override AI state laws in favor of a national policy. Titled "Ensuring a National Policy Framework for Artificial Intelligence," it directs the Department of Justice to establish an AI Litigation Task Force and challenge "cumbersome" state laws. It also asks the Secretary of Commerce to consider withholding federal funds from states found to have restrictive AI laws. In this podcast, Michael Bennett discusses what the EO means for states like New York and California, which already have established laws in place, and how they might respond.  Featuring: Michael Bennett, Associate Vice Chancellor for Data Science and Artificial Intelligence Strategy, University of Illinois Chicago  In this episode, we cover how:  The EO aims to prevent conflicting state laws on AI.  States with existing AI regulations are likely prepared to resist the EO.  The U.S. has a more laissez-faire approach to AI regulation compared with the EU and China.  The order could lead to significant political battles leading up to the midterm elections.  The effectiveness of minimal regulation in winning the AI race is uncertain.  To learn more about AI regulations, check out AI Business, and please subscribe to our newsletter to keep up to date on the most important AI news.  To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.  References:  Navigating Big Tech’s Influence on the AI Regulatory Landscape in 2025  Big Tech Firms Ask for AI Regulation but Quietly Hedge Their Bets  US State Attorneys General Demand Greater AI Safety From Tech Giants

In this episode of the Targeting AI podcast from AI Business, host Esther Shittu interviews Oren Michels, of 2024 startup Barndoor.ai, an AI data and access management vendor, about how to effectively secure enterprise agentic and generative AI systems. The approach is different from traditional cybersecurity paradigms designed to prevent outside intruders from doing harm within an organization's IT system, according to Michels. With agents, security procedures need to focus on the agents themselves to ensure they are performing as their human counterparts intend. The podcast was recorded at the AI Summit conference in New York City on Dec. 10. Featuring Oren Michels, founder and CEO of Barndoor.ai In this episode, we cover: How enterprises can secure agentic and generative AI systems. What mistakes businesses make that make them vulnerable to security threats to AI systems. Some of the biggest security threats to large-scale business users of generative and agentic AI technology. How to use the Model Context Protocol standard with cybersecurity measures to protect and govern AI agents. To learn more about security for generative and agentic AI systems, check out AI Business, and please subscribe to our newsletter to keep up to date on the most important AI news. To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech. References: AI Agent Security: Whose Responsibility Is It? Governance Is Top Priority for Companies Using Agentic AI: Survey What Agentic AI Means for Cybersecurity  

In this special news analysis edition of the Targeting AI podcast from AI Business, Esther Shittu and Shaun Sutter interview R "Ray" Wang of Constellation Research, with Wang live from the AWS re:Invent 2025 conference in Las Vegas. Wang says AWS's new frontier AI agents represent a major step in the development of agentic AI, and other AI vendors are likely to follow AWS's lead. He also notes that AWS's new Trainium AI chips position AWS to be less reliant on AI chips from Nvidia, though the AI hardware giant continues to be a major chip provider to AWS. Wang also notes that AWS's new "AI factories" are crucial for the growing sovereign AI movement, as countries and regions worldwide are establishing their own AI industries and are less dependent on the U.S. and China. Featuring R "Ray" Wang, founder and analyst at Constellation Research In this episode, we cover how: The demand for AI chips is growing rapidly. AWS's Trainium AI chips offer cost-effective options for developers. Pre-built models are essential for speeding up development. AWS is focusing on providing choices for developers. The integration of AI into existing systems is crucial for businesses. AWS is catching up in AI capabilities compared to competitors. The importance of governance and security in AI deployment. Startups are increasingly building on AWS infrastructure. The future of AI will involve multi-agent systems across platforms. To learn more about AWS, generative AI, agentic AI and sovereign AI, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news. To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech. References: AWS Launches Frontier Agents AWS Opens First Innovation Hub for APAC AWS Developing High-Performance Autonomous AI Agents    

It’s no secret that generative AI  has led to exponential growth in AI technology. However, one area continues to seem to be lacking. Years ago, Asha Saxena, of the World Leaders in Data and AI (WLDA) organization, attempted to shift the landscape by creating an organization that emphasizes the importance of diversity in AI and the ethical challenges organizations face when implementing AI systems. In this Targeting AI podcast from AI Business, she emphasizes the need for women to have a bigger role in the AI community and the role of men as allies in this mission. Featuring: Asha Saxena, CEO of World Leaders in Data and AI In this episode, we cover how: Diversity is essential for innovation and excellence. Men must be included in the conversation about women in leadership. AI can help detect and rectify bias in data. Organizations face challenges in obtaining diverse data sets. Lifelong learning is crucial in the rapidly evolving AI landscape. Personalization in AI applications is a significant trend. To learn more about generative AI and diversity, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news. To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

In this episode of the Targeting AI podcast from AI Business, Esther Shittu and Shaun Sutner interview Justin Liu of B2B platform Alibaba.com., discussing his extensive experience in e-commerce and the evolution of B2B sourcing in the age of AI. Liu shares insights on the complexities of B2B transactions, the innovative AI tools being implemented to enhance buyer and seller experiences, and the rapid adoption of these technologies by small businesses. He also highlights the importance of supplier verification and security in B2B commerce, and how AI is transforming traditional roles in the industry. The conversation concludes with a look at Alibaba's global expansion efforts and the future of AI in the e-commerce sector. Featuring: Just Liu, general manager, Alibaba.com U.S In this episode, we cover how: B2B sourcing is more complex than B2C transactions. AI can simplify the tedious processes in B2B sourcing. Alibaba.com focuses on helping buyers and sellers with AI. AI adoption is growing rapidly among professional buyers. AI enhances supplier verification and transaction security. AI is transforming traditional sales roles in B2B. AI helps lower the entry barrier for small businesses. To learn more about generative AI and agentic AI, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news. To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech. References: Alibaba, Nvidia Unite for AI Development and Cloud Growth Alibaba Cloud targets full-stack AI dominance Alibaba unveils Accio Agent for global trade  

Some think AI is just a trend and that we are on the verge of a bubble. That is not the case for Arun Subramaniyan, of Articul8. This enterprise AI vendor offers customers a platform for developing and deploying customized generative AI applications. In this Targeting AI podcast, Subramaniyan discusses some of the misconceptions enterprises have about implementing AI technology and the significance of measuring ROI. Featuring: Arun Subramaniyan, CEO and founder of Articul8 In this episode, we cover how: AI is a necessity for solving complex problems, not just a trend. Enterprises struggle with data synthesis and knowledge discovery. Customer data remains secure within its environment. Open source is crucial for the evolution of AI technology Many enterprises misunderstand the complexities of AI implementation. To learn more about generative AI, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news. To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech. References: How business leaders are measuring generative AI's ROI AI regulation and the open source community Intel-Backed Generative AI Company Launches Aerospace Platform at Paris Air Show  

In this special news analysis edition of the Targeting AI podcast from AI Business, Esther Shittu and Shaun Sutner discuss Nvidia's historic achievement on Oct. 29 of becoming the first company to reach a $5 trillion market valuation with R "Ray" Wang of Constellation Research. The conversation explores the implications of this milestone for enterprise AI technology, the current AI boom, and the potential for a bubble in the market. They also touch on Nvidia's market position and the concerns surrounding monopoly in the context of the ongoing U.S.-China AI war. Featuring R "Ray" Wang, founder and analyst at Constellation Research In this episode, we cover how: Nvidia's valuation reflects the growing importance of AI technology. The AI market is expected to continue expanding significantly. There is a potential for an AI bubble if job creation does not keep pace with AI advancements. Entrepreneurship in AI is thriving, with small companies achieving significant revenue. The emergence of AI exponentials is disrupting traditional business models. Nvidia's dominance is partly due to geopolitical factors, particularly the U.S.-China AI war. Concerns about monopolistic practices exist but are complicated by the competitive landscape. The future of AI jobs remains uncertain as automation replaces traditional roles. To learn more about Nvidia, generative AI and agentic AI, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news. To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech. References: Nvidia unveils new AI hardware-software approach for industrial AI Nvidia's deal with rival AI chipmaker Intel The AI chip giant becomes first company to cross $5 trillion threshold    

In this breaking news analysis episode of the Targeting AI podcast from Informa TechTarget's AI Business, Esther Shittu and Shaun Sutner discuss the recent AWS outage that disrupted numerous websites and services, including AI applications such as widely used generative AI models from OpenAI and Anthropic. Tech analyst David Nicholson provides insights into the causes of the outage, emphasizing the importance of multi-site redundancy for enterprises relying on cloud services. The discussion also touches on the implications for AI applications and the need for businesses to consider redundancy options to prevent future disruptions. Featuring: David Nicholson, analyst, The Futurum Group In this episode, we cover how: AWS experienced a major outage due to DNS problems. The outage affected several large language models. Multi-site redundancy is a way to prevent future disruptions. Enterprises need to invest in redundancy for cloud services. AI applications are not the cause of outages but are affected by them. Cloud services have become more resilient over time. Companies must be proactive in ensuring service continuity. The cost of redundancy can be high, but it is necessary. Smaller cloud providers may not offer the same level of resilience. To learn more about generative AI, agentic AI and AI cloud services, check out AI Business from Informa TechTarget. To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech. References: Prepare for a cloud outage with these preventive steps Beware of over-reliance on U.S.-based cloud giants Generative AI models from Anthropic and OpenAI

In this episode of the Targeting AI podcast from AI Business, Shaun Sutner and Esther Shittu interview Sean Falconer of streaming data platform vendor Confluent. They discuss Confluent's AI strategy, the importance of real-time data management, and the integration of generative AI and multi-agent systems into business processes. Falconer emphasizes the need for high-quality data and the advantages of open source technologies like Apache Kafka and Flink. The conversation also touches on the challenges of implementing AI systems and the future direction of AI technology at Confluent. Featuring: Sean Falconer, senior director of AI Strategy at Confluent. In today's episode, we cover how: Confluent focuses on real-time data processing and management. Generative AI requires fresh, relevant data to be effective. Data quality should be enforced at the source, not downstream. Multi-agent systems can operate continuously and autonomously. Confluent partners with major AI model providers for integration. Reliability and testing are critical challenges in AI development. The future of AI at Confluent includes building support for ambient agent experiences. To learn more about AI, open source and agentic systems AI, check out AI Business. To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.  References:  Confluent, streaming data and agentic AI Confluent and Databricks work together to simplify AI development What is data streaming?