Targeting AI

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?    

In this episode of the Targeting AI podcast from AI Business, hosts Esther Shittu and Shaun Sutner discuss the role of AI in healthcare with Madhav Thattai of Salesforce and John Oberg of Precina Health. They explore the concept of being AI-first, the integration of AI in patient care, and the impact of agentic systems on healthcare outcomes. The conversation highlights how AI can enhance clinical practices, improve patient interactions, and streamline business processes, ultimately leading to better health outcomes and operational efficiency. In this episode, the conversation revolves around the transformative role of AI in healthcare, particularly focusing on patient experience, the integration of Salesforce Health Cloud, and the balance between AI automation and human clinical judgment. The speakers discuss the supportive role of AI in clinical decisions, innovative applications in mental health, and the importance of trust and ROI in AI deployments. They emphasize the need for clear KPIs and the potential for AI to unlock efficiencies in healthcare delivery. Featuring: Madhav Thattai, SVP & COO of Agentforce product management at Salesforce, and John Oberg, founder and CEO of Precina Health In this episode, we cover how: AI is used extensively in healthcare to enhance patient-provider interactions. Being AI-first can lead to improved clinical and financial outcomes. Salesforce's agentic technology is being used for customer support and marketing. AI can automate routine tasks, allowing healthcare providers to focus on patient care. The integration of AI in diabetes management has shown significant success. AI can personalize patient care through meal planning and recipe suggestions. The future of healthcare involves a collaborative approach between technology and human providers. AI is not the focus; it's a catalyst for patient experience. AI supports clinicians without replacing their judgment. To learn more about agentic AI and generative AI, check out AI Business.  To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.  References:  Salesforce Agentforce   Applications for AI in healthcare AI and type 2 diabetes risk   

In this breaking news analysis episode of the Targeting AI podcast from Informa TechTarget's AI Business, hosts Esther Shittu and Shaun Sutner discuss the latest innovations in agentic AI technology unveiled at Salesforce's Dreamforce conference in October with guest Madhav Thattai of CRM and CX giant Salesforce. The conversation covers the new Agentforce 360 platform, including hybrid reasoning, enhanced control and context for agents, and the importance of the user experience and data privacy. Thattai emphasizes the need for a balance between creativity and control in enterprise AI applications. Featuring: Madhav Thattai, SVP and COO of Agentforce product management at Salesforce In today's episode, we cover how: Hybrid reasoning combines LLMs with structured processes. Control and context are essential for agent functionality. UX features are being enhanced for agents. Data privacy is important to Salesforce. AI agents must respect user permissions and access. Salesforce aims to democratize agent development. Context indexing improves agent accuracy. To learn more about agentic AI, generative AI and Salesforce, check out AI Business. To watch videos of our podcasts, subscribe to our YouTube channel, @EyeonTech.

In this podcast, Mark Geene of robotic process automation (RPA) vendor UiPath discusses the evolution of RPA and the emergence of agentic AI. He explains how these technologies are transforming business processes, the importance of governance and compliance, and the future of work with digital workers. Geene also highlights the role of data in enabling effective AI agents and shares insights on the competitive landscape of RPA vendors. The discussion concludes with predictions about the future of AI in business. In the episode, we cover how: RPA automates repetitive tasks and is limited to deterministic workflows Agentic AI combines deterministic and ad hoc processes for greater flexibility Governance and compliance are critical for successful automation Orchestration allows for effective collaboration between agents, robots, and humans Data is essential for providing context to AI agents Narrowly scoped agents can operate with more autonomy The future of work will see agents supervising business processes Featuring: Mark Greene, senior vice president and general manager of AI products and platform at UiPath To learn more about agentic AI, RPA and generative AI, check out AI Business from Informa TechTarget. To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech. References: UiPath AI agents blend with RPA amid industry hype, doubts Governance Is Top Priority for Companies Using Agentic AI: Survey Startup aims to upend old-school RPA with large action model | TechTarget          

In this special breaking news analysis edition of the Targeting AI podcast from AI Business, hosts Shaun Sutner and Esther Shittu dive into the latest developments in the AI industry with Torsten Volk, an analyst with Omdia. Both AI Business and Omdia are owned by Informa TechTarget. This episode covers AI cloud computing vendor CoreWeave's groundbreaking $14 billion AI compute deal with Meta Platforms, exploring its implications for enterprise AI and the future of data center services. Join us as we unravel the complexities of AI infrastructure, the race for GPU power, and the strategic moves shaping the tech landscape. Don't miss this discussion on the forces driving innovation and competition in AI.  Takeaways: CoreWeave's partnership with Meta underscores the growing need for specialized AI infrastructure. Efficient GPU utilization is crucial for AI companies to maintain competitiveness. The AI sector is rapidly evolving, with significant investments in infrastructure and talent. Meta's strategy involves collaborating with various vendors to enhance its AI capabilities. The deal may signal the emergence of a new sector within the AI industry, focusing on data center services.  

As one of the biggest financial institutions in the U.S., Capital One isn’t running away from generative AI and agentic AI. Instead, the $490 billion company is using the technology to enhance both internal operations and customer experience. In this Targeting AI episode, the chief scientist and executive vice president at Capital One discusses some of the challenges and opportunities the financial giant is facing in customizing LLMs, and how the company continues to prioritize risk management and safety. Featuring: Prem Natarajan, executive vice president, head of enterprise AI and chief scientist, Capital One In today's episode, we cover: Capital One's enterprise AI strategy is focused on creating customizable platforms using open source or open weight models Capital One uses its proprietary data to customize AI models The company uses GenAI and agentic AI for internal operations, such as with agent assist tools for customer service and customer-facing experiences like chat concierge The enterprise has a focus on long-term transformation and not short-term ROI To learn more about AI, open source and agentic AI, check out AI Business and SearchEnterpriseAI from Informa TechTarget. To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech. References: Capital One AI partnerships aim to build trust and grow talent Compare proprietary vs. open source for enterprise AI The importance and limitations of open source AI models

As a legacy organization, IBM has long been a champion for open source, especially in the age of GenAI. In this episode of Targeting AI from Informa TechTarget, Bruno Aziza, vice president of data, AI and analytics at IBM, discusses how the vendor has had to rebrand and shift in the age of GenAI and agentic AI. Aziza shares insights on talent challenges, IBM's data strategy with Watson X, and the significance of customer-centric AI solutions.  Featuring: Bruno Aziza, vice president of data, AI and analytics at IBM  In today’s episode, we cover how:  The shift to agentic AI is crucial for modern enterprises.  Open source plays a vital role in AI development.  IBM focuses on enterprise AI, rather than consumer-facing solutions.  Talent scarcity is a significant challenge in AI innovation.  99% of enterprise data remains untouched by AI.  To learn more about AI, open source, agentic AI, check out SearchEnterpriseAI.  To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.  References:  IBM customers assess the performance of AI agents  IBM to buy open source data platform and AI vendor DataStax  IBM targets agentic AI orchestration 

When it comes to diversity, AI systems often fail. In this episode of the Targeting AI podcast, Christina Mancini, CEO of Black Girls Code, discusses the importance of inclusion in tech, the strategies Black Girls Code employs to empower girls of color, and the need for ethical considerations in AI education. Christina emphasizes the role of communities of color as creators in AI and the necessity for equitable development in technology. She also outlines the future goals of Black Girls Code and how organizations can support their mission. Featuring: Christina Mancini, CEO of Black Girls Code In today’s episode, we cover: Communities of color are often super users of technology but need to be creators too. AI technologies must be built by diverse teams to avoid bias. Organizations should pause to consider the impact of their products on all communities. Collaboration with tech partners is essential for achieving their mission. To learn more about AI, bias and diversity check out SearchEnterpriseAI. To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech. References: Generative AI will force diversity in AI systems Federal report focuses on AI diversity and ethics Diverse data, ethical use key to responsible AI engineering

In a market dominated by Nvidia H100 GPUs, Cerebras Systems seeks to develop the world's largest AI chip, the Wafer Scale Engine. In this episode of Targeting AI from Informa TechTarget, James Wang, the vendor's director of product marketing, discusses the importance of the inference market for AI technology and how the company's strategic partnerships are essential for growth. He elaborates on the evolving landscape of AI, including the significance of agentic AI and touches on Cerebras' future direction as a cloud and API company. Featuring: James Wang, director of product marketing at Cerebras Systems In this episode, we cover how: Cerebras approaches competing against Nvidia. Cerebras approaches differentiating itself from other AI inference vendors The company is evolving into a cloud and API product company to meet market demands. Agentic AI represents a new frontier in AI applications, enabling complex tasks through multiple requests. To learn more about AI and Cerebras Systems and other hardware news, check out SearchEnterpriseAI.  To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.  References:  Cerebras launches Alibaba model, forms key AI partnerships Cerebras' inference AI tool challenges Nvidia, but faces hurdles Microsoft, AWS and Cerebras launch DeepSeek-R1 model  

In this two-year anniversary episode of Targeting AI from Informa TechTarget, Michael Bennett discusses the rapid evolution of generative AI technology, and its implications for society, legal frameworks and creative industries. He highlights the public's growing awareness and understanding of AI, the legal challenges surrounding copyright and fair use, and the moral questions that arise from the use of AI in creative fields.  Featuring: Michael Bennett, associate vice chancellor for data science and artificial intelligence strategy at University of Illinois Chicago  In this episode, we cover:  The public's awareness of AI technology has significantly increased since the release of ChatGPT.  Legal challenges surrounding generative AI focus on copyright and fair use, creating uncertainty for the industry.  The disparity in AI infrastructure may lead to unequal benefits and negative consequences globally.  The future of AI, including AGI (artificial general intelligence), is uncertain and requires careful consideration.  To learn more about AI and the other regulation and governance news, check out SearchEnterpriseAI.  To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.  References:  AI regulation: What businesses need to know in 2025  XAI releases Grok 4 amid furor over antisemitic comments  Anthropic’s early lawsuit win pushes courts forward on fair use 

In this special news edition of Targeting AI from Informa TechTarget, government reporter Makenzie Holland discusses President Trump's AI action plan and executive orders aimed at promoting AI development and ensuring U.S. dominance in the AI race. Featuring: Makenzie Holland, Informa TechTarget senior news writer In today’s episode, senior new director Shaun Sutner and AI news writer Esther Shittu cover these topics: President Trump’s new executive order and its intent How the executive orders differ from the president’s previous orders and President Biden’s 2023 executive order Woke AI and political bias concerns To learn more about AI and the other regulation and governance news, check out SearchEnterprise AI and SearchCIO To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech. References: White House AI plan places scrutiny on state AI laws Senate’s ‘One Big, Beautiful Bill’ affects AI, U.S. energy S. policy moves reflect big tech issues with state AI laws  

In this episode of the Targeting AI podcast from Informa TechTarget, Ben Flast, of NoSQL database vendor MongoDB, discusses the company's rapid integration of generative AI technologies, including vector search and real-time updates through Atlas Stream Processing. He emphasizes the importance of community engagement and the role of agentic AI in enhancing developer productivity. The conversation also explores the differences between open source and proprietary models, the impact of model sizes on performance, and MongoDB's approach to AI governance. Flast shares customer applications that highlight the transformative potential of AI in various industries and concludes with insights into future innovations at MongoDB. Featuring: Ben Flast, director of product management at MongoDB In today's episode, we cover these topics: Vector search enhances the capabilities of GenAI applications. Agentic AI represents a new application pattern for AI capabilities. Model size affects performance and cost for developers. To learn more about AI and the importance of GenAI in database platforms, check out SearchEnterprise AI and SearchDataManagement. To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech. References: Atlas Stream Processing MongoDB vector search Model Context Protocol GenAI standard

The lack of diversity in AI systems has been an issue since the birth of the technology. In this episode of the Targeting AI podcast from Informa TechTarget, Karen Panetta discusses the importance of diversity in tech, and the ethical implications of AI. She emphasizes the need for inclusive design in engineering and AI systems, the role of digital twins in education, and the challenges of AI bias. Featuring: Karen Panetta, an IEEE fellow and dean of graduate engineering education at Tufts University In today's episode, we cover these topics: AI should focus on solving real-world problems rather than being applied indiscriminately. Ethical AI must prioritize the principle of “do no harm” to individuals and communities. AI bias can lead to significant real-world consequences, especially in healthcare and hiring. and more. To learn more about AI and the importance of diversity in AI systems, check out SearchEnterprise AI. To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech. References: Generative AI will force diversity in AI systems Federal report focuses on AI diversity and ethics Diversity in hiring a key to eradicating AI bias  

In this episode of the Targeting AI podcast, Shaun Sutner and Esther Ajao interview Alan Trefler, founder and CEO of Pegasystems, discussing the evolution of AI technology, particularly generative AI, and its integration into business processes. Trefler shares insights on the differences between design time and runtime applications of AI, the importance of workflow engines, and the challenges of AI safety and reliability. He emphasizes the need for collaboration between AI and human expertise, and outlines Pegasystems' roadmap for effectively using AI in business process automation and legacy transformation. Featuring: Alan Trefler, founder and CEO, Pegasystems. In today's episode, we cover how: GenAI has significantly advanced Pegasystems' offerings. GenAI coaches differ from traditional generative AI assistants by focusing on design time. Design time is crucial for ensuring reliable AI outcomes in business settings. GenAI can enhance business process automation by streamlining workflows. References: Pegasystems expands agentic AI for business automation | TechTarget Pegasystems expands generative AI in CX, BPA cloud platform | TechTarget Pegasystems unveils AI assistant for knowledge management | TechTarget CRM and BPM vendor Pegasystems adds new AI features | TechTarget To learn more about AI and Pegasystems, check out Informa TechTarget news sites, including SearchCustomerExperience and SeachEnterpriseAI To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

Nvidia's hardware strategies are powering AI technologies. Recently, networking has become the critical backbone of modern AI systems. In today’s episode, Kevin Deierling provides practical insights for enterprises looking to implement AI technologies effectively. Deierling contrasts traditional data centers with the emerging concept of AI factories, revealing how these specialized environments are reshaping enterprise computing. Featuring: Kevin Deierling, senior vice president of networking, Nvidia. In today's episode, we cover: Nvidia hardware and software approach AI factories and data centers Agentic AI and the shift toward complex reasoning and more. To learn more about AI and Nvidia, check out SearchEnterprise AI. To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech. References: Nvidia AI platform for cloud GPU providers widens supply Nvidia, AMD and others tout partnership with Saudi Arabia Nvidia aims at agents, physical AI with reasoning models

Generative AI has led to many fears about the workforce. However, for work management platform vendor Asana, GenAI and agentic AI can be effective tools in the workforce. Instead of replacing humans, AI technology can work alongside humans. Despite the potential for collaboration, not all tasks require the use of AI technology. Featuring: Saket Srivastava, CIO of work management platform, Asana. In today's episode, we cover: The collaboration between AI technology and humans Employees need training and support in AI How GenAI can significantly improve project management tasks and more. To learn more about AI and Asana, check out SearchEnterpriseAI. To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech. References: Project management vendor Asana brings AI to Work Graph 6 of the top change management applications Connected workspace apps improve collaboration management

As an AI writing assistant, Grammarly has used AI technology from its inception. The popularity of large language models has led to a shift in which the writing assistant vendor moved from natural language processing to including large language models to help enterprise employees improve their writing as they work. This has led Grammarly to see a possibility in the part it can play in transforming the future of work. Featuring: Luke Behnke, head of Enterprise Product at Grammarly, an AI-powered assistant writing platform. In today’s episode, we cover: Grammarly’s AI evolution Agentic AI and the future of work AI technology as an assistant and not a replacement for work and more. To learn more about AI and Grammarly, check out SearchEnterprise AI. To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech. References: Grammarly AI and an update to the writing tool What will be the future of the workplace? Top 4 AI writing tools for improved business efficiency

A key truth about AI is that regulation has long lagged innovation. However, this has not removed the responsibility of enterprises to deploy AI systems responsibly or for AI vendors to create responsible systems. What are the key metrics to understanding a safe AI system? Featuring: Stuart Battersby, CTO at Chatterbox Labs, vendor of a quantitative AI risk metrics platform, and Danny Coleman, CEO at Chatterbox. In today’s episode, we cover: The difference between AI safety and responsible AI The need for standards in AI safety The future of AI safety in Enterprises and more. To learn more about responsible AI, check out SearchEnterprise AI. To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech. References: Assessing if DeepSeek is safe to use in the enterprise EU, U.S. at odds on AI safety regulations Responsible AI vs. ethical AI: What's the difference?

Industrial AI is less familiar than consumer AI, but represents a critical and growing sector within AI’s influence. What unique AI applications are surfacing in this area? Featuring: Olympia Brikis, director of Industrial AI research at Siemens In today’s episode, we’ll cover… Understanding Industrial AI and its distinctions from consumer AI AI and, specifically, generative AI adoption at Siemens The role of digital twins in testing AI recommendations and more. To learn more about AI in healthcare, check out Search Enterprise AI. To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech. References: CES 2024: Siemens eyes up immersive tech, AI to enable industrial metaverse How businesses are using AI in the construction industry Siemens forges digital twin deal with Nvidia for metaverse