Targeting AI

Changing enterprises' misconceptions about AI

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
 

Breaking news: What Nvidia's $5 trillion milestone means for AI

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
 
 

Breaking News: Impact of the AWS Outage on AI Applications

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

Streaming data and generative AI: Confluent's approach

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?
 
 

The Future of Patient Care: AI-First Approaches with Salesforce Agentforce

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 
 

Salesforce advances its agentic AI platform with Agentforce 360

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.

UiPath moves from RPA to agentic AI

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
 
 
 
 
 

CoreWeave signs $14.2 billion deal with Meta Platforms

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.
 

Capital One takes on GenAI in a big way

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

IBM, agentic AI and the future of enterprise tools

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