Targeting AI

Most people looking for a job usually spend hours scouring job search engines and LinkedIn. However, the professional network has changed the way its search engine works, shifting from a keyword-based, taxonomy-driven system to an AI-powered semantic search that understands natural language. In this podcast episode, Caleb Johnson of LinkedIn dives into how LinkedIn uses AI and large language models (LLMs) to revolutionize job search, improve search relevance, and ensure data privacy. Featuring: Caleb Johnson, principal staff software engineer In this episode, we cover: AI-powered job search and semantic understanding Use of LLMs and transformer architecture Bias mitigation and fairness in AI systems Data privacy and compliance in AI applications Future directions: voice, visual search, and interactive AI To learn more about AI search, 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: Indeed Unveils AI Agents for Job Seekers and Recruiters Google's AI-Powered Chrome Further Transforms Search LinkedIn Unveils AI Updates for Business Users, Job Seekers

With the rise of generative AI and agentic AI, there has also been a push for AI PCs within the enterprise. Companies like Lenovo and Microsoft are providing enterprises with devices that help create these devices. However, there is no AI PC without AI chips. In this podcast, Michael Nordquist of chipmaker AMD discusses the evolution of AI chips and AMD's role in the rapidly changing AI landscape. He highlights the features of AI PCs, the impact of AI on enterprise efficiency, and AMD's strategy against competitors such as Nvidia. Featuring: Michael Nordquist, corporate VP of product marketing, AMD In this episode, we cover: AMD's position as a key player in AI technology. How AI PCs integrate NPUs for enhanced performance. The need for vendors to focus on security when developing AI PCs. How adoption of AI PCs is influenced by perceived value. The future will see a blend of personal and enterprise AI agents. To learn more about AI PCs, 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: AMD Competes With Intel With AI New Chips AI PCs Are Going Mainstream, Says AMD's Jason Banta Microsoft Aims for AI PCs While Apple Unveils M5 Chips  

Agentic AI has grown rapidly in the past two years, and with that growth comes different tools that help agents work. Among those tools is the Model Context Protocol from Anthropic. In this episode of the Targeting AI podcast from AI Business, Jim Zemlin and Mazin Gilbert dive into the importance of agentic AI, the relevance of MCP, the nuances of openness in AI, and the responsibilities surrounding AI security and ethics. The conversation also touches on the future of personal agents and the evolving role of developers in the AI landscape, with the popularity of OpenClaw. This episode was recorded on-site in New York City last week at the MCP Developer Summit presented by the Agentic AI Foundation. Featuring: Jim Zemlin, CEO of the Linux Foundation, Mazin Gilbert, executive director of the Agentic AI Foundation In this episode, we cover how: Agentic AI is crucial for driving information exchange and financial transactions. Standardization is necessary as we move from experimentation to production in AI. The Linux Foundation provides a neutral space for collaboration among tech companies. Openness in AI includes varying degrees of access to data and models. Ethical AI usage is a priority for the AI industry to prevent bias. Developers' roles are shifting from coding to system architecture and security. The future of AI will involve both open and closed data. To learn more about 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: MCP is Alive, But Faces Challenges How to Prepare Supply Chains for Agentic AI The Growing Need for Cybersecurity in Agentic AI    

The future of work is continuing to change with AI, and many agree that AI co-workers are becoming part of everyday work. However, many enterprises still find it challenging to understand the various use cases for AI, the role AI can play in enhancing productivity, and the need to approach AI implementation thoughtfully, focusing on real problems rather than succumbing to FOMO. In this conversation on the Targeting AI podcast from AI Business, HP's Faisal Masud shares insights on the future of work and HP's commitment to integrating AI into its offerings. Featuring: Faisal Masud, President, digital & lifecycle services, HP In this episode, we cover how: Consumers are more advanced in using AI than enterprises. AI at the edge enhances privacy and security. Enterprises need to understand specific use cases for AI. How HP approaches its differentiation strategy. ROI in AI projects should consider productivity and cost reduction. AI should augment human capabilities, not replace them. The future of work will involve AI as a co-worker.          To learn more about AI adoption, 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: HP's New Keyboard Gives New Meaning to All-in-One AI Innovation vs Adoption: Why They Are Misaligned Generative AI Adoption Grows Fivefold, Capgemini Reports

In a special episode of the Targeting AI podcast from AI Business, host Esther Shittu interviews Christopher Campbell of Lenovo about the challenges and considerations surrounding AI governance, emphasizing the importance of human impact, safety, and accountability. They explore the evolving perspectives on bias and hallucinations in AI, the role of hardware in AI development, and the implications of personal AI agents. The discussion highlights the importance of selecting the right AI partners, maintaining governance in hybrid AI environments, and addressing the complexities of shadow AI and AI governance sovereignty. The episode concludes with advice for organizations on effectively adopting AI governance practices. The podcast was recorded on-site at the Gartner Data & Analytics Summit in Orlando. Featuring: Christopher Campbell, director of AI governance and global products and services security leader at Lenovo In this episode, we cover how: The human impact and safety of AI are paramount. Trust in AI systems is essential for their success. Bias and hallucination perspectives have matured over time. Accountability in AI governance lies with leadership. Choosing AI partners with aligned philosophies is crucial. Governance standards apply equally to local and cloud models. Shadow AI presents a complex challenge for organizations. Sovereignty in AI gives regions more control over their data. Understanding technology is key to effective AI adoption. There is no one-size-fits-all approach to AI governance. To learn more about AI governance, safety and sovereignty, 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: AI data governance guidance that gets you to the finish line The AI bias playbook: Mitigation strategies for CIOs Major sovereign AI funding deals kick off India AI Impact summit      

In this interview on the Targeting AI podcast from AI Business, Amy Lenander of financial services giant Capital One discusses the critical role of talent in building AI-ready data ecosystems. She explores how organizations can cultivate the right skills, develop foundational data platforms and use AI to drive business value. The interview was recorded on-site at the Gartner Data & Analytics Summit 2026 in Orlando. Featuring Amy Lenander, chief data officer, Capital One In this episode, we cover how: Talent agility outweighs technical experience in AI success. Organizations that develop learning agility and curiosity foster talent capable of navigating rapidly evolving AI landscapes. Instead of hiring for a specific toolset, focus on candidates who demonstrate rapid learning, problem-solving, and collaboration—traits that enable mastery of new AI methods as they emerge. Building a unified data ecosystem creates a competitive moat. A well-designed data ecosystem, prioritized over immediate AI application, provides a robust foundation that supports all future data and AI initiatives. Investing in governance, data trustworthiness, and accessibility shields organizations from fragmentation, enabling scalable innovation regardless of future technological shifts. AI adoption is a cultural shift, not just a technology implementation. Domain-specific data products enhance AI interpretability and trust. Specialized data teams responsible for understanding business nuances ensure AI systems interpret data context correctly for strategic use. To learn more about generative and agentic AI and AI-ready data ecosystems, 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: The Shift Toward AI Data Quality as a Core Product Data Quality in AI: 9 Common Issues and Best Practices Data and AI Governance Must Team Up for AI to Succeed            

In this episode of the Targeting AI podcast from AI Business, hosts Shaun Sutner and Esther Shittu engage with Abel Sanchez and John Williams from MIT to discuss the evolving landscape of generative AI. The conversation covers the motivation behind their initiative, Gen AI Global, the dynamics of their professional relationship, and the societal implications of AI technologies. They explore concepts such as "vibe living," the energy demands of AI, and contrasting perspectives on AI's future, including the debate between optimists and skeptics. The episode concludes with a discussion on the sustainability of the AI boom and the importance of human involvement in an increasingly automated world.  Featuring: Abel Sanchez, a research scientist and executive director of MIT's Geospatial Data Center; and John Williams, professor of civil and environmental engineering at MIT and director of the Geospatial Data Center and Intelligent Engineering Systems laboratory at MIT.  In this episode, we cover how:  Learning is social; community enhances educational outcomes.  Generative AI is rapidly changing industries and education.  AI's impact on society is both exciting and concerning.  The relationship between Abel and John is built on trust and differing perspectives.  Generative AI can empower non-experts to achieve expert-level results.  Energy consumption for AI is a growing concern.  The future of AI models may involve new architectures beyond transformers.  Human intuition and emotion remain valuable in AI applications.  The AI boom is characterized by rapid adoption and innovation.  Organizations must adapt to integrate AI effectively.  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:  Gen AI Global  How much energy do data centers consume?   Debate Rages Over AI Bubble vs. Boom    

AI is changing digital payments, and Coinbase is trying to lead that change. Last year, the cryptocurrency exchange provider partnered with Cloudflare, AWS, Anthropic and others to create the x402 protocol, a standard that enables AI agents to make transactions online. In this conversation, Coinbase’s Dan Kim talks with Targeting AI hosts Esther Shittu and Shaun Sutner AI about how generative AI is critical in creating a new class of AI agents that can autonomously engage in trading and transactions.  Featuring: Dan Kim, vice president, head of digital asset listings & services at Coinbase In this episode, we cover: Coinbase's mission is economic freedom through cryptocurrency and blockchain. AI is transforming software to be more intelligent and adaptive. The X402 Foundation aims to standardize how payments are processed over the internet. AI agents are becoming a new class of customers in the trading space. Stablecoins are crucial for secure transactions between AI agents. To learn more about generative and agentic AI and RPA, 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: X402 Aims to Enable Agentic Payments with Digital Dollars Blockchain for businesses: The ultimate enterprise guide What is a Stablecoin?

In this episode of the Targeting AI podcast from AI Business, Manuel Haug, of Germany-based process mining vendor Celonis, discusses the intricacies of process mining and its integration with AI technologies. He explains how Celonis differentiates itself in the market, the evolution of its strategy in light of generative AI, and the practical applications of AI agents in various industries. Haug emphasizes the importance of operationalizing process mining findings and preparing for the future of work as the workforce ages. He also touches on the complementary nature of AI and traditional automation methods, such as RPA, and the need to capture organizational knowledge before it is lost. Featuring: Manuel Haug, field CTO of Celonis In this episode, we cover how: Process mining connects to various IT systems to analyze business processes. AI can improve and automate manual processes in companies. AI agents can assist human teams in decision-making. Operationalizing findings from process mining is crucial for improvement. The aging workforce necessitates capturing knowledge effectively. RPA and AI can coexist and complement each other in automation. Understanding processes is foundational for effective AI implementation. AI technology is becoming more reliable and powerful. The future of work will involve a blend of AI and human oversight. To learn more about generative and agentic AI and RPA, 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: 5 Benefits of Using Process Mining Process Mining Software Comparison: What CIOs Should Look at Top Enterprise Process Mining Challenges, Ways to Solve Them      

If most sales representatives spend nearly a quarter of their time on administrative tasks, they are losing opportunities to generate revenue and be productive in sales. This is why Eilon Reshef of AI sales platform vendor Gong sees AI technology as a supportive co-worker that can offload menial admin tasks from sales agents so they can focus on their new jobs. He shares insights into Gong's mission to enhance sales team productivity and the importance of data in AI applications. Featuring: Eilon Reshef, co-founder and chief product officer, Gong In this episode, we cover how: AI's effectiveness is heavily dependent on the quality of data. "Gong" symbolizes success in sales. Agentic AI is about automating complex tasks intelligently. Sales roles are evolving, not disappearing, due to AI. The future of sales will involve more AI-driven insights. 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: AI and automation: Transforming sales CRM Phenom’s Acquisition: AI, Automation and the Future of Work Salesforce Launches AI Cloud to Bring Generative AI to the Enterprise

At the start of the mass popularity phase of generative AI, large language models were the star of the show. Vendors released bigger and newer models. However, the conversation has recently shifted from considering big or small models to a deep focus on data. In this episode of the Targeting AI podcast from AI Business, Yasmeen Ahmad, of Google Cloud, discusses the transformative effect of generative AI on the data landscape. She emphasizes the importance of treating data as a product, the shift toward multimodal data, and the role of AI agents in enhancing data management and decision-making processes. Featuring: Yasmeen Ahmad, managing director of product management for data and AI Cloud, Google Cloud In this episode, we cover how: The era of multimodal data is upon us, integrating various data types. Agentic AI enhances the understanding of unstructured data. Databases must evolve into cognitive reasoning engines for AI. Gemini Enterprise provides a unified platform for AI and data. Data security and responsibility are critical in AI deployment. To learn more about the role data plays in 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: Generative AI is the Future of Data Management Without Data There Is No AI Google Invests $40B in AI Data Centers in Texas

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?    

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

Traditional, generative, agentic—in the past couple of decades, AI metamorphosed into an indisposable tool for enterprises wanting to streamline their processes and improve their impact. In this episode, we dive into the different types of AI, best practices for implementation, and the challenges faced in the industry. Featuring: Deepak Singh, Vice President at AWS In today’s episode, we’ll cover… The difference between traditional AI, generative AI, and agentic AI The role of agentic AI in software development Best practices for implementing agentic AI and more! To learn more about agentic AI, check out Search Enterprise AI. To watch the video version our podcast, subscribe to our YouTube channel, @EyeOnTech. References: AWS intros new foundation model line and tools for Bedrock Amazon Q, Bedrock updates make case for cloud in agentic AI Amazon to spend $100B on AWS AI infrastructure

In the couple of years since the popularization of ChatGPT, generative AI technology has quickly taken hold in the legal profession. It has backfired in some cases, such as when an attorney filed a legal brief written with ChatGPT's help and the AI platform hallucinated some of the cases in the brief. That case and others have led some law firms to block general access to AI tools. Most recently, Hill Dickinson, a law firm in the U.K., asked its staff not to use generative AI tools like ChatGPT. Many law firms are using generative AI tools, and some even market their own AI systems. AI vendors are also partnering with law firms and companies in the legal profession. In February, LexisNexis and OpenAI agreed to integrate OpenAI's large language models across its products. The success, and uncertainty, surrounding AI tools in the legal profession led James M. Cooper and Kashyap Kompella to write the book A Short and Happy Guide for Artificial Intelligence for Lawyers. Cooper is a law professor at California Western School of Law, while Kompella is CEO of AI analyst firm RPA2AI Research. In the book, Cooper and Kompella explore how lawyers can understand and use AI technology. "We saw an urgent need to upskill lawyers on AI," Kompella said on the latest episode of Informa TechTarget's Targeting AI podcast. "How do you move AI ethics and responsible AI into practice? You have to move them through lawyers. Lawyers are a big part of that equation." Kompella and Cooper argue that while numerous books for lawyers about AI exist, few focus on using the technology ethically. The authors also argue that while the legal profession has traditionally been slow to adopt new technologies, it can benefit from AI for several reasons. For example, AI technology can provide access to legal services for those in underserved areas like rural communities in the United States, Cooper said. "AI can be a game changer in terms of provision of legal services," he said. However, providing more education is the key to helping legal professionals understand AI technology. "The law school curriculum is not teaching AI or any technologies to the students, so there is a huge skill gap," Kompella said. Cooper added, "The skill sets of prompt engineering, of knowing how to use these AI tools and the dangers that come with them, should be rote in law schools now right from the first year. Those law schools around the world that embrace this idea are future-proofing their students. They're not going to have to play catch up." Esther Shittu is an Informa TechTarget news writer and podcast host covering artificial intelligence software and systems. Shaun Sutner is senior news director for Informa TechTarget's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. Together, they host the Targeting AI podcast series

Without a good data strategy, generative AI becomes unusable technology for enterprises. This was true when ChatGPT started becoming popular, and it is even more accurate years later. The most recent example is the AI Chinese startup DeepSeek. While most AI cloud providers like Google, AWS and Microsoft now offer the DeepSeek-R1 reasoning model, many AI experts believe that enterprises might be hesitant to use it due to the data it was trained on. Despite DeepSeek's R1's innovation, it all comes down to the foundation, said Michelle Bonat, chief AI officer at AI Squared, an AI and data integration platform. "As GenAI expands and expands ... the fundamentals are the fundamentals," Bonat said on the latest episode of Informa TechTarget's Targeting AI podcast. She added that while many organizations may have started with GenAI by just putting up a chatbot, many have found that if they do not have good quality data, they might have to pause their GenAI initiatives. The reason is that the nature of generative AI systems is to produce responses. Therefore, if they do not have good-quality data, they tend to hallucinate. Thus, Bonat said the growth in GenAI initiatives across organizations has also led to an increase in conversation around data strategy, data quality and data cleanliness. "They're very much connected," she said. "GenAI has become important in the conversation that connects with data strategy, data quality, data cleanliness and also, ultimately, in responsible AI and governance within the organization." She added that enterprises should pay attention to data and responsible AI because it benefits their businesses. "It's a competitive advantage to have responsible AI," she continued. "Customers want AI systems they can trust. ... Being transparent and having responsible AI helps increase your brand reputation." Esther Shittu is an Informa TechTarget news writer and podcast host covering artificial intelligence software and systems. Shaun Sutner is senior news director for Informa TechTarget's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. Together, they host the Targeting AI podcast series.  

While some vendors are working to ensure large language models become better at reasoning, other AI vendors are making them compatible in multiple languages. Writer is a provider of a full-stack generative AI platform for enterprises. While the vendor provides a generative AI platform that enterprises can use to build generative AI capabilities into their workflows, it also offers a family of LLMs: Palmyra. The models support text generation and translation in numerous languages, including Spanish, French, Hindi and Russian. "Multilingual training data and models that can be as good in dozens of other languages as they are in English is something everybody should strive for," said Writer cofounder and CEO May Habib on a recent episode of Informa TechTarget's Targeting AI Podcast. Writer also uses large volumes of synthetic data to help build legal confidence in generative AI technology, Habib said. Writer also publishes data on how its models score for bias and toxicity. "We really want to make sure that we are compliant with folks' ESG [equity, sustainability and governance] guardrails and guidelines," Habib said. Writer recently raised $200 million in series C funding, bringing its valuation to $1.9 billion. Esther Shittu is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems.  

The contact center world is a difficult place, packed with frustration and stress. Digital communications giant Cisco sees its mission as easing that experience for human contact center workers and the customers they deal with every day. For that undertaking, the vendor has seized on generative AI and agentic AI as the vehicles to both automate and augment the work of humans, in essence, smartening up the traditional chatbots that have long helped companies interact with their customers. "We're to see a lot more of what I call event-based communication, proactive communication outbound that we do particularly well, powered by AI," said Jay Patel, senior vice president and general manager for customer experience at Cisco Webex, on the Targeting AI podcast from Informa TechTarget. "And then the response path to that is we think there will be AI agents involved in some of the more simple use cases. "For example, if you haven't paid a bill, they can obviously call you in the outbound call center, but probably a better way of doing it is probably to send you a message with a link to then basically make the payment," Patel continued. Like many other big tech vendors, Cisco deploys large language models (LLMs) from a variety of specialist vendors, including OpenAI and Microsoft. It also uses open models from independent generative AI vendor Mistral, as well as its own AI technology developed in-house or acquired by acquisition. "Fundamentally, what we are looking at is the idea of an AI engine for each use case, and within the AI engine you would have a particular LLM," Patel said. Among the generative AI-powered tools Cisco has assembled are Webex AI Assistant and Agent Wellness, to tend to the psyches of busy contact center human workers. "Customers call very frustrated; they may shout at somebody. And then if you've had a difficult call, the agent wellness feature will mean that the supervisor knows that this set of agents has had a set of difficult calls," Patel said. "Maybe they're the ones who need a break now. So, there are ways of improving employee experience inside the contact center that we think we can … use AI for." Shaun Sutner is senior news director for Informa TechTarget's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. He is a veteran journalist with more than 35 years of news experience. Esther Shittu is an Informa TechTarget news writer and podcast host covering artificial intelligence software and systems. Together, they host the Targeting AI podcast.  

Box has been in the AI game for a long time. But when generative AI mushroomed into a transformative force in the tech world, the cloud content management vendor opted to turn to specialists in the new and fast-growing technology to power the arsenal of tools in its platform. "We've been doing AI for many years. But the really cool thing that happened … AI got to the point where the generative AI models understood content," said Ben Kus, CTO at Box, on the Targeting AI podcast from Informa TechTarget. "For us, this whole generative AI revolution has been this great gift to everybody who deals with content. It's almost like having a very dedicated, very intelligent person who stands next to you, ready to do what you want." When generative AI exploded with OpenAI's release of ChatGPT in November 2022, Box turned to OpenAI for its first batch of generative AI tools. Box CEO Aaron Levie had known OpenAI CEO and co-founder Sam Altman for many years. However, when a passel of other independent generative AI vendors sprang up and the tech giants started releasing their own powerful large language models (LLMs) and multimodal models, Box decided to broaden its generative AI palette. "Azure and OpenAI are partners of ours and we think they have great models, but we are not at all dedicated to any one model," Kus said. "In fact, at Box, one of our goals is to provide you with all of the major models that you might want." These include generative AI models from Google, IBM, Anthropic and Amazon. One example of how Box uses an outside model is Anthropic's 3.5 Sonnet LLM, which Kus called "one of the best models out there right now." One application is at a financial firm that deals with long bond offerings. The company needs to analyze many of these complex financial vehicles to evaluate which bonds in which it wants to invest. "They use [the model] to extract key info. It takes the [job] of looking through these bonds. From hours or days to … hopefully, minutes," Kus said. "If the model is very good, it can give you very good answers. If it's not as smart, then it can be off a little bit. So, this particular company really wants to have the best models so they can get the best sort of use of this kind of AI." Shaun Sutner is senior news director for Informa TechTarget's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. He is a veteran journalist with more than 30 years of news experience. Esther Shittu is an Informa TechTarget news writer and podcast host covering artificial intelligence software and systems.  

This is the year of AI agents. The last few months of 2024 brought much talk about and expectations for AI agents that can operate autonomously and semi-autonomously. Many vendors have capitalized on the enthusiasm to introduce new agentic products: Salesforce came out with Agentforce, and Microsoft introduced Copilot agents. With 2025 here, questions about whether the momentum on agents will continue. Some see the agentic hype, and real progress, persisting this year. Craig Le Clair, a Forrester Research analyst and author of the soon-to-be-published book Random Acts of Automation, is among those who think AI agents will continue to gain momentum in the new year. "It's the biggest change toward AGI [artificial general intelligence] that I've seen," Le Clair said on the latest episode of Informa TechTarget's Targeting AI podcast, referring to the concept of AI that is as smart or smarter than human intelligence. Enterprises will likely adjust the ways they use applications that use AI agents as copilots to augment humans, because many of those applications are not profitable, he said. However, AI agents will be the driving force in helping enterprises build platforms that use generative AI technology to spur business value, he said. "When you really start to turn piles of data into conversations with people ... that's the opportunity for this," Le Clair said. "For an employee to have a conversation with standard operating procedures to get advice on what to do, or for standard operating procedures to be taken out of that PDF repository and actually put into a prompt and generate tasks that are then followed by an agent to get something done -- the potential is really there." As with all new technology, AI agents involve a trust issue. Enterprises still do not trust the technology to be fully autonomous and perform tasks from start to finish all on its own, Le Clair said. However, organizations can rely on AI agents to perform part of the work with the assistance of a human in the loop. With the speed of the technology's maturation, progress toward fully autonomous agents by 2028 is likely, Le Clair predicted. Esther Shittu is an Informa TechTarget news writer and podcast host covering artificial intelligence software and systems. Shaun Sutner is senior news director for Informa TechTarget's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. Together, they host the Targeting AI podcast series.  

The AI application startup, which was founded in 2016 and was valued at more than $2.1 billion in 2021, uses a reasoning engine to help employees search for information across the enterprise. Since its inception, a key ingredient in the company's success has been AI and generative AI technology. "We were the first company after Google to deploy BERT in production," said co-founder and president Varun Singh on the latest episode of Informa TechTarget's Targeting AI podcast. BERT was Google's first model with bidirectional encoding that enabled computers to understand large text spans. It was pretrained, so Moveworks did not have to train it from the ground up. It also did not require a lot of data. After using BERT to train its automation platform, Moveworks started using GPT-2  from OpenAI in 2020. This is two years before the mass popularization of the generative AI vendor's ChatGPT chatbot, mostly to generate synthetic data. Singh added that he and his team had failed to realize right away that the model could also be used for reasoning tasks. "It's not so much a mistake that was made or not, but it was just sort of as technology evolved, the moment a paradigm shift actually comes into full focus, you look back and you're like, 'We could have done that sooner because we had access to the models, but we didn't see how powerful they could be,'" he said. Since the shift, Moveworks has evolved from a platform with a reasoning engine to a platform for building AI agents. On Oct. 1, Moveworks launched Agentic Automation as part of its Creator Studio offering. The system enables developers to build AI agents. Throughout the evolution of its business, Moveworks has differentiated itself with its use of AI technology, Singh said. "Without AI, there's nothing Moveworks has to offer to the world," he said. "There's only value from Moveworks because of AI." Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems. Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. Together, they host the Targeting AI podcast series.  

When generative AI became the next big thing in tech, enterprise software giant Oracle bet heavily on a startup to provide it with foundation and large language models rather than scramble to develop its own. That then-fledgling company was Cohere. Founded in 2019, the generative AI vendor raised $270 million in a Series C round, and its investors included Oracle, Nvidia, Salesforce Ventures, and some private equity firms. In July, Cohere raised another $500 million and reached a market valuation of $5.5 billion. Cohere's open generative AI technology is now infused in many of Oracle's databases, a fixture among large enterprises. The tech giant has also tapped Cohere's powerful and scalable Control-R model for Oracle's popular vertical market applications, including those for finance, supply chain and human capital management. But while Oracle has put Cohere at the center of its generative AI and agentic AI strategy, the tech giant is also working closely with Meta. The social media colossus has gained a foothold in the enterprise AI market with its Llama family of open foundation models. Oracle is customizing Llama for its Oracle Cloud Infrastructure platform, along with Cohere's models. "We have made a decision to really partner deeply around the foundation models," said Greg Pavlik, executive vice president, AI and data management services at Oracle Cloud Infrastructure, on the Targeting AI podcast from TechTarget Editorial. "What we're looking for are companies that are experienced with creating high-quality generative AI models," he continued. "But more importantly … companies that are interested in enterprise and specifically business solutions." Pavlik said Oracle values the open architecture of the models from both Cohere and Meta, which makes it easier for Oracle to customize and fine-tune them for enterprise applications. "The advantage really of having a deep partnership is that we're able to sit down with the foundation model providers and look at the evolution of the models themselves, because they're not really static," he said. "A company will create a model and then they'll continually retrain it. "We see our role as to come in and proxy for the enterprise user, proxy for a number of verticals," Pavlik continued. "And then try to move the state of the art in the technology base closer and closer to the kinds of patterns and the kinds of scenarios that are important for enterprise users." Oracle also uses generative AI technology from other vendors and enables its customers to use other third-party models, he noted. Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, analytics and data management technologies. Esther Ajao is a TechTarget Editorial news writer and podcast host covering AI software and systems. Together, they host the Targeting AI podcast.

At the beginning of the wave of generative AI hype, many feared that generative models would replace the jobs of creatives like artists and photographers. With generative AI models such as Dall-E and Midjourney seemingly creating unique works of art and images, some artists found themselves at a disadvantage. Some say the generative systems took their artwork, copied it and used it to produce their own images. In some cases, the generative systems allegedly outright stole the creative work. Two years later, artists have to some extent been reassured by the support of stock vendors like Getty Images. Instead of trailing behind generative AI tools such as Stable Diffusion, Getty created its own image-generating tool: Generative AI by Getty Images. Compared with other image generators, Getty has taken great lengths to restrict its model through the data set. The stock photography company maintains what it calls a clean data set. "A clean data set is really a training data set that a model is trained on that can lead to a commercially safe or responsible model," said Andrea Gagliano, senior director of AI and machine learning at Getty Images, on the latest episode of TechTarget Editorial's Targeting AI podcast. Getty's clean data set does not contain brands or intellectual property products, Gagliano said. The model's data set also does not include images of well-known people or likenesses of celebrities like Taylor Swift or presidential candidates. "We have taken the very cautious approach where our generator will not generate any known person or any celebrity," Gagliano said. "It will not generate Donald Trump," she said, referring to the President-elect. "And it will not generate Kamala Harris," referring to the vice president and former presidential candidate. "It has never seen a picture of Donald Trump," she continued. "The model has never seen a picture of Kamala Harris." Gagliano added that removing this possibility also guards against those who want to misuse the technology to create deepfakes. Therefore, any generated output is labeled synthetic or AI-generated. "We don't want any situation where we start to undermine the value of a real image," Gagliano said. Finally, the data set that Getty uses produces images with licenses on them, ensuring that creators get compensated. Thus, a portion of every dollar made by Generative AI by Getty Images is given to the creator who contributed to the data set. "The reason for that is the more unique imagery that we bring into the training data set, the more additive it is," Gagliano said. Getty updated its generative AI tools Tuesday. The new capabilities include Product Placement, which lets users upload their own product images and generate backgrounds, and Reference Image, which enables users to upload sample images to guide the color and composition of the AI-generated output. Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems. Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. Together, they host the Targeting AI podcast series.  

President-elect Donald Trump during his election campaign offered clues about how his administration would handle the fast-growing AI sector. One thing is clear: AI, to the extent that it is regulated, is headed for deregulation. "It's likely going to mean less regulation for the AI industry," said Makenzie Holland, senior news writer at TechTarget Editorial covering tech regulation and compliance, on the Targeting AI podcast. "Being against regulation and [for] deregulation is a huge theme across his platform." Trump views rules and regulations on business as costly and burdensome, Holland noted. The former president and longtime businessman's outlook presumably includes independent AI vendors and the tech giants that also develop and sell the powerful generative AI models that have swept the tech world. President Joe Biden's wide-ranging executive order on AI has been the strongest articulation of how the federal government views AI policy. However, it's unclear which elements of the Democratic president's plan Trump will scrap and which he'll keep. Trump established the National Artificial Intelligence Initiative Office at the end of his first term as president in 2021. David Nicholson, chief technology advisor at Futurum Group, said on the podcast that Trump will likely retain some aspects of the executive order with bipartisan support. Among these is the federal government's recognition that it should guide and promote AI technology. "[Trump will] definitely not scrap it wholesale," Nicholson said. "There's something behind a lot of those concerns ... and pretty bipartisan concern that AI is a genie that we only want to let out of the bottle, if possible, very carefully." Holland, however, doesn't expect many regulatory proposals in Biden's executive order to survive the next Trump presidency. Trump is also likely to dramatically de-emphasize the AI safety concerns and regulatory proposals that feature prominently in Biden's executive order, she said. Meanwhile, concerning Elon Musk -- a major Trump backer and owner of the social media platform X, formerly Twitter, and generative AI vendor xAI -- the issue is complicated, Nicholson said. Musk has been a trenchant critic of xAI competitor OpenAI, alleging in a lawsuit that the rival vendor abandoned its commitment to openness in AI technology. However, Nicholson noted that Musk's definition of transparency in training large language models is unorthodox, insisting that models be "honest" and not contain political bias. "Having the ear of the president and the administration, I think he could be meaningful in that regard," Nicholson said. "[Musk] is going to be the loudest voice in the room when it comes to a lot of this stuff." While Trump is expected to try to reverse or ignore much of Biden's agenda, one major piece of bipartisan legislation passed during Biden's tenure, the CHIPS and Science Act of 2022, is likely to survive because it emphasizes reviving manufacturing and technology development in the U.S., Nicholson said. But the Federal Trade Commission's and Department of Justice's active stances on AI rulemaking and big tech regulation -- the DOJ successfully sued Google for monopolizing the search engine business -- are ripe for a Trump rollback. "The FTC is likely to face a shake-up, as far as Lina Khan's job probably is on the line," Holland said, referring to the activist FTC chair, who has vigorously pursued a number of big tech vendors. "Trump's entire platform is about deregulation and being against regulation. That's automatically going to impact these enforcement agencies, which, in some capacity, can make their own rules," Holland said. In the absence of meaningful federal regulation of AI, the U.S. is moving toward a state-by-state regulatory patchwork. Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems. Together, they host the Targeting AI podcast series.