Targeting AI

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