Targeting AI

The Future of AI in Process Intelligence

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
 
 
 

From Predictive to Agentic: The Future of AI in Sales

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

Google AI exec says data is the next phase of generative AI

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

Vertical AI Systems and Open Source Flexibility

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

The Impact of the "One Rule" AI Executive Order

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

Securing autonomous enterprise agents in the age of generative AI

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
 

Breaking news: AWS moves at re:Invent make the cloud giant an AI player

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
 
 

Generative AI and Diversity: What WLDA is Doing About It

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.

Alibaba.com B2B marketplace buys and sells with AI

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
 

Changing enterprises' misconceptions about AI

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