Targeting AI

AI Co-Workers and the Future of Work

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

Understanding Human Impact and Safety in 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
 
 
 

How Capital One is building an AI-ready data ecosystem with creative talent

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
 
 
 
 
 
 

Navigating the generative AI landscape with two MIT profs

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 
  

Coinbase, crypto, blockchain and the outlook for digital payments

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?

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