The future of work is humans and AI collaborating. Despite developments in which technology companies blame AI for the decision to lay off large numbers of workers, Nikhil Krishnan of C3 AI says there is still a need for a human in the loop. On the latest Targeting AI podcast from AI Business, Krishnan said the future of work will involve a pyramid-type system. At the bottom, the AI will automate certain processes, but at the middle and top levels, there should be collaboration between the human and AI.

Featuring: Nikhil Krishnan, CTO at C3 AI

In this episode, we discuss:

• The pyramid structure of the future of work
• The need for the human in the loop
• The industry that is currently not seeing AI replacing humans
• C3 AI’s differentiation from hyperscalers and competitors
• The AI boom versus bubble debate
• The importance of operational efficiency in any economic environment

To learn more about C3 AI and the future of work, 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:

C3 AI Launches C3 Code for Businesses Seeking Domain Expertise

Phenom’s Acquisition: AI, Automation and the Future of Work

Fourth Industrial Revolution: How AI Agents Are Transforming the Future of Work

For many organizations, the advent of AI has necessitated a transition from a human-centric to an intelligence- or even agentic-based approach. Real-time event, threat and risk vendor Dataminr began its transition to intelligent automation in 2018, marking the start of its AI journey. Since then, the vendor has transitioned to a more agentic and generative AI approach. In this interview, Joel Tetreault of Dataminr discusses the evolution of real time intelligence platforms, the integration of generative AI and agentic AI, and the importance of data strategy in AI development.

Featuring: Joel Tetreault, chief AI officer, Dataminr

In this episode, we discuss the:

• Evolution of Dataminr’s platform pre- and post-generative AI
• Integration of GPT and modern models in real-time data processing
• Role of data strategy and domain-specific models in AI effectiveness
• Use of multimodal AI for security and threat detection
• Impact of agentic AI and future trends in cybersecurity

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:

Top 10 Cybersecurity predictions for 2026

How to enhance OSINT investigations using AI

Company Using AI to Strengthen Cybersecurity Raises $50M

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