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