Welcome back to Meeting of the Minds, a special podcast episode series by EM360Tech, where we talk about the future of tech.

In this Big Data special episode of the Meeting of the Minds, our expert panel – Ravit Jain, Podcast host,  Christina Stathopoulos of Dare to Data and a data and AI evangelist, Wayne Eckerson, data strategy consultant and president of the Eckerson Group and Kevin Petrie VP of Research at BARC, come together again to discuss the key data and AI trends, particularly focusing on data ethics. 

They discuss ethical issues related to using AI, the need for data governance and guidelines, and the essential role of data quality in AI success. The speakers also look at how organisations can measure the value of AI through different KPIs, stressing the need for a balance between technical achievements and business results. 

Our data experts examine the changing role of AI across various sectors, with a focus on success metrics, the effects on productivity and employee stress, changes in education, and the possible positive and negative impacts of AI in everyday life. They highlight the need to balance productivity with quality and consider the ethics of autonomous AI systems.

In the previous episode, new challenges and opportunities in data governance, regulatory frameworks, and the AI workforce were discussed. They looked at the important balance between innovation and ethical responsibility, looking at how companies are handling these issues.

Tune in to get new understandings about the future of data and AI and how your enterprise can adapt to the upcoming changes and challenges. Hear how leaders in the field are preparing for a future that is already here.

Also watch: Meeting of the Minds: State Of Cybersecurity in 2025

Takeaways

• Generative AI is creating a supply shock in cognitive power.
• Companies are eager for data literacy and AI training.
• Data quality remains a critical issue for AI success.
• Regulatory frameworks like GDPR are shaping AI governance.
• The US prioritises innovation, sometimes at the expense of regulation.
• Generative AI introduces new risks that need to be managed.
• Data quality issues are often the root of implementation failures.
• AI's impact on jobs is leading to concerns about workforce automation.
• Organisations must adapt to the probabilistic nature of generative AI.
• The conversation around data quality is ongoing and evolving. AI literacy and data literacy are crucial for workforce success.
• Executives are more concerned about retraining than layoffs.
• Younger workers may struggle to evaluate AI-generated answers.
• Incremental changes in productivity are expected with AI.
• Job displacement may not be immediate, but could create future gaps.
• Human empathy and communication skills remain essential in many professions.
• AI will augment, not replace, skilled software developers.
• Global cooperation is needed to navigate the evolving AI landscape.
• Data quality is critical for mitigating risks in AI applications.
• Organisations must prepare for the proliferation of AI models.

Chapters

• 00:00 Introduction to the Future of Data and AI
• 03:30 Significant Shifts in Data and AI Landscape
• 06:52 The Role of Education and Training in Data Literacy
• 09:59 Regulatory Perspectives: GDPR vs. US Approaches
• 13:06 Risks and Challenges in Implementing Generative AI
• 16:53 Data Quality: The Foundation of AI Success
• 21:59 The Impact of AI on Jobs and Workforce Dynamics
• 27:49 The Future of Workforce and AI Literacy
• 30:15 Job Displacement and the Importance of Junior Roles
• 32:05 AI's Impact on Professional Roles
• 34:26 The Role of AI in Software Development
• 39:54 The Necessity of Human Involvement in AI
• 41:10 Data Governance and Global Cooperation