Artificial intelligence is no longer a distant ambition, it is actively reshaping how businesses operate, innovate, and compete. But what does AI truly mean for the workplace of today and tomorrow? And as the pace of advancement accelerates, are organizations truly ready for what comes next?

In this episode of AI at Work, we explore these questions with Louis Landry, newly appointed CTO of Teradata. With over two decades of experience in software architecture, engineering leadership, and technology innovation, Louis brings a grounded and insightful perspective on how businesses can harness AI responsibly and effectively.

Together, we unpack some of the defining trends for 2025: the maturation of retrieval-augmented generation, the evolution of large-scale personalization, and the rise of agentic AI systems that blend generative AI with traditional software architectures. Louis explains how enterprises are moving beyond experimental AI projects to focus on outcome-driven deployments that deliver measurable business impact.

Throughout our conversation, Louis stresses a recurring theme: trust. Building trusted AI, grounded in transparency, human accountability, and high-quality data, is essential for sustainable success. He shares practical strategies for managing emerging challenges such as vector data governance, navigating regulatory uncertainty, and balancing innovation with responsible risk management.

We also explore the vital role of data harmonization in achieving faster, more confident decision-making, and how open-source technologies are enabling more accessible and customizable AI solutions across industries. Louis highlights why data quality, explainability, and clear business outcomes should be the North Star for any organization looking to thrive in an AI-driven future.

As businesses face an increasingly complex digital environment, what strategic investments should they prioritize? How can they build AI systems that remain trustworthy, scalable, and truly transformational? And what leadership mindset is needed to unlock the next era of workplace innovation?

Tune in to hear Louis Landry’s insights on the future of AI, and join the conversation: How do you see AI shaping the future of work in 2025 and beyond?

What happens when artificial intelligence moves beyond assisting individual developers and solves problems across thousands of codebases simultaneously?

In this episode of AI at Work, we explore how AI is being used to tackle one of the most complex challenges in modern software development: large-scale code migrations. Justine Gehring, AI research engineer at Moderne and author of AI for Mass-Scale Code Refactoring and Analysis, joins the show to explain how she and her team are helping enterprises rethink how they approach code changes across massive environments.

While many are familiar with tools like GitHub Copilot and ChatGPT that assist with writing or suggesting code snippets, Justine shares how mass-scale refactoring calls for a very different set of tools and methods. At Moderne, AI is applied with precision inside an open-source framework called OpenRewrite, which enables consistent and verifiable code changes while maintaining enterprise-level reliability and security.

We discuss how Moderne's approach blends deterministic automation with targeted machine learning to make code migrations faster and more trustworthy. From onboarding new developers to simplifying upgrades across legacy systems, the real-world impact of this work is becoming increasingly visible in sectors like banking and insurance, where complexity and risk have historically slowed down innovation.

This episode also dives into how AI enhances collaboration between developers and machines. Justine highlights the potential for AI to become a quiet partner in understanding, searching, and maintaining vast repositories of code and why this shift may help organizations reduce technical debt and increase maintainability over time.

For business leaders evaluating how AI fits into their development strategy, this conversation offers a practical look at how to make meaningful progress without cutting corners. Whether you're leading a digital team or managing critical systems, Justine's insights reveal what it truly takes to put AI to work at scale.

In this debut episode of AI at Work, part of the Tech Talks Network, we sit down with Gautam Singh, Head of the Business Unit at WNS Analytics and co-founder of The Smart Cube, to uncover how analytics and AI are helping organisations navigate a rapidly evolving digital-first business world. With decades of experience in data strategy and management consulting, Gautam brings a grounded yet forward-looking view on integrating intelligence into the enterprise.

We explore how WNS helps clients cut through the noise of AI hype by anchoring innovation in practical use cases and structured strategy. Gautam shares a compelling example of a global retail client achieving a 13.5x return on analytics investments, and unpacks why businesses should start small with “data ponds” rather than aim for comprehensive “data lakes” from day one.

He also challenges popular misconceptions about AI, explaining why not everything needs a model and how Excel sometimes still does the job. We examine the importance of traceability, regulation, and a “maker-checker-consumer” framework that ensures human oversight remains central to AI implementation.

Looking ahead, Gautam discusses how collaboration across industries, adaptability, and a clear North Star are key to staying resilient and competitive. This is a conversation for leaders who want to move beyond buzzwords and make meaningful progress with AI and analytics.

How can your business approach AI in a way that delivers real outcomes instead of just more complexity? Tune in to hear Gautam’s adv