AiThority Interview with Louis Landry, CTO of Teradata

Louis Landry, CTO of Teradata discussed the key challenges and opportunities in delivering UX across Multi-cloud, solutions to ensure data integrity, security, and compliance across the cloud, the role of AI and ML in enterprise decision-making, and more in this quick chat…

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Could you share the experiences that have helped shape you as a leader? 

One of the most impactful experiences that shaped me as a leader was heading up a massive global project in my early career when I was working in open source. Among other things, I had the responsibility of overseeing translation teams for 100 languages and localizations across multiple countries, which required coordinating diverse volunteer developers and engineers. 

Navigating the complexities of collaborating with people from various cultures and backgrounds — like working with a retired Frenchman and a young Indian developer — taught me invaluable lessons in leadership. It was a baptism by fire, especially as I was in my 20s, and I made plenty of mistakes along the way. Fortunately, it was in a friendly environment which allowed me to learn from my missteps and grow quickly. This experience was crucial in shaping my approach to collaboration and problem-solving, and ultimately, how I lead today. It gave me a head start in understanding what executive leadership truly requires. 

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As Chief Technology Officer, what are the key challenges and opportunities in delivering a world-class user experience across multi-cloud environments? 

Hybrid cloud architecture is often described as the “having your cake and eating it too” approach: it allows businesses to maintain on-premises infrastructure while also tapping into the innovation and scalability of public clouds. This enhances flexibility, cost-efficiency, and data accessibility, enabling faster decision-making, better insights, and more agility. Businesses can scale resources on demand and easily adapt to changing data needs, without the cost, compute and efficiency challenges that cloud-only solutions tend to have. 

However, a key challenge remains in ensuring that users of the system don’t need to worry about the underlying complexity of the system they are using – where each type of data lives, or which type of environment they are using for a particular task. For most users, the goal is to make it possible for them to focus solely on their data, business problems, and solutions. Achieving this seamless experience is where the true opportunity lies and what Teradata is working towards. Our core focus is solving the complexity of infrastructure management so that users can simply focus on their business needs and data, without worrying about the cloud environment behind the scenes. 

Data security and governance are top concerns for organizations today. What steps is Teradata taking to ensure data integrity, security, and compliance across cloud environments, particularly with the growing complexity of multi-cloud infrastructures?  

A key part of our approach is ensuring that data governance is comprehensive, not only for traditional business processes and dashboards, but also for the data types that feed generative AI and other advanced analytics. Vectors are a good example, because they can take unstructured data such as PDFs, medical imagery, voice recordings, hand-written notes and more — and make them understandable to AI models. Data governance, integrity, and security are foundational to everything we do, whether it’s for business insights or the next-gen AI models that are revolutionizing industries. The market is still immature in this area, and customers need to see that Teradata’s strength in data governance extends to these emerging use cases as well. 

Additionally, we’ve introduced innovations like bring-your-own LLM (BYO-LLM) support, empowering our customers to bring small- or mid-sized open LLMs to their data, versus the other way around. This reduces costly data movement while enhancing privacy, security, and compliance — especially important in regulated industries such as banking. By providing this flexibility, we help customers reduce infrastructure costs while ensuring their data remains secure and compliant. 

There is understandably a heavy focus on clean data for solutions like GenAI — using the “garbage in/garbage out” argument. But it’s also vital to remember that the authenticity and reliability of data are just as important as data cleanliness. Starting with clean, trustworthy data lays the foundation for producing accurate and meaningful results, underscoring the critical role of data integrity and security in today’s multi-cloud environments. 

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What role do you see AI and machine learning playing in enterprise decision-making, and how is Teradata leveraging these technologies? 

In enterprise decision-making, AI and machine learning are poised to play a transformative role by enabling more data-driven, personalized, and efficient outcomes. A critical element of this transformation is trust. Enterprise decision-making, especially at the executive level, must be grounded in AI and data that is trustworthy. Ensuring that these technologies are reliable and transparent is key to their success. As AI-native data platforms become more prevalent, they will be specifically designed to store and manage data in ways that optimize AI workflows, including training, tuning, and inferencing. These platforms will go beyond traditional data management, enabling enterprises to curate high-quality, topic-specific training datasets that fuel powerful agentic AI systems, small models, and advanced applications like enterprise Retrieval-Augmented Generation (RAG). 

Speaking of RAG, this technology will be a game-changer for large-scale enterprises, especially in the context of Generative AI (GenAI). While RAG has been available in early forms, it will soon scale to support enterprise-level applications. RAG leverages existing enterprise knowledge to deliver comprehensive answers and actionable insights, enabling faster, more informed decision-making across various business functions. It’s particularly valuable in large organizations with complex data landscapes, allowing for enhanced efficiency and more nuanced responses to business challenges. 

Teradata is jumping headfirst into this evolution. We offer the world’s most complete cloud analytics and data platform for AI, empowering organizations to move beyond traditional analytics and unlock the full potential of their data. In a landscape where AI tools and platforms are becoming increasingly complex, and data scientists face growing pressure to maximize productivity and deliver higher AI output, Teradata simplifies the process. We provide harmonized data and trusted AI solutions that enable more confident decision-making, accelerate innovation, and drive the impactful business results that organizations need most. 

What’s your vision for the future of data-driven decision-making with AI and real-time data? 

I see the future of data-driven decision-making with AI empowering organizations to make faster, more informed decisions through predictive analytics and advanced machine learning. AI can analyze patterns in real-time, enabling proactive decisions rather than reactive ones. A critical aspect of this is higher transparency — for example: reasoning models are beginning to provide their “chain of thought” in responding to prompts, which builds trust by ensuring its decisions are logical, understandable, and can be internalized by users. 

Greater personalization will be another powerful element. By leveraging generative AI, users can receive insights and responses specific to them, aligned with their preferences and context. This level of personalization ensures that decision-making is not just accurate, but also relevant and intuitive for each individual. 

Ethical considerations and data transparency are central to this vision. As AI becomes increasingly embedded in decision-making, it’s crucial that users understand and trust the data and models driving these insights. We’re committed to ensuring that AI-driven decisions remain explainable, accountable, and aligned with each organization’s ethical standards, so that the journey toward data-driven decision-making is as responsible as it is transformative. 

On a lighter note, is there any latest tech that excites you the most?  

I’m particularly excited about the blending of traditional software with generative AI. We’re seeing more AI-powered agents that can handle multistep productivity tasks, using either broad or customized domain-specific training, while also understanding the user’s intent and context. This is a huge step forward in how businesses can automate complex processes. 

What really excites me is how generative AI can react to imprecise data and combine it with precise control. For example, take inventory management. Today’s systems rely on rigid rules like “when it’s cold outside, do X” or “when January hits, do Y.” But with generative AI, the system can dynamically pull in external data — like news about which sports teams are popular or which teams are in the Super Bowl — and update inventory based on this evolving context, without needing pre-set rules. It can even suggest improvements, such as optimizing the distribution of sporting goods at a retailer. 

This merging of deterministic software with AI’s more flexible, dynamic capabilities is the key to automating processes in ways that were previously unimaginable. We’re at the point where AI agents can handle routine tasks, but they’re also smart enough to make decisions on the fly, and this technology is accelerating at an impressive rate. 

Additionally, tools like DeepSeek-R1 show how quickly we’re becoming more efficient with AI, and OpenAI’s deep research agent shows how effective we can now be. The speed at which these models are evolving is outstanding, and the potential for transforming industries is huge. In the future, I envision these AI agents not just automating tasks but actually orchestrating complex processes—like managing shipping and delivery across multiple enterprises or negotiating contract terms. That’s going to happen much sooner than most people think. 

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