We consider a problem-solving or use case-based approach where we first articulate the problem statement we are trying to solve — especially ensuring that we approach it from a design stage. We make sure that the problem statement and the ask are clearly defined.

Additionally, we verify that the required data for building the AI solution is not only available but also qualified. We also articulate the potential risks, as this helps us set expectations from day zero.

Applying AI involves navigating uncertain outcomes. However, defining the problem statement with a clear owner — whether it’s a stakeholder who will be using the solution or the function that will consume it — ensures alignment on why this problem deserves attention.

Given the market is flooded with numerous AI solutions, selecting the right tool for the right use case is critical. At the end of the day, we have evolved from ERP and legacy systems, carrying tech debt along the way. However, our focus remains on building a strong foundation in data science, aligning with data governance and AI principles, and continuously learning alongside hyperscalers.

As technologies evolve, success depends on adapting and growing with them. This journey requires continuous feedback and learning from one another.

AI governance should first be anchored in the organization’s mandate and purpose. Additionally, establishing foundational principles — particularly ensuring that data governance supports AI governance.

It’s more of a Yin and Yang dynamic; data governance should come first, followed by AI governance.

I would suggest building a strong foundation in data science and developing an understanding of how to use data effectively. It’s important to grasp the key aspects of training an AI model, as well as adopting a persona-based learning approach. Not everyone needs to be a data or AI practitioner, and not all practitioners need to be consumers of AI.

The critical distinction lies in whether you are a consumer of AI-powered solutions — meaning you’re the decision-maker integrating AI into workflows — or a practitioner developing these solutions. Clarifying your role in how you leverage AI is a great starting point for someone new to the field.

I always recommend focusing on becoming a consumer or user of AI. The more you engage with these technologies, the better you understand their evolution and how to adapt to AI-driven solutions. This approach sets you up for success. Not everyone needs to train AI models, but it’s essential to be pragmatic and recognize the opportunities AI presents in driving value within your organization.

AI is empowering us; it’s not going to replace the human in the loop. Instead, the human remains the core value proposition that AI is designed to support.

There are three key aspects that define the landscape of the CDO, CDAO, or CDAIO role. As a CDO, a good starting point is mapping the pain points and ambitions of your organization.

“How can we leverage data effectively? Specifically, how do you facilitate its development and use within the organization while ensuring that you act as the mediator for standards, safety, risk management, and overall data governance? Additionally, how do you ensure that the data you oversee becomes interoperable across the organization?”

Understanding your organization’s landscape and aligning your priorities at the organizational level is essential. These are some of the challenges, but I also see them as opportunities for CDOs in this sector today.