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Council Post: Evolving Role of Chief Data Officers (CDOs) in AI Investment Decisions

Our panelists bring diverse perspectives from retail, healthcare, and finance—sectors that are not only data-rich but also highly regulated.

The dawn of the AI era has catapulted Chief Data Officers (CDOs) from the back rooms of data management to the forefront of corporate strategy. As organizations scramble to harness the power of AI, CDOs find themselves in a unique position—balancing the exhilaration of technological promise with the sobering realities of data quality, regulatory compliance, and strategic alignment.

This seismic shift in the CDO’s role is the focus of our discussion on “Evolving Role of Chief Data Officers (CDOs) in AI Investment Decisions.” To navigate this complex terrain, we assembled a panel of trailblazing leaders: Ambika Saklani Bhardwaj, Data & Analytics Product Leader at Walmart; Phanii Pydimarri, Head of Strategic Planning & Partnerships at Health Care Service Corporation; and Erum Manzoor, Executive Technology Leader at a Financial Company.

Our panelists bring diverse perspectives from retail, healthcare, and finance—sectors that are not only data-rich but also highly regulated. They candidly share their experiences managing the “balancing act” CDOs must perform: tempering executive enthusiasm with data realities, ensuring AI investments align with risk appetites, and fostering a culture of data ethics and quality.

The CDO’s Balancing Act in Navigating AI Excitement and Reality

In my opinion a CDO is a person who knows about the data more than a CFO or anyone else in the company. They know about the data structures, data quality and its potential on what value it can unlock for the organization. So definitely the CDO’s play a great role in the upcoming AI technology because AI is wholly based on Data. So if your data quality is good and fully governed then you can think of having a really good and useful output out of that. 

Ambika Saklani Bhardwaj, Data & Analytics Product Leader at Walmart

What I’ve been hearing from fellow CEOs and industry leaders is that there is definitely tons of excitement around AI and the opportunities it presents. Sometimes, I also feel like there are false expectations, too. As CDOs, one of the first foundational settings we need to establish is managing those expectations. It’s important to bring everybody down to reality without taking the excitement away. There’s still a lot of excitement, so it’s a very thin line between maintaining that excitement and setting realistic expectations.

To the point about bringing reality from a data standpoint—not every organization is as mature in terms of data capabilities. When it comes to investments, there’s tons of excitement and opportunities to invest in the edge and the latest technologies. These vendors with point solutions are doing an amazing job, offering super cool solutions that can solve niche problems. However, within a corporate setting, especially in large organizations, things get pretty messy. Things are all over the place. All three of us are in large to extra-large organizations, and things are not as clean as we would like them to be. There’s a lot of excitement from executives and business partners about these shiny new tools being sold to them, so data and AI leaders are expected to perform a balancing act.

The second aspect of balancing is from a compliance and regulatory standpoint. As health insurance providers, our company wants to be risk-oriented in our decisions and investments in adopting certain solutions. We want to be risk-averse, which is okay for us. We don’t want to be the drivers or have the first-mover advantage here.

The last item is the quality of the data itself. What does your current state really look like? Understanding where you stand to be able to reach the goal of achieving a top-notch AI solution is crucial. Identifying the gaps and working to fill them can be a long process in some cases.

Phanii Pydimarri, Head of Strategic Planning & Partnerships at Health Care Service Corporation

During COVID and around that period, you must have noticed that a lot of CFOs actually became CEOs of companies. They were highly paid and focused on figuring out operational efficiency and transformation to achieve savings. Over the last three years, the advancements in AI and automation have been phenomenal and amazing. We want to ensure that these solutions are safe, secure, and deliver everything they promise.

I want to talk about a challenge and a bone of contention regarding the role of the Chief Data Officer (CDO). Are CDOs truly empowered to impact investment decisions today? I feel that shift is finally happening. In the past, CDOs were expected to present their findings in a nice deck or report, and then step back. But today, they are part of the conversation, helping to figure out solutions for various challenges. I believe that in the future, there will be even more integration and merging of roles.

A decade ago, data professionals worked behind the scenes, and their efforts were often overlooked. Now, the CDO is establishing Centers of Excellence (COEs) and evolving into a prominent discipline. The role is no longer about just one person but encompasses a whole team. Leadership qualities are emerging, and CDOs are now empowered to provide feedback on investments in application technology and front-end development. It’s essential to ensure that foundational systems and back-end infrastructures keep pace with these advanced technologies.

Erum Manzoor, Executive Technology Leader at a Financial Company

Cornerstones of AI Advancement in Modern Organizations

Data governance, data technology, and data security play a really, really important role in the AI world today. As I mentioned earlier, data is the code for our AI technology—that’s what this technology is working on. In all organizations today, including mine, there is a comprehensive framework around data governance. We conduct regular audits and checks to manage data quality.

In addition to the data framework, we have established Centers of Excellence (COEs) to oversee data quality. Given the immense scale of an organization like Walmart, these frameworks are essential. The key to these AI technologies coming up and the use of AI in industry is training and education in data ethics, data compliance, data accuracy, and quality. This should be a continuous effort by each organization if we want to excel in AI technology. Expertise has no level—it can come from any level within the organization.

While companies typically follow a top-down approach for ownership, responsibility, and accountability, learning about and initiating AI technologies is the responsibility of each and every employee working in data in any organization.

Ambika Saklani Bhardwaj, Data & Analytics Product Leader at Walmart

The Role of Data Leadership in Application Development

There are certain processes that are manual today. In business, when they do product development, their requirements are often based on user experience rather than on the underlying complexities. Technology brings in the aspect of executing, building, and addressing all the pain points. When it comes to data, we are the ones who not only worry about the consumer data but also take care of critical data, determining which applications will interact with it, what we will feed to the data warehouse, and what kind of reports we will create. In our world, we consider ourselves masters of that.

In one instance, a business area got access to a co-pilot, trained their people, and when requirements started coming in, they coached and taught the application developers and solution leads. They suggested real-time processes instead of stored procedures, sparking a fun conversation on the technology side. It became mandatory for all team members to install the co-pilot and ensure business engagement.

These situations highlight the diverse mix of people, personalities, and leaders, and how we evolve and learn differently. Sometimes we are forced to adapt due to a launch, sometimes business pushes for changes, and sometimes technology mandates them. For example, with end-of-life happening for many tools on the infrastructure side and the push to remove technical debt, discussions now increasingly involve data. People have realized the significant impact data has on applications and the consumer market.

I believe the role of the CDO and the entire realm of data has an amazing future in application development.

Erum Manzoor, Executive Technology Leader at a Financial Company

Balancing Speed and Accuracy in Data and AI Leadership

One comment is “slowing down to get faster.” It’s as simple as that—you have to slow down before you start getting into that marathon or even a sprint. Foundational elements are key. Do we know everything within the spectrum? No, but we know enough to take a pause and assess. This ties back to an earlier question about setting expectations from an executive standpoint. There’s often impatience at the executive level regarding the value delivered.

As data leaders, analytics leaders, or AI leaders, it’s our responsibility to level set those expectations and present the real picture. You don’t want to end up in a lawsuit. So, let’s take a pause, slow down, and do it right the first time. That would be the mindset I would approach with.

Phanii Pydimarri, Head of Strategic Planning & Partnerships at Health Care Service Corporation

Picture of Anshika Mathews
Anshika Mathews
Anshika is an Associate Research Analyst working for the AIM Leaders Council. She holds a keen interest in technology and related policy-making and its impact on society. She can be reached at anshika.mathews@aimresearch.co
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