Fireside Chat Interview with Lalit Thakur, CDAO, Nissan Motors

 

CJ: Lalit, thanks for making time to talk today. The role of the Chief Data Officer has evolved dramatically over the past several years, especially with AI and machine learning becoming central to enterprise strategies. Across your leadership at Solera Holdings, Premier Inc, American Tire Distributors, and Nissan, how have you seen this role change, particularly in such diverse industries?

 

Lalit: Thanks, CJ—it’s a great question. The CDO role has transformed from being mainly about managing data quality and basic operational reporting, to being a core driver of business outcomes through data science, AI, and advanced analytics. Across insurance, healthcare, distribution, and automotive, while the sectors differ hugely, the fundamentals remain the same: driving measurable value, fostering a data-driven culture, and bridging technology with business. For example, at Solera, Premier, Novant Health and ATD, I had to tailor these principles to each industry’s unique challenges and opportunities.

 

CJ: Let’s take them one at a time. Starting with Solera Holdings, which operates in insurance, you led transformative analytics initiatives there. Could you share a standout AI use case that had a major business impact?

 

Lalit: Certainly. One of our biggest projects was around claims estimation and fraud analytics. Solera provides software platforms that insurance carriers use to estimate the cost of vehicle and property damages. We used machine learning models to analyze vast historical claims data alongside exploring new images and sensor inputs to precisely predict repair costs and detect anomalies indicative of fraud. But firstly, we had to centralize disparate data across different countries in a data hub. This significantly shortened claims processing times and boosted the accuracy of reserves, helping insurers improve customer satisfaction and financial stability. It was a classic example of how algorithms can streamline complex decisions in highly regulated industries.

 

CJ: That’s impressive. Moving to healthcare, Novant Health operates in a space with massive complexity and stringent requirements. What kind of analytics or AI initiatives did you lead that stood out in that environment?

 

Lalit: Healthcare data is enormously rich but complex—electronic health records, patient outcomes, claims data, , clinical quality metrics—all come with privacy and compliance constraints. At Novant Health, one flagship initiative involved analytics in value-based care to reduce readmissions and improve clinical outcomes. We developed models that integrated diverse data sources to identify high-risk patients, enabling more proactive interventions. On the claims side, models had to be created to calculate cost of care episodes to minimize waste—critical in healthcare. Implementing these initiatives required a delicate balance of innovation with compliance and trust-building among care delivery personnel.

 

CJ: Healthcare certainly brings unique challenges around trust and privacy. How did you foster data trust and ethical AI usage there and across your other roles?

 

Lalit: It’s always about transparency and governance. Across Premier, Solera, Novant Health and ATD, we implemented rigorous data governance frameworks—including audit trails, bias detection, and multi-stakeholder ethics reviews. We trained teams on responsible data use and took an open approach to explain how decisions and predictions were made, particularly in sensitive areas like healthcare and insurance claims. Trust isn’t optional in these fields; it’s foundational. For example, involving legal, compliance, and customers early in the design process drastically improved adoption and outcomes.

 

CJ: Speaking of ATD (American Tire Distributors), logistics and distribution come with their own sets of challenges. What role did data and AI play in optimizing operations there?

 

Lalit: Distribution is a great example of a traditionally complex, fragmented industry ripe for digital transformation. At ATD, one major analytics focus was demand forecasting and inventory optimization across distribution centers, integrating market trends, seasonal variances, and supplier constraints using AI-driven predictive models. This helped reduce stockouts and excess inventory, improving customer fulfillment rates and lowering carrying costs. We also implemented advanced route optimization algorithms to enhance delivery efficiency and reduce carbon footprint, tying operational improvements directly to sustainability goals.

 

CJ: You’ve worked across very different sectors. Are there common leadership lessons or strategies that other CDOs and data leaders can learn from your cross-industry experience?

 

Lalit: Definitely. First, data and AI success always starts with clear business alignment—understanding the highest-value problems and focusing resources there. Second is culture—breaking silos and building cross-functional trusts is critical, whether it’s actuaries at Solera, clinicians at Novant Health, Supply Chain teams in Hospitals (members of Premier) or distribution managers at ATD. Third, governance and ethical responsibility can’t be afterthoughts. Lastly, scaling AI is a marathon, not a sprint—quick wins matter, but you also need a sustainable foundation of technology, talent, and leadership commitment. For example, at Premier, the data stewards needed to bring in clinical expertise and thus posed unique recruitment needs.

 

CJ: Looking forward, what are your biggest bets on AI and data that will shape your work and leadership in coming years?

 

Lalit: I’m excited about multi-agentic AI systems that doesn’t just explain what happened but guides what to do next, why and how, all in real-time. Techniques like reinforcement learning and streaming analytics will become mainstream. Personally, I’m also focused on developing leaders who can manage the human and ethical dimensions of AI—building resilient teams that blend creativity with accountability. Across industries, the future demands leaders who can marry technical sophistication with empathy and strategic foresight. Picking the right talent and not necessarily “doing the same thing and expecting different results” is the key to accelerate.

 

CJ: Before we close, a quick one on talent. Firms like AI81Works specialize in sourcing top data and AI leadership. From a CDO’s standpoint, why is a specialist executive search partner valuable in building these critical teams?

 

Lalit: The competition for data and AI talent is fierce and highly specialized. The needs of data visibility, quality and overall governance is now being recognized across the landscape. Partnering with firms like AI81Works means faster access to leaders who deeply understand the nuances of these roles and industries. Their credibility and technical insight reduce risk—especially if they’ve walked the same ‘lines of fire’ that we as data leaders face. They’re more than recruiters; they are trusted strategic advisors who help build ecosystems of leadership aligned to strategy and culture.

 

CJ: Great insights, Lalit. Thank you for sharing your broad and deep experiences across these critical industries. It’s been a pleasure.

 

Lalit: Thank you, CJ. I appreciate the opportunity. Sharing lessons across industries strengthens all of us leading digital transformation in these evolving times.

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