Nan Li, AI leader and former Nationwide VP of AI/ML and Statistical Practice, speaks with Gavroshe Founder Derek Strauss in a video interview about organizations leading the change with AI, going back to the fundamentals of business, building an  AI execution strategy, and the need for a human-centric edge.

Leading through change in the age of AI

In a rapidly shifting business landscape increasingly shaped by AI, it takes courage and clarity to lead the charge. Li, an experienced leader in AI strategy and transformation, offers a thoughtful, grounded framework for organizations navigating this evolving terrain.

In her view, success is not just about deploying AI; rather, it is about learning and accelerating growth. “Kudos to the leaders who dare to take the first step. They probably have a lot of learning, and learning will not be wasted. It’s all about how to take the learning and accelerate the growth of the company.”

Rediscovering business fundamentals and how AI is an amplifier

Next, Li emphasizes the importance of returning to the fundamentals. She urges leaders to understand the business and why it exists. Li notes that many companies exploring AI are not, in fact, AI companies. They operate in industries like banking, education, logistics, healthcare, and retail.

The key, she says, is to focus on the business’s core purpose and determine how AI can amplify that purpose, and she advises companies to 

 • Involve corporate strategy teams in AI strategy development.
• Review the alignment between business and AI strategies.
• Revisit assumptions and challenges that underpin both strategies.

It’s not about what AI you have — It’s about how you use it

Moving forward, Li suggests companies look at how AI is integrated across the entire value chain. “To realize business value, you need to improve the whole value chain, not just certain steps.”

According to her, a comprehensive value chain framework includes suppliers, employees, customers, regulators, competitors, and the broader marketplace environment. For example, Li explains that when AI is applied internally to support employees, the focus is often on boosting productivity. However, using AI in customer-facing areas directly affects the products or services being delivered, which introduces higher risk.

Similarly, automating processes for efficiency could influence interactions with suppliers — raising the question of whether those suppliers are prepared to adapt. Speaking of regulatory concerns, she states that even in the insurance industry, it becomes challenging to get approval for certain models, as the regulations have not yet caught up with the model.

Moreover, it is critical to take account of the competitor’s advancement in the domain and assess whether there is real improvement or just assumptions.

However, Li believes that early adopters have a solid advantage but they must reprioritize use cases, connect those to strategies, and map them in a value chain. Secondly, she suggests shifting the focus to people, identifying stakeholders, articulating the roles, and organizing a cross-functional team.

Positioning AI inside the organization

Speaking of organizational challenges, Li discusses how positioning AI in business and positioning AI teams in organizations is critical. Based on the organization’s level of readiness and maturity, it could have a centralized or distributed, or federated model, but the focus should be on people.

Thereafter, Li reminds that the organizational governance processes are related to its people, activities, and operating model. She adds, “If you already have an investment, evaluate and adjust your investment expectations based on the exercise.”

For organizations that have invested already, Li recommends aligning investments with strategy and organizational maturity. Ultimately, she urges developing and launching an enterprise-wide AI literacy program and change management with a tailored approach to the four personas she mentioned in part 2 of the discussion.

Building a robust AI execution strategy

Organizations should develop an AI execution strategy, says Li. The key, she says, is to start with strategy and then work on people, process, technology, and data, while also stressing risk management, governance, and education.

“AI is like the old internet boom,” says Li. “If you don’t have AI, your business is going to disappear pretty soon, but just using AI wouldn’t guarantee your success either.” She emphasizes the importance of keeping pace in the productivity race without falling behind, while also exploring how AI can be used to strengthen current competitive advantages or create new ones.

In some cases, Li notes, efforts around productivity and innovation can be integrated within the same team, but it can also be more effective to separate them into distinct workstreams — one focused on productivity, the other on competitive advantage.

AI: A super engine that’s becoming ubiquitous

As a key takeaway, Li calls AI a super engine and says while early adopters may have advantages, eventually AI will be as pervasive as the internet.

She affirms, “Everyone will have it and use it. What distinguishes you and your business from others is still how you can better serve your customers for their needs and provide them with better value and benefits, which is delivered by your people working together.”

The human-centric edge

Li concludes by sharing a key insight she picked up from another webinar: As intelligent or AI-enabled businesses become the new standard, the real differentiators will be how companies harness their people, data, and brand.

This, she says, is what fuels her passion for human-centric leadership. She hopes that the framework and ideas she’s provided will serve as a guide to help organizations navigate this journey and achieve lasting success.

CDO Magazine appreciates Nan Li for sharing her insights with our global community.