Northwestern Mutual

Sushma Niwalkar is a seasoned professional with over 15 years in AI/ML leadership. She has led digital transformations in finance and has forged trusted partnerships in business strategy, operations, and tech. Niwalkar has been instrumental in establishing responsible AI infrastructure and collaborating across privacy, legal, compliance, and data governance. Currently, she focuses on laying foundations for Generative AI, covering talent acquisition, tech integration, and business applications.

Through this interview, Niwalkar shares her insights on the challenges and emerging trends in the Generative AI space and modern ways to adapt to the volatile industry.

Could you elaborate on the primary roles and responsibilities that you undertake daily?

My primary role involves steering the data science and analytics division at Northwestern Mutual. My team and I are tasked with the development and implementation of data science solutions that cater to various business segments. Our focus presently encompasses enhancing the customer experience throughout their entire lifecycle by leveraging both traditional AI and generative AI (Gen AI) technologies.

What are some of the significant challenges currently faced within the industry, particularly those you've encountered in your role?

 Are there any recent trends that have influenced these challenges? Regarding Gen AI, our emphasis at Northwestern Mutual is twofold: we are committed to advancing the technical prowess required for these innovations while simultaneously catalyzing organizational transformation.

Our strategy extends beyond merely developing technical acumen; it encompasses fostering a culture and infrastructure conducive to integrating Gen AI across a range of applications.

Our focus presently encompasses enhancing the customer experience throughout their entire lifecycle by leveraging both traditional AI and generative AI (Gen AI) technologies

This approach is aligned with the sector’s prevailing trends, prioritizing both technological progression and the organizational agility necessary to navigate and lead in the evolving landscape of AI.

Could you share insights on any current projects or initiatives within your department?

Transitioning from a business-to-consumer (B2C) model at Citigroup to a business-to-business-to-consumer (B2B2C) model at Northwestern Mutual has been invigorating. We are currently focused on crafting Gen AI solutions tailored for our advisors and field force. Our project portfolio includes developing applications aimed at enhancing service capabilities for operational efficiency, leveraging knowledge management to boost conversion rates, and creating tools to increase employee efficiency.

The current phase involves setting up the technical and cost infrastructure, implementing solutions like retrieval-augmented generation (RAG) frameworks, and searching and retrieval for specific use cases. A crucial aspect is establishing the right talent pool to execute these technical solutions and driving cultural changes within the organization to ensure successful implementation. This aligns with the broader industry trend of navigating similar paths in adopting Gen AI solutions.

How do you see Gen AI shaping the future? What kind of impact might it have on the market?

Gen AI is traversing a unique trajectory, and perspectives vary widely depending on whom you consult. However, a consensus is emerging that Gen AI represents a highly democratized form of AI technology, with a wider array of applications than most recently popularized technologies. The key for organizations lies in their ability to fine-tune the balance between accuracy and cost to realize tangible returns on investment.

The industry is today navigating the initial phase of leveraging Gen AI to lay the groundwork for future success. It’s anticipated that within the next couple of years, we’ll gain a clearer understanding of its impact. The deployment of Gen AI can be categorized into three types, point solutions, application solutions, and systemic solutions. We’re presently at a stage where point solutions are being enhanced, with a gradual shift towards application solutions across various domains. Systemic solutions, which will likely redefine industries, are still on the horizon.

The discourse on the progression of Gen AI varies, with some experts predicting we are at the precipice of an exponential growth phase, while others believe we’ve reached a plateau. Only time will provide clarity on these predictions.

In terms of practical applications, industries are currently focused on application solutions that deliver specific operational efficiencies or revenue opportunities, particularly in service industries. System-wide solutions, aside from our advanced language understanding capabilities, are yet to be fully realized and tested. Still, the potential for Gen AI to revolutionize fields such as the legal and medical sectors is significant. The trajectory is set, and it’s a matter of time before its full implications unfold.

As an industry veteran, what advice would you offer to aspiring professionals or your peers?

Continuous learning is essential in our industry. It’s a constant process, and while it may seem challenging to keep up with everything, maintaining a growth mindset is key to ongoing growth, ensuring you can adapt to changes and stay at the forefront of developments in the field.