Summary:

In this episode of Insurance Unplugged, Lisa Wardlaw interviews Janet Faircloth, SVP Health Solutions Thought Leadership at Aon, about benefits and healthcare innovation. They discuss the concept of lifestyle assurance and the evolving needs of employees in terms of health, wealth, and life. They also explore the macro trends in health benefits, including the increasing longevity of the workforce and the importance of employee health in driving business outcomes. Janet highlights the need for employers to focus on the ROI of healthcare benefits and to provide access to quality care.

They also discuss the rise of concierge services and the importance of personalization in employee benefits. The conversation concludes with a call to action for employers to embrace new design models, focus on outcomes, and continue to prioritize the health and well-being of their employees.

Takeaways:

Employers need to focus on the ROI of healthcare benefits and provide access to quality care to improve employee health and drive business outcomes.

The rise of concierge services and personalization in employee benefits is a macro trend that aims to meet the evolving needs of employees.

Employers should embrace new design models that guide employees to high-quality, cost-effective care within a network.

Measuring engagement in employee benefits should go beyond sign-ups and focus on outcomes and milestones.

Employers should continue to prioritize the health and well-being of their employees and understand their evolving needs.

Sound Bites:

"People don't really care which circle or vertical or line of business they're in, meaning customers are people that we protect and serve. They are really focused on ensuring and assuring their lifestyle."

"We're already seeing the rise of non-traditional, multi-career self-owned businesses. By 2025, more than 55% of six-figure salaries will be from multi-career self-owned businesses."

"Most of the clients that we work with would struggle to get approval to make some investments that only had a soft link between health and business performance."

Keywords:

benefits, healthcare innovation, lifestyle assurance, macro trends, employee health, concierge services, personalization, ROI, outcomes

Summary:

Sastry Durvasula, Chief Information and Client Services Officer at TIAA, discusses the transformation of TIAA into an AI-first company and the balance between innovation and regulation. He emphasizes the importance of grounding AI initiatives in the core mission of the company and the impact on clients. Sastry highlights the need for a responsible AI policy and the implementation of robust controls and processes.

He also discusses the democratization of AI and the importance of upskilling and reskilling all colleagues, no matter what type of organization they sit in. Sastry shares examples of AI use cases at TIAA, such as leveraging generative AI to simplify complex retirement/financial topics and AI-powered phishing emails for cybersecurity training.

Takeaways:

Ground AI initiatives in the core mission of the company and the impact on clients

Implement a responsible AI policy and robust controls and processes

Democratize AI and upskill and reskill all colleagues

Create value through AI use cases that improve productivity and enhance client service

Sound Bites:

"AI is for everyone."

"AI is not just for AI or data science professionals, it is a cultural change for everybody."

"Don't get locked into one set of models and technology, keep an open architecture."

Keywords:

AI-first, innovation, regulation, responsible AI, mission-driven, generative AI, use cases, democratization of AI, upskilling, reskilling, AI training, value creation

Summary:

In this episode, Lisa Wardlaw interviews Frank Schmid, the Chief Technology Officer of Gen Re about the application of generative AI in the insurance industry. Frank, an economist by training, discusses the concept of generative AI as a general-purpose technology and the importance of understanding how technology diffuses and makes an impact. He emphasizes the need to focus on building foundations and creating options for the future, rather than chasing the latest tools. Frank also highlights the role of workflow redesign and the potential for reinventing business models with the integration of generative AI.

Takeaways:

Generative AI is considered a general-purpose technology that has the potential to transform the insurance industry.

Understanding how technology diffuses and makes an impact is crucial for making informed technology decisions.

Building foundations and creating options for the future is more important than chasing the latest tools.

Workflow redesign and reinventing business models can lead to productivity gains and competitive advantage.

Patience is key, as the full productivity benefits of generative AI may take time to materialize.

Sound Bites:

"Generative AI, economists look at it as general purpose technology."

"Emphasize foundations. Build the foundations. And then things will fall into place."

"Real options valuation is really helpful in making decisions."


Keywords:

generative AI, insurance industry, technology diffusion, workflow redesign, business models, foundations, options, productivity gains

Summary:

In this episode of Insurance Unplugged, Lisa Wardlaw interviews Nigel Walsh, the Global Head of Insurance for Google Cloud. They discuss the role of technology, specifically generative AI, in transforming the insurance industry. They emphasize the importance of choosing the right model for the right task and the need for agility in implementing and switching technologies.

They also highlight the accessibility and ubiquity of generative AI, making it a game-changer for insurers. The conversation touches on the significance of data and the role of data management in leveraging AI capabilities. The conversation explores the challenges and opportunities of adopting generative AI in the insurance industry. It emphasizes the importance of data and the role of a Chief Data Officer. The discussion also touches on the need for a nonlinear mindset and the ability to reimagine processes.

The conversation concludes with a call to action to think big, start small, and act quickly in embracing generative AI.

Takeaways:

Choosing the right model for the right task is crucial in leveraging AI capabilities in insurance.

The insurance industry is at an inflection point with the accessibility and ubiquity of generative AI.

Agility and the ability to switch technologies quickly are essential in the rapidly changing insurance landscape.

Data management is a critical factor in successfully implementing and leveraging AI technologies in insurance. Generative AI has the potential to transform the insurance industry, but it requires a strong foundation of data and a Chief Data Officer to drive its implementation.

A nonlinear mindset is crucial in reimagining processes and finding innovative solutions with generative AI.

The adoption of generative AI should follow a three-step approach: think big, start small, and act quickly.

Standards may be redefined or eliminated with the use of generative AI, but a balance between innovation and regulatory compliance is necessary.

Failure should not be feared, and a willingness to learn and adapt is essential in the journey of adopting generative AI.

Sound Bites:

"Nothing moves without us."

"There's no better organization in the world that will help you understand, deal with, and process data than I think Google."

"The right model for the right task is a really important piece."

"You've got all these levers and pulls against going, but to your point, I love this part about data. It needs to be there, but in my opinion, it doesn't have to"

"They've trained the models and they've got models coming in and it's like self-training."

"The chief data officer role for me is a permanent fixture because that's all we do. We don't manufacture anything else other than data."

Keywords:

insurance, technology, generative AI, digital transformation, data management, generative AI, insurance industry, data, Chief Data Officer, nonlinear thinking, reimagine processes, call to action

Summary:

In this episode, Lisa Wardlaw interviews Gary Hoberman, CEO of Uncork, about generative AI and its impact on various industries. They discuss the difference between traditional AI and generative AI, the need to challenge the status quo, and the importance of creating a vision for the future. They also touch on the adoption curve of codeless technology and how it relates to the adoption of generative AI.

In this conversation, Lisa and Gary discuss the importance of meeting customer expectations in the age of hyper-personalization. They emphasize the need for businesses to be agnostic to different models and tools in order to adapt to changing consumer demands. They also explore the use cases of generative AI in insurance, such as Intelligent Document Processing (IDP) and explain how it can improve efficiency and decision-making. They highlight the significance of traceability and lineage in AI models and the need to show the work behind decisions.

They conclude by urging companies to do a mark-to-market evaluation of their technology investments and embrace generative AI to create a competitive advantage.

Takeaways:

Generative AI creates something that didn't exist before based on various sources and is not constrained by predefined models.

Challenging the status quo and having a relentless pursuit of new ideas is crucial in the adoption of new technologies.

Codeless technology, such as Uncork, allows for the creation of applications without the need for traditional coding, reducing costs and increasing scalability.

The adoption of generative AI is similar to the adoption of codeless technology, as both require a shift in mindset and a willingness to embrace new approaches.

The future of AI lies in the ability to imagine and create new possibilities, rather than relying solely on past data and models. Businesses need to be agnostic to different models and tools in order to meet customer expectations in the age of hyper-personalization.

Generative AI, such as Intelligent Document Processing (IDP), can improve efficiency and decision-making in insurance.

Traceability and lineage are crucial in AI models to show the work behind decisions and gain regulatory approval.

Companies should do a mark-to-market evaluation of their technology investments to identify areas of improvement.

Embracing generative AI can help companies create a competitive advantage and drive innovation.

Sound Bites:

"Generative AI is creative, it's really, I'm creative. It's responding to me in some ways."

"Every application in my mind is a dark warehouse filled with boxes and the boxes keep coming in a conveyor belt."

"Our goal is how do we get out? Stop forcing yourself to think in those constraints. They don't exist."

"Your expectations are... And you know, for people that talk about consumers and, you know, consumer experience and customer experience, kind of like the hyper... It's like, ugh, nobody ever uses the word hyper -personalization because I'm like, you're faking it because you think you know me."

"I always tell everyone you have to be agnostic."

"There should be no one opening documents anymore."

Keywords:

generative AI, traditional AI, codeless technology, adoption curve, challenging the status quo, future vision, customer expectations, hyper-personalization, agnostic, generative AI, Intelligent Document Processing, traceability, lineage, mark-to-market evaluation, competitive advantage

Summary:

In this episode of Insurance Unplugged, Lisa Wardlaw interviews Alex Taylor, Global Head of Emerging Technology at QBE Ventures. They discuss the implications of generative AI and its impact on the insurance industry. Alex shares insights on the democratizing power of AI, the rise of large multimodal models, and the future of AI adoption in insurance.

Takeaways:

Generative AI has the democratizing power to enable non-technical business stakeholders to optimize model behavior through prompt engineering.

The rise of large multimodal models (LMMs) and the emergence of multi-stage reasoning in AI are shaping the future of AI adoption in the insurance industry.

The pace of technology innovation, particularly in the compute capabilities behind AI models, is rapidly advancing, making AI adoption more feasible and cost-effective for the insurance industry.

Sound Bites:

"It's the person that plays chess differently. It's the company that has the people that I used to say, people say it's not checkers, it's chess. And I was like, I think it's actually three dimensional chess."

"Could we just not have to structure data? I mean, how ahead of my time was I, I might add."

"AI is rapidly advancing to your point. The democratization of it is making it kind of iPhone -esque in the sense of you can, it's easy to use."

Keywords:

insurance, AI, generative AI, technology, innovation, QBE Ventures, large multimodal models, AI adoption, insurance industry

Summary:

In this episode of Insurance Unplugged, Lisa Wardlaw interviews Dev Ganguly, Chief Operating Officer at Jackson, about the complexity of retirement products, the impact of AI on the insurance industry, and the importance of customer and advisor experience.

Dev emphasizes the need to focus on outside-in experience design, prioritize technology modernization, and enhance digital dexterity for all associates. He also advocates for a unified effort to drive change in the insurance industry.

Takeaways:

Focus on outside-in experience design for customers and advisors

Prioritize technology modernization and cybersecurity

Enhance digital dexterity for all associates

Unify efforts to drive change in the insurance industry

Chapters:

00:00 Introduction to Dev Ganguly and His Role at Jackson

02:21 Complexity of Retirement Products and Customer Experience

06:55 The Impact of AI on Customer Experience and Industry Transformation

Sound Bites:

"AI will impact the industry in the areas of automation, experience, and growth."

"Ownership of technology investments should move into different business areas."

"Be laser focused on the outside-in advisor and customer experience."

Keywords:

insurance, retirement products, AI, customer experience, advisor experience, technology modernization, digital dexterity, industry transformation

Summary:

Rama Venkat, CTO and Chief Data Scientist of Charlie.ai, discusses the role of AI and NLP in the insurance industry. He shares his background in AI and machine learning and how he got involved in the insurance domain.

Rama explains the difference between traditional AI and generative AI, highlighting the advancements in computing power that have enabled the development of large language models (LLMs). He emphasizes the importance of selecting the right LLMs and training them on specific insurance data.

Rama also discusses the need for a holistic solution beyond LLMs, including workflow, process, and orchestration. He shares examples of effective use cases, such as claims summarization and predictions, and emphasizes the importance of ethical and effective use of AI in the industry.

Takeaways:

Selecting the right LLMs and training them on specific insurance data is crucial for effective use of AI in the industry.

A holistic solution beyond LLMs is necessary, including workflow, process, and orchestration.

Claims summarization and predictions are effective use cases for AI in the insurance industry.

Ethical and effective use of AI is important, and regulations should be set in place to ensure responsible use.

AI is here to stay, and embracing it is essential to avoid being left behind.

Chapters:

00:00 Introduction and Background

04:23 Traditional AI vs. Generative AI

11:23 The Role of LLMs in Insurance

23:54 Building a Holistic Solution Beyond LLMs

29:08 Effective Use Cases: Claims Summarization and Predictions

34:06 Ethical and Effective Use of AI

37:06 Embracing AI in the Insurance Industry

Sound Bites:

"AI is not gonna take away your job, but the person using AI, ethically and effectively will take away your job."

"Don't be left behind. It's here. Organize it, leverage it, understand it, partner with people that understand it."

Keywords:

AI, NLP, insurance industry, traditional AI, generative AI, LLMs, claims summarization, predictions, workflow, process, orchestration, ethical use of AI

Summary

In this episode of Insurance Unplugged, host Lisa Wardlaw interviews Jeff Saviano, an expert in emerging technology strategy and governance. They discuss the importance of AI governance and ethics in the business world. Jeff explains that many organizations have adopted responsible AI principles, but there is a need for executable frameworks and real actions. They also explore how boards are thinking about AI governance and the challenges they face in understanding and managing AI risks. Jeff emphasizes the importance of board composition and the need for citizen representatives to provide diverse perspectives. He concludes by highlighting the role of governance in enabling innovation and responsible AI use.

Keywords

AI governance, AI ethics, responsible AI principles, AI risks, board composition, citizen representatives, innovation

Takeaways

Many organizations have adopted responsible AI principles, but there is a need for executable frameworks and real actions.

Boards are thinking about AI governance and the challenges they face in understanding and managing AI risks.

Board composition matters, and there is a need for citizen representatives to provide diverse perspectives.

Governance enables innovation and responsible AI use.

Titles

The Importance of AI Governance and Ethics

Enabling Innovation and Responsible AI Use

Sound Bites

"What is the boundary of tolerance that you have as a board, that you have as an institution to data privacy?"

"Put a citizen representative on every board. Wouldn't the world be a better place?"

"Good governance will enable innovation and won't stifle it. Like brakes, they'll allow us to go faster, but to do it responsibly."

Chapters

00:00

Introduction and Background

04:46

AI Governance Frameworks and Principles

10:52

Boards and AI Governance

27:59

AI's Impact on Risk Assessment

35:33

Behavioral Psychology and Governance

38:53

Wrap-up and Call to Action

Summary:

In this episode of Insurance Unplugged, Lisa Wardlaw interviews Anil Vasagiri, SVP and Head of Property Solutions at Swiss Re, about the transformative opportunities and challenges of AI in the insurance industry. They discuss the potential of AI in extreme event modeling, behavioral sciences, and generative AI.

They also explore the integration of AI with other technologies, such as IoT, blockchain, and federated learning. Anil emphasizes the importance of data quality and the need for organizations to rethink their tech stack and data strategy. He encourages the industry to be open to experimentation and to focus on quality over quantity when it comes to data.

Takeaways:

AI has transformative potential in the insurance industry, particularly in areas such as extreme event modeling and behavioral sciences.

The integration of AI with other technologies, such as IoT, blockchain, and federated learning, requires organizations to rethink their tech stack and data strategy.

Data quality is crucial for obtaining accurate and reliable insights from AI.

Organizations should be open to experimentation and willing to test out new technologies and approaches.

Focusing on quality over quantity when it comes to data can lead to more impactful outcomes.

Chapters:

00:00 Introduction and Background

03:44 Transformative Opportunities and Challenges of AI in Insurance

09:34 Integrating AI with IoT, Blockchain, and Federated Learning

14:05 Rethinking Tech Stack and Data Strategy for AI Implementation

24:02 Embracing Experimentation and Testing in the Insurance Industry

Sound Bites:

"AI no doubt as you've described, right, is a tech that has truly transformation potential and it has already had quite some material impact on different facets of insurance value chain and it'll continue to have an outsized impact going forward."

"Any use case that has a human in the loop is a fair game, right? So if you look across end to end within the spectrum of your business processes, whether it's a question of dealing with high velocity, high volume data streams and sort of, or it is computationally intensive, a very structured, structure is probably not right the way."

"For a long time when people talked about data strategy, it was always quantity, right? So I have like these 450 attributes I'm looking at or 3000 attributes or leave alone the fact that many of these attributes that you have, are they current or are they stale? What really is the quality consistency across?"

Keywords:

AI, insurance industry, extreme event modeling, behavioral sciences, generative AI, integration, IoT, blockchain, federated learning, data quality, tech stack, data strategy