The Reality Layer

In part one of this two-part conversation on The Reality Layer, Dave Tobias sits down with Lynn Thompson, Partner and Global Head of Strategy and Partnerships at QBE Ventures, to explore what it takes to turn emerging technology into meaningful insurance outcomes.
Lynn argues that innovation has to start with a real problem, not speculation about what technology might make possible. She shares how QBE has used data, imagery, and AI to help frontline teams identify potential coverage gaps and have better conversations with customers, while earning the trust required to scale those capabilities more broadly.
This conversation also tackles what Lynn calls the “pilot to nowhere” problem. Pilots can launch quickly, but real impact requires a deliberate path to production, clear ownership, strong executive support, and a willingness to iterate several times before value appears.
Dave and Lynn also explore how frontier AI models are changing what insurers can do with large volumes of data, why regulated workflows must remain predictable, repeatable, auditable, and governed, and why the right partner often matters more than the flashiest technology.

Ultimately, Lynn’s message is simple: start with the problem worth solving, build for scale from day one, and choose teams with the judgment and domain knowledge to keep adapting as the technology changes.

What You’ll Learn:

• How data and imagery can help insurers identify coverage gaps and support better customer conversations
• Why underinsurance requires more than simply extending today’s insurance models
• Why innovation projects need strong conviction, executive support, and a clear definition of success
• What Lynn means by the “pilot to nowhere” problem and why so many POCs never reach production
• How current AI models can help insurers process far more data across underwriting and portfolio decisions
• Why regulated AI workflows need to be predictable, repeatable, auditable, and governed
• Why specialized InsurTech partners still matter even as general-purpose AI becomes more capable
• How choosing the right team can matter more than choosing the most impressive technology
• Why deep domain expertise is becoming even more valuable as the cost of building technology falls
  
  

Lynn Thompson is Partner and Global Head of Strategy and Partnerships at QBE Ventures, the venture investment arm of QBE Insurance Group, where she leads strategic investments and partnerships with emerging technology companies.
Her career sits at the intersection of large organizations, startups, data, and innovation. Before joining QBE Ventures, Lynn led go-to-market at Data Republic and worked on an AI R&D hub in Singapore. She began her career in management consulting at Deloitte and has worked across the APAC region.

Lynn is also an Advisory Board Member at The Spark, a prevention-focused insurance lab bringing insurers, reinsurers, and technology companies together around major industry challenges, and a Board Member at iluminr, a capability intelligence platform focused on organizational resilience.

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Episode Resources:

• Lynn Thompson on LinkedIn
•  QBE Ventures Website
• Dave Tobias on LinkedIn
• Nearmap Website

In this episode of The Reality Layer, Dave Tobias sits down with Jeremy Greenberg, a veteran emergency and risk management leader with more than two decades of federal service, to explore what insurers can learn from the way FEMA prepares for and responds to disasters.
Having helped coordinate responses to hundreds of presidentially declared disasters, Jeremy explains why growing unpredictability is forcing organizations to rethink preparedness. His advice starts with a simple principle: do the basics brilliantly. Know your plans, understand your resources, practice under stress, and identify gaps before a real event exposes them.

This conversation also explores a growing challenge: organizations have more data than ever but still struggle to turn it into knowledge. In the first 24 to 48 hours after a disaster, imagery, property intelligence, and faster analysis can help answer critical questions about damage, accessibility, infrastructure, and where resources should go first.

Jeremy also emphasizes the value of relationships built before disaster strikes. Insurers should know local emergency managers, make communication easier for people under stress, and prepare for the possibility that their own teams and systems could be affected by the same event.
Ultimately, better response comes down to knowing how information moves, turning it into action quickly, and building a trusted network before you need it.


What You’ll Learn:

• Why disaster patterns are becoming less predictable and what that means for insurer preparedness
• Why training cycles and surge capacity matter when there is no longer a clear “off-season” for major events
• What Jeremy means by “do the basics brilliantly” and why checklists still matter during complex incidents
• Why well-written plans can still fail when they depend on resources being used elsewhere
• How to move from raw data to information, knowledge, and ultimately better decisions in the first 24 to 48 hours
• How aerial imagery, geospatial intelligence, and AI-assisted analysis can accelerate damage assessment and resource allocation
• How stronger public-private relationships can improve catastrophe response and recovery
• How better property intelligence can reduce the burden placed on policyholders during one of the worst days of their lives
  
  

Jeremy Greenberg is a Senior Advisor at The Aspen Institute and an emergency and risk management leader with more than 23 years of federal service, including five years as a member of the Senior Executive Service.

Most recently, Jeremy led FEMA’s Response Operations Division, overseeing national capabilities responsible for identifying, alerting, and stabilizing threats and hazards. His portfolio included the National Watch Center, FEMA Operations Center, National Response Coordination Center, and National Urban Search and Rescue program.

During his FEMA career, Jeremy also served as Chief of the National Response Coordination Center and FEMA Team Leader for the Domestic Emergency Support Team, helping coordinate responses to hundreds of presidentially declared disasters spanning hurricanes, wildfires, cyber incidents, terrorist attacks, and other major emergencies.

Beyond his federal career, Jeremy has spent more than 20 years serving his community as a volunteer firefighter, emergency medical technician, and instructor.

If you enjoyed this episode, make sure to subscribe, rate, and review it on Apple Podcasts, Spotify, and YouTube Podcasts. Instructions on how to do so are here.


Episode Resources:

• Jeremy Greenberg on LinkedIn
• The Aspen Institute Website
• Provincial Strategies Website
• Dave Tobias on LinkedIn
• Nearmap Website

In this episode of The Reality Layer, host Dave Tobias sits down with AI Strategist, Technologist, and former AWS Executive in Residence, Jake Burns. Together, they analyze why enterprise AI initiatives stall after early success and how leadership can fix it.


With four decades of hands-on experience advising over 1,000 executive teams, Jake brings sharp builder intuition. He breaks down why building a proof of concept is easy, while engineering a secure, scalable production system trips up most organizations.


For insurance carriers, legacy data and technical debt create friction. Jake explains why waiting for pristine data environments is a mistake. Instead, teams should build with current datasets and leverage AI to clean up legacy debt.


Technology is only half the battle. People and internal culture dictate ultimate success. Employees need a clear executive roadmap and personal incentives to support AI execution. AI is a capability multiplier designed to expand what your business can achieve.


What You’ll Learn:

• Why enterprise AI proofs of concept succeed easily but stall during production scaling.
• How to build effective AI capabilities immediately without waiting years to fix legacy data.
• How execution gaps and scarce agentic talent convert promising builds into expensive failures.
• Why people, culture, and employee buy-in are fundamental to successful AI transformation
• How companies can combine existing industry knowledge with new AI skills instead of relying entirely on outside specialists
• Why executives should use AI themselves to develop a practical mental model of what the technology can and cannot do
• Why regulated industries such as insurance can adopt AI securely without treating compliance as a reason to delay
• How smarter model selection, architecture, and token usage can dramatically change the economics of enterprise AI
  
  

Jake Burns is an AI Strategist, Technologist, and former Executive in Residence at Amazon Web Services, where he spent nearly seven years as an Enterprise Strategist.


With four decades of technical experience, Jake has guided over 1,000 global executive teams through AI strategy, cloud adoption, enterprise optimization, and change management.


Today, Jake spends much of his time researching and building AI systems while advising executive teams on AI strategy and execution.


If you enjoyed this episode, make sure to subscribe, rate, and review it on Apple Podcasts, Spotify, and YouTube Podcasts. Instructions on how to do so are here.


Episode Resources:

• Jake Burns on LinkedIn
• Dave Tobias on LinkedIn
• Visit Nearmap
• Learn more about Nearmap