Security investment decisions are easier when there’s shared understanding of what level of risk the organization is willing to accept. Without that foundation, every security decision requires individual justification and approval. Many organizations find these conversations challenging – legal and leadership may be hesitant to formally acknowledge any acceptable risk level. Yet having even a basic framework helps prioritize investments and align technology and business perspectives. This session explores how peers are approaching risk tolerance conversations, what frameworks or tools are helping, and how to build productive dialogue with leadership about security priorities.

Discussion points:

• How are you approaching risk tolerance conversations with leadership?
• Frameworks that help quantify and communicate risk
• Building shared understanding across technical and business teams
• Tools that have helped (X Analytics, risk quantification approaches)

Building retention schedules that align with new state mandates, such as those in California, proves challenging when balancing insurance-specific retention requirements. Are you using automated data discovery or anonymization to stay compliant in 2026?

Discussion points:

• Where are you seeing the biggest conflicts between privacy law retention limits and insurance-specific mandates?
• Is anyone using automated data discovery or classification to identify what falls under which rule?
• How are you approaching anonymization or pseudonymization as a middle ground?
• What does your retention schedule governance process look like: legal-led, IT-led, or somewhere in between?
• Are vendors helping here, or are you mostly building internal processes?

The lack of standardization in carrier data formats and APIs is a universal pain point that came up repeatedly in previous sessions. Everyone’s building custom integrations, dealing with inconsistent data structures, and spending resources on problems that could be solved with better industry coordination. Some organizations have found middleware tools that help, others have built creative workarounds. There’s also the question of how to prepare for upcoming platform changes from major AMS vendors. This is a chance to compare notes on what’s actually working and commiserate about what isn’t.

Discussion points:

• Creative workarounds for carrier data limitations
• Middleware tools and integration platforms that are helping
• Broker-to-carrier feedback – how do you handle it?
  • Preparing for upcoming AMS platform changes and integrations

Your data architecture determines what’s possible – whether that’s real-time reporting, advanced analytics, or simply getting a single view of the customer. Yet many organizations are still dealing with data fragmented across systems, stuck in 30-minute sync cycles, and poorly organized for anything beyond basic queries. The decisions you make about data platforms today will determine your capabilities for years to come. This session focuses on the foundational questions: what does your data architecture need to look like to support modern analytics and automation? How are organizations structuring their data lakes and warehouses? What data quality issues are blocking your initiatives, and how are you addressing them?

Discussion points:

• What data quality issues are limiting your initiatives?
• Data architecture patterns that enable analytics and automation
• Real-time sync strategies getting past 30-minute frequencies
  • AMS data consumption and integration approaches

Everyone is being asked to “do AI” but the playbook for getting there isn’t one-size-fits-all. A firm scaling through 30 acquisitions a year needs a fundamentally different data and technology foundation than an independent building for the next generation. And both look different from a firm’s positioning for its next transaction.

Drawing from the wins and scars of building a unified platform across multiple acquisitions and powering growth to a $2B firm, this session offers a practical framework for getting AI-ready by getting the foundation right first.

We’ll walk through the three layers that determine whether AI investments pay off or fall flat: strategy, data foundation, and workflow automation. Then we’ll pressure-test how the priorities shift depending on your firm’s business strategy.

The goal: Walk out with some practical steps you can use next week calibrated to where your firm is headed.

Discussion Points

• Why most AI initiatives stall — and the 90% of foundational work nobody talks about
• A three-layer model: strategy, data foundation, and workflow reengineering
• How the playbook varies for aggregators vs. transaction-ready firms vs. forever firms
• Crawl / Walk / Run: a self-assessment framework for AI readiness
• Practical lessons from building a data foundation across 35+ acquired firms on different AMS platforms
• Preparing your data layer for carrier integration, real-time analytics, and AI-driven workflows

This session is designed to build on the earlier discussions on carrier data standardization and modern data foundations — offering a framework the group can take home and apply based on their own firm’s direction.

How do you manage fragmented “copilot” solutions across the Enterprise while giving users the freedom to innovate? What strategies are you using to consolidate into unified platforms and integrations to reduce complexity?

Discussion points:

• How many AI/copilot tools are actually in use across your organization right now — do you even know?
• Where are you drawing the line between “approved” and “shadow AI”? How are you discovering unsanctioned tools?
• Are you consolidating around a single platform (e.g., all-in on Microsoft Copilot) or taking a best-of-breed approach? What’s driving that decision?
• Has AI adoption forced you to finally address data permissions, classification, and access controls you’d been putting off?
• How are you handling costs? Are you licensing copilots broadly or limiting to specific roles? Is anyone actually measuring ROI?
• What does your governance model look like? formal AI review board, acceptable use policy, approved tool list, or still figuring it out?
• How do you give teams room to experiment without creating a mess you’ll have to clean up in 18 months?

Roundtable format where anyone can bring questions, share recent wins, or ask for help with challenges they’re facing. Some of the best insights from our previous meetings came from spontaneous conversations where someone shared what worked for them. This is also a chance to follow up on yesterday’s discussions, go deeper on topics that resonated, or surface new issues that didn’t make the agenda. Come prepared with something you’re proud of, something you’re struggling with, or a question you’ve been wanting to ask this group.

Discussion points:

• What’s working at your org right now?
• What’s keeping you up at night?
• Quick asks for the group
• Follow-ups from yesterday’s discussions