Conference Intelligence: AI Wins at Guidewire Events

Insurance conferences used to be about roadmaps and release notes. Now they’re about execution: who can ship measurable outcomes with AI—faster quoting, better retention, lower servicing cost—without breaking compliance.

That’s why Guidewire Connections continues to matter long after the lanyards are packed away. It’s one of the few places where carrier leaders, system integrators, and InsurTech teams can compare notes on what’s actually working inside core systems and distribution workflows. And it’s exactly where companies like Zelros chose to show up—because the market has moved from “AI pilots” to AI in production.

This post is part of our “AI for Event Management: Conference Intelligence” series, where we look at how to plan, run, and measure conferences using AI—while also extracting the business intelligence hidden inside the event itself. Guidewire-style gatherings are a goldmine for that.

Why Guidewire Connections is an AI signal—not just an event

If you want a fast read on where insurance tech is headed, follow the conversations at platform conferences. They compress a year of strategy into a few days of demos, partner meetings, and hallway reality checks.

Here’s the core signal: AI is no longer a standalone product category. It’s being embedded into the workflows insurers already live in—policy administration, billing, claims, CRM, agent portals, and contact centers. That embedding is what makes AI “real.” It’s also what makes conference intelligence valuable, because you can track:

• Which workflows vendors are targeting (quote, FNOL, renewal, endorsements)
• Which operating models are winning (agent assist vs. self-serve vs. back-office automation)
• Which metrics buyers care about (conversion, retention, cycle time, loss adjustment expense)

From the RSS source, Zelros positioned itself in this exact lane: a vertical AI platform using reinforcement learning and generative AI to recommend products and messaging in real time, with reported outcomes such as 30% improvement in acquisition/cross-sell/up-sell and up to a 50% increase in quote conversion.

Those are the numbers people look for on the expo floor because they translate directly into budget approvals.

The conference intelligence angle

For event planners and sponsors, Guidewire Connections also highlights a trend: buyers come to conferences with a shortlist. They’re not browsing; they’re validating.

That changes how you should plan your event presence:

• Your booth isn’t a billboard—it’s a decision room.
• Your best content isn’t a whitepaper—it’s a 5-minute workflow story.
• Your success metric isn’t badge scans—it’s qualified meetings and next-step commitments.

What insurers are trying to solve right now (and where AI fits)

The most valuable conversations at insurance tech conferences cluster around a few stubborn problems. AI can help—but only when it’s tied to a specific workflow and a measurable KPI.

1) Distribution friction: quoting and conversion

The easiest revenue lift in many carriers isn’t a new product. It’s reducing the time and cognitive load it takes to get from “lead” to “bound.”

AI that improves quoting typically does three things:

• Next-best-action recommendations (what to ask, what to offer, what to clarify)
• Personalized messaging (tone, channel, and product framing that matches the customer)
• Agent coaching in the moment (nudges, scripts, objection handling)

Zelros’ reported up to 50% quote conversion lift aligns with what distribution leaders want: more wins with the same headcount.

My take: most carriers don’t have a “lead problem.” They have a consistency problem—different agents and channels produce different outcomes for similar customers. Recommendation systems and real-time guidance are one of the cleanest ways to standardize best practices without turning every interaction into a rigid script.

2) Service cost: contact center and back-office load

Service teams are being asked to do more with less, while policyholders expect instant answers. AI can reduce service cost, but only if it’s designed for operational reality:

• Summarize long histories across policies and interactions
• Draft responses and documentation that match compliance rules
• Route work to the right queue with fewer handoffs

This is where generative AI is finally earning its keep. Not by “chatting,” but by compressing administrative work that drains time from complex cases.

3) Claims and underwriting: automation with guardrails

Conferences like Connections are also where you’ll hear the same line repeatedly: “We can’t risk hallucinations.” Fair. But it’s not a reason to avoid AI.

It’s a reason to implement it correctly:

• Use retrieval and grounded generation for policy language and procedures
• Separate “drafting” from “decisioning” (humans approve material actions)
• Log every model input/output for auditability

In practice, many carriers start with AI in underwriting and claims as assistive (summaries, extraction, triage) and only then graduate to automation.

Zelros at Connections: what their positioning tells us

The source article is short and promotional, but the details matter:

• Zelros described a vertical SaaS platform combining reinforcement learning and generative AI.
• They emphasized real-time product and messaging recommendations.
• They pointed to measurable commercial outcomes: 30% improvement in acquisition/cross-sell/up-sell and up to 50% higher quote conversion.
• They named recognizable enterprise customers (global insurers and banks).

Here’s what that positioning tells me about the market:

1. Personalization has moved from marketing to distribution operations. It’s not just “targeting campaigns.” It’s “help the agent say the right thing right now.”
2. Reinforcement learning is a quiet differentiator when you have enough interaction data to learn which actions lead to better outcomes.
3. Vendors are being forced to lead with ROI metrics, not model specs. No one is buying “GenAI.” They’re buying conversion lift, retention gains, and lower service cost.

A practical lens: where this kind of AI sits in the stack

For carriers running Guidewire, the question is rarely “Do we want AI?” It’s:

• Where does it integrate into the producer experience?
• How does it get context (policy data, product rules, customer history)?
• How do we control risk (PII, consent, model drift, audit trails)?

When an AI vendor shows up at a core-platform conference, they’re implicitly saying: “We can fit into your ecosystem without making you rebuild it.” That’s a strong message—if it’s true.

How to use AI for event management at insurance conferences

Conference intelligence isn’t just a theme for attendees; it’s a competitive edge for sponsors and event planners.

Build an “AI meeting engine” instead of a booth schedule

The highest-performing teams treat the conference like a pipeline sprint.

A simple approach that works:

1. Define your 3 meeting outcomes (example: confirm use case, confirm data access, book technical deep dive).
2. Pre-score accounts using firmographics + intent signals (industry, region, tech stack, hiring patterns).
3. Auto-personalize outreach by persona (CIO vs. Head of Claims vs. Distribution).
4. Generate a one-page “workflow brief” per meeting so your team walks in aligned.

This is attendee matching with a purpose: not “networking,” but deal progression.

Use AI to optimize agendas in real time

If you’re sponsoring, you can’t attend everything. If you’re organizing, you can’t predict everything. AI helps both sides.

Conference schedule optimization ideas that are actually useful:

• Recommend sessions based on current pipeline stage (technical sessions for late-stage deals, vision sessions for new prospects)
• Detect overlapping interests and propose “micro-meetups” (15-minute topic huddles)
• Predict no-show risk and overbook intelligently (with guardrails)

Make post-event analytics non-negotiable

Most teams still do post-event reporting like it’s 2015: badge scans, email opens, and a vague “good conversations.” That’s how budgets get cut.

Better post-event analytics look like this:

• Meeting-to-opportunity conversion rate
• Opportunity acceleration (days shaved from stage progression)
• Top 10 objections heard (clustered by AI from notes)
• Top 10 feature requests (tagged to product backlog)
• Content impact (which assets were referenced in meetings that progressed)

If you only measure leads, you’ll optimize for volume. If you measure movement, you’ll optimize for revenue.

What to ask vendors at Guidewire-style events (a buyer’s checklist)

When you’re evaluating AI solutions for underwriting, claims automation, or customer engagement, conference demos can be misleading. You need questions that expose implementation reality.

Here’s a checklist I’ve found effective:

1. What decision is the AI influencing, and who remains accountable?
2. What data is required on day one vs. month six?
3. How do you prevent “confident wrong answers” in regulated workflows?
4. Can we control outputs by product, jurisdiction, and appetite rules?
5. How do you measure ROI—what metric moves first, and how fast?
6. What’s your audit trail: prompts, sources, outputs, approvals?

For AI in insurance, governance isn’t a side feature. It’s the product.

The next 12 months: where conference conversations are headed

As we head into 2026 planning season, three themes are likely to dominate insurance conference agendas:

• AI embedded in core workflows (less “innovation theatre,” more operational adoption)
• Model governance and security as buying criteria (especially in enterprise deployments)
• Distribution productivity as the fastest ROI path (quote conversion, retention, cross-sell)

Events like Guidewire Connections will keep acting as a pressure test: if a solution can’t integrate, can’t prove ROI, or can’t satisfy risk teams, it won’t survive beyond the demo.

If you’re planning your next conference presence—or deciding which conferences to attend—treat it like a conference intelligence project. Capture signals, run post-event analytics, and turn conversations into a roadmap.

The insurers who win with AI won’t be the ones with the flashiest models. They’ll be the ones who operationalize AI inside the workflows that make money.

Next step

If you want to apply AI for event management to your next insurance conference—attendee matching, meeting prioritization, schedule optimization, and post-event analytics—start small: pick one workflow and one metric, then instrument it end-to-end.

Which conference KPI would you most like to improve in 2026: qualified meetings, pipeline acceleration, or conversion from demo to pilot?