The conversation about AI in events has been loud for a while. At Cvent CONNECT 2026, three financial services event leaders had a more practical conversation: what AI is doing right now inside real teams managing governance, data quality, adoption, and pressure to move faster.
Key lessons in AI from financial services event leaders
The panelists’ discussion laid out a repeatable path for event and marketing leaders, showcasing how AI in financial services events can create capacity, improve trust, and sharpen decision-making:
- AI creates the fastest capacity gains by reducing repetitive work such as data cleaning, list reconciliation, note summaries, and first-draft reporting.
- Data quality is the foundation: clean inputs make every downstream use case more reliable.
- Governance is a process, not a permanent blocker. Teams can create a review path that answers security, compliance, and defensibility questions.
- Adoption grows when leaders connect AI to painful work, create change champions, and reinvest the time saved.
- The highest-value outcome is often a better decision, not simply a faster task.
Now, let’s dive into each lesson and explore how financial services event leaders can put these ideas into practice.
Where does AI create the fastest capacity gains for event teams?
Most event AI conversations begin with personalization. The panelists described a more immediate opportunity: remove the manual work that drains capacity before an event begins. Opportunities for fast capacity gains included:
- Clean attendee records and surface typos, omissions, and inconsistencies.
- Cross-check rooming lists against hotel requests, air manifests, and confirmation lists.
- Summarize meeting notes and create first drafts of weekly reports.
- Analyze survey feedback across the full dataset instead of relying on the loudest voice in the room.
Their examples were intentionally unglamorous but highlighted the workflows that give teams hours back every week.
AI is not replacing the event professional. It is changing where we spend our time.” — Joel Reilly, Executive Director of Public Affairs Strategy and Transformation, Wells Fargo
This distinction set the tone for the discussion: AI surfaces patterns and drafts options, while people decide what is relevant, appropriate, and strategically sound.
Why does data quality come first for AI in financial services events?
The speakers approached data quality from different vantage points, but they landed in the same place. Before an event team asks AI to personalize an experience, summarize feedback, or recommend a portfolio decision, it needs confidence in the underlying records.
A typo in an attendee profile, a mismatch between a rooming list and a hotel request, or a missing field can create friction onsite and undermine trust in every output that follows.
The practical starting point is to inventory the sources that feed the workflow, compare them against one another, and use a controlled AI workflow to surface inconsistencies and gaps for human review. The goal is not to automate judgment. It is to make exceptions visible earlier, measure how often they occur, and establish a cleaner foundation for the next use case.
This is why the panelists treated data cleanup as an immediate value opportunity rather than a prerequisite that teams should postpone. It creates measurable time savings now while making later applications more reliable.
How can financial services teams make governance a process rather than a verdict?
For financial services teams, governance is where AI conversations often stall. The panelists described a shift from asking whether a tool can produce an impressive demo to asking whether the organization can explain, defend, and audit what the tool produced.
That means the conversation must include data residency, model-training boundaries, retention, prompt-injection protections, audit logs, intellectual-property terms, and the point at which a human must review an output.
The demo question is, ‘Can it generate something impressive?’ The enterprise question is, ‘Can I defend what it generated?’” — Richard Stoia, Manager of Experiential Technology & Innovation, Capital Group
The panelists connected that governance work to a broader operating-model challenge: teams are managing more third-party dependencies, more technology requests, and higher expectations for speed at the same time. What’s more, there’s the human dimension to consider: adoption cannot be separated from trust.
Taken together, the speakers’ message was not that governance should be relaxed but that teams should build a clear path through review so governance moves the work forward instead of leaving it in limbo.
How to build a team that actually uses AI
The discussion moved beyond tools and into team behavior: knowing that AI can help and building a team that consistently uses it are two different challenges. For adoption to stick, leaders have to make clear choices about where AI applies, which work can be deprioritized, and how the time saved will be reinvested. Without shared standards for adoption, AI risks becoming another disconnected innovation project.
That is why the panelists described embedding AI into the rhythms of work already underway within their teams, including meetings, one-on-ones, and annual gatherings. The same principle is reflected in CventIQ, which brings AI into the workflows teams already use rather than treating it as a separate tool.
This practical approach also shaped who became the most credible change champions. They were not necessarily the most technical people. More often, they were the people closest to a painful workflow, and therefore best positioned to judge whether an AI-generated output was genuinely useful.
From there, teams created simple structures to make experimentation visible and repeatable. One panelist described a four-tier adoption framework with 50+ AI Champions across its marketing organization. Another shared real wins, honest frustrations, and practical prompts through an internal newsletter. Although the tactics differed, the common thread was the same: people had permission to learn in public and improve through repetition.
AI won’t take your job. But someone who knows AI better than you may.” — Todd Sloan, Director of Event Services, Nationwide
That line works because it is both a warning and a permission slip: teams do not need to have every answer before they begin, but they do need to create regular opportunities to practice.
When does AI change the decision, not just the task?
Time savings were the baseline, not the finish line. One panelist described survey analysis becoming more objective because AI could surface patterns across the full dataset instead of reflecting whichever interpretation was most persuasive in the room.
They also described connecting operational data, stakeholder feedback, project updates, and team-capacity signals that no individual had enough bandwidth to synthesize. In those moments, AI did more than produce a faster summary; it changed what leadership could see and therefore changed the decision that followed.
The bigger opportunity is portfolio intelligence, helping teams answer whether they’re investing in the right events, with the right capacity, for the right business outcomes." — Richard Stoia, Capital Group
That is the broader opportunity for financial services event teams: use AI to make the portfolio more legible. The question shifts from “How do we do more with the same team?” to “Do we have the right operating model for what we are now managing?”
What is a practical 30-day starting plan for a financial services event team?
The real opportunity for financial services event teams is to use AI to turn disconnected event data into clearer, more strategic decisions. The panelists’ discussion points to a practical sequence for getting there: start with work that drains team capacity, improve the quality of the data behind it, establish governance that makes outputs defensible, and give people a structured way to test and adopt the workflow.
The goal is not simply to save time. It is to help teams make clearer decisions about their events, resources, and operating models.
The 30-day plan below translates those themes into a practical starting point for implementing AI:
- Days 1–5: Choose one high-friction workflow, document its current steps, and establish a baseline for time, error rate, and review effort.
- Days 6–12: Clean and reconcile the underlying data. Record the exceptions that require human judgment.
- Days 13–19: Define the governance checklist, approved tools, data boundaries, and human-review points.
- Days 20–26: Run a small pilot with a change champion and collect examples of useful, incorrect, and questionable outputs.
- Days 27–30: Review the results with stakeholders, decide whether to scale, and document where the recovered time will be reinvested.
Want to keep exploring?
AI adoption does not have to happen all at once. For a practical introduction to AI use cases, prompting, and responsible adoption, start with Mastering AI for Events: 2026 Guide. Then, browse the AI Learning Center for checklists, best practices, and practical resources for using AI in event planning and marketing.
For a financial services perspective, visit the Financial Services Industry Learning Center for resources on privacy, personalization, reporting, and event strategy.
