Why This Global Event Agency Moved AI Into the C-Suite
INVNT has created a global chief AI officer role and named James Nicholas Kinney to lead it. This is a signal to event and hospitality leaders: AI is no longer a side project. It's a core operating system that touches creative, production, data, and client outcomes.
The agency isn't new to tech-forward experiences. INVNT previously worked with General Motors on a digital CES keynote that moved audiences through mixed-reality scenes built with Unreal Engine. The new role extends that mindset from one-off activations to everyday practice.
From pilots to platform
Most teams test AI in pockets-copy assists here, predictive modeling there. INVNT is embedding it across the business: strategy, creative development, production workflows, and data systems. The goal is a consistent way to learn from audiences before, during, and after events, and feed those learnings back into design and delivery.
Why AI belongs in the C-suite
Kinney's stance is direct: AI changes how agencies operate, create, and compete, so leadership must own it. This isn't a tools question; it's a business model question. Security, governance, and data privacy are central-not downstream tasks to delegate.
"Human data is effectively permanent now," he notes. Decisions about capture, storage, use, and protection set the tone for trust and regulatory exposure for years. Creativity is both tool-dependent and tool-agnostic, which means leaders need to guide adoption without losing human judgment or ethical clarity.
The biggest gap: usable event data
Events generate enormous signals-registrations, dwell time, content choices, movement, interactions, and post-show behaviors. Most of it goes dark. "Data is storytelling," says Kinney, and right now the story often stops at impressions and satisfaction scores.
What's changing is the ability to interpret micro and macro moments across an experience and take action. Think session-level content adjustments, staffing shifts based on live flow, and post-event personalization that continues the conversation without feeling intrusive.
INVNT's year-one AI priorities
- Build end-to-end data structures that connect registration, content, on-site behavior, and post-event engagement.
- Define measurement models that tie experience design to revenue and retention outcomes.
- Stand up AI governance: security reviews, model access, approval flows, and bias checks.
- Operational maximalism: automate repetitive prep and reconciliation so producers and creatives focus on craft.
- Creative augmentation: AI-assisted concepting, content versioning, and real-time show intelligence.
- Education at scale: shared language, playbooks, and role-based training so teams learn continuously.
Or as Kinney puts it, "We are transforming the way we work and cultivate so we can put a brand on the moon."
What this means for jobs on your team
"We see AI as a multiplier," he says, "amplifying teams and missions rather than replacing human-first creative and production roles." The intent is simple: offload busywork, expand creative options, and keep humans in the loop for taste, ethics, and client context.
His favorite analogy: "AI should feel like gravity-always there, shaping the environment-but never replacing the multimodal human experience that makes live events meaningful."
A culture-first approach
Kinney's background in people and culture informs his framework. He favors "human in the loop" systems: people make the calls, AI assists. That mindset keeps adoption grounded in behavior change, shared language, and clear incentives-not feature chasing.
What event and hospitality leaders should do next
- Start with purpose: write down the outcomes your event must drive (pipeline, retention, learning, community) and design measurement to match.
- Map your data lifecycle: pre, during, post. List what you collect, where it lives, who owns it, and how it's used. Kill anything you don't need.
- Set guardrails early: consent language, data minimization, retention timelines, and approval workflows for new AI tools.
- Adopt a simple measurement model: define the 5-7 metrics that matter and how AI will help you report and act on them.
- Build your AI stack: content assistance, audience insights, routing and scheduling, and on-site signal capture-integrated with your CRM and event platform.
- Pilot in low-risk areas: agenda recommendations, session tagging, asset versioning, and debrief analysis.
- Upskill your team: train producers, creatives, and ops on prompts, review standards, and data handling. Create a clear RACI for AI tasks.
- Document everything: naming conventions, prompt libraries, QA checklists, and rollback plans.
- Protect trust: make privacy visible, give attendees control, and communicate how insights improve their experience.
For governance structures you can adapt, review the NIST AI Risk Management Framework from the U.S. National Institute of Standards and Technology (NIST AI RMF).
Tech should add color, context, and texture
Kinney warns against novelty for its own sake. Unnecessary tech creates noise and extra load on staff. The filter is simple: Does this add color, context, or texture to the moment? If the human senses don't respond, cut it.
What this signals for the industry
Expect more agencies to give AI a seat at the table where strategy, security, and client value are decided. Events will be judged not just by how they look and feel, but by what they learn and how responsibly those learnings inform future experiences.
As Kinney says, "People are data," not to reduce anyone, but to respect that every movement, reaction, and choice contains insight. The agencies that win will convert human energy into learning while protecting trust at every step.
Quick checklist for your next show
- One sheet: the 7 metrics that define success and where each comes from.
- Consent language: clear, concise, and attendee-first.
- Model access: who can use which AI tools, and for what tasks.
- Creative QA: human review gates for anything AI-touched that goes public.
- On-site signals: plan for session scans, dwell, and feedback you'll actually use.
- Debrief pipeline: 48-hour analysis that feeds sales, marketing, and content teams.
Level up your team's AI fluency
If you need a structured way to upskill producers, creatives, and ops, explore role-based learning paths here: AI upskilling plans by job function. Keep the training practical: prompts, review standards, data handling, and live-event use cases.
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