August 21, 2026
Keep the Meaning Human
We built ITT around a simple belief: a story can open something in a person that a slide deck never will. The reflection that follows is personal. The conversation is human. The decision about what to carry forward belongs to each participant. So when people ask me where AI fits into all of this, my answer starts there, with what we refuse to let it touch.
The story is human. The reflection is human. The conversation is human. The choice about what matters is human. AI works behind the scenes, and only where it helps people and organizations make more of what they've already created.
Consider what happens when someone finishes an ITT Experience. They've sat with a story, answered a few reflection prompts in their own words, and chosen something to carry forward. We can use AI to bring those responses together into a concise personal summary, not to interpret the person, not to tell them what to think, and certainly not to diagnose them. The participant creates the meaning. AI simply reflects it back in a way that's easier to revisit, keep, and return to.
Now imagine that same experience across an annual conference: hundreds of participants, dozens of group conversations, hundreds of commitments to carry forward, and weeks later, follow-up reports on what actually happened. Without AI, making sense of that volume of open-ended response would take enormous staff time. Important patterns would be missed. And too often, rich answers would get reduced to simple counts simply because counts are easier to analyze than language.
That's where AI changes what's possible for us. It helps us synthesize large volumes of participant-created response and surface recurring patterns: what captured people's attention, what they kept returning to, what they realized, why the conversations mattered, what they chose to carry forward, and, later, what they reported happening.
We use it to create, on demand, an end-to-end loop: from the story a person reads, to the reflection they write, to the conversation their group has, to the intention they set, to the follow-up they complete weeks later, and finally to a report an organizer can actually use. Each step is created by people. AI helps the steps connect into something a whole organization can learn from.
Organizations already collect enormous amounts of data — surveys, assessments, attendance, performance measures. AI gives us a way to work with a different kind of information: the words people use when they describe what they noticed, the ideas that stayed with them, the questions they're still thinking about, the actions they chose for themselves. At scale, those responses become a richer picture of what's happening across an experience.
The boundaries matter to us. AI doesn't decide what a participant should believe. It doesn't replace reflection or conversation. It doesn't determine whether someone succeeded or failed. It doesn't manufacture consensus or turn individual responses into certainty about an entire organization. The original, participant-created response is always the foundation. AI supports interpretation and synthesis within defined limits, to help people see more clearly, not to overstate what the information can prove.
We believe the future isn't human experience or artificial intelligence. It's learning to use AI in ways that make human experiences more valuable. Stories create recognition. Reflection creates understanding. Conversation creates perspective. People decide what matters. AI helps make sense of what emerges.
Keep the meaning human. That's the principle. AI does the rest, where it adds real value.
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