Rise of AI and the Readiness Gap.
By Tracy Judge, Founder & CEO, Soundings
A year ago, Soundings set out to answer a question we couldn't find good data on anywhere: how is AI actually changing the way people work in business events, not in theory, but in practice? Four survey phases and twelve months later, we have an answer. It's not the one I expected.
AI adoption in this industry didn't stall. It surged. What's lagging is everything that's supposed to come after adoption: the training, the workflow redesign, the organizational rethink that turns a new tool into a new way of working. That gap between how fast we've picked up AI and how slowly we've prepared for what it actually requires is the story of this report.
Adoption is accelerating. Transformation is lagging.
AI usage in our industry climbed from 59% to 77% over the past year. That's not incremental change, that's mainstream adoption happening in real time. But more people using AI isn't the same as an industry becoming AI-ready. The conversation has shifted from whether to use these tools to how to use them well, and most organizations haven't caught up to that second question yet.
Engagement is driven by practicality, not innovation.
When we mapped how people actually engage with AI, the results didn't point to a workforce chasing disruption. They pointed to one solving immediate problems. The largest segment of respondents, the Resourceful Realists, are using AI to get more done with what they already have, not to reinvent how they work. Innovation-driven adoption exists, but it's the minority position. This industry is pragmatic first.
AI is accelerating outputs, not transforming workflows.
The tasks seeing the heaviest AI usage, content creation, marketing, chatbots, are exactly the ones that are easiest to adopt and fastest to show a return. Operational work like logistics, scheduling, and budget tracking lags far behind, because it requires deeper integration into systems that weren't built with AI in mind. We're in an efficiency-first phase. The next stage of maturity won't come from more tool access. It will come from organizations actually operationalizing what they've adopted.
Commitment isn't at the scale adoption requires.
Here's the number that should concern every leader in this industry: more than 80% of professionals will put less than 10% of their learning and development budget toward AI training this year, and nearly 70% will do the same with technology spend. We are using these tools faster than we are investing in the people and systems needed to use them well. Enthusiasm has outpaced commitment.
Workforce strategy hasn't kept pace either.
Most organizations are integrating AI into the roles and teams they already have. Far fewer are rethinking team structure, hiring models, or how work should actually be organized around the flow of tasks rather than legacy job descriptions. AI is accelerating faster than the workforce strategy needed to support it, and that mismatch is where risk is quietly building.
Here's what I take from a year of this data.
The next phase of transformation in this industry won't be won by whoever adopts the most tools. It will be won by whoever gets honest first about the gap between adoption and readiness, and starts closing it deliberately: understanding how people actually engage with AI, investing in the training that adoption alone doesn't provide, and redesigning workforce structures around real work instead of static roles.
That's the work ahead. I'd rather we get there intentionally than get pulled there by circumstance.
If you want the full picture, including the AI Engagement Persona breakdown and everything behind these numbers, the complete 12-Month Benchmark Report is available now. I'd love to hear what stands out to you in it.