ResearchPod Summary
This report investigates the integration of artificial intelligence within the event planning industry, a sector historically characterized by slow technological adoption. Through a survey of 124 experienced meeting professionals conducted between August and September 2025, the study examines current usage patterns, motivations, and obstacles planners face when adopting generative AI tools.
The survey reveals a high level of enthusiasm among seasoned professionals, with 79 percent of users reporting immediate productivity gains or time savings. OpenAI’s ChatGPT is the dominant platform, utilized by 80 percent of respondents, while Microsoft Copilot is used by over half. Currently, usage is concentrated on general productivity tasks, such as brainstorming, note-taking, and content summarization. While the market for task-specific AI tools remains highly fragmented and unfamiliar to many, there is strong demand for future applications in industry-specific areas like hotel sourcing, contract negotiation, and personalized event management.
Planners are driven by the need to streamline operations as their responsibilities expand, with 91 percent anticipating that AI will improve overall efficiency. However, the adoption process is marked by caution. Major barriers include concerns over data security and privacy (reported by 70 percent of participants) and doubts regarding the accuracy and quality of AI-generated content (78 percent). Additionally, a notable segment of the industry is contemplating the broader implications of AI, including potential job displacement for entry-level roles and the environmental impact of increased computing demands.
Despite these concerns, the professional consensus is that AI will allow planners to delegate logistical burdens to technology, thereby creating space for more strategic and creative decision-making. Respondents express optimism that AI will eventually enable more personalized attendee experiences and better real-time data analysis, provided that human oversight remains central to the process.
Sam: Experienced event planners are embracing AI, but their enthusiasm is not a performance test. In this survey, most already use it, and most users say it helps them finish tasks faster. The distinction is between reported usefulness and demonstrated gains.
Alex: That sounds like a shift for a profession the report describes as slow to adopt technology. Where does this evidence come from?
Sam: It’s an industry survey report from Northstar Meetings Group, sponsored by Visit San Jose, called “Buying In: AI Adoption in Event Planning.” It draws on 124 qualified responses from professional meeting and event planners.
Alex: For researchers studying technology at work, the question is whether adoption means meaningful change, rather than experimentation. And for people organizing events, it’s where these tools might actually help.
Sam: We’ll separate what planners already do from what they hope to do, then ask how much the evidence supports. The useful tension is that enthusiasm and concern appear together.
Alex: Start with what was uncertain. Does the report challenge an earlier finding, or mainly a belief about this industry?
Sam: Mainly an industry reputation. The report describes meeting planning as traditionally slow to embrace new technology, despite extensive discussion of AI’s potential. Its contribution is a snapshot of actual reported use and attitudes, not a measured trend against an earlier survey.
Alex: So we should not hear “adoption is accelerating” just because the current level looks high. Who answered the survey?
Sam: The respondents include corporate, independent, and association planners, with smaller groups in other categories. Nearly half have worked in meeting planning for more than two decades. The sample also skews toward older professionals, rather than people just entering the field.
Alex: That makes the enthusiasm harder to dismiss as simply newcomers trying new tools. But experience alone doesn’t tell us whether the sample represents the profession.
Sam: It doesn’t. The online survey ran in late summer 2025. The report gives the field dates and respondent profiles, but not enough recruitment or response-rate information to judge representativeness. I’d read it as evidence about these respondents, not a census of the industry.
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Alex: What did the survey ask that helps distinguish genuine use from a generally positive attitude toward AI?
Sam: It separated current use, future intentions, perceived benefits, and concerns. It covered generative AI, meaning software that produces content from prompts, alongside tools designed for particular tasks. It also asked which work activities people use them for.
Alex: Give us the headline adoption result, but keep the unit clear. Are these individual planners using AI, or organizations?
Sam: Roughly four in five said they or their organization currently use it for work. That wording matters: it does not establish how frequently each respondent personally uses a tool. Another group intended to start soon, while a minority reported no current use.
Alex: And when the report says AI saves time, is that an observation from completed work or the planner’s own assessment?
Sam: It’s the planner’s assessment. Among users, about four in five said AI lets them complete tasks faster. The remaining users expected eventual benefits; none chose the response saying that learning new apps or processes took too much time.
Alex: That last result sounds unusually unanimous. Could someone use AI and still find it distracting, or helpful for one task but costly overall?
Sam: The reported responses don’t resolve those possibilities. The question offers a broad judgment about time and productivity, not a task-by-task accounting. There are no measured completion times or comparisons with equivalent work done without AI.
Alex: So the strongest conclusion is that users perceive value. We don’t know the size of any efficiency gain, or whether the finished work is better.
Sam: That’s the boundary I’d keep. The survey is informative about acceptance and expectations. It cannot establish that using AI caused productivity to improve, and it does not independently assess the quality of the output.
Alex: Let’s move from that broad judgment to actual work. What are planners doing with these tools now?
Sam: Brainstorming leads the current uses, with note-taking, transcription, and summarization also prominent. Data analysis appears too, though less widely. These are largely general workplace activities rather than functions unique to organizing an event.
Alex: Is the next step simply more of those activities, or do respondents want something more specific to their profession?
Sam: They want more specialized help. For example, nearly six in ten hope to use AI for negotiating pricing or contracts. That’s a future-use response, not evidence that AI can already negotiate successfully on their behalf.
Alex: The same distinction would matter for sourcing: finding hotels, venues, or services. Wanting help with that is different from trusting a system to choose a venue.
Sam: The survey also finds strong future interest in sourcing and personal event assistance. It doesn’t test those capabilities or specify how much control planners would delegate. Its useful comparison is between established general tasks and hoped-for event-specific applications.
Alex: Does enthusiasm extend across lots of tools, or does adoption mostly mean using familiar platforms?
Sam: ChatGPT is the clear favorite, followed by Microsoft Copilot. Beyond the major platforms, the report describes a fragmented market. Many respondents had never heard of the selected task-specific tools, while transcription and note-taking tools were more familiar.
Alex: So a planner can be an active AI user without exploring the whole market. Does the report tell us why some people have not started?
Sam: Among nonusers, lack of training and unclear benefits were the leading reasons. Security concerns around proprietary information also stood out. That suggests practical barriers, but it does not show that training alone would bring those respondents into use.
Alex: We’ve heard a lot of optimism. What concerns remain even when people see potential benefits?
Sam: Nearly four in five respondents worried about data quality or false information. That’s especially relevant when the attraction is finishing work faster. The survey doesn’t measure how much checking the output requires, or whether that changes the perceived time saving.
Alex: And event work involves information about businesses and attendees. How prominent is privacy in the responses?
Sam: Seven in ten cited data privacy or security. Respondents also raised bias, environmental demands, and possible effects on jobs. These are reported concerns, not measured harms in the survey.
Alex: The jobs issue seems to cut both ways. Does the report treat working with smaller teams as a benefit or a threat?
Sam: Both perspectives appear in respondents’ comments. Some welcome help with a growing workload; others fear job losses and fewer entry-level opportunities. Those comments reveal competing expectations, not a forecast of what will happen to employment.
Alex: And the more positive comments are about freeing time for relationships and creativity, rather than removing people from events altogether.
Sam: One planner puts it as handling the predictable so they can customize and personalize. That captures the aspiration running through the report. But the survey has not tested whether saved time actually moves into those activities.
Alex: Who should read the full report, and where should they begin?
Sam: Researchers studying workplace adoption, event professionals, and developers of planning tools should read it. Start with “Evolving Use Cases,” which separates present activity from future demand. Then read “Bracing for Drawbacks” and the respondent profile before treating the findings as market-wide evidence.
Alex: And what should everyone else remember?
Sam: These experienced planners report enthusiasm for AI alongside substantial concerns. That is useful evidence of acceptance, not proof of better performance. Adoption tells us people are using a tool; it does not tell us how well it works.
Alex: Thanks for listening.