When I re-read the title of this piece, I thought to myself: “That sounds like a pretty good description of Hell.” But trust me, I actually meant for the title to describe something potentially good.
What does a never-ending meeting look like? I think we’re getting a sense of it from some of the new AI-powered features that collaboration vendors are starting to add to their meetings software. One example comes from cloud video provider Blue Jeans, which offers a platform called Smart Meetings, whose features start with automatic transcription, off of which it builds such capabilities as:
- In-line action items that can be tagged with assigned task owners and alerts, to increase the potential for effective follow-up
- Automatically curated video highlights and emailed meeting summaries
- Attendee-provided ratings that can be attached to critical ideas or moments within a meeting
- Social tagging and other opportunities to contribute to the discussion post-meeting
If these new types of features are implemented effectively—whether in the Blue Jeans product or others—and if they’re adopted appropriately in the enterprise, they could change the culture of meetings. People who can’t make it to a meeting can still keep up, and additional input can be sought from those who the organizers didn’t think to invite in advance. Good ideas have a better chance of being followed up and brought to fruition by the people who are best suited for the task. Nagging issues that never really get solved can be the centerpiece of ongoing discussions that happen both in meetings and in the team collaboration applications that will likely host these ongoing topic-based chats.
So in that sense, a meeting might never end. It can spur online discussions that interact directly with a meeting video or transcript, or use portions of the meeting as raw material for additional work. And the meeting’s attendee list will never be complete, as any team member with something to contribute can join the post-meeting discussion.
This won’t all be a completely good thing. While the software promises automated summaries that avoid the need to watch a whole meeting, there’ll still be lots of opportunities to get bogged down not just in the meetings you go to, but also the meetings you missed. And context will be critical; I think most enterprise people will want to start out skeptical of the AI’s ability to truly represent a meeting’s discussions not just accurately but with accurate context. Social rankings may add the more nuanced human element to the evaluation—but with the attendant human biases as well.
And because all of this involves human beings, there’ll be lots of occasions where people get the wrong idea—say, based on a summary that just didn’t capture some critical nuances of a conversation. And each enterprise will have to ensure its system is finely tuned so that the AI captures the subtleties and peculiarities of its industry, relationships, and values. AI is getting better all the time, but I honestly don’t know when it will truly reach this ultimate level of sophistication.
In short, some powerful and promising new tools are on the way for meetings, but we’re still at the beginning of the process for learning how to use these tools most effectively, and how to train the tools themselves to do the job they’re supposed to do.
But we all want meetings to be better, so this will be an exciting area of workplace strategy to follow in the coming years.