During the InfoComm Latin America On the Road event held in Buenos Aires, Shure focused on a new challenge for collaboration spaces: preparing meeting rooms so that artificial intelligence can receive reliable information.
Arthur Fabrício, the brand’s Market Development Manager for Latin America, analyzed why audio quality, video, and participant identification are beginning to become part of the infrastructure required to take advantage of new AI tools.
When AI also participates in the meeting
The incorporation of artificial intelligence is quietly changing the dynamics of corporate meetings. Automatic transcriptions, summaries, task assignments, translations, identification of decisions, and the generation of insights are beginning to become part of everyday work. But behind all these possibilities lies a basic condition: AI needs to receive information that is clear enough to interpret.
That was the starting point for Shure during its participation in InfoComm Latin America On the Road, produced by AVIXA and held in Buenos Aires. Speaking to AV industry professionals, Arthur Fabrício presented a perspective that shifts the conversation from software capabilities to something that happens much earlier: capturing the meeting.
“AI does not correct poor capture: it infers from it. If the input fails, the reasoning also fails,” Fabrício stated during his presentation.
Until relatively recently, evaluating a videoconferencing room primarily meant asking whether participants could hear and see each other correctly. The incorporation of AI now adds another user to that equation: systems that process conversations and subsequently turn them into actionable information.
A word captured incorrectly, a voice attributed to the wrong person, or a conversation affected by noise and reverberation can have consequences that go far beyond a poor videoconferencing experience. When that information feeds automated processes, it can also alter a summary, a decision, or the assignment of a task.
“The difference is that now it is not only people who are listening to us: a system is also listening to us, and it is going to transform that conversation into notes, tasks, decisions, and insights,” Fabrício explained.
From a good room to an AI-Ready room
Shure’s presentation thus introduced the concept of an AI-Ready Room, which does not refer to a specific function or to the isolated addition of artificial intelligence. Instead, it involves designing the space while considering from the outset the quality and consistency of the data that these tools will subsequently use.
The proposal includes consistent voice and facial capture throughout the room, controlling noise and reverberation at their source, accurate speaker attribution, and integration with the platforms used by each organization. It also incorporates a variable that is often difficult to anticipate: people’s actual behavior, as they move, interrupt, speak simultaneously, and do not necessarily use spaces in the ideal way envisioned during the design process.
“Stop asking what AI can do. Start asking what it can hear and see,” Fabrício summarized.
This change in perspective also introduces a particularly interesting question for integrators and technology managers: where should artificial intelligence not listen? Determining which areas should remain outside the capture process also becomes part of designing a room prepared for these new applications.
Audio and video therefore cease to function solely as support for communication between people. They become a data input layer that can subsequently feed transcription, analysis, and automation systems.
Consistency becomes part of the infrastructure
The challenge takes on another dimension when it moves from a single room to dozens or hundreds of spaces within an organization. Using the same AI platform does not necessarily guarantee equivalent results if capture conditions vary significantly from one room to another.
Microphones positioned too far away, air conditioning, reverberation, uneven coverage, or overlapping voices can introduce variations that later appear in transcriptions and, based on those transcriptions, in automatically generated processes.
“At an enterprise scale, room variability stops being a technical issue and becomes a business risk,” Fabrício warned.
This is precisely where Shure positions its longstanding expertise in voice capture within a much broader conversation. The company proposes thinking about collaboration standards according to the type of space—from small and medium-sized rooms to executive rooms, classrooms, or flexible environments—seeking to maintain consistent input quality regardless of where the meeting takes place.
It is therefore not about adding AI to AV equipment, but about ensuring that platforms that already incorporate these capabilities can work with reliable information. As tools such as assistants, agents, automatic summaries, and tracking systems gain a greater presence within organizations, the physical infrastructure that captures every conversation takes on a significance it did not have before.
“Clean audio and consistent video are not luxuries. They are infrastructure for AI,” Fabrício stated.
Shure’s presentation in Buenos Aires thus left a much more interesting question than the usual discussion about what the next generation of artificial intelligence will be able to do. Before expecting better summaries, automated decisions, or smarter assistants, perhaps we should look up, at the table, and around the room, and ask ourselves something much more fundamental: what information are we giving them to work with? For Shure, the answer begins there: by designing rooms capable of listening and seeing with the precision that artificial intelligence now also requires.
