Already, we$$re seeing features that add AI$-$powered feedback to

familiar tools. On Zoom there$$s an $$AI companion,

$$ helping you

catch up when you arrive late to a meeting, and on Teams,

$$Copilot$$ will help you summarize key discussion points. Read AI

$($a smart AI tool that records, transcribes, and analyses

interactions and incorporates into summaries$)$ goes further,

measuring meeting participants$$ engagement, sentiment$,$ and

airtime. These applications can be integrated into work so

seamlessly users begin to use them almost without realizing.

These tools offer productivity and feedback benefits, but there are

also downsides to them joining our conversations. If people

outsource their listening wholesale to the technology, skipping

the work of thinking through for themselves the key messages,

then meetings may be efficient, but understanding and

commitment to act might well be lacking.

From what we see, conversations about how we should have AI$-$

enabled conversations are absent in many organizations,

alongside discussions about mitigating risks and maximizing

benefits. Incurious acceptance of the technology and uncritical

implementation means that missteps $$ when topics or people are

silenced $$ are not $$intelligent failures,

$$ to use Harvard Business

School professor Amy Edmondson$$s phrase. Conversational

failures are inevitable when we venture into new territory,

however they are only intelligent if we do our homework before

we experiment, engage in thoughtful consideration as we make

choices and anticipate outcomes.

Building on our decade of research into speaking truth to power,

this article champions the need to pay attention to how we talk

when using AI and how we talk about AI$.$ It highlights five highly

intertwined areas of opportunity and potential problems arising

from how technology is used in the specific context of an

organizational culture with its habits, ingrained through

perceptions of power differences, of who has the right to speak

and be heard.

Who Speaks and Gets Heard

We all have suspicions about who gets airtime, gets interrupted,

and interrupts in meetings. AI allows us to move from suspicion

to fact, by providing hard data around the share of voice.

With curiosity and positive intent, those who take airtime might

be motivated to dial it down, creating space for others. In a

conversation Megan had with David Shim, CEO of Read AI$,$ he

told the story of a venture capital executive shown data through

the app that revealed he had spoken for $80\%$ of a pitch $$ too

much, given the aim of the meeting was to hear about the

potential investment in detail. His raised awareness meant he

spoke less and listened more in subsequent conversations.

But the reasons for speaking a lot or a little are complicated. If you

simply focus on the amount of contribution as a conversational

goal, people who don$$t want to speak or who contribute by being

active listeners, might get sucked into speaking when they have

nothing they want to say.

The quiet ones might also be those who least trust being recorded

and having their words made permanent. The AI tool could raise

the stakes for speaking up$,$ ingraining patterns where the most

secure and powerful voices dominate.

What Gets Said and Heard

In virtual meetings, many are busy taking notes, creating their

record of what has been said, so missing visual signals and relying

on listening biases. Only afterwards do they realize there are

multiple perspectives on what was said and agreed upon that

don$$t align to what is in their notebook. Now an AI bot can do the

note$-$taking, produce the summary, and list action points and

responsibilities, and we can turn our full attention to the people

on screen $$ with just one shared version of what was said

available to all.

However, subjects such as failures, mental health, or criticism of

strategy could be pushed underground as people silence

themselves because they fear that their perspective will be put

onto the public, permanent record. This may especially be the

case if AI attaches subjective labels, such as $$low sentiment$,$

$$

which could be translated as shorthand for critical or

unenthusiastic, when someone raises a particular subject. While

actionable positivity is important, so is tentative uncertainty, and

our work frequently highlights situations where more doubt and

humility would have contributed positively to the quality of

conversation.

To address this, Read AI announces itself in the meeting and

allows participants to opt out from recording and delete data

before reports are generated. In a world without social hierarchy

this would be fine, but in workplaces filled with it$,$ the question is

whether employees would feel empowered to make that choice

when their boss is running the meeting?

When We Speak and Listen

As humans, our energy varies through the day and from day to

day. AI can factor in these consider