• AI tools can help reduce the need to attend some work meetings and boost productivity.
• These apps can summarize meetings, answer workers' questions, and train employees.
• This article is part of "Transforming Business," a series on the must-know leaders and trends impacting industries.

Matt Martin knows about meetings run amok.

He's the CEO and cofounder of Clockwise, which aims to help people manage their work calendars so they have more time to get things done — and not just sit in meetings.

Earlier this year, in a bid to be more efficient, he started using an artificial-intelligence tool called Granola to help him take notes in meetings and summarize takeaways and to-dos.

The result for Martin is time saved and "actually pretty damn good notes," he told Business Insider.

Efforts to reduce the sting of meetings are perhaps as old as meetings themselves. Yet the imperative can feel more urgent thanks to our propensity, hardened during the pandemic, to wedge more gatherings into our calendars.

Now, thanks to AI, we might soon have fewer work meetings — or at least attend fewer. It's likely, according to execs leading the development of the technology, that corpulent calendars will be no match for AI-powered notetaking apps capable of being everywhere all at once.

And AI meeting bots won't serve just as digital scribes. They'll resemble all-knowing, indefatigable assistants able to take on tasks like answering questions on our behalf, interviewing job candidates, and training workers, execs told BI.

The boss' avatar

Sam Liang generally has as many as 40 meetings a week.

It's not practical for him to attend each one, so sometimes he sends an AI stand-in. This is easy enough for Liang since he's the CEO and cofounder of Otter, an app that records audio from meetings and produces a real-time transcript using AI.

Liang told BI he uses Otter to forgo some meetings. He then reads the summaries or listens to the recording. Liang expects more leaders will soon do this.

He estimated that perhaps 20% of C-level executives would use AI avatars to attend routine meetings on their behalf by the end of 2025.

In his case, Liang has an avatar that acts like a "personalized agent." Otter trained the AI using seven years' worth of Liang's meetings, along with emails and some Google docs he wrote on topics like product principles, Otter's strategies, and why the company does certain things.

"When people ask me those questions, my avatar can answer probably 90% of those," Liang said.

This knowledge can flow to new hires at Otter. Liang said his AI avatar could use what he's said and written to explain his vision for the company, its strategies, and its origin, for example.

A view of the future

The ramifications of having an ever-present AI available to document our workdays — and beyond — will be similar in scale to that of the introduction of the internet, said Terry Sejnowski, a distinguished professor at the University of California, San Diego, who's a neuroscientist and the author of the book "ChatGPT and the Future of AI."

"Nobody predicted the impact it was going to have on our lives," Sejnowski said. "Same thing here. It's going to take decades."

He said keeping track of meetings and other interactions would go well beyond capturing audio or video. Sejnowski sits on the scientific advisory committee for Softeye, a startup developing glasses intended to work with a smartphone to serve as an AI assistant. Similar attempts have been made, of course. Remember Google Glass?

Ray-Ban Meta glasses allow users to take photos and videos. In September, Meta CEO Mark Zuckerberg said updates to the glasses aimed to let users translate certain languages, scan QR codes, and capture images of what they've seen so they can refer to them later when, for example, they need to buy something.

Softeye's plan, Sejnowski said, is to have glasses that constantly recognize objects and people around the wearer and provide related information. He said they would also take snapshots and store them, along with the time they were taken. That would make it possible, he said, to reconstruct where a user was — and rely on the AI assistant.

"You can ask it questions," Sejnowski said. "Did I promise anything to this person?"

Highlight reels of meetings

Richard White, like so many other desk workers, found himself stuck on endless Zoom calls during the pandemic.

He found it frustrating to take notes, jump to another call, and have little time to clean up his takeaways in between. Plus, White said, even good notes weren't always reliable after too long.

"Do you really remember what was important?" he said.

Four years ago, White started Fathom, a company that uses AI to capture video and generate notes from meetings.

People don't necessarily want a transcript, he said, though it's often necessary for AI to work its meeting magic — including generating notes, making to-do lists, and updating data on customer-relationship management.

White said that what most meeting-goers are after, aside from a list of action items, is a better recall of the ephemeral and unstructured information that's often delivered at these gatherings. Showing up, White said, is often the only way to access it.

He said AI notetakers would be able to produce highlight reels of key meeting moments. The goal, White said, would be to reduce "meeting inflation" by enabling fewer people to attend them while maintaining information flow.

"You'll have an AI that actually goes out and listens to every meeting in your org and comes back and tells you, 'Here's the five minutes of content you should pay attention to today,'" White said.

White said an accessible record of all but the most sensitive meetings within an organization could serve as a basis for identifying gaps in training or generating feedback. That's in part because AI can now accurately discern sentiment and tone — something that's become possible only in the past six months to a year, he said.

Beyond that, he said, AI meeting bots will be able to act on ideas. So if someone in a meeting proposes creating a document, the AI would have a draft ready soon after.

White doesn't expect we'll necessarily each have individual bots that go to meetings on our behalf. He said that would quickly result in meetings swimming with AI avatars.

The best approach, White said, would be to use a "federated" system where all the meetings are accessible. That way, anyone not in the meeting could access the content through a personal agent that lives in the cloud, he said.

White said bosses could ask AI for instances in which a meeting was positive or when participants grew frustrated. A search might take the form of, "Give me a pricing discussion that didn't go well," he said. That goes well beyond parsing a transcript for the word "price," he added.

"The tech is finally there, and it's really good," he said.

An interview with AI

AI could also help document meetings with prospective employees, said Alan Price, the global head of talent acquisition at Deel, a global human resources company that helps employers hire abroad. Price told BI that Deel had begun using AI meeting tools to reduce the time and personnel needed to hire for roles like customer service.

That's important because when Deel posts that type of job, Price said, the company might soon have some 4,000 applications. So Deel uses an AI bot to conduct an initial interview with promising candidates. Then, a recruiter can evaluate the summary of the interview and, if necessary, review the audio and video to determine whether the candidate should move on to an interview with a person.

Price said that rather than spending 30 minutes on a single interview, a recruiter could review five or six interview summaries in that same time.

That bump in efficiency has enabled a single recruiter to hire 30 to 35 candidates within about two weeks, he said.

"The recruiter makes the decision," Price said, "but it's streamlined."

• A few months ago, I started using an AI notetaking app, Granola, in meetings.
• I take notes and then after the call, the AI builds a more fulsome outline of the conversation.
• Taking notes on what's most important helps us get more from meetings, Granola's CEO said.

A few minutes after I'd hopped on a call with a tech founder, he mentioned that he'd started using an "amazing" AI notetaking app.

It was helping him capture the various decisions and to-do's that came up in the many meetings that punctuated his calendar.

I was intrigued. I'd tried artificial intelligence tools for summarizing interview notes and transcripts. The results were often great at capturing themes, yet the AI tended to sweep past the details, pithy comments, or intriguing ideas I would tend to highlight.

It was like getting a book report from someone who'd only skimmed the reading.

Not long after my call with the tech founder, I downloaded the app, which is called Granola, on my Mac. It's a desktop tool, for now. An iOS version is on its way and Windows after that.

I've been using Granola since midsummer, and it's changed my meetings. To be clear, I also use a different app to get a full recording of the call to ensure my reporting and quotes are accurate. But what delighted the founder who tipped me off to Granola is also what I like best: I get to shape the outline for the notes that the AI generates.

My kind of notes

When I began using it, I allowed Granola to synch with my calendar. A few minutes before, I get a prompt to join a meeting. When the call begins, I then get permission from whomever I'm talking with to record the conversation. (Granola also has a prompt that pops up at the bottom that reminds users to get the OK to transcribe calls.)

The notetaking window in Granola is pretty much a blank page, which I like because it's a clean UX. I can drop in a title or use the one populated by what's on my calendar.

Once things begin, I only type what's most important, and the AI follows my lead. I can type just a few words and know that, after the call, with a click, Granola will build an outline around the points I flagged.

That's a huge help and different from the summaries I often get from other AI tools. Plus, I also always look back at the untidy notes I took in case something in the AI version feels off.

If I take no notes at all — which is rare — Granola will still deliver a pretty sharp summary complete with subheads and bullets.

The biggest benefit for me is that I worry less about scribbling down each thing that I might later deem important. In essence, I can be more present.

That's a frequent comment from users, Chris Pedregal, Granola's CEO, told me over a call in which we each took notes with the app.

In fact, given the whac-a-mole way many of us work — quickly triaging the messages that bombard us throughout the day — AI notetaking apps could have our back.

Pedregal said he was surprised when the company began hearing from users that they'll often zone out during a meeting to respond to an urgent Slack or WhatsApp message, then go back to Granola and pop up the transcript to read what they missed.

That's notable, in part, because in a recent survey, 57% of Granola users reported being in leadership roles. Pedregal said that supports the narrative that many top execs might be more excited about AI than some rank-and-file workers.

Pedregal, 38, cofounded Granola in March 2023. He's from the US, though he and the company's staff are based in London. Granola is focused on the American market and has US investors, he said. The company recently completed a $20 million Series A round. Google acquired Pedregal's prior startup, Socratic, in 2018.

Finding the sweet spot

The benefit of having an AI notetaker, I've found, is more than knowing I don't have to worry as much about details in the moment (though I'll always double-check afterward). Pedregal said the reason the app doesn't record audio is to make it less invasive.

The things I type are often the points that stand out because they're unique — or questionable — and that I want to think or ask more about.

Pedregal says jotting down a few notes during a meeting — but not being slavish about capturing everything — is the sweet spot. Unless we're trying to multitask, that middle path often enough, he said, to keep us tethered to the conversation and engaged with what speakers are saying.

I admit I've felt good while in meetings on busy days knowing that the safety net is there.

Not every app or service wants to monetize your personal data. Here are some of our favorite alternatives to popular apps.

© 2024 TechCrunch. All rights reserved. For personal use only.

This article is part of "CXO AI Playbook" — straight talk from business leaders on how they're testing and using AI.

The future of software-development jobs is changing rapidly as more companies adopt AI tools that can accelerate the coding process and close experience gaps between junior- and senior-level developers.

Increased AI adoption could be part of the tech industry's "white-collar recession," which has seen slumps in hiring and recruitment over the past year. Yet integrating AI into workflows can offer developers the tools to focus on creative problem-solving and building new features.

On November 14, Business Insider convened a roundtable of software developers as part of our "CXO AI Playbook" series to learn how artificial intelligence was changing their jobs and careers. The conversation was moderated by Julia Hood and Jean Paik from BI's Special Projects team.

These developers discussed the shifts in their day-to-day tasks, which skills people would need to stay competitive in the industry, and how they navigate the expectations of stakeholders who want to stay on the cutting edge of this new technology.

Panelists said AI has boosted their productivity by helping them write and debug code, which has freed up their time for higher-order problems, such as designing software and devising integration strategies.

However, they emphasized that some of the basics of software engineering — learning programming languages, scaling models, and handling large-scale data — would remain important.

The roundtable participants also said developers could provide critical insight into challenges around AI ethics and governance.

The roundtable participants were:

• Pooya Amini, software engineer, Meta.
• Greg Jennings, head of engineering for AI, Anaconda.
• Shruti Kapoor, lead member of technical staff, Slack.
• Aditi Mithal, software-development engineer, Amazon Q.
• Igor Ostrovsky, cofounder, Augment.
• Neeraj Verma, head of applied AI, Nice.
• Kesha Williams, head of enterprise architecture and engineering, Slalom.

The following discussion was edited for length and clarity.

Julia Hood: What has changed in your role since the popularization of gen AI?

Neeraj Verma: I think the expectations that are out there in the market for developers on the use of AI are actually almost a bigger impact than the AI itself. You hear about how generative AI is sort of solving this blank-paper syndrome. Humans have this concept that if you give them a blank paper and tell them to go write something, they'll be confused forever. And generative AI is helping overcome that.

The expectation from executives now is that developers are going to be significantly faster but that some of the creative work the developers are doing is going to be taken away — which we're not necessarily seeing. We're seeing it as more of a boilerplate creation mechanism for efficiency gains.

Aditi Mithal: I joined Amazon two years ago, and I've seen how my productivity has changed. I don't have to focus on doing repetitive tasks. I can just ask Amazon Q chat to do that for me, and I can focus on more-complex problems that can actually impact our stakeholders and our clients. I can focus on higher-order problems instead of more-repetitive tasks for which the code is already out there internally.

Shruti Kapoor: One of the big things I've noticed with writing code is how open companies have become to AI tools like Cursor and Copilot and how integrated they've become into the software-development cycle. It's no longer considered a no-no to use AI tools like ChatGPT. I think two years ago when ChatGPT came out, it was a big concern that you should not be putting your code out there. But now companies have kind of embraced that within the software-development cycle.

Pooya Amini: Looking back at smartphones and Google Maps, it's hard to remember how the world looked like before these technologies. It's a similar situation with gen AI — I can't remember how I was solving the problem without it. I can focus more on actual work.

Now I use AI as a kind of assisted tool. My main focus at work is on requirement gathering, like software design. When it comes to the coding, it's going to be very quick. Previously, it could take weeks. Now it's a matter of maybe one or two days, so then I can actually focus on other stuff as AI is solving the rest for me.

Kesha Williams: In my role, it's been trying to help my team rethink their roles and not see AI as a threat but more as a partner that can help boost productivity, and encouraging my team to make use of some of the new embedded AI and gen-AI tools. Really helping my team upskill and putting learning paths in place so that people can embrace AI and not be afraid of it. More of the junior-level developers are really afraid about AI replacing them.

Hood: Are there new career tracks opening up now that weren't here before?

Verma: At Nice, we have something like 3,000 developers, and over the last, I think, 24 months, 650 of them have shifted into AI-specific roles, which was sort of unheard of before. Even out of those 650, we've got about a hundred who are experts at things like prompt engineering. Over 20% of our developers are not just developers being supported by AI but developers using AI to write features.

Kapoor: I think one of the biggest things I've noticed in the last two to three years is the rise of a job title called "AI engineer," which did not exist before, and it's kind of in between an ML engineer and a traditional software engineer. I'm starting to see more and more companies where AI engineer is one of the top-paying jobs available for software engineers. One of the cool things about this job is that you don't need an ML-engineering background, which means it's accessible to a lot more people.

Greg Jennings: For developers who are relatively new or code-literate knowledge workers, I think they can now use code to solve problems where previously they might not have. We have designers internally that are now creating full-blown interactive UIs using AI to describe what they want and then providing that to engineers. They've never been able to do that before, and it greatly accelerates the cycle.

For more-experienced developers, I think there are a huge number of things that we still have to sort out: the architectures of these solutions, how we're actually going to implement them in practice. The nature of testing is going to have to change a lot as we start to include these applications in places where they're more mission-critical.

Amini: On the other side, looking at threats that can come out of AI, new technologies and new positions can emerge as well. We don't currently have clear regulations in terms of ownership or the issues related to gen AI, so I imagine there will be more positions in terms of ethics.

Mithal: I feel like a Ph.D. is not a requirement anymore to be a software developer. If you have some foundational ML, NLP knowledge, you can target some of these ML-engineer or AI-engineer roles, which gives you a great opportunity to be in the market.

Williams: I'm seeing new career paths in specialized fields around ML and LLM operations. For my developers, they're able to focus more on strategy and system design and creative problem-solving, and it seems to help them move faster into architecture. System design, system architecture, and integration strategies — they have more time to do that because of AI.

Jean Paik: What skills will developers need to stay competitive?

Verma: I think a developer operating an AI system requires product-level understanding of what you're trying to build at a high level. And I think a lot of developers struggle with prompt engineering from that perspective. Having the skills to clearly articulate what you want to an LLM is a very important skill.

Williams: Developers need to understand machine-learning concepts and how AI models work, not necessarily how to build and train these models from scratch but how to use them effectively. As we're starting to use Amazon Q, I've realized that our developers are now becoming prompt engineers because you have to get that prompt right in order to get the best results from your gen-AI system.

Jennings: Understanding how to communicate with these models is very different. I almost think that it imparts a need for engineers to have a little bit more of a product lens, where a deeper understanding of the actual business problem they're trying to solve is necessary to get the most out of it. Developing evaluations that you can use to optimize those prompts, so going from prompt engineering to actually tuning the prompts in a more-automated way, is going to emerge as a more common approach.

Igor Ostrovsky: Prompt engineering is really important. That's how you interact with AI systems, but this is something that's evolving very quickly. Software development will change in five years much more rapidly than anything we've seen before. How you architect, develop, test, and maintain software — that will all change, and how exactly you interact with AI will also evolve.

I think prompt engineering is more of a sign that some developers have the desire to learn and are eager to figure out how to interact with artificial intelligence, but it won't necessarily be how you interact with AI in three years or five years. Software developers will need this desire to adapt and learn and have the ability to solve hard problems.

Mithal: As a software developer, some of the basics won't change. You need to understand how to scale models, build scalable solutions, and handle large-scale data. When you're training an AI model, you need data to support it.

Kapoor: Knowledge of a programming language would be helpful, specifically Python or even JavaScript. Knowledge of ML or some familiarity with ML will be really helpful. Another thing is that we need to make sure our applications are a lot more fault-tolerant. That is also a skill that front-end or back-end engineers who want to transition to an AI-engineering role need to be aware of.

One of the biggest problems with prompts is that the answers can be very unpredictable and can lead to a lot of different outputs, even for the same prompt. So being able to make your application fault-tolerant is one of the biggest skills we need to apply in AI engineering.

Hood: What are the concerns and obstacles you have as AI gains momentum? How do you manage the expectations of nontech stakeholders in the organization who want to stay on the leading edge?

Ostrovsky: Part of the issue is that interacting with ChatGPT or cloud AI is so easy and natural that it can be surprising how hard it is actually to control AI behavior, where you need AI to understand constraints, have access to the right information at the right time, and understand the task.

When setting expectations with stakeholders, it is important they understand that we're working with this very advanced technology and they are realistic about the risk profile of the project.

Mithal: One is helping them understand the trade-offs. It could be security versus innovation or speed versus accuracy. The second is metrics. Is it actually improving the efficiency? How much is the acceptance rate for our given product? Communicating all those to the stakeholders gives them an idea of whether the product they're using is making an impact or if it's actually helping the team become more productive.

Williams: Some of the challenges I'm seeing are mainly around ethical AI concerns, data privacy, and costly and resource-intensive models that go against budget and infrastructure constraints. On the vendor or stakeholder side, it's really more about educating our nontechnical stakeholders about the capabilities of AI and the limitations and trying to set realistic expectations.

We try to help our teams understand for their specific business area how AI can be applied. So how can we use AI in marketing or HR or legal, and giving them real-world use cases.

Verma: Gen AI is really important, and it's so easy to use ChatGPT, but what we find is that gen AI makes a good developer better and a worse developer worse. Good developers understand how to write good code and how good code integrates into projects. ChatGPT is just another tool to help write some of the code that fits into the project. That's the big challenge that we try to make sure our executives understand, that not everybody can use this in the most effective manner.

Jennings: There are some practical governance concerns that have emerged. One is understanding the tolerance for bad responses in certain contexts. Some problems, you may be more willing to accept a bad response because you structure the interface in such a way that there's a human in the loop. If you're attempting to not have a human in the loop, that could be problematic depending on what you want the model to do. Just getting better muscle for the organization to have a good intuition about where these models can potentially fail and in what ways.

In addition to that, understanding what training data went into that model, especially as models are used more as agents and have privileged access to different applications and data sources that might be pretty sensitive.

Kapoor: I think one of the biggest challenges that can happen is how companies use the data that comes back from LLM models and how they're going to use it within the application. Removing the human component scares me a lot.

Verma: It's automation versus augmentation. There are a lot of cases where augmentation is the big gain. I think automation is a very small, closed case — there are very few things I think LLMs are ready in the world right now to automate.

• The office — and the role it plays in companies — is at the center of workforce change.
• Neil Murray, a Workforce Innovation board member, discussed workspace purpose, leadership, and AI.
• This article is part of "Workforce Innovation," a series exploring the forces shaping enterprise transformation.

Commercial real estate has experienced a tumultuous few years, with pandemic-related office vacancies and high interest rates. The sector is also at the epicenter of significant changes to the global workforce.

"It is the most incredible time to work in this industry," said Neil Murray, the CEO of Work Dynamics at JLL. "We are at the center of some really important strategic conversations about the very nature of work."

Work Dynamics is a division of the global real-estate corporation that collaborates with corporate clients on technology, employee experience, and design strategies. Murray says its goals are to help client companies attract and retain employees and foster productivity.

In its annual global Future of Work survey, which involved 2,300 corporate real-estate and business decision-makers, some 64% of respondents said they expected to increase their head counts by 2030.

JLL's third-quarter earnings beat estimates — it reported revenue of $5.87 billion, an increase of 15% from the same period in 2023.

Murray talked about companies mulling the purpose of the office, how leaders can incentivize employees to willingly go into their workplaces, and how to harness AI for concrete breakthroughs.

The following has been edited for length and clarity.

How have the priorities of your clients changed in the aftermath of the COVID-19 pandemic and the changes that brought to office life?

What we do for a living changed dramatically through the pandemic. Previously, corporate real estate may have been seen as a sort of factor of production. We weren't intentional about why we had space and where we had it, what we wanted that space to do, and its function. Is it a cost line, or is it an investment?

Suddenly every chief executive in the world had a view on real estate. It brought much more intentionality about its function within the organization and its ability to contribute to broader organizational goals.

Our business now is about helping our clients navigate that complex situation where they're planning to grow their workforce over a number of years, balancing what that might look like in the macro environment we're living in. It's a very complex environment for leaders to think through.

What's the state of return-to-office you're seeing among your clients?

There's a fairly even split between companies that are embracing some sort of hybrid policy and those that want their people back full time.

In our Future of Work survey, we found that 85% of organizations had a policy of at least three days of office attendance per week, and 43% expected the number of days in the office to increase by 2030.

It's still very much an evolving scenario. The metrics of productivity that we've relied upon to make database decisions don't always capture the challenges that businesses are facing. The time people spend doing emails or logged in doesn't necessarily translate to productivity.

One client, for example, has found that while their productivity metrics looked just fine, the number of patents had fallen off a cliff from prepandemic levels.

That led to this notion that what we're missing is, as the phrase goes, people painting on the same canvas at the same time.

Now we've seen some high-profile companies coming out, wanting more time spent in the office, saying there's something lost around culture and the collective sort of personality and purpose of an organization because of remote working. Companies are finding it really difficult to balance that.

What aspects of the workplace are most effective for enticing workers to return to the office?

The overwhelming evidence is that it's not a single amenity but it's other people — and, in particular, leaders. Companies that are intentional about their leaders being present have seen the greatest results in terms of people coming back.

What people crave is proximity to leadership for personal development. So without getting leaders back into the office, you can add whatever amenities you want and you'll still have significant challenges.

Clients that enacted three-days-a-week mandates but didn't focus on leadership presence have exactly the same attendance as those who didn't have three-day mandates.

Could that be attributed to people just wanting to be visible when the boss is around?

I wouldn't purport to understand entirely the psychology of humans, but I do believe that our research and my own experience is that people enjoy other people. The most important amenity in any workplace is that notion of community and other folks to chat to.

The notion of apprenticeship in all aspects of what we do is very real. The ability to learn from others, to absorb how things are done or navigate the complexities of an organization, is really difficult to do among 30-minute slots. You don't get to sort of naturally observe through osmosis what's happening in the world around you.

You mentioned in one of our roundtables that companies need to focus on consistent, breakthrough innovation across the organization as opposed to incremental innovation from a centralized department or team. Why is that important, and how can leaders work toward that goal?

When you centralize innovation, you can get stuck in the paradigm of trying to incrementally improve a particular way of working. But the technology breakthroughs mean that it's fundamentally shifting how we do business.

In my business alone, the rapid adoption of AI tools in daily business use has surprised us all. We are an organization with 250 years of data on everything from how buildings are occupied and used to what they cost to run to their utilities to their capital values.

The tools available to us now to cut and splice and curate and make connections in that data, which we were never able to make before at scale, are driving us to think about the business in completely different ways.

Breakthrough innovation comes about when you use a large language model to interpret multiple data sets and then you start to ask the second, third, and fourth questions, going deeper and deeper into a particular topic. You find things that you could not have possibly seen or connected otherwise.