The AI Blueprint for Improving Team Collaboration with Dan Chuparkoff

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Hi, I'm Susan Drum, and welcome to the Enlightened Executive, where your personal evolution sparks your leadership evolution. Each episode we feature groundbreaking techniques and strategies to help you get the edge in personal and leadership effectiveness. Are you curious about your own development path to enlightened leadership? Take our free 5 minute quiz@susandrum.com to discover your superpower and the roadblocks that could get in the way of effective leadership. Today we're focusing on leveraging AI to improve team collaboration. And I'm thrilled to introduce my guest, Dan Chuparkov. Dan is a keynote speaker on innovation and the future of teams. Formerly a technology leader at Google and McKinsey, he's invested 60,000 hours over three decades reinventing teams using his proprietary formula for surviving. As a partner at Reinvention Labs, Dan helps teams to adapt to the impact of exponential collaboration challenges for remote work, AI and robotics so they can lead and succeed in their industry. Welcome, Dan.

Thank you so much, Susan. You're too kind. I'm excited to be here. I'm looking forward to the conversation today. We're going to get into some really interesting stuff that's hitting all our teams, regardless of what industry we're in and so looking forward to digging in. Thanks for having me.

Yes. I'm so glad you're here to talk about this. I know. Look, people have heard so much about AI and what I find is that it sounds very theoretical, like, oh, AI is going to do this and it does this and does this and I'm always left with, okay, and what do I do about that? Right, what exactly? Like, show me what to implement, what exactly to get, what steps do I take? So I think you're someone that has really practical, real, real world experience with teams on applying this stuff and can't wait to learn from you.

Thank you so much. Yeah, I think we will definitely dig into some of the actual things that I suggest that teams should do, like my prescriptions for the future of work. Over the time I happen to be exactly the right age to have seen the PC revolution and then the Internet revolution and then the mobile revolution and then the cloud revolution and all, all, all of these changes hit our teams in the way we interacted as people and the way we stored and accessed information and all those things changed the way we work. AI is just another one of those transitions. AI is just another one of those transitions, although it's probably the biggest one that we will ever see. And so I help teams take the, the tips and successes that I used when I was trying to learn PCs at the beginning of my life and apply them to. To learning AI. Bringing AI into your team so that you can succeed more and build better value for your customers or whatever it is that you're trying to do as a business.

Yeah, absolutely. So looking at how do we collaborate more effectively if we can leverage AI to do so? Well, let's dive a little bit into that. What do we need to know?

I think first, fundamentally first, the thing that many teams are wrestling with is, you know, AI's in the news, there's a hype cycle. Are we at the peak of the hype cycle? Is there going to be an AI crash? It's going to be part of the future. But how much and when? That's the first hard question that people need to answer. And so the first thing that I help people do is crack AI open a little bit and make sense of what it even means. Lots of people are saying, you know, AI has been around for a long time. Our team has been working on AI for decades, or this scientist has been working on AI since the 70s. And then other people are saying, oh, my God, ChatGPT came out 18 months ago and it, you know, had 100 million users in two months. Is it new or is it old? That's the first question that we have to answer. And first, I would say AI isn't a thing. There are chat assistants and there's machine learning and image recognition and all those are really different things. And machine learning came out decades ago. Chat assistants came out exactly 18 months ago. Right. And that's new. And those are different things. And so first you have to figure out, like, should we use machine learning or should we use chat assistants? And those are different questions. And I help teams sort of figure out how to, how to answer that question.

And so how do you do that? What is the key consideration there?

I think so, first of all, I would say every team is probably capable of using chat assistance. You can go to ChatGPT or Claude from Anthropic or Gemini from Google. If you go to a chat person, chat assistant as a person, you can ask IT questions, you can use it to do research. It's really just like search a search engine on stereo. Right? Everybody should be using that. Everybody should be playing around to see what it's good at and what it's not good at. We're in the exploratory phase with chat assistance on the other end of the spectrum, machine learning, you've got to be a machine learning specialist to understand that. And I think most teams should have a few scientists on their team, data analysts, data scientists that are doing some of that machine learning stuff. But you're probably already, if you have a machine learning specialist on your team, I guarantee you they're already doing that. And that's why they call themselves that. Everyone else should be playing around with chat assistants to see what its boundaries are.

I don't know many teams that have a machine learning specialist on their team.

But it's probably there. There probably ought to be more. And the number of them is growing up. It's one of the fastest growing fields, for sure. If you have children, maybe have them figure out how to be one of those when they grow up.

But everyone else be an example of that. Like I'm not. Like I, again, because I've already played in the world of chat assistance, using it for research, even analyzing team dynamics, started to do that a little bit. But what's an example on the machine learning side?

First I'll give you it's an example, but it's also an analogy that will help you recognize what it does at the same time. So imagine you go to the airport and before you get to your gate, you have to go through airport security. And in airport security, you have to put your suitcase or your briefcase or your backpack on the belt and it goes under the scanner, and that scanner takes a picture of your suitcase and a red alarm goes off. If there's something in there that looks weird or it doesn't and the bag just goes right through. That's what machine learning does. It looks at something and makes a determination about whether it's in a group or not in a group. And that can be something bad or something good. But what happens there is it doesn't know if there's a knife or a gun in your bag. It just says there's something in here that might be suspect and a person should take a look at it. And that's why there's still a person sitting there at the airport scanner, because every time it sees a new shape that it's never seen before, the red alarm has to go off. And then somebody has to, has to look at it and see if it's bad or good. And there's examples of that scanning thing happening in every industry. There's in manufacturing, there are machine learning scanners that look at quality products on a production line to try to decide if they're high quality or poor quality. And there are, in education, there are machine learning things that look at test scores and essays to Try to decide if this is a passing test or if failing test. Every industry has that sort of scanner. If there's, if there's something that you're trying to look at and you're trying to decide if it's good or bad, machine learning is a great way to make that more reliable, faster and, and higher quality.

Yeah, yeah, BR Got it. Okay, well, tell us a little bit about some of the opportunities to use AI to improve team collaboration.

Yeah, absolutely. I think, you know, as we get into the heavily scientific things, there are people that are, you know, deeply studying AI and what, what it can do, and those people should keep doing that. I'm not saying that's bad or wrong or any of that, but what I am saying is for all the rest of us that aren't scientists, there's something that we do every week and we do it a lot, and that's communicate with our team. Separate studies from both McKinsey and from Microsoft showed that most knowledge workers spend about of their time in meetings, email and chat. The Microsoft study said 64%. The McKinsey study said 68%. So about 2/3 of our time is spent communicating with our colleagues. That's a lot of time. Now. It's probably good. Communicating with our people is important. Telling them what we're working on. Ask them, asking them what they're working on, talking to our customers about what they need and when. All of that stuff is really important. But we're really inefficient as people when we talk about stuff. Most of the time we sit in a conference room or we jump on a Zoom meeting and we talk about all of this rich intellectual property and then it just evaporates into the air. And maybe we have a really great note taker in the room with us and they're transcribing the important points. But even in that case, the fidelity of their note taking is poor. Right. And so AI will give us the ability to get perfect notes for every conversation that we have. You know, I know, Susan, that you use Fireflies for note taking, which is a tool that you can use. Fireflies AI is a note taking tool that will plug into any of your meeting tools, whatever, whatever meeting tool you use, whether that's Zoom or WebEx or Teams. I use one. It's called Otter. Otter AI is also a meeting note taker. And both of these tools will pay attention to your conversations. It'll give you a perfect transcription of it so you can go back and see, what did we say about that one important Thing you could read it later. You can have a new person join your team and they can catch up on old conversations. And not only do you get the transcript, but you get a summary. So you could scan a quick outline of the meeting that happened that maybe you didn't go to and you can see, should I listen to this transcript or not? Is it important? Are there things in there that apply to me that is going to make teams dramatically better at recording and keeping track of the conversations they have so they don't have to keep having the same conversations over and over and over again?

Yeah, I mean we're using that to like we are doing that. Right. So I think a lot of teams at this point are doing some type of recording where they're using one of the note takers and getting, but it's in the use of it. So one of the things that we found, and I think you probably have other tips like this is to say the word action item and you, you know, when we're talking about, okay, what were you going to do next? Okay, wait, stop. Action item. Here's what we're going to do next. So you can either just search by that, but also it starts to pick up when you say action item and it's really good. I mean, how many times is misunderstandings happen because you think you're aligned on the action item but you're actually not. So actually forcing you to say the word action item and what it is allows people to hear it back and either correct it if it's wrong or at least now you're going to find it in a summary. So everybody's on the same page.

A hundred percent agree. That's a fantastic tip. That actually makes it actionable. The action item is to make your meeting notes actionable.

Yes, because then otherwise I get all these meeting notes. I'm like, oh my God.

It's like now, now, you know, right now. I think the, my prescription for teams is to start using it so that you're familiar. Right. You want to, you want to know how accurate is it? How much can I rely on it? Can I stop going to meetings Sometimes right there, there in the world, in just in the US alone and every, let's say there are 50 million people that are knowledge workers going to meetings often. Like 50 million is a random estimate. It might be 80 million, it might be some other number. But if it's 50 million people in meetings a day and Microsoft and McKinsey say 2/3 of our time is spent in meetings, that's 250 million hours a day spent in meetings. Like, that's massive. And so if meeting items and action note capturing or action item capturing can get just 5 or 10% more efficient, that's millions of hours of created time every day. And with that created time, you can do stuff that serves your customers a little bit better. And that will make a huge improvement for teams, for sure.

Yeah, great, great. What else do we need to know?

I think the second thing is anticipate. One thing that you'll do is you'll go in, you'll start using meeting notes and then you'll start accumulating this trail of meeting notes. Right. And that, that will eventually you're like, I never go back and read those anyway. Do I even need this stuff? What you will get soon is the meeting notes. People will start giving you summaries of summaries. So you could look back at the last month and say, like, what are the important things that were discussed over and over again on our team? What are the problems that keep getting raised over and over again on our team? You'll start to get aggregation of meeting notes over long periods of time. And that's where it starts to get much, much smarter. So for now, just play around with it and trust that there's some really interesting, really exciting stuff coming in the future.

Yeah. And I think leveraging it too, so that you just don't have volumes of meeting notes which can be helpful at some point. But to really call out when there's an important piece, you could use other code words for yourself and that way you can just get those pieces.

Yeah, yeah, yeah, exactly. And, and these things will apply to meeting conversations, but they'll also apply to email and chat as well. So you'll start to get, instead of coming in Monday morning and open up your, opening up your email inbo and seeing 300 messages or whatever the number is for you, you will get a communication dashboard that summarizes what's being sent to you in email, clusters it into groups and highlights the important ones for you. The kinds of technologies that are being worked on by teams like Google and Microsoft and the independents like Otter and Fireflies is really exciting. There's some amazing stuff coming in the next year that will really, really transform the way our teams work.

So, so what else should we be doing today to enhance our team collaboration?

I think there's another thing that as I talk more and more to teams, especially larger global teams, I show them some of the progression that's happening in translation, AI based translation of Our words. I have a video that I share at conferences where I say the words. We live in a global world now, and people deserve to collaborate in the language that's most comfortable for them. And then I take that 20 seconds of words and I translate it into Mandarin and Japanese and Hindi and German and Spanish and Portuguese, and it's perfect. It's a video of me saying it. My lips are synced. It's my voice speaking languages that I don't speak. And it's amazing. And so if you're a global organization that's collaborating with people in other countries, you have probably been benefiting from the fact that every other country in the world has decided to just learn English because it makes global collaboration easier. That's actually not required anymore. Only 94. I'm sorry, only 6% of the people in the world speak English at all. And that's a very small number your companies are probably ignoring. 94% of the world now. Another billion people. Beyond that, 6% have learned English, but they prefer to speak Spanish or Chinese or Hindi. And so we will start to have automatic translation so that people that are in a meeting with us can listen and speak in their own native language. And these tools will automatically translate for us into the. Into the listener's preferred language. And that's going to make the world a lot smaller.

Is it able to do it now in real time or is it still. You'd have to record it. Let's say a leader was delivering a town hall globally. Can it translate it in the moment or do you need to just do it first in English and then we'll send you the Chinese transcription or the actual video of it in Chinese.

Yeah. So right now it's it. You need a recording that you then upload and that recording is processed.

Yeah.

However, it's changing quickly. If I was betting, I don't have evidence that this is true, but I bet by the end of this year, 2020, for I will be able to deliver a keynote live from a stage and have it heard in whatever language I. I want it to be heard in or my listeners want it to be heard in.

Amazing. What else will enhance team collaboration?

I would encourage users to start there for sure.

Yeah.

Getting. Getting really, really comfortable at knowing an AI assistant that you're going to rely on, I think is the. Is the thing. So the thing number one was have a meeting notes tool. Thing number two is start playing around with language translation. Thing number three is the thing that many people are already probably doing a little bit and that's use Chat GPT just to see what it's like. What is it good at doing? What, what is it not good yet at doing? The chat assistants in the world, There are really three big ones. The first is ChatGPT, which many people have heard of. You get that from a company called OpenAI. That was the first one that really came to the world with a chat interface so that anyone that wasn't a developer could use it. There's a second one called Claude C L A U D E and that's from a company called Anthropic. But you can get it at Claude AI and it's just like ChatGPT except it works under the covers in a slightly different way that is allegedly rigidly more private with your data and more bias free based on some things that Anthropic is saying that they're doing. Those things are great. And so you should, you should experiment with both and see what the results look like for you. And you should read both of their privacy policies so that you know what's happening to your data. And the third big one is called Gemini and that's from Google, where I used to work. Last September I left Google to just do this. And Google's tool is called Gemini because it does images and text together in one model. Gemini, like twins.

Got it, Got it. So is it true that when you're working with AI and doing research, it doesn't really have anything super current. It's only pulling from information back from, you know, 2021 or earlier. I know originally was that way. Is it still that way? It's something like that.

It's changing. The answer is slightly nuanced. But let me first start with generative. Pre trained Transformers, that's what the GPT in Chat GPT stands for are generative because they make something like they make a answer to a question or a poem or whatever you ask it to make, but it's pre trained. That's what the P stands for. It takes all the information that it can find and creates a model that it's going to use later and then transform it takes your prompt and turns it into the answer. But that P is important because it pre trains itself so that it can run later. And the original models took about a year to train and then it started going more quickly. Right now ChatGPT takes about four months to train a new model. So there's a four month lag between what the new version of ChatGPT, ChatGPT 5, which isn't out yet, but it will took about four months to train and so it will have a four month lag on knowing real time things. However, because everyone knows they want kind of a hybrid between the ChatGPT answer and what they could get from search. Each of these Gemini with Google and OpenAI from Bing are combining their GPT Assistant answers with search results as well. So it's hard to answer that question because you're really starting to get a mix of both things. You are.

Okay, yeah.

But really the answer that's coming from AI is has a lag and that lag is at least four months depending on which version you're using.

Got it, got it. Well, going back to the use in teams, what do you think of some of the technology that I've seen that's analyzing the collaboration effectiveness? It's not just doing a transcript, but it's actually saying, you know, some of them already do. How much was speaker time? You know, how much. But it's coming back with a descriptor of that. How many questions were asked?

Right, right, right.

You know, how many agreements were made versus like, you know, affirming statements versus not affirming statements. I've seen some of this and it just feels a little rudimentary. I mean, maybe it's helpful, I think it's helpful in terms of speaker time, let's say a team, you know, but maybe there's very good reason for that. But maybe just be aware of that. So what do you, what do you think about some of the analysis of collaboration?

Yeah, I think that, you know, people love seeing dashboards, they love numbers, they love metrics because it feels like they have something to try to make better there. It's difficult. It's very, very difficult. I've been studying collaboration for 32 years now, and it's very difficult to, to measure good collaboration. Right. The number of questions you ask or the number of meetings that you have, or the number of whatever new ideas you put on a list, none of those are things that your customers are buying from you. And so a great team might be able to have, you know, build great customer value with one meeting and another great team might be able to build great customer value with 17 meetings. You can't measure the meetings, you have to measure the customer value. And so mostly what my, my hypothesis is, your customers aren't buying meetings, they're not buying email, they're not buying chat from you. And so the less of that stuff you have, the better. Try to measure the thing that you're doing when you're not in meetings. And watch for that number to go up and then who cares what happens to the meeting dashboard numbers? Just try to measure customer value and customer you happiness and, you know, money that customers are paying you for.

The thing that they're clear on the KPIs, right. What's their key performance indicator? And let's, let's, let's be monitoring and measuring that and then see how we're producing that result.

100% agree.

Yeah. Yeah. Well, what's one thing that you would suggest? And we've talked about a couple of different things. Any, any other sort of like tomorrow you gotta try this and maybe it's a, it's, it's an AI platform that people haven't heard of. I don't know.

Just give it 10 minutes a day while you're drinking your coffee, getting situated, open up ChatGPT or Claude or Gemini or Otter or Fireflies or whatever tool you decide to start with and just click something and see what it does. And then at the end of a year, you'll have done that 300 times and you'll know 300 more things about AI than you knew when you first heard this podcast. And so that, I think, is my number one recommendation for people. Learn a little bit at a time. Just give it five or ten minutes a day and it will change your life.

What's the time to be a little creative and play around? You know, as kids we played in a sandbox, you know, and we weren't really clear exactly what we were building, but we would just do it. And maybe it's just returning if, if only for 10 minutes of your day. You sort of treat it like that play time in a, in a unique way.

That is an analogy. Amazing because yeah, playing in the sandbox is how you, you know, figure out what to do later when it really matters. And, and so I think that's my biggest recommendation for people.

Awesome. Where can people learn more about you and your work?

I am pretty easy to Google on the Internet. My last name is Chu C H U Park like a car and off like a light switch. If you Google to park off, I'll, I'll come up. I, I, There are only like 28 of us in the country and I'm the best at search engine optimization. So find me, connect on LinkedIn or YouTube or, you know, my own website, which will let me, you email me. I would love to start a conversation with some of your listeners. This is really fun for me to talk about and, and please let me know if I can ever be helpful. Helpful.

Thank you so much. Really appreciate everything that you shared today, and particularly last point of let's just get in there and start playing.

Awesome. Well, thanks so much, Susan. Thanks for having me. It was a great conversation. I'm looking forward to collaborating with you again in the future.

Yes. And if you like this episode, you're not going to want to miss my interview with David Meltzer, who epitomizes the definition of enlightened leadership, where he takes us on a journey to to achieving great success alongside genuine happiness. Let's lead the way. Hope you enjoyed today's episode, and I'd like to point you to the next important step. Hit the subscribe button and the bell to get notified when we release new content. I'll see you on the next episode of the Enlightened Executive.

Sam.