262 AI Unleashed: Exploring Tools, Tactics, and Strategies for Launching Your Team into the New AI Workplace, Tom Taulli and Mahan Tavakoli | Partnering Leadership AI Conversation

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[00:00:00] Mahan Tavakoli: Welcome to Partnering Leadership for a conversation where we are going to focus on the applications of artificial intelligence as we lead our teams and organizations, which comes primarily from your feedback, which is why I love hearing from you. Keep your comments coming mahan@mahantavakoli.com. 

There's also a microphone icon on partnering leadership.com. Really enjoy getting those voice messages as well. It's energizing for me to hear from you, and I wanna give a special shout-out to VJ Shinde, who's been very supportive both on LinkedIn as well as being one of our listeners who has binge listened from the very first episode on.

I truly appreciate you, vj, and I appreciate it. All of you, the listeners of this podcast, as I say, and I mean it sincerely, you have a choice with what you do with your time, and it is truly an incredible honor for me that you choose to listen to these conversations. With a growth mindset, looking to learn and have a positive impact on your team and the people around you.

There's lots of studies showing that the people we choose to surround ourselves with have a significant influence on us, whether it's their dietary habits. Their studying habits, their habits impact us.

And the most beautiful part of this is that you are choosing to surround yourselves with some of the smartest. People from across the globe sharing their leadership perspectives, their perspectives on the future of work, and on artificial intelligence with you. So to me, that says a lot about you. I am.

Energized. I'm excited as we have more of these conversations. For this episode, I've invited Tom Tulley back for us to have a conversation about the approaches that leaders can take to the tactical applications of artificial intelligence in their teams and organizations. In thinking about AI's transformative impact, I want you to think about four key areas that will be impacted.

There are strategic implications and with many of my clients, I'm urging them to focus on the strategic implications of artificial intelligence, and I urge you to do the same thing. It's important if you're involved in a leadership team or lead an organization , that you think about strategic implications there are gonna be significant strategic changes as a result of artificial intelligence, and we need to be nimble and agile to transform our teams and organizations. Now, I've run into some people that think because they're involved in leading a nonprofit or a quasi-government organization, they won't be impacted.

I would beg to differ. I think all of our organizations will be significantly impacted on the strategic level as well. So one element to consider is the strategic implications of ai. I will continue to have conversations with thought leaders around that. 

In addition to that though, there are also operational efficiencies that can be gained from implementing artificial intelligence in organizations. We can do that even if we are not involved at the strategic. Level in our organizations. There are also societal impacts. There are the pure optimists that say, Every technology. In the past, people have said we would lose jobs and we ended up gaining jobs. There are the pessimists that say AI will end humanity as we know it. I would say let's be pragmatic optimists.

There are lots of opportunities with artificial intelligence. However, we need to learn how to use it, and we need to guide the technology as it moves forward. Then think about the impact on how leadership will change as a result of artificial intelligence.

I'm doing a talk for clients and a lot of outside organizations as well, which focuses on how AI significantly improves efficiency, provides data-driven insights, but it can't replace human abilities to inspire and connect.

At least it can't do that yet. And if you think about the traditional functions of management, Those are the key elements that AI enhances controlling, planning, staffing, and organizing. The only one that AI doesn't impact as much. Is leadership and a heavy element of human coaching involved in that leadership because effective coaching requires deep trust.

So leadership in organizations will also need to change as a result of artificial intelligence. And then the final area for focus are tactical applications where each individual can improve his or her productivity, and the team's productivity can improve and we can produce better results through artificial intelligence.

Those who don't will be left behind. If you want to go from DC. To LA you can choose to ride a bike, and I'm sure there is a lot of joy in that. I love riding bikes. I'm not sure I could do it from DC to la. However, in the work environment, if there are others who are taking cars, going from DC to LA or others are flying from DC to la, the ones that are taking cars and especially the ones that are flying, get there.

A lot faster and won't use as much energy and won't be as tired so, AI tools will help us become a lot more efficient, a lot more effective, getting better, faster results when we learn how to use them effectively, which is one of the reasons I encourage all teams to start experimenting and using AI tools.

And that's why I asked Tom Tulley back. He is an expert in the area, spends a lot of time studying it, is writing a book on generative AI as we speak. So in this conversation with Tom, we focus specifically on some of the latest in artificial intelligence and practical applications of artificial intelligence by professionals and teams in organizations. Here is my conversation with Tom Tulley.

[00:00:00] Mahan Tavakoli: Tom Taulli. Great to have you back on partnering leadership to talk about AI, specifically how people can apply it and teams can apply it in their organizations.

So what are you seeing in the AI space as it relates to organizations and executives working in organizations?

[00:00:22] Tom Taulli: Yeah, great to be back. The last time we talked it seemed like a different era. It moves pretty quickly in these circles. But yeah, at least on the software front, the big players right now are Google and Microsoft which should be no surprise. And it looked like Google was fading out.

And then their IO conference changed some hearts and minds and the stock started to move upward along with Microsoft and others. It's a race out there. And even, this week Nvidia.

Reported its earnings the growth was so fast and furious that the stock went up close to 30% in one day, added over 200 billion in market cap and 200 billion a market cap is like an industry, it's real. Companies are investing huge amounts of money in this. And, from a business and leadership standpoint, it's a time to not so much think about it, but do about it because it's happening in real time and the biggest companies in the world are pushing hard on this. But when you look at Google in particular they have Bard, which is their answer to chat GPT. It's free. There's no paid version. Maybe they will.

But there is no paid version yet. One of the differences it's uses real-time data. At che e p t it cuts off at 2021. It's like October of 2021. You might ask it a recent question, it just wouldn't know what to do with it. The system's really good with reasoning.

Mathematics. Mathematics has been actually one of the more challenging parts of this technology but it looks like the Google system does much better with math. It's also trained on a lot of healthcare and scientific data, so they're moving it into medicine and biotech where there's so much information. It's very complicated information and trying to figure out how to deal with it and make better use out of it.

And they're also implementing this technology across their workplace. Apps like docs and all these productivity apps that they have. So Google's been very busy. The irony of this whole thing is that Google is the pioneer of this technology. They created the core of this technology and then they just weren't quick enough to commercialize it as.

Open AI and Microsoft where and then when you look at Microsoft, they had their BUILD conference. And they're continuing like Google to roll out this generative technology in their own systems office 365 and so forth. And then, they have this vision the co-pilot where everything will have a co-pilot.

It'll be your assistant, your virtual assistant. So if I le, if I'm a lawyer, I'll have my law co-pilot, if I'm a doctor, I'll have my doctor, co-pilot. And then Microsoft is trying to make these tools to make it easier for just about anyone. To be able to create their own co-pilot for their own.

Use cases. And so that's the vision that Microsoft is promoting. There's a lot happening. But I think we're still in the early innings with this. But like I said get ready cuz it's here now. It's not something that

It is just out in the future. 

[00:03:37] Mahan Tavakoli: As you said, it's incredible. And just to put in context, NVIDIA's market cap in one day. Went up more than Intel's market cap. Before we go deeper, we'd love to get your thoughts on a couple of other things. Tom, Sam Altman, and a couple of others came to Congress asking for. Further regulation of ai. 

And I also heard a recent conversation with Jeff Hinton who left Google, because of his concern about where AI is going and the future of humanity. And he said we don't fully understand how these chatbots. Actually, do what they do. And Jeff Hinton, who is known as one of the fathers of modern artificial intelligence, wants to better understand how it is that they do what they do.

Would love to give your thoughts on some of the safety conversations, regulations, and thoughts around where this is headed.

[00:04:45] Tom Taulli: We have a brain and we're not even too sure how this brain works. But it works. Somehow it works and it does a pretty good job. So I don't necessarily buy the argument that you have to completely understand something to get value out of it. 

But then again, I think we'd feel a little more comfortable if we do know why certain things work. Just so we're prepared to deal with some of this open AI when it was created in 2015, its focus was on building AI for humanity that was safe.

They start as a nonprofit, but then they realized they had to pay the bills, so they got investment from Microsoft and became a little more commercial. So there's this, tug of war between, making money and saving the world. And, a lot of times the making money part, wins out at the end of the day shouldn't be too surprising.

But I do think for open AI, one of the interesting questions to Sam Altman in the hearing was one of the senators asked him, do you have any equity in open AI? And he says he does not. I don't even know if he makes a salary. He's a very wealthy person cuz he's made money at other ventures.

But he actually has no equity in open AI, which I thought maybe his idea of safe AI is truer than it would be. Say someone at Microsoft or Google. And they do a lot of research on this. They publish it, they open source, that research. They have something called reinforcement learning with human feedback.

So they have people that will take, what's going on with these models try to improve 'em. And there's a thumbs up and thumbs down on, the results. So you can tell the system if it's what you think is proper or not. 

I think the industry as a whole, is fairly responsible. At least at the bigger companies. They're big brands. They don't want to tarnish themselves and they've seen how things can go off the rails. So I do think there's some self-interest in there from the standpoint of making it so it's used properly. As for regulation, Europe is trying to implement its own regulations, it's been a challenge because things are moving so fast. When they were looking and creating this law, generative AI wasn't on the radar screen.

All of a sudden it's the biggest thing now, and now they're scrambling to figure out what to do with it. So it's just a constantly moving target. So it makes it difficult for regulations to work and you don't wanna stamp out. The innovation. And then some may say the regulation may only help the bigger players.

It's kinda like the banking system, JP Morgan can afford all the lawyers and compliance officers, whereas the bank down the street, can't maybe, there's probably some of that. It's probably a legit concern. My feeling on regulation at least in the United States, is it's gonna be slow and it's probably not gonna be too impressive. We tend to have a more of freewheeling approach to technology and business in this country as opposed to Europe or China. Don't expect a lot with regulation.

As for Hinton's comments about the models these models are generally known as black boxes. Cuz they're so complicated, if you have a model that has close, a trillion parameters and has been trained on trillions of pieces of content or tokens, those kinda like words how does a human kind of conceptualize that and even give you a little education?

These models will take words, and they find the relationships of these words, but what they do is they try to find the spacial differences between all these words, and it's not a 3D space. It's like a dimension of thousands. So how is our brain to visualize spatial relationships of, and that's just one word, that one word could have thousands of different dimensions?

Now the computer figures that out with all this matrix math, is very complicated math, but we can't figure it out. So I don't even know if we could figure it out if we wanted to.

What's interesting though is that it works. I don't know why. It really just works when you take a lot of that information, you use these models, and the way you approach it gives back words that seem like a person wrote it.

And why does I have no idea? But it does. And maybe our brain operates on similar principles. We don't know yet. Maybe we are some kind of l m and we just haven't figured out the biochemistry for that at this point. But it does work. It is definitely a big breakthrough.

But I suspect in the years to come we'll have other types of breakthroughs. And we probably won't know what these mean because it's going to build on even more sophisticated models, more sophisticated engineering. I would say good luck to Mr. Hinton. It took him most of his life to create these deep learning systems that revolutionized deep learning.

He's the guy that takes on the big challenges though. 

[00:09:23] Mahan Tavakoli: Tom, I've been getting emails from listeners and some of the people that I interact with mentioning they are excited, they're energized, they are learning from the conversations, including the conversations with you, with respect to AI.

Incorporating it into their workflow, their team's workflow, and in their organization. and one of the top questions I'm getting is, where should I start?

What should I do? Would love to spend the rest of our time together focusing on that, whereas managers of teams and leaders of organizations are looking at incorporating AI into their workflow and their team's workflow, where do you advise them to start?

[00:10:11] Tom Taulli: Yeah, so actually a day before entries in Horowitz, which is one of the top venture capital firms of Silicon Valley, put out a post called the AI canon, it's a curated list of links to every topic you would want to think about. And some of it's technical, some of it's easy for your immortals to understand. 

It covers stuff like prompt engineering, how to create prompts, what is an L M, what's a transformer, how it's gonna impact business, and, what are the differences.

Systems and so forth. So I think that, a good resource. Something you really can't read in one day, but it be a good reference point. But the other thing too is technology is amazing, but a lot of times it just sits there. People don't know where to begin they get set in their way. Every morning. They power up Excel, they copy-paste this, and then they go to Word and they have a certain workflow and it works for them.

Changing that workflow sometimes freaks people out. So they can be disruptive when you're making these changes to an organization. There's this idea of change management, there's different approaches to that, but sometimes it's better to just focus on a couple of things first.

And let that set in and become part of the process. And then, maybe take it to another level and another level over time. And then you start seeing improvements. If you try to do the Big Bang and everybody, completely rethinks their approach, usually there's gonna be a lot of pushback and it's probably not gonna end well.

So I do think the old-fashioned principles of change management with new technology or critical some of it could just be as simple as a workshop or a Zoom. I get random requests just outta the blue. I got one from the Caribbean.

They want me to speak to them about some basic stuff about chat GPT and how they can use it, and what it means. Different approaches in a Zoom call. Cuz like you said, they just don't know where to start. And they're just trying to figure out, some strategies.

So I think, this AI can, YouTube, and then also change management I think are some of the preliminaries to get off to a good start. Now, the other thing this is something I, I wrote about in my AI basics book is that, don't go and just start doing an AI project.

One, you probably need a data scientist and experts and you gotta figure out a problem to solve and things like that. That's probably not the best approach unless you are a high-tech company and have those resources. Most companies don't have that. 

So the thing is that, again, if this IO conference, Google is announced Bard and they're implementing this technology in their systems go to Google Docs, and on the left side of the margin for docs, there's a little button there click it and it'll actually, suggest some ideas for prompts, and then you can say write me a blog about, generative ai and I'll say, okay here's something we put together.

Do you like it? We can put it in that doc. If not, we can try it again. Or, maybe you can change the prompt and so forth. So start experimenting with what you already have as tools. If you're Office 365, do that. You're working on the workplace, Google. Start there. , if you're in these applications all day long, definitely take advantage of that because you might realize you could be saving a lot of time using some of these tools versus the old ways you were doing that.

So I think those are some of the low-hanging fruit to get off to a start. And then you build on that over time. 

[00:13:56] Mahan Tavakoli: I love the way you approach it, Tom, and in some of the teams that I've been working with, there are a couple of conversations. One, I will separate from this. There are strategic implications for organizations, and that's a separate conversation but in terms of the tactical implementations, I go to your point. First of all, on change management.

I've seen some resistance. For good reason. People have different relationships with technology. In many instances. They have heard scary things or seen scary things about artificial intelligence and they are wondering whether it is beneficial or not.

So change management is a big part of the process. It's not just rolling out a bunch of tools. And secondarily, they are accessible in Microsoft and in Google. In addition to that, are there business issues problems or ways to determine where AI could be most useful to addressing team and organizational issues? 

[00:15:06] Tom Taulli: I come to the question more of automation. AI is one of those terms that can be applied to any type of technology when it may not even be AI.

There's something called robotic process automation or RPA, which is like macros that automate certain processes that you do over and over again. And, is that AI? It probably isn't. Although RPA companies try to make it sound like it's AI but it really isn't.

So sometimes, it's better just to take that term and put it aside. And then look at, okay, what is my process? Am I doing a lot of cut and paste? That's probably like an RPA automation, macro-type automation. It's really not ai. But you could save a ton of time, and improve productivity to a great extent.

RPA has shown that the ROI is pretty high off that so there's those types of automation that are really not ai, but are still important and, should not be ignored. There are other types of AI that are more cognitive

I look at AI or what is known as generative ai, and I look more at what are those knowledge-based capabilities and functions that this technology can carry out. We talked about this earlier. You take your Zoom calls and you transcribe 'em and then you summarize them, right?

Now just imagine the amount of time it would take for you to go through and read it and then put it into a summary. You're probably taking three hours of your day. That's gone because of just summarization. Old RPA could not summarize that because it didn't have the cognitive capability. The AI can. So it will go through that content. It'll transcribe it in a very little amount of time and is it perfect? Probably not. But you gotta weigh the benefits of your time. Plus the fact that if you did it yourself, it wouldn't be perfect either. You're probably gonna miss some stuff, you're gonna get bored or maybe think, this is not an important topic, but this is so we're talking a little bit more subjective factors, but is the overall benefit we're getting.

So much better in terms of the time saved through that automation. And I would say generally the answer is yes unless it's like a very serious, important topic. But one thing that generative AI is really good at is summarization because you're just dealing with that content if you ask it some open-ended questions.

Then that's when the answers start to get fuzzy. So if I go to chat GPT and I ask about my bio, doesn't really know me and it'll try to figure me out and it'll probably get things wrong. But with this approach to Geneva, I'm telling it your universe is this transcript.

Just stick to that. Don't look at anything else. Just tell me only about this and the way you do that is you limit it. So maybe you put a couple of pound signs at the top, and when you do this in chat GPT and a couple of pound signs at the bottom and at the top the prompt, you say, summarize this.

Be as detailed as you can with that prompt saying, I want to know what are the three topics that were, discussed in this or the top, topics that spent, people spent the most time on or whatever, what is it that you want the summary to say?

And you could say, make it three paragraphs. You could say, put it in bullet points. You could say, make it into a chart or whatever. But the amount of time savings, this is off the charts. Off the charts, and it'll. Look like it was written by a human. And it'll probably get most of it cuz you are delimiting it to that specific content.

So I think that's just an incredible use case that could just save you a lot of time and make your organization run so much more efficiently. And that is not hard to do. That is not hard to do at all. It's very easy actually. So I think that's why this technology really is powerful and it's driving a lot of.

Value in the markets and a lot of excitement and that's just one of the use cases right off the top.

[00:19:26] Mahan Tavakoli: That's a very practical use case. And to your point, it doesn't require a significant investment of time and energy to learn anything. So in a couple of instances, what

I have used it with some of the teams that I work with, it's fathom or read ai, they transcribe the meeting, come up with the key conversation points in the meeting, and who spoke how much.

These are insights. Now you might think it's appropriate that someone took up 50% of it, and the rest of the people took up a lot less than that. Key action items that came out of the meeting, right? So what follow-up needs to be done? 

So this is both saving time and insights that in many instances, help the team become more effective to your point, this doesn't require a lot of effort. It saves time and it is incorporating AI into your regular workflow.

[00:20:38] Tom Taulli: Compare that to the old way of doing AI, I would have to go and get a Python programmer who knows how to parse text. They would have to come up with some type of model. I'd have to somehow figure out how to interpret that text and convert it into tokens. It'll then have to write a program it would probably take months and it'd be expensive.

It would probably cost tens of thousands, if not hundreds of thousands of dollars, to create a summarization model. Custom-built for this, and you can now just go to chat GPT for free or Bard for free, and do that without having any experience. You don't need a data scientist for that.

All you need to do is write the prompt. That's pretty basic and you'll get the results that are probably even better than what a data scientist was doing several years ago. So that's the thing about this is that it's abstracting out a lot of what had to be done with ai.

AI was very bespoke a few years ago, and maybe even just a year ago. You had to create these custom systems. You had to wrangle the data. You had to build these models, you had to test the models. I have to do all these different tasks and even if you can find a data scientist, could you afford the data scientist?

And how do even know the data scientist is as good as they say they are? And that is revolutionary. That really is because it's making this technology so much more accessible than it has ever been before. And I think that's powerful for businesses and for organizations.

It's more of a matter of going back to the change management or just being aware that these tools are out there and understanding the value of these tools and then also understanding, what it takes to implement 'em. 

There's still not some implementation. It's not magic. Still needs to be some change in management, but it's nothing like it used to be just a few years ago when it came to AI.

[00:22:37] Mahan Tavakoli: And unleashing that power Tom. One of the things that I've done in working with my clients in many instances, the more experience they have and the higher up they've moved into the organization, the they end up doing better in their conversations with chat GPT because they're more clear in their prompting and in what they're asking for, so they get better responses. What has been your experience in terms of

The kind of prompting that gets better responses, more useful responses in interacting with, whether it's Bard or chat GPT. 

[00:23:17] Tom Taulli: So prompt is whatever you tell the system. You enter the information and also keep in mind that what you enter into the system can be also your voice. You can go to chat GPT and talk to it or Bard and so forth. It's multimodal you can, it be written or voice. 

[00:23:33] Mahan Tavakoli: That's what I actually love about the app that they rolled out. Yeah, they have a whisper on the app and you have a conversation with them. Of it, in voice.

[00:23:41] Tom Taulli: Exactly. Exactly. We're seeing these multi-model capabilities or we saw the chat GPT for presentation. From open AI where the president drew a website. And then fed it into chat GPT and it actually figured out how to create the website.

So it's also going to understand charts, visuals, or PDFs. Think of all the PDFs that exist in businesses, you can feed those PDFs and it will summarize those PDFs. You can extract data from those PDFs. So, when it comes to prompt engineering don't just think about prompt engineering as entering text in the future. It's going to be multimodal. 

And I agree, a Senior executive probably might be better as a prompt engineer because chat GPT not gonna say, do you mean this or do you mean that? It's just gonna say, okay, based on what you told me and what I can figure out, and even those are some ambiguities I'll take in this direction, and that may not be the direction you wanna take it.

So feel free to be as detailed as you want with your prompt. And that usually gets better results when you do that. You also get fewer problems with hallucinations, false information, or misleading information when you're more detailed with these comments. Talking about resources, open AI does have it's in their technical part for their API, but they do have an explanation of prompt engineering and the three or four factors to consider when creating prompts.

That's a really good resource to look at if you want to improve your prompt engineering. But part of it is being very specific and also telling it what format you want the result what the AI system comes back with is called the completion of the complete response.

So you could, you should tell it what format you want it in. Do you want it as a Shakespearean on it? Or do you want it as a chart? Do you want it as Python code? You can tell whatever you want. Or do you want it in another language or multiple languages tell it what format you want this data to be in. 

And then, also when I mentioned before the Del Limiters, that's really important, and I think that's something that a lot of people miss, is when you're telling 'em to look at certain data, make it so the computer figures out what data you're talking about.

Because if not, it may not necessarily know what it's working with and can get some wrong results. Now prompt engineering, we're talking about, like a workshop to do a company. I think prompt engineering would be one of the first practical things to talk about. Because even though there are these tools that are starting to pop up in these applications like Google Docs and so forth.

I still may not get as much value out of 'em because I may not know how to write a good prompt. The other thing too, it's a chat. Most likely you're not gonna come up with a perfect, pristine prompt for the first try. You usually need a couple of shots at it before it works. 

[00:26:45] Mahan Tavakoli: As I'm thinking about it, Tom, If you don't know how to on a car and drive a car, it is not going to give you the benefits of moving faster than a horse.

If anything, it's gonna be this big, clunky thing that you can't push, and I've seen people test using chat GPT or Bard, and saying where what it gave me wasn't helpful. In many instances, it's knowing the potential and how to have those conversations and ask questions. To your point, I've had conversations on PDF documents that have been outstanding in many instances because.

I can also use chat GPT as a sparring partner. How would you argue against these points? Making counter-arguments back and forth. So it's an interesting, sparring partner going back and forth and trying to understand content deeper.

And to your point, the more specific you are with the content you're providing, the less likely it is to go off the rails and hallucinate now, one of the concerns that I've heard from some. Has been with respect to the safety and security of having their team members in the organization use these tools.

What are your thoughts and your guidance around that, Tom? 

[00:28:16] Tom Taulli: First of all, probably need to talk to your compliance people or HR to see what the policies are abusing this technology. 

[00:28:26] Mahan Tavakoli: Yeah. Oh, a lot of times, compliance people their first answer is No. No, 

[00:28:31] Tom Taulli: Exactly. Yeah. But, sometimes, if you do something, you may get fired too. There's this case with Samsung. Where employees in their semiconductor division were putting proprietary information in the chat GPT and that didn't go down so well with the organization.

I do think it depends on what type of information you're using too. So if it's general information I don't think that is a particular problem, but if you're in a regulated industry you would think, there's already been some discussion from compliance about it.

But if you're using personally identifiable information or PII, then that's something where you gotta talk to someone and say, is that okay to do? Now it's not necessarily clear what the security is for something like open AI, cuz it's an API.

So it takes the information and it's transported over to open AI systems and their data centers and so forth. It's process and then it comes back to you on your application. Now apparently it's supposed to be encrypted and it may be anonymized, so there are definitely security precautions that are taken, but there are some organizations that can't have data moved to another location or another country again, it depends on what it is your company is doing.

So there is a risk to security. That's a risk for every type of application that's just not ai. And that's why, you need a good security policy and maybe when you're using these systems, be careful with the type of information you put in there.

But I think that's a good role in general. If you're putting credit card numbers in chat GPT is probably not a good idea. Just be reasonable with it. But nothing is risk-free that's for certain when it comes to technology.

[00:30:20] Mahan Tavakoli: And part of

[00:30:21] Tom Taulli: the point that, 

[00:30:21] Mahan Tavakoli: You make is thinking about what is the content that you are asking about. if it's content that is identifiable. to an individual or to a specific company, and it's information you don't want out there. So it is the health information for a specific individual and you have their name and health information in there.

Now you are entering this and this could be accessed in the future. You don't wanna do that. However, if you're just asking for generalized health information or if it's generalized conversations you're having about some document from within the company that is not about the secret strategy to introduce right, a new product, then that's fine. Most of the organizations that I've interacted with and teams, that's the bucket they fall in.

There is nothing highly sensitive or secret that people would be entering, but they have heard about the concerns about security and safety, and that raises the concern. So the points that you make are relevant to keep in mind.

So as this progresses, Tom would love to know your thoughts on where you see the future of the workplace, going with AI over the next year to two,  One of the quotes I love is the future is already here. It's not just not evenly distributed. Yes. So you are getting a sense of interacting with organizations and teams. Of what that future is, what a year or two from now, most of our workplaces, what they will look like.

Would love to get some of your thoughts on that. 

[00:32:14] Tom Taulli: Making predictions is always a hazardous activity. But I would say that if you're in the workforce, you're gonna be hearing a lot about AI and you're gonna start working with new systems.

And I would say it's not gonna hurt to be good at it. Prompt engineering is probably one of the best things you could do for your career at this point. There's job ads for that, but these systems work better when you're doing prompt engineering.

And for the companies themselves, I think they're gonna start realizing that as they start rolling this technology out, maybe they're gonna have to do more education or re-skilling for the AI-type workforce. 

But it will definitely be unevenly distributed. Some companies will be at the forefront and will benefit significantly from these technologies and others will be left behind and they may not know why they're being left behind. The frog and the hot water, it seems fine.

And then you. I think there's gonna be some companies come under pressure and they're not gonna figure out why they are coming under pressure because some of their competitors are gonna be using these technologies and getting efficiencies out of 'em.

In terms of the workforce, there's a lot of talk about this technology and replacing jobs and we've always had that discussion, from the early days of computers, it's always been a concern. It never really seems to have happened, at least for knowledge workers.

Now I don't know if that's going to happen in the next year or two, but I do think it's going to happen. And that's gonna be a wake-up call for a lot of people in their careers because these technologies are getting better and better at such a  rapid pace. And there's gonna be replacement certain types of roles people and organizations.

We gotta think seriously about the societal impact that this technology could have on people's careers. And I think in the next few years, there could be a rude awakening for certain people who are very qualified, very smart, and are gonna have a hard time finding jobs because of automation. Verizon, I think this week announced, major cutbacks in their call centers.

It's not that they're gonna have fewer calls. It's just because probably this technology can handle a lot of tier one and tier two support a hundred percent without any human intervention. Now for businesses, this might be good, they're gonna cut a lot of costs and save a lot of operating expenses but it'll definitely have a big impact on people and their livelihoods.

So I think that's something that should not be discounted.

[00:34:44] Mahan Tavakoli: It will have a significant impact, which will require us to constantly learn and reinvent ourselves. Which is one of the reasons I enjoyed the conversations with Tom. you are finishing a book on generative AI. It's called Generative AI:  How Chat GPT and other AI tools will Revolutionize Business. I look forward to having a conversation on that book, but to get the audience primed to go out and start reading the book soon as it's out.

How do you see Generative AI revolutionizing business?

[00:35:26] Tom Taulli: In terms of how to revolutionize business, what we've been talking about, the automation, all these great capabilities that may have taken three or four hours can be done in minutes, and done very well. Good performance and outcomes, and I talk about different industries.

Like I even talk about Hollywood, making films or gaming healthcare, and financial services. There are so many industries that are gonna be revolutionized. Today when I woke up, I saw that JP Morgan is creating their own chat GPT system for financial analysis, for example.

They've already got a trademark on it and sounds like they're pretty serious about it. There's all these industries jumping on the bandwagon. So I talk about that. I also talk about how this technology is impacting different components of a business, whether it be hr, legal sales, and marketing support, all those types of things.

And I talk to a lot of companies and lots of case studies about what they're doing and how they're using this technology. I also talk about how the technology works. I do talk about change management implementation and the risks of this technology. And what can be done with that as well, the security and so forth.

So it's a soup to nuts. Look at generative AI. I started writing it before chat GPT came out because I thought this was a category just from my standpoint looked very interesting and I knew some big investors were moving into it. And then chat GPT came out. Oh my God. And then created a lot of urgency. The problem was that as I was writing this book when I started, it was only GPT3 and then I'm almost done with the book, and then all of a sudden there's GPT4. So then I gotta go back and, write about GPT.

So a lot of that was involved is trying to keep it as, as, Updated as possible. But I do have the latest as of right now in there. Things will obviously change, but the idea with this book is not to have all the current information, but, to provide some tools and ways of thinking about, this incredible technology, how to implement it, and get value out of it.

So that's really the main focus in the last chat I talked about the future. So I do talk about jobs, new models or breakthroughs or quantum computing, and all these different things that are on the horizon that can have a big impact on the world.

I think we're still in the very early innings with this technology. It's gonna be really exciting. And as you mentioned, you gotta be nimble and adapt to change it's not easy to do that, but I don't know if we have a choice in the matter.

And it is certainly gonna be a very, exciting area and for businesses and leaders, I know it is top of mind and it's something to really focus on now because it will drive great outcomes of performance.

[00:38:11] Mahan Tavakoli: I wholeheartedly agree and appreciate the work that you have done and continue to do in the space, including your book, Tom, because part of what I believe in is that this is going to continue transforming at an accelerating pace.

The way we live works in our organizations. There are strategic implications, there are tactical applications.

However, there is also a lot of noise. So I look for sources of signals and you serve as a source for reliable signals for leaders and organizations, hich is why I really appreciate you, Tom, and look forward to reading the book.

I know after that you will have a course coming out on it. We will have more conversations specifically on generative ai. Thank you much for another outstanding conversation, Tom Taulli.

[00:39:18] Tom Taulli: Thanks very much. It's been great to be here