1: Welcome to 'Using AI at Work'

Welcome to Episode 1 of "Using AI at Work"! I'm Chris Daigle, and in this episode peeled back the curtain on the AI revolution in the business world (timestamps below)

For businesses, ambitious employees and coaches/consultants/entrepreneurs, 2023 was the year of awareness ... 2024 is the year of adoption for those who don't want to be left behind.

We discuss how it's not too late to harness AI for 'human augmentation,' boosting productivity, creativity, and performance. 

You'll also learn how leaders in the industry are educating their teams on AI's potential, anticipating at least a 20% gain in efficiency as a result. From ground-breaking studies to practical advice, we explore how AI, like Chat GPT, can be the co-pilot transforming your workflow, elevating your team's output, and reshaping roles across all departments.

We're stepping into an era where integrating AI isn't just an option – it's a strategic imperative. Jumpstart your AI journey today and stay ahead in the game. Tune in, and let's leverage AI to truly realize the promise of transformation from Using AI at Work.


 0:22 Welcome to 'Using AI at Work'

 8:29 Intro to Generative AI Tools

 13:43 Practical Use Cases for AI

 21:45 Getting Started with AI

 30:57 Ethics & Using AI in the Workplace

 35:48 AI's Impact on Team Structures & Roles

 39:06  The Advantage of the 'Chief AI Officer'


Be sure to download this week's Smartcut/s:
🔸 Mastering Facebook Ads with Writesonic — https://smartcuts.io/1

And check out the related prompts at: https://PromptVault.studio

Pre-roll add for my private practice

This gives an example from US Compliance and promotes the $50,000 Opportunity Call

This version of the post role mentions Chief AI officer and my private practice

Transcript

Copy Transcript Copied

SPEAKER_00 0:52

Welcome to Using AI at Work. I'm your host, Chris Daigle. Each week we'll be learning how today's business owners, entrepreneurs, and ambitious professionals are getting more done with smart use of tomorrow's tech. Let's get started. Okay, everybody.

Welcome to 'Using AI at Work'

SPEAKER_00 1:13

Welcome to the Using AI at Work podcast. As I mentioned in the intro, my name is Chris Dagle, and I'm your host. Now, instead of starting episode one as being some showstopping interview, I thought I'd start with the basics. There's a lot of people out there who have heard about quote-unquote AI, and maybe they're seeing a lot of uh stories in the press that are giving them some FOMO about their inability to participate because just a lack of understanding. The intention of this first episode is to clear all of that up for you so that you feel comfortable understanding exactly what it is that these tools can do, how to use them, where to use them, what their limitations are, um, risks associated, just how to intelligently communicate on the topic of AI in business in today's global conversation about this. So as we get started, I want you to keep in mind the who, not how concept. What I mean by that is just because you learned something doesn't mean you have to be the person who does the work. What this means for you is that if there's a particular part of the spectrum of AI opportunities that you learn about or that we discussed, but you're not interested in or comfortable doing them, just find someone who can do them for you. Remember, you don't have to know how to use the tools to benefit from the tools. To put it simply, you don't have to do this alone. And also, remember that we here at Using AI at work advocate for a human augmentation environment, not pure outsourcing to AI for everything. Now let me explain that. This school of thought is called human in the loop. As you've probably seen, there's a lot of fear about job loss. And the statement that seems to be making the rounds is that AI is not going to take your job. Someone who knows how to use AI will take your job. Even with that being the case, the output from the tools that we'll talk about, it still benefits from human oversight. Um, it allows you, as the user, to get more output faster and of higher quality. But again, advocating for the use of AI as an independent environment is not what we stand for. We really want to make sure that humans continue to participate in the production of, again, high-quality, fast output using these tools. The way we talk about it is we want to set it up so that the human has more time to focus on tasks that require human involvement, not the data-driven, the uh repetitive type tasks. That's where AI and automation come in. Now, let's talk about why it's important that you know how to use AI in today's business environment. There was a survey conducted in September of 2023 by EdX. They're an online education platform partnered with Harvard. Now, they interviewed uh 1,400 employees, half of those being uh executives, C-suite, half of those being everything from middle management to frontline workers. And here's some of the information that they pointed out. When surveyed, 74% of the executives said that those with AI know-how should be promoted more frequently. That's most of the C-suite is saying if you know how to use AI, you deserve better opportunities in your career. 83% of those executives said employees who know how to use AI should be paid more than those who don't. So, if for nothing else, just to improve your economic viability if you're an employee or part of a team, it would make sense for you to understand what these tools are and how to use them. Now, some further data points from that survey. Almost 40% of those executives surveyed are requiring employees to start learning this stuff. If it hasn't happened in your company, it's coming. And then to take it even further, 31% of those have created a role for something called a chief AI officer, which we'll talk about a little bit later in this podcast. Okay, so now let's start with the most common question I get. How late am I? Well, let's talk about that. Uh, a survey conducted by Pew Research, some of you may be familiar with them. They're a notable research firm here based in the U.S. They conducted a survey in late July of this year. Of those that had heard of ChatGPT, only 24% of those adults were using it, which indicates about 18% of U.S. adults in the U.S. are using ChatGPT. So if you're using ChatGPT, you're already in the top 25% or so of individuals currently conducting business in an AI driven world. Now, of those, 90% of them had only used ChatGPT for entertainment purposes, just to get some kicks or to see how it works. So that leaves only about 10% of those surveyed were using it for something other than entertainment. So let's assume they're using it for business, strategy, that sort of thing. Of those who are doing that, only about 10% of them have a paid GPT plus account, which is the paid version of ChatGPT currently built at around $20 a month. So when you break those numbers down, that means that only about 118th of 1% are seriously using GPT in their business. So you're not late, you're early. However, AI is advancing rapidly, and the tools that are you're able to use now will soon be surpassed. To shape the future of work with AI, organizations and individuals like yourself should start actively experimenting with productive ways of using AI as a partner, an assistant for your role, a co-pilot, whatever the case. However, the sooner you get started, the sooner you're going to have what we like to call the O moment. That's typically when a new user gets in front of Chat GPT, and a lot of times they'll use it almost like they use Google. We've all been trained, you know, for search engine usage. This is not a search engine, can act as a search engine. However, the true power of it does not come from that. That oh moment is when somebody's working with the model and they're going back and forth with it, looking to continue to refine their communication with Chat GPT or any of the other AI models. And then suddenly they get that moment where the output from the model makes them go, oh, like I get it now. The challenge, though, is changing your thinking the way you think currently, so that you can start to include using the tools instead of relying solely on gray matter. It can happen pretty quickly, usually within an hour or less of you sitting down in front of the model with an intended outcome and making the commitment to go back and forth, refining the output, refining your questions, starting to understand how the model thinks. Within an hour or less, I guarantee you'll have that O moment and you will not be looking back. So when it comes to AI, we classify two types of AI. Now, there's the AI side of data science. That's the one that you'll generally hear when it comes to big business. Amazon uses AI, Google's an AI company. They are, but they're more focused on a data science application of the tool. Now, we don't dive into that environment very much on this podcast because we're geared for the more tech non-technical business person who wants to use AI in their business or role. Think small to medium businesses, not necessarily large enterprise. So in our case, generative AI is where we are focused. We're looking at ways for you to use AI as an assistant in your role, a co-pilot in your job, an advisor in your business. So simply put,

Intro to Generative AI Tools

SPEAKER_00 9:20

generative AI is just a type of artificial intelligence technology that can produce or generate various types of output or content for you, including text, imagery, audio, and synthetic data, which we'll talk about. Now, some of these tools are paid, but there are many free tools that you can use. So let's talk about some of the ones that are most commonly uh popping up in conversation and in the media. We've all heard of ChatGPT, or if you haven't heard of ChatGPT, welcome to Generative AI. Uh Claude is another version like ChatGPT. These are more the text inputs looking for text outputs typically used. You have a business case that you need to solve, you use the models, you provide it with context and input on what that business use case or business issue is, and you start to have dialogue with these models. Now, in dialogue, it's gonna usually be an iterative process. And what I mean by that is that if you type in, especially if you're a new user of these tools, if you type in your query into the uh chat input on any of these, like ChatGPT or Claude or Googlebard, maybe you've heard of Microsoft Bing, and any of those, usually the first output, the first answer that you get from the model is not going to be the final best answer that you were looking for. That requires that you go, you you take a look at the response, and then you, well, what I meant was blah, blah, blah, blah, blah. Or that's great, can you expand on this point? Or I need more details. So it's it's you going back and forth with the models till you ultimately get. Um, I usually look at it like 80-20. AI, when you know how to use the tools, can generate generally about 80% of the solution or the output or the content that you're looking for. Yet it still requires, remember, human in the loop, it still requires human interaction to get that final push to a finished product. Don't uh fret about that. Don't be disappointed that current models can't read your mind and give you ideal output from the get-go. This is the skill, that that iterative process, that iteration, that going back and forth with the models until you get that 80% or higher output that you were looking for. That is the real skill here. It's not knowing the tools, it's knowing how to use those tools. So those are the primary tools in our business. We tend to rely a lot on ChatGPT, although we have used all the models and we regularly test them against each other, uh, providing same inputs, same user inputs to measure the quality of outputs. Now, as far as generating images, perhaps you've heard of Midjourney. It's a very popular one. Although very high quality output comes from Midjourney, it is based in a Discord server, which for most individuals, even getting to the Discord server is more technical than you need. Therefore, to get started easily with a paid GPT Plus account, again, around $20 a month as of this recording, they have built-in or native image generation using their DAL E3 technology. Same as typing, uh, it's the same as using it, uh, the models to get text output. You simply type in the description of the image that you want, allow AI to generate that image, and then again go back and forth. No, what I was looking for was make it daylight, um, make it a cinematic style. It's again a process, an iterative process to get that final result that you're looking for. Most of these generative AI tools are going to be very user-friendly. Interfaces that are easy to navigate and understand, uh, output that allows you to uh work with the models to get that the best output that you can get before the human steps in. And then as far as uh voice generating voice, we tend to focus on two tools, Hey-Gen, H-E-Y-G-E-N, or we also use 11 labs. And it's been pretty incredible. A simple case study was just to test it. I had recorded uh instructions to some of our team using a Loom video. I uploaded that Loom video, and in about, I don't know, less than five minutes, that entire Loom video had been translated into French using my voice, my accent, and it had modified the video so that my lips were moving as if I was actually speaking French to my team. You know, it seems like a novel use case, however, if you have a business or you work in an environment where it's a distributed team and you've got resources that are across the globe. Tools like this can make the communication on your team much easier. Now, also consider using something like that. If you've got content that you sell or if you've got a product, it's very easy to instantly translate that product into a new language and open up your market in a half hour or less to go just from English speaking to whatever language you're looking for.

Practical Use Cases for AI

SPEAKER_00 14:34

So in general, these uses of AI have a wide range of practical application in business for content generation, image generation and editing, as we've discussed, music generation, as we just discussed, voice generation as we just discussed. Now, some areas where you can immediately apply AI to your business or role would be to start with any data-driven or repetitive tasks, the ones that aren't anyone's favorites. And some common areas would include uh marketing and business processes in general. So let's take a look at marketing. Um, as a matter of fact, this episode was AI assisted, and this is uh a bit of our content effort. Marketing can you know automate such tasks as lead generation, lead scoring, customer attention efforts, create personalized marketing materials like emails, landing pages, social media posts. It can be used to generate new product ideas, designs. It's uh I mean, if you were a marketer and you're not using this yet, um, get ready to be surprised. If you aren't a marketer, understand that the marketing department is typically where a lot of companies will start to see first use because marketers are used to uh leveraging new technologies in their marketing efforts to generate more leads for the company, uh, create more engagement. A simple use case from sales would be leveraging AI to predict you kind of like a predictive categorization of prospects based on those most likely to buy, so that your sales team is spending more time with a higher close rate because they've got this targeted list. If you've ever seen Glenn Gary, Glenn Ross, these are those leads, right? AI can help with that. And it can also support with personalized follow-up for prospects that maybe have already had a call with your sales team or have expressed interest in your products. AI can automate personalized follow-up. And personalization is always shown to increase conversions from marketing efforts and sales efforts. And HR, we use it to create uh job descriptions. And we happen to use uh Upwork, an outsourcing platform pretty frequently. And rather than starting from scratch, we use AI to assist us with a clear definition of the job, resulting in us spending a lot less time sorting through candidates who have replied to that job post because we were very specific and targeted, and AI knew exactly what to write. Um, we also use it internally in an interesting way. Uh we're big fans of the Colby Index, which is a personality test that's used to kind of determine how to best work with somebody, what somebody's tendencies are, their preferences are. There's many tests out there like that, the disk, the strengths finder, all of them. Any of those can be used. In our case, when we have a candidate that applies for a role, we have a very clear expectation of what the outcome is for that role. Whoever's in that role needs to be able to do blank. We will go to GPT and tell GPT to act as an expert at reviewing Colby scores. Trust me, the models know all this stuff. We'll give it a copy of that uh applicant's Colby test, and it will quickly tell us the highlights of that test. The next thing that we do is say, okay, here is the job role that we've got. How does this individual match with that role? And it will start to give us what risks would we have by hiring this person for that role? It gives us answers. How would this person um truly thrive in this role? It gives us those answers. Now that may seem like a simple thing. However, if you've gone through the process of hiring and firing, that's pretty painful. Any, you know, small effort that you can do at the front end to make sure that you get the right person in the role, turnover is very expensive. It's uh, you know, I see reports uh coming out pretty regularly indicating that the wrong hire can cost a small business anywhere from $100,000 to a million dollars or more in missed opportunity, uh delayed momentum, all of those things. So using these tools initially, and this is something your AR, your HR team can do in about 10 minutes, start to screen these candidates and determine who's probably the best one. Customer service, great applications for a generative AI in it. Uh you may already be interacting with customer service chatbots that can answer questions and resolve issues. Now, usually what those chatbots are doing, they've been fed uh you know, chat logs and maybe recorded calls from customer service. And the models can look at that and understand what the typical queries are from customers or prospects and be prepared to handle those. Now, what does that mean for your customer service team? It means that your team is left to focus on the issues that require more human interaction or more discretion on a human's part. But that's again, that's about 80-20. 20%, 20% of those customer service queries are going to be requiring humans. 80% of them can probably be handled by a trained chatbot, which isn't a complicated task if you know how to do it. Content. I mean, every business that is marketing is doing some form of content, whether you know it or not. If that has been an issue for you, because for me, I know personally, creating content hasn't been an easy task for me to get around to. Now, leveraging AI, once again, 8020, I'm able to very quickly create written content that I'm able to take the output from the models and then start to introduce my own tone into it, correct some stuff. And what's what the output ends up being is very engaging content for our target avatars and any of our businesses that didn't require a whole lot of effort. Now, the increase, if you've got a if you've currently got a content team, surprise, surprise, they're probably already using this. Now, I'm not suggesting that you use strict output from the models as your content. That's what we would call synthetic content. If you're not familiar with output from the different ChatGPT and Bing and everything, if you ask it to create some content and read that content, it's not going to have the depth or the nuance that a human can introduce to it. So our position is that we have a topic that we want to use as part of our content strategy and to um educate and engage individuals who are interested in generative AI and business, we'll use, we'll enter those ideas and we'll start to play with the output. And again, what this does, not 100%. That's not content that you're going to copy and paste into LinkedIn or Facebook. But what it does do is it eliminates that problem of the blank sheet of paper. And if you've ever been a content creator or attempted to create content, starting from scratch is the hardest part. Once you get into the flow, it's easy. The we use the tools to help us get past that blank sheet of paper stage and move into an environment where we can uh feed off of the ideas or suggestions that were provided by ChatGPT. So when you're getting started with a uh using or if let's let's say you're in a small business, generative AI or AI use in the business has been a casual conversation, but there hasn't been like an active effort by any leadership or individuals in the company to introduce it. That's a pretty common scenario. That's where most businesses in 2023 found themselves, fewer in 2024, because this is 2023 was more like the year of awareness. And we'll say 2024 is going to be uh the year of adoption for a lot of these companies. So if you're at the stage where you're ready to explore adoption or introduce that conversation into the business, here's what we do. When we go into a company, the first thing that we want to do is we want

Getting Started with AI

SPEAKER_00 23:33

to educate and align the leadership team. Just like you, if you're on this and you're learning a lot from the conversation. That I'm sharing here, the leadership team's the same way. They are running a business, usually. Maybe they've attended a conference or a conference call or a webinar or something where AI was discussed. But very few leaders have been able to make the dive into, okay, how do I use this? Not just on a strategic level or a theoretical level, but in a workflow or process, what impact is this going to have? Is it going to help the business? So the first thing that we do is we come in and we actually just share with them very similar content to what we're sharing on this podcast. We want them to understand what it is, what its limitations are, what its capabilities are, what the risks are, and the easiest places to get it introduced or deployed into the company. So once you've educated them, it's important for them for you to get true buy-in on the next step. You need to show the leadership team the forecasted ROI as early as possible. I have a saying that I like a lot: Arithmetic is not an opinion. If you go in there and you're talking to the decision makers in a company, whether it's your boss, whether it's your executive team, whether it's your clients, if you're a consultant or coach that's introducing AI into the business, your opinions matter, but they're not the deciding factor. The numbers are going to be the deciding factor. And it's not uncommon for a once you run through a strategy similar to what we do in our business, for you to quickly see at least a 20% performance increase in the workflows and business processes where AI is a fit. Okay. So again, forecasting, you can show, uh pick a half dozen processes within the business, introduce an average 20% performance increase. Those numbers matter. Doesn't matter how big or small the company is, 20% is 20%. That's free, that's more free time to focus on higher leverage activities. That's more uh capital that the company can deploy towards more marketing, better hires, whatever the case might be. Once you've gotten the buy-in from the leadership team, we like to establish an AI coalition or the AI team. And this is basically made up of individuals who have raised their hands and said, hey, I'm already using it or I've got a lot of interest in it. Because one of the things that when when this discussion comes up, those individuals in a business who aren't familiar with it, they start to have that feeling of, oh, AI is going to take my job. If you've got those individuals participating in anything that you're doing with AI, initially at least, there's going to be a lot of resistance and quite possibly overt sabotage of your efforts. They don't want AI to take their job. So by selecting a handful of individuals who can participate in the discussions and the planning related to AI deployment within the company who have raised their hands and said, hey, I'm interested. I want to learn this stuff. I love it. I'm already using it, whatever. Those individuals on your AI coalition or your AI quick start team are going to be the ones who help make sure they're going to go the extra mile to make sure that the initial pilot projects are a success. Now, once you've got that team, it's time to start looking at some of the low-hanging opportunities in the business. Some of those we've already discussed: customer service, marketing, sales, content. Those are usually the easiest places for companies to get started. There's not a lot of risk with that, provided human is in the loop. And those are areas that can easily be measured. What's the engagement on the content? How many more tickets are we handling? And at what speed compared to previously? How much more effective is the marketing? How many more sales are being closed? So by identifying a handful of processes or workflows within the company that can benefit from the introduction of AI, the next step for you is to be to prioritize that list that you've created based on impact and likelihood of success. And it's a very simple matrix you can draw up. What's the proposed impact, high, medium, low? What's the proposed risk of it not being completed due to technical issues, user issues, whatever, high, medium, low. And you look for the ones that have high impact, high likelihood of success. Doesn't mean you have to start there, but again, when you're introducing AI into your business, the fastest way to get momentum is to show, hey guys, this works. Start with some pilot projects that have high impact and high likelihood of success. You get results, you advertise them throughout the company, throughout the team, throughout the leadership meetings. And once people see that, other leaders at all levels of the organization start to say, I want some of that. Can you do that for me? I want a 20% performance increase. I want more bandwidth that I can use to focus on higher leverage activities in the business. So how do you find that low-hanging fruit? A simple way to get started for you personally, like right now or after this episode is done, is to run something called a delegate and elevate exercise. This is a concept that we learned from EOS, Entrepreneurial Operating System. We're operators of that in all of our businesses. And we'll actually provide a link in the show notes for you to see an example of a delegate and elegate document. So what it is, it's simply a document, and you can draw it up right now. Take a piece of paper in front of you and draw a big cross right in the middle of it. All the way down, all the way across, an XY matrix. And what you want to do is in the bottom right, that's where we're going to start to list things that you do on a daily or weekly basis that you don't like doing and you're not good at them. Right? The aim here by listing those things is to spend as little as time, little time as possible working on those things. Those are the areas where AI, automation, human augmentation can do a lot of the heavy lifting for you, or at least you want to start. The quadrant next to that to the left will be don't like doing them, but you're good at them. You might spend some time here, especially if you're learning and actually growing from that. However, the ultimate goal here is to either become good at those tasks or get them off your plate to either, again, automation, augmentation, or human in the loop or AI. The next quadrant up in the top right will be things that you like doing and you're good at. Many people find themselves stuck here, right? Because they're competent at the skill, but they're just not engaged enough in it to really have that level of virtuosity or impact on it. The goal here is to elevate those tasks again by automating, delegating, or committing to for you to become more passionate about that. The top left quadrant, this is the quadrant where you should aim to spend the majority of your time for job satisfaction, for impact, for all of the above. And this is the quadrant that's called, I love doing it and I'm great at it. So again, the four quadrants. Now, once you've kind of listed out all the things that you do, you know, on a on a day-to-day or weekly basis, and you've categorized them into those four quadrants, the low-hanging fruit is in that bottom right quadrant, which again was I don't like doing it and I'm not even good at it. A lot of us have those types of things that either officially or unofficially kind of fall into our lap or are part of our uh expected contribution to the company. Anything there, not only does it create uh a clear category of workflows or processes for you to focus on to start getting those low-hanging fruit, but you you want some real buy-in, go and start addressing those don't like and not good at quadrant tasks for an individual on your team or in your company. Once those things are off their plate, boy, they love AI. They become evangelists, they become fans of it. They're talking about at the water cooler, they're talking about on the Zoom meetings before they start. Oh my gosh, right? That's an area where you can get a huge win, not only for like real-world ROI impact, but culture impact as well, uh, employee morale, job satisfaction. You start to introduce uh automation and AI, AI efficacy into those areas, and you're gonna you're gonna build a lot of momentum. People will start coming to you saying, hey, can you do that for us? Okay, now a lot of concern about the ethics and data privacy environments with AI, right? These models, like ChatGPT and Claude, being BARD, mid-journey, all of that, they've been trained on huge amounts of

Ethics & Using AI in the Workplace

SPEAKER_00 32:45

data that was found on the internet. Some of it copyrighted materials that probably shouldn't end up in the training modules. The fact that this happens raises concerns about ethical practices and data privacy and that sort of thing. So it's important that you have a human in the loop, again, to review the output. So most companies right now, uh, if they are using AI, it's kind of an ad hoc or by the individual basis. It hasn't become like formal doctrine of the company's operations. And I know that because a survey conducted by the conference board found that only 26% of organizations have a policy related to the use of AI by their employees. Look, people in the company are already using it, whether the boss knows about it or not, right? So it's it's important for you to uh at least as a foundation create a generative AI use policy. Now we have a template, it's in the show notes, we have this internally, we use it with clients. And what it does is it just makes a formal declaration of what is and is not okay for applying AI within that business. And again, we're talking about generative AI and not necessarily the data science side, which does the heavy number crunching and all that, and we'll talk about that next. If your company does not have one and you bring this to the table, you will be a hero. Because again, the leadership team, as we mentioned, one of the first steps we do is educate and align the leaders of the company because they don't know about this stuff. They don't know enough to be able to make informed decisions on next steps in deploying AI use into their entire business or specific departments within that business. And if you bring them a generative AI use policy, a couple things are gonna happen. One, they're gonna say, I didn't know we needed this. Oh my gosh, thank goodness. It's also gonna make you look like a hero. There's nothing wrong whether you are a business owner, you're a solopreneur, you're a coach or a consultant, you're an employee at any level within an organization. It will only benefit your career or your business if you are recognized as somebody who understands how to use these tools, where they're used, and how to get the real results from that. All right. The generative AI use policy is an easy place for you to start. Again, we've got a link to it in the show notes. Uh, please feel free to honestly pass it around. If you know of uh other companies or departments within your business who don't have one in place, they should. Okay, another area that that people have a lot of concern about is well, with AI, you know, our our data is not our data hygiene is is, you know, it's it's not that great. Listen, with what we're doing with generative AI, it's not that important. On the data science side, for big businesses who are doing a lot of number crunching and they're looking for trends based on analysis of past and historical data within the company, the cleaner the data, the cleaner the output. That's just a fact. However, most of you listening to this aren't at that stage. And if you are at that stage, again, reference that who, not how concept that I shared with you earlier. You don't know, you don't have to know how to go in there and fine-tune an LLM or a large language model or uh know how to code and any of the AI-specific languages. You simply need to know that it needs to get done and find that individual with a quick Google search or tapping into your network. You don't need to do that. You don't need to have super clean data to use generative AI. Because remember, what we're doing is we're interacting with these models so that it can generate new output for us. It doesn't hurt if you've got data. Let's say you had a spreadsheet of top 100 clients and you had a bunch of data points on them. You can certainly upload that into a generative AI tool like ChatGPT or Claude and ask it to look for trends and identify it. If the data on that spreadsheet is wonky or there's fields missing, obviously it's going to do the best it can, but it's not gonna be as great as it would be if your data was um hygienic, I guess is the word. Uh high integrity data, let's say. But I don't want you to think that that that is an impediment to you or your company starting to use AI to get some real impact. Now, that's all I want to say on that. It's a big topic, uh, but it's a topic for a strata of business that most of us don't exist at. Okay. So uh let's talk about AI's impact on team structures and roles, right? As we've mentioned, generative AI is already transforming roles and boosting performance across customer service, marketing and sales, software engineering, research

AI's Impact on Team Structures & Roles

SPEAKER_00 37:36

and development, a ton of areas. Now, there was a very interesting uh case study that was done recently by a group called Boston Consulting Group. If you're not familiar with them, they're uh a well-known and very capable consultancy that supports larger businesses with challenges that they come up with or that they encounter in the course of conducting their business. So Boston Consulting Group took a sample of their workforce, about 1,400 of their consultants, right? And here was the study. Half of you get to use Chat GPT. Half of you keep doing what you're doing. Now, to be employed at the level of Boston Consulting Group, you've got to be a smarty pants, right? You've you've got to be a capable individual, uh, you've got to have uh solid experience, you've got to be able to deliver results to these big companies where you know the consequence of not delivering is extremely expensive and painful for these large companies. So all of these individuals that participated in this were highest intellect, highest capability that you can find in the business consulting world. Now, what was interesting is after about an eight-week period, they found that the consultants with access to ChatGPT outperformed other consultants. No surprise there. They outperformed them in 18, all 18 areas where performance was measured between the two groups, they outperformed in all 18. On average, consulting consultants using AI finished 12% more work. They completed their tasks 25% faster, and the product that they were creating, the output they were doing, 40% higher quality than those not using the tool. Okay. But what the real surprise was here is that the lowest skilled in the Chat GPT user group of those consultants, the lowest skilled had a huge boost in performance. Now, what the big win for that came out of this study, though, was that the obviously not all consultants are created equal. There's some that have 20, 30 years of experience and some that just graduated with their MBA. It was that lower tier or lower performing consultant, generally speaking, lower performing performing consultant who had the biggest boost. It now, of course, the the better consultants, the best on the list, did get a performance increase, but the biggest boost was for those who didn't have the skills that the highest performers had. Now, I want you to think about that. That means anybody in any organization who's a mediocre performer or doesn't quite have the experience of your best person can use these tools and level up significantly to the performance level of somebody who has many more years' experience, much more know-how about their particular role. So it's not just that ChatGPT can help you, and it's not just that it can help the best performers, it helps everyone that uses it in a structured environment, again, according to that generative AI use policy we mentioned. So we talked about the role of the chief AI officer early, I mentioned it, but I would expect to see more chief AI officers on the org chart. That's again, this isn't somebody who knows

The Advantage of the 'Chief AI Officer'

SPEAKER_00 40:54

data science. Now, if you were to go to LinkedIn, as someone argued with me the other day, and type in chief AI officer, today that role is identified as somebody who is on the geekier side. Not to mean that they're, you know, there's nothing wrong with that. They just tend to be more skewed towards the engineering or the data science environment. But the new definition of a chief AI officer isn't that individual. It's somebody who understands the landscape of AI, generative AI primarily, and is seeing how it's being used in the marketplace, understanding what new tools are coming out, and is able to translate that, those optics on the landscape of AI, translate that into specific business application depending on your role, your industry, whatever the case might be. So expect to see chief AI officers who are leveraging the who not how model. The new definition is hey, we need data scientists, great. I'll find them and manage them, or I'll work with them, I'll liaise with them. But the real application for most small businesses and for you and your small business or your role in that business is how do I use these tools? That's great that these tools are out there. I didn't even know those tools were out there. That's great that I understand them now. But how do I use them? Where do I apply them? All right. That is the true contribution that you as an AI-informed or AI-enabled individual can bring to any company that you work with. And again, you know, I'm not excluding enterprise folks. People, you know, in every business, the one commonality is that there's people. And any individual who has an interest in leveraging these tools, the generative AI tools, will perform better in their role and will get recognized for that, even if they acknowledge that they are AI enabled and they're using these tools. Companies want those people. They don't have the answers. And honestly, it's kind of hard to find individuals who are uh either have the AI capabilities for small business, but have the bandwidth to come and work with your small business. So it's better for you to understand the stuff, or uh again, who not how, find somebody who understands the operational side of business. I've heard it called AI ops. That's what we're talking about here. So I hope that this uh episode was uh helpful in kind of demystifying uh some of the applications for you in using AI at work. Now, look, I'll admit AI is a huge topic. We could have talked about natural language processing and machine learning and generative adversarial networks. Nobody wants to hear that stuff. I'm being flip, I don't mean that necessarily. However, if you're on this episode, that's probably not what you're looking for. And if it is, there are fantastic podcasts out there that do dive into the more uh computer science, data science, data engineer, chief data officer types of conversations. However, if you're like me and like most of the people that I interact with, you're an individual, non-technical, you are interested in how do I leverage these tools, how do I not fall behind first, but how do I use these tools to, as we talked about in that study with Boston Consulting Group, better quality product, faster, and more of it. If that's what you're looking for, and again, maybe maybe your role is a customer service frontline rep, right? Tough gig, I've been there. Maybe your role is a uh uh commissioned salesperson, tough gig, I've been there. Any role within an organization that is can can leverage these tools will benefit from it once they have that oh moment. So uh in closing, here's what I want to tell you. Expect some fantastic interviews with uh notables in the in the business space who, again, they're not the CEO of uh you know Amazon or anything like that. These are individuals who run small businesses or work in small businesses, and they have kind of cracked the code on how to use AI for what they do, whether that's e-commerce, whether that's uh warehousing, um, info products, you know, personal training, it doesn't matter. You knowing how to use these tools, it doesn't mean that you're gonna be able to go sit on the beach and AI is gonna take over all of your work, at least not yet. But what it does mean is that by knowing how to use these tools and seeing how other people are using them and going, oh, never thought about that. Let's try that. That's what this podcast is built for. It's for you to hear how others have cracked the code so that you can go take it back to your own company or your own job and say, I'm gonna do it like they mentioned in that thing. Let me try it. And here's what I can tell you. You will, if you stick with it and you get past that very, very slight learning curve, you will have that oh moment and you'll never look back. So with that, thank you so much for listening to this episode. I look forward to uh sharing a ton of insights on future episodes. And with that, last bit of advice, go use some of these models today. Start playing with it. Don't use it for entertainment purposes only. Start playing with it. Get that learning curve out of the way so that as you're listening to future episodes and I'm sending you resources, you are able to quickly adopt and deploy what you're learning. I'll see you on the next episode. Thanks, everybody. Thanks for tuning in to Using AI at Work. Don't forget to subscribe for more conversations about how to use AI at work. And a special thank you to our sponsor, Chief AI Officer, for empowering businesses with AI education and training. Visit their website for free AI readiness assessment and AI strategy guide to help you get started using AI at work. That's www.chiefaiofficer.com. So thanks to our producer, Evan Desaulnier, for making this episode possible. Follow us on Twitter at the handle UsingAI at Work and visit www.usingaiatwork.com for free resources to help you harness AI in your role.