An AI Committee Needs Every Department and Real Authority

GovernanceAdoption
Written By Christina Ellwood

Episode Summary

Isar Meitis, CEO of Multiplai, spends most of this episode on one structure. After continuous education, which he makes step one of his blueprint, he wants an AI committee: at least one person from every department, somebody from the leadership team and preferably the CEO, and a few people who will stay up late playing with a new tool. The departmental spread gives you the needs and the limits from everywhere and a champion inside each function. Leadership gives the committee authority, because a group that has to route every decision through a VP loses opportunities while AI moves. The committee then owns the guardrails, the education, the tool selection, the processes and the budget. He argues guardrails widen what people try rather than narrowing it, and says tools do absolutely nothing for efficiency until the process around them is right.

Key takeaways

  • Step one is continuous education, and he frames it as a standing duty rather than a launch. Find a way for you, your team, your leadership and your employees to stay on top of what AI can do, where it is going, what the potential is and what the risks are

  • Step two is the one everything else hangs on. Starting an AI committee is what makes the effort survive and get across the entire company, rather than staying with the one or two people already tinkering

  • His reason for wanting the departments represented is two-sided, and he numbers the halves himself. You collect the needs, the struggles, the technical limitations and the data privacy constraints from everywhere, and you get a champion inside each department so the implementation meets less resistance when it reaches the people in the trenches

  • He wants somebody from the leadership team on the committee, preferably the CEO, and is explicit that it does not have to be. The first reason is that leading by example shows the company actually cares and that the work is important to it

  • His second reason for putting leadership on the committee is authority rather than symbolism. He wants an actionable committee that can make a decision and run with it, because waiting three or four weeks to route something through a VP loses opportunities while AI keeps moving

  • The committee’s first job is to define the rules, guidelines, regulations or guardrails, some of them tactical and some of them ethical, covering what may and may not be done with what kind of data in what kind of scenario

  • Education stops being an individual duty and becomes a committee one. The group divides and conquers across newsletters and YouTube channels, aggregates once a week, and distributes what is already digested and summarized to the people it is relevant to

  • His argument for putting tool selection under the committee is that the choosing is already happening without one. He says the research, from McKinsey or the others publishing on this, shows about 75 percent of employees are using AI tools they are not reporting to their leadership

  • He calls the next point a little secret, and it is the one that reorders everything above it. Tools do absolutely nothing for your efficiency on their own; using them properly in the right process is what does, so the committee owns the processes and the procedures for using them

  • Budget and infrastructure are committee work too, and he includes time in that. Budget for tools, budget for training, and protected time to experiment, because a committee also handed another marketing project and a strategy piece will not get to the experimenting

  • Asked what feeds the committee, he names three assessments and takes the strategic one first. His premise is that almost every industry will change dramatically in how people use things and in what products and services they will pay for

  • His worked example is a law firm billing on paralegal time. Nobody three, five or seven years from now will agree to pay for research, he argues, because they will know one prompt returns the information

  • He puts a number on the consequence and treats it as a planning problem rather than a prediction. If 30 percent of a firm’s income is paralegal time and that goes away inside five years, the strategic question is how you plan for it now

  • The second assessment is an HR skills gap analysis. What do people know today, what will they need to know tomorrow or in two years for the company to stay competitive, and do you hire for those skills or train for them

  • The third assessment is low-hanging fruit, and he means tactical things available immediately. What small changes right now save time, save money or make operations more efficient across every aspect of the business

  • His answer on adoption puts the people ahead of the technology. It is another exercise in change management, and you can have the best AI tools and the greatest infrastructure and still fail the transformation, because it is the people who make it or break it

  • Celebrating a win is a mechanism rather than a morale exercise, and he sizes both halves of it. His example is a marketing colleague doing in 37 seconds something that used to take three days, and his instruction is to spend ten minutes letting that person explain how, because it shows the thing is doable and it shows the company cares

  • His arithmetic on small wins is the case for gamifying them. Forty small wins across 40 different people in different departments leaves the whole organization about 5 percent more efficient than it was, which he calls significant

  • Asked how a company differentiates once everyone has the same tools, he answers human relationships and calls it the more important of his two answers. Relationships have always mattered, especially in B2B, and he expects them to matter a lot more

  • His reason is a research finding about who gains most from AI. Bottom performers using it get a significantly bigger efficiency increase than top performers, so the spread between people narrows, and if you aggregate that, companies converge too

  • What is left when output converges is relationships, and he names three kinds. Your employees, who you can drive and motivate; your ecosystem of suppliers, distributors and people who are not direct competitors; and your clients and prospects

  • His closing ask is to stop reading about it. You can listen to a million podcasts and follow YouTubers, and if you do not try it and start playing with it you will not learn, which he says holds for himself, for his company and for his own children equally

About Isar Meitis

Isar Meitis is the CEO of Multiplai, and at the time of this conversation had spent a year and a half teaching an AI course to business executives, with hundreds of them through it, alongside consulting work with companies putting AI in place. He describes the blueprint on this episode as something that refined itself over that year and a half on real scars rather than in theory. He also hosts the Leveraging AI podcast. His argument here is that an AI effort survives and reaches a whole company only when a standing cross-functional committee owns it: one person from every department for the inputs and the champions, someone from leadership so it can decide and act, and clear guardrails so people experiment more rather than less. Underneath that he is a change management person more than a technology one, and says it is the people who make or break the transformation.

 

In this episode

00:41 Welcome, and who Isar Meitis is
01:16 The question: a blueprint for implementing AI
01:26 The biggest question people ask, and where the blueprint came from
01:59 Step one: continuous education
02:27 NotebookLM, and testing a new tool the day it ships
03:33 Step two: start an AI committee
04:19 Inputs from every department, and a champion in each one
04:45 Someone from the leadership team, preferably the CEO
05:05 An actionable committee that can decide and run
05:23 And you want geeks on it
05:42 Committee job one: rules, guardrails, do’s and don’ts
06:22 Why guardrails prevent the wrong things
06:45 The playground fence, and why boundaries widen the field
07:24 Committee job two: divide and conquer on education
08:20 Committee job three: tools, and the 75 percent nobody reports
08:54 How a tool request becomes a test case and then licenses
09:09 Tools do nothing; the process around them does
09:54 Committee job four: budget, infrastructure and time
10:57 Committee job five: the strategic conversation, kept smaller
11:23 Three assessments, starting with the strategic one
11:38 The law firm and its paralegal hours
12:02 Thirty percent of income that goes away
12:44 Assessment two: the HR skills gap
13:03 Assessment three: low-hanging fruit
13:33 Fifty-seven to 230 processes per department
14:00 How to drive adoption and create excitement
14:21 It is change management, not a technology problem
14:42 What a committed CEO looks like, and what the other kind looks like
15:20 Celebrate wins: three days to 37 seconds
16:03 Gamify it, and what 40 small wins add up to
16:29 Encourage experiments inside the guardrails
16:51 Differentiating when everyone has the same tools
16:57 The first answer: human relationships
17:17 Bottom performers gain more than top performers
18:12 Relationships with employees, ecosystem, clients and prospects
19:03 What listeners should take away
19:12 Do not be afraid
19:14 Get your hands dirty, or you will not learn

In Guest’s words

“The process on how this thing survives and gets across the entire company, is starting an AI committee.”

— Isar Meitis  (03:33)

“You want somebody from the leadership team in that committee, preferably the CEO, but it doesn’t have to be.”

— Isar Meitis   (04:45)

“You wanna be able to move very quick because the AI moves very quick.”

— Isar Meitis   (05:05)

“When there was a fence, kids were playing at the fence, so they filled out the entire playground.”

— Isar Meitis   (06:45)

“Tools do absolutely nothing as far as your efficiency.”

— Isar Meitis   (09:09)

“Nobody, three years, five years, seven years from now will agree to pay for research.”

— Isar Meitis   (12:02)

“Human relationships have always played a very important role in businesses, especially B2B businesses, but it’s gonna become a lot more important.”

— Isar Meitis   (16:57)


 

Resources

Isar Meitis

Named on air

  • NotebookLM: The Google tool he uses at 02:27 as his example of staying current. He describes it as having shipped the week of the recording and says listening to the audio overview it generated blew his mind

  • McKinsey: Named at 08:20 as one source among others for the figure of about 75 percent of employees using AI tools they do not report

  • The playground fence experiments: The psychological research he refers to at 06:22 without naming it, in which children used the whole playground once there was a fence and stayed away from the edge without one

Ideas and terms discussed

  • The AI committee: A standing cross-functional group with one person from every department, somebody from leadership, and the authority to decide and act

  • Guardrails: His preferred word among rules, guidelines and regulations. Tactical and ethical boundaries on what may be done with what data, set by the committee before anything else

  • Continuous education: Step one of the blueprint and, once the committee exists, one of its duties. Divided across the group, aggregated weekly, and distributed already digested

  • The three assessments: Strategic, HR skills gap, and low-hanging fruit. What feeds the committee rather than what the committee produces

  • Change management: His framing for the adoption half of the episode. The transformation is a people problem, and the best tools and infrastructure will not save it if the people are not on board

Related AI Realized episodes and events

 

Frequently Asked Questions

  • An AI committee is a standing cross-functional group inside a company that owns how AI gets implemented across it. Isar Meitis of Multiplai makes it the second step of his blueprint, after continuous education, and describes it as the thing that lets an AI effort survive and get across the entire company. It holds at least one person from every department, somebody from the leadership team, and a few natural enthusiasts, and it is responsible for the rules, the education, the tools, the processes, the training and the budget.

  • An AI committee should be staffed department by department, with somebody from the leadership team and a few people who enjoy the technology for its own sake. Isar Meitis of Multiplai wants that spread for two separate reasons: you collect the needs, the struggles, the technical limitations and the data privacy constraints from everywhere, and you get a champion inside each department who reduces resistance when the work reaches the people who have to use it. He wants leadership on it, preferably the CEO though he says it does not have to be, both to lead by example and to give the group authority to decide and act.

  • An AI committee sets the rules, runs the education, selects and tests the tools, designs the processes around them, trains the people who need training, and owns the budget. Isar Meitis of Multiplai works through those in order on this episode. The rules come first because they prevent the obvious mistakes and because clear boundaries make people experiment more. Education is divided across the group and aggregated weekly. Tool requests go to the committee, which tests them, checks the license cost and the data question, and distributes licenses if a test case works. Budget covers tools, training and protected time to experiment.

  • Guardrails widen what people are willing to try rather than narrowing it. That is why Isar Meitis of Multiplai puts them first among the committee’s jobs, and his illustration is a set of psychological experiments he does not name, in which children playing near the edge of a playground stayed well away from it when there was no fence, and used the whole field once a fence was there. His reading of that for AI is direct: give people the boundaries so they feel safe experimenting inside them.

  • About 75 percent of employees use AI tools their employer does not know about. That is the figure Isar Meitis of Multiplai cites here, and he attributes it loosely, to McKinsey or all the other ones that publish information about this, rather than to one named study. His point in raising it is that the choice of tools is already being made across the company whether or not anyone is governing it, so putting selection under the committee brings something back under control rather than adding a new control.

  • A company should run three assessments before implementing AI: a strategic one on how the industry itself changes and what customers will still pay for, an HR skills gap analysis covering what people know now against what they will need in two years and whether that gap gets hired for or trained for, and a scan for low-hanging fruit, meaning the tactical things available immediately that save time or money. That is the account Isar Meitis of Multiplai gives. He says some of the three are ongoing and some are more of a one-off every now and then, without saying which is which.

  • A CEO shows support by using the tools personally and visibly, not by sponsoring the work from a distance. Isar Meitis of Multiplai contrasts two kinds of client from his own consulting: the CEO who reads everything, tests things himself, spends time on it at night and shares what he finds with his team, whose company is energized around it, and the CEO who says you take care of it and tell me what you found, where he says it is just not the same. His conclusion is that real buy-in from leadership makes a very big difference.

  • A company differentiates on relationships once every competitor has the same tools, because the tools stop being the variable. Isar Meitis of Multiplai calls that the more important of his two answers, and his reasoning starts from a research finding that bottom performers gain far more from AI than top performers do, so the spread between people narrows and, aggregated, companies converge on similar output. What is left to choose between suppliers is trust, which he locates in three relationships: with your own employees, with an ecosystem of suppliers and distributors who are not direct competitors, and with clients and prospects.

 

  • [00:41] Christina Ellwood: Welcome to AI Realized, the podcast for enterprise executives leading AI deployments. From addressing security, data, and operations challenges to managing the organizational and management changes, AI deployment presents the opportunity to redesign our organizations from the inside out. I’m Christina Ellwood, your host for today’s episode. We’re talking today with Isar Meitis, the CEO of Multiplai. Isar, welcome to AI Realized.

    [01:12] Isar Meitis: Thank you so much for having me. I’m really excited to talk to you about this.

    [01:16] Christina Ellwood: Yeah. So let’s talk today about your blueprint for a successful implementation of AI in an organization. I think that’s relevant to everyone who’ll be listening to our podcast.

    [01:26] Isar Meitis: Yeah. I think that’s the biggest question people are asking themselves, right? How do I get started? Or if I got started, how do I make this a company-wide initiative versus I have Gina in HR and John in marketing, like tinkering with this. How do I make this a, a thing? And I’ve been, I’ve been teaching a course to executives for a year and a half now, so hundreds of business executives have been through the course, and I’ve been consulting to businesses, so this has refined itself through the last year and a half based on, uh, real scars that, that I got in the year and a half that I’ve been doing this. But the very first step of those things that you gotta do is continuous education. You’ve gotta find a way for you, your team, your leadership, your employees to stay on top of what AI capabilities is, where is it going, what the potential is, what the risks are, and so on. Now, it’s not easy to do, but it’s very rewarding if you figure out how to do this. And I’ll give you a great example that I talked about this pre-show. There’s a new tool by Google that’s called NotebookLM, and that NotebookLM tool, it literally came out last week. It allows you to take notes and analyze stuff in a notebook style thing, but with some very cool AI capabilities. The coolest thing in there is you can take a file or a link, upload it, and turn it into a podcast that is just like this. So it’s not reading the content, it’s not summarizing the content. It’s a conversation of two people, a guy and a girl discussing and geeking about the topic of the document that you gave it. And when I listened to it the first time, which was the day they sent it out, so I like to check these things, it blew my mind. And I already have several use cases on how I’m using this for myself and for my clients and so on. So staying educated and on top of things is critical. The easiest way to do this is to follow the right people. Follow people who are practitioners, follow people who are thought leaders, follow people from the big companies, and just stay... A- and this could be on anything, right? You can do this on TikTok, you can do this on YouTube, you can do this on Instagram, on LinkedIn, like wherever it is, podcasting, wherever it is you consume content, it’s fine. You can do that in order to stay educated. So that would be the very first thing. The second thing that ties into that, and then that becomes the process on how this thing survives and gets across the entire company, is starting an AI committee So let’s talk a little bit about who’s in the committee, why you need a committee, what do the committee does, and how does it work. The committee’s role is to really help in implementing AI successfully across the organization. So let’s talk initially about people. You want at least one person from each department. So you want somebody from HR, somebody from marketing, sales, et cetera. You want that for several different reasons. Reason number one is you get inputs from all the different departments as far as what your needs are, what you’re struggling with, and so on. Limitations, technical limitations, and so on, data privacy. So you get inputs from everybody. The second reason is you get champions in each and every one of the departments, so the implementation gets less resistance, and somebody can help in actually getting this to the people at the end, like the people in the trenches who actually need to use this the most. So this is number two. Number three is you want somebody from the leadership team in that committee, preferably the CEO, but it doesn’t have to be. Why? Two reasons. One, you lead by example. It shows that the company actually cares about this and that it’s important to the company. But two, you want it to be an actionable committee. You wanted them to be able to make a decision and actually run with it versus, “Okay, now let’s submit this as thing to this VP, and they will present it, and then in two weeks, we’ll get...” You don’t want that. You wanna be able to move very quick because the AI moves very quick, and if you wait three to four weeks every time before you can start implementing something, you will miss a few good opportunities. So these are the people in the committee. Now, the other type of people you want in the committee, and that doesn’t matter which department they’re in, you want geeks. You want people like me because then they will spend 10:00 p.m. to 2:00 a.m. playing with this new tool because they find it cool and interesting, and you’re just gonna make more progress faster. So that’s the people in the committee. But what does the committee needs to do? So the very first thing the committee needs to do is define rules, guideline, regulations, guardrails, call them whatever you wanna call them. And some of them are tactical, and some of them are ethical, but they gotta define the do’s and don’ts of what you should and shouldn’t do with what kind of data and what kind of scenario in your company with AI. You wanna do this for two different reasons. Reason number one is it’s going to prevent people from doing the wrong things, releasing data that they shouldn’t, getting in trouble in places that they shouldn’t, saying stuff that they shouldn’t with... Because the AI, the AI sent it. I’m like, “You’re, should be in charge.” I’m like, “How am I supposed to know that?” So rules and regulations. And then the second reason is once you have guardrails, once you have a fence, there’s actual psychological experiments, but they let kids play with a ball next to not a cliff, but a cliff, like the edge of their playground. And when there was no fence, the kids were playing very far away f- from the edge. When there was a fence, kids were playing at the fence, so they filled out the entire playground and playing at the whole field. It’s the same thing with AI. You want to encourage people to use AI. We’re gonna talk about later how to do that But you wanna give people the boundaries so they feel safe to experiment within these boundaries. So that’s the very first thing that the committee needs to do. As I mentioned, both tactical as well as ethical. Like it’s very tempting to do things with this. Let’s say, no offense to salespeople, I was a salesperson myself when I started my career. Many salespeople get compensated on results, and it’s very tempting to close another deal, to close the quarter, to get the bonus, to buy the car. And what’s acceptable and not acceptable as far as using these tools to close the next deal or whatever the case may be. And so rules and regulations are number one. Number two is what I said in the beginning, continuous education. The committee needs to be the group that divides and conquers. You listen to this, I’m gonna watch that, you follow that YouTube channel, I’m gonna read this newsletter, and we’re gonna aggregate this once a week, and the stuff that is relevant, that already has been digest, summarized, so it’s easy to consume, is gonna be distributed to the relevant people in the organization, so they can benefit from that. So not everybody has to follow everything all the time. They have to plan training sessions and tests and so on to actually check that people are following and so on. So that’s the second part of the committee. The third part of the committee is what tools are we going to use? Then that’s the million-dollar question, because there’s three new tools every about six minutes, and which one is best for us? And how much budget can we afford for that? So you want... You don’t want each person in your organization trying stuff out randomly. By the way, it’s happening right now. Like the research is showing, if you go to McKinsey or all the other ones that publish information about this, about 75% of employees are using AI tools that they’re not reporting to their leadership. So people are already using the tools, you just don’t know about it. So you want to have it under control, so there’s clear governance on which tools, which data, and so on, and check the tools. Now, anybody can raise their hand and say, “Hey, I heard Isar on this podcast, and he suggested this, and it sounds perfect for our use case. Can I use it?” Goes to the committee, tests this out, checks if how much is a license, can we use it, what about the data, blah, blah, blah, blah, blah. And then they decide if they wanna do a test case about it. They do a test case. If the test case works, they distribute it, the right licenses to the right people. So then again, you have more control on what’s actually happening in your company with AI. The follow-up step of that is once you have a tool, I’ll let you in on a little secret, tools do absolutely nothing as far as your efficiency Using the tools properly in the right process is. And so the committee is in charge of developing the right processes and the right procedures on how to use the tools to get these specific business outcomes that the business wants. And, and then training the people, the relevant people, so you don’t have to train everybody, like could be different training to different kinds of people on how to use the tools. So that’s another part of the committee’s work. Uh, another part of the committee’s work is budget and infrastructure, right? Somebody has to define the resources for this whole thing to work. It doesn’t happen in a vacuum. So you need budget for tools, you need budget for training. Guess what? You need time for the committee to experiment. If you’re just gonna tell, “Oh, you guys are the committee, by the way, here’s another marketing project, and go and work on this, uh, business strategy,” then when do you expect them to work on that? So there’s resources that needs to be allocated, and the committee is the one that needs to define the required resources. They can obviously adjust it and so forth, so on and so forth. But they need to be able to be in charge of resources for AI, and they need to come up with a plan that will verify that there’s an ROI on the investment on these resources. “So here’s what we’re going to do, here’s how we’re going to test it, here are the test parameters, here are the KPIs that we’re gonna put in place, both leading and trailing KPIs,” and so on. So that’s about the committee. The other thing that a committee needs to do, and sometimes we do this with smaller committees, is the strategic thinking of how’s that gonna impact the industry, your business, your competitors, your clients. And so usually it’s a smaller part of people because you don’t wanna expose everybody to these kind of conversations, but it’s a smaller part of the committee that needs to be involved in these kind of conversations as well. So I’ll stop here and let you ask any questions if you have any.

    [11:09] Christina Ellwood: Thank you. That was very helpful and clear, and I can see where as an input to that committee, some assessments might be necessary. And what do you recommend in that area?

    [11:23] Isar Meitis: Great question. I’m glad you asked. The three different assessments that needs to be done, that some of them are ongoing and some of them is more of a one-off every now and then, but the first one is strategic, as I mentioned before. Almost every industry will change dramatically in how people will use the things and the types of products and services they would be willing to pay for. Let’s take two quick examples. The legal world today works on billable hours. X percent of them goes to par- based on paralegal time. So let’s say you run a law firm and 30% of your income is paralegal time. That’s gonna go away Nobody, three years, five years, seven years from now will agree to pay for research because they will know that with one prompt you get the information. Now, if you’re a paralegal, start thinking of how to diversify your capabilities in your career. But if you’re a law firm and sometime in the next five years you’re gonna lose 30% of your income, that’s significant. How do you plan for that from a strategic perspective? Same thing, people writing code. All these code writing tools are already very good right now. Where are they gonna be in three to five years? If I’m running a software company, how many code writers and developers I actually need? What roles do they need to have? What do I need to teach them in order to stay ahead of the game? Which leads me to number two. Number two is HR skills gap analysis. What do my people know today? What do they need to know either tomorrow on some of the things or in two years for some of the things in order for my company to stay competitive? So do the mapping of where the world is going, what skills you need to be successful in that new world, and then do you need to hire for those skills? Do you need to train for those skills, and so on. And the third thing is low-hanging fruits Right? So what are tactical small things you can do right now that will save you time, save you money, make your operations more efficient across every aspect of your business? So every department has between 57 to 230 processes that they do. And first of all, you can use AI to map these processes. But once you mapped the processes, you now need to figure out excuse me, how much of these you can automate or use AI in order to accelerate the process and make it more efficient. So strategic, tactical, skills gap analysis.

    [14:00] Christina Ellwood: Got it. Okay. Now, this is a big change for a lot of the folks in our organizations. How do you, uh, recommend driving adoption and creating excitement for people about this change?

    [14:12] Isar Meitis: I’m glad you asked. It’s an awesome question. And, and when I talk to people, when I do my consulting on my courses, I tell everybody, people think it’s the tech part of it that is exciting. At the end of the day, it’s another exercise in change management, right? It’s the people that will make it or break it. Like, you can have the best AI tools and invest in all the greatest infrastructure, and you’re going to fail in this transformation because you have to get the people on board. And to get the people on board, you need them to be on board, and that comes from several different things. The first thing I mentioned before, lead by example. Like, I work with companies where the CEO is completely committed. He reads everything. He tests stuff out himself. He spends times at night. He shares it with his team, and it shows. The company’s all energized around this AI capabilities and so on. And I have companies in which the CEO doesn’t care. It’s like, “You guys take care of it and tell me what you found,” and it’s just not the same. So getting the actual real buy-in from leadership plays a very big difference. So that’s number one. Number two, you wanna encourage AI usage. So how do you encourage usage? That’s a very big word, but what’s the practical aspect of this? So number one, you want to talk about this regularly. You have an all-hands, have an item about AI implementation in your all-hands every single week. You have team meetings, talk about what you’re doing with AI in your team meetings. Share wins. Celebrate wins. Dave in marketing was able to do this thing that used to take him three days and now takes him 37 seconds. And people go, “Holy crap,” and invest 10 minutes in letting Dave s- explain what he did and how he got to it. And so when you celebrate wins, two things happen. First of all, people see that it’s doable. It’s not just this, “Oh, people saying AI, but I couldn’t get it to work.” So it’s actually working. And the second thing is it shows that the company cares, right? If you gamify this thing, saying, “Okay, anybody who is able to do this and that gets a PTO Gets three tickets to take his kids to the movies. Like, whatever, it doesn’t matter. Like, there’s different ways to build excitement around celebrating small wins, like small stuff that is day to day, but you aggregate 40 small wins across 40 different people in different departments, and your entire organization now is 5% more efficient than it was before, which is significant. So this is how you build excitement and encourage people to actually use. The other thing that I said is encourage people to experiment within the boundaries of the guidelines that you have defined. So tell people, ”Don’t be afraid. Try this out. Just don’t send this automatically to the client before you..." Like, go check your work before, before you do that. But that’s another way to do this.

    [16:51] Christina Ellwood: Great. So in a world where everyone’s using AI tools, how do you differentiate?

    [16:57] Isar Meitis: So there’s two answers to that. I’ll, I’ll answer the first one, which is the more important one first, and then I’ll go to the other one. The important one is human relationships And the reason for that is human relationships have always played a very important role in businesses, especially B2B businesses, but it’s gonna become a lot more important. Research is showing that people that are bottom performers that are using AI are getting a significantly bigger increase in efficiency compared to top performers. And it makes sense, right? So if you know very little and now this tool can do a lot for you, you can double your results. If you’re in the top percentile in your company, your industry, your department, your niche, it’s gonna be harder to get additional benefits, so it’s gonna improve your capabilities 5% instead of 100%. What that means is that the spread between the bottom performers and the top performers are gonna get smaller and smaller over time. What that means, if you aggregate that, is that companies are gonna get closer and closer together. The results of a company, you are now the leader in your industry, and there’s people who are 30 places below you in the ranking may close up the gap very quickly. So what’s gonna make the difference? Your relationships with your employees, the people you can drive and excite and motivate to do things for the business, your relationships with your ecosystem, so your suppliers, your distributors, your people that are not your direct competitors, and your relationship with your clients and prospects. Because they will say, “Okay, everything’s vanilla. Like, I can go with you, I can go with this, like, you can all do this because none of you is actually doing it. AI is doing it for you or X percentage of it. I will pick you because I trust you, Christina.” And so doing that comes from go to trade shows, create your own events, be on podcasts, share your own content on social media. Like, all these things that are connecting you, building trust and relationships with other people will play a very big role in this new AI world.

    [19:03] Christina Ellwood: Thank you. That was very good advice. As we wrap up, what would you like our listeners to take away from our conversation today?

    [19:12] Isar Meitis: Everything. I, I think the first thing is don’t be afraid. Like a lot of people are really scared from this whole thing, and they get into a paralysis of, “I don’t even know where to start.” So first of all, now we have a blueprint on how to start. Second is it really is about getting your hands dirty. You can listen to a million podcasts, and you can follow YouTubers, and you can do all of that. If you don’t try and start playing with this, you will not learn. And if you do try, you may actually find this exciting and cool and interesting, especially if you have some bit of a geek in you. And so I do this now for myself, I do this for my company, I do this with my kids. My kids now love AI. They do this for work. They do this for fun. They do this for... It’s really easy to build excitement around this once you start experimenting and seeing results.

    [19:59] Christina Ellwood: Isar, CEO of Multiplai, thank you for talking with me today on AI Realized.

    [20:06] Isar Meitis: Thank you. This was absolutely awesome.

 
Listen to other episodes
 
 
 
Join AI Realized
 
 
 
Subscribe to the newsletter
 
 
 
Christina Ellwood
Previous

AI Is a Tool for Augmenting People, Not Replacing Them