Zohra: 0:01

Hello listeners. Welcome to season 7 of Inside Techcom with Zara Mutabana. In this season, we have a series of standalone episodes, each tackling a critical issue shaping tech and content today. From hallucination-resistant content and AI adoption to ethics and equity in tech, this season is about navigating change with clarity, integrity, and impact. Let's get started. Hello listeners. Welcome to another episode of Insight Techcom. This is not part of season six. We are doing this as standalones. I was fortunate enough to get in touch with Monica, Marcus, and we connected and we discussed what more we could add to the whole AI conversation that's been happening. And even though I ended season six, this was a conversation worth sharing. And I thought, how about just kind of pivoting a little bit and having these standalone conversations? So that's what this episode is all about. We have Monica here with us. Hey Monica, welcome to the show.

Monica: 1:07

Thank you. I appreciate uh the opportunity again. Thank you for give doing this standalone episode. I know you mentioned that your season had ended, but we had an amazing conversation. And I think the two of us felt like this would be fun and a great addition for your audience.

Zohra: 1:23

Absolutely. And to the audience, we had talked about several things, but one thing that really caught my when Monica shared with me the whole AI adoption at organizations, be they big or small, and what her experience has been and how her company is working on AI adoption and AI fluency. I will have Monica introduce herself. Monica, please introduce yourself.

Monica: 1:47

Certainly. And my through line has always been working with the people in the organizations, helping people maximize their potential and really be able to navigate their career journey, you know, as far and as fast as they'd like. And so about, I would say, six years ago, about five and a half, six years ago, I left my corporate job to really start having broader impact because I loved all the work that I did at many of these organizations, but I was always restricted to the four walls of the organization I was working for in helping individuals in their just professional development, their career development, in really transforming themselves so that they could, you know, give their best work and feel fulfilled in the work that they were doing. And so left about five years ago and you know, have launched multiple companies since then and exited companies and our current company, uh, we really started thinking about it about you know a year ago when we saw how AI was really going to disrupt just work, right? And the idea about what does this mean for everybody? What does this mean for the workplace? What does this mean for people? And one of the things that, you know, my area of expertise in terms of adult learning, people kind of adopting and kind of growing and all of those things, I knew that this change was going to be happening faster than people would be able to change. And so we really started thinking about how do we help people embrace this change. And we started seeing some of the reports out where companies were investing millions of dollars in AI tools and, you know, deploying them out to their organizations. But we're starting to see now the residual effects of the strategy in which they rolled it out isn't necessarily working because the adoption rates have been extremely low. And part of it is because we forgot along the way that people really need to be at the core and at the center of the of really the return on investment of AI, because AI itself can't make the the changes that or can't really lead to the productivity or the efficiency. It's the people. And people don't realize that the difference with AI tools is it's not like here's a calculator, learn how to use this calculator. It's a it's the opposite, where people actually have to train the tool in order to get the outcomes that they're hoping for. And so this idea is it's not really that you have to transform yourself, you know, to AI. You actually have to partner with AI and you have to reinvent the way that you do work. And there's a lot of things that are stopping people, you know, behavioral things, just identity in terms of who am I and how do I leverage AI to, you know, do the work. And there's a lot of fear around am I going to be replaced in all of those things? And so that is why our company that we have launched is Flipwork, where we've partnered with some really big, significant partners and Fortune 500, Fortune 100 companies to really start to think about the agentic human reinvention, right? How do you create exponentially capable people who can successfully adopt emerging technologies very quickly so that the companies can start seeing the ROI, right? Because the research has come out that 78%, really almost 80% of enterprises that have deployed AI, but really single-digit numbers are actually seeing the ROI. And so, how do we help people close that gap? So, workforce transformations, they can't reinvent people at the speed of technology change. And so that's who I am in a nutshell, is really trying to help people reinvent themselves so that they can adopt to the AI age.

Zohra: 5:58

Thank you for that very detailed background. And I think you gave a great context about how we are going to approach this conversation. The very first thing that comes to my mind, uh Monica, is the company that I work at, I believe I've been in this career as a technical writer for 20 years, 20 plus years. And I've worked at several companies, and where I am at currently, I believe personally, that they have had a pretty good rollout strategy for employees. But like you said, the adoption has been slow. Now everybody's kind of waking up. They've made certain training mandatory, and I would say there is these, there is this reservation. And as you were talking about the low human adoption of AI and the percentage, I think you said about 80% of companies are facing low adoption. Is that what that percentage is?

Monica: 6:54

Low adoption or low, um 80% are not seeing the return on investment they were expecting to get from the AI, you know, tools and the deployment of all of these tools.

Zohra: 7:05

Right, right. So the one question that cropped in my mind, I think I which I hadn't even thought about was many companies are doing well with this AI adoption, are having people train and then share their learnings. And as companies realize that many things can be automated, there has been an impact on talent. Yes. What are your thoughts on that? I don't want to start on a negative note, but I think it's a very pressing question. And I wanted to kind of start with that as our context here, because there is this fear. So there are people that there are employees who are scared. Okay, I will I will start training, I will show what my learnings are, and then I can be replaced. So people are coming at it with fear. So I can I can understand why the AI adoption might be low. But yeah, what what is your take on that?

Monica: 7:57

I mean, the reality is exactly what you shared, Zora, is that the truth is that yes, jobs are going to be disrupted. People, employees, talent is going to be disrupted, but not necessarily in the way that we all think, right? Yes, there's worst case scenario that AI is going to replace you. But the reality is, and many of the experts have said, the AI itself is not going to replace you. But people who leverage AI or who train the AI are going to replace people who don't embrace and learn to use AI. And so that is where we're trying to get people to understand you have to disrupt yourself before you get disrupted, or you are going to get left behind. And so, yes, the way that you do work is probably going to change. And the way that I tried to really kind of visualize this for people is that if you get ahead of this and you start to really train and work with the AI and kind of partner with technologies, you will start to become what we here at Flipwork are referring to as agentic human. And an agentic human is like people squared. Imagine yourself like exponentially having more capacity because some of the more, I would say, administrative or kind of your time-sucking tasks are going to be relieved by you training or leveraging the AI to do those things, allowing you to be able to do more of what we like to say leverage your zone of genius. Where is your area of expertise? What are the things that you bring to certain projects, to certain tasks, whether it's discernment or you know, the judgment or the lived experience or just the kind of the devil in the details, right? Of like making finessing and making a product or an end output better, right? And so for all intents and purposes, say like your job is to, if you're a creative designer or if you're someone who's an analyst and you're pulling together some RFPs for your company, on any given week, you could probably churn out, I don't know, I'm throwing out an arbitrary number, three RFPs, because it takes you about a day and a half to pull together the research on the company, all of these types of things. It takes you roughly about a day and a half, two days to pull one RFP together. And if we were to break it down for you and reverse engineer your workflow, we would say, where is the bulk of the time that you're doing it coming from? And it might be, oh, gathering all the data, pulling it all together, all of these things. And then the last few hours is where I come in and I apply my discernment, my judgment, my all of these things. Well, if you start to say, here's your workflow, and here are some areas that we've identified that you can leverage AI to pull all this data together, to clean it up, to synthesize it for you and all of these things. Now all of a sudden, rather than a day and a half per RFP, it's really taking you five hours, right? And so now you start to do the math, and you're like, you know what? I used to be able to turn out three RFPs that were good quality that I was proud of, but now I can actually give them six RFPs or eight RFPs because all of the grunt work is getting done and the heavy lifting is getting done by the AI and the way that I've trained it and worked with it. And now I have time to actually apply my expertise, my preference, my taste, everything that I do that makes it unique and authentically me. I have more time to do that for all of these reports. So the quality and the impact of your reports are much more significant. And so that's what we're trying to get people to understand is that it may not replace you, but it may help give you more capacity to do the things that you do best. And sometimes those are the human things that AI cannot replace, right? Right. And contrary to what a lot of reports that we're seeing, right, we're seeing Amazon like let go of 10,000, 14,000 people or whatever that number was, it was huge. The reality is, is for those of us who are familiar with HR processes or with talent management, you know, processes, the reality is, is that right now on the forefront is that it's disrupting and replacing people. But the reality of some of the layoffs that you're seeing in that huge bulk is that they are right sizing from the overhiring that they did during COVID. So if you think about Amazon, I mean, their deliveries and sales went out the roof because people couldn't leave their homes. And so they needed more delivery people. They needed all these kinds of, when you think about the people that they needed to hire, they had to hire a bunch of people. Now they're right sizing, but it's also in tandem with some of the AI things where yes, they have probably taken AI and tools and really made efficiencies and cut some of these things back. But all of those jobs or all of those people that you let go aren't being replaced by AI. They're also some level setting going on. So are 50% of those because of AI and 50% because of overhiring? Possibly, we don't know. But the headlines, of course, clickbait, you know, it's just like all these jobs are leaving because of AI. But the reality is, is there are a lot of reasons why. But there are some really great examples, right? Recently I was at a conference in Palm Springs, which was Ernst Young's strategic growth forum conference, and they all the topics were about AI. But one of the leaders talking talked about some great examples where IKEA leveraged AI and machine learning and machines to really optimize their customer service piece of it, which was going to displace roughly, if I'm getting the number right, 8,000 people. But instead of replacing those people over the past two years, IKEA decided to retrain and upskill those people to become design consultants or design advisors for IKEA. And the return on investment on that, supposedly IKEA's revenue jumped up by a billion dollars because they took those people and repotted them somewhere else opposed to getting rid of that talent. So some companies are looking to figure out how do we upskill our people? But the reality is, the truth is that yes, people are gonna get disrupted. And if you don't really, like you said, wake up and really think about how I might start to leverage AI so that I can amplify my zone of genius and my strengths, then yes, you may be one of those that gets left behind. But it's not too late. And this is where you can really start to think about how do I reinvent myself in this new age of AI disruption, machine learning, whatever it is. And how do I make sure that I'm riding that wave opposed to getting to crushed by it?

Zohra: 15:00

I love that getting crushed by this wave. As you were giving me the all these tidbits of information, I was starting to kind of tick off. Oh, yeah, that resonates with me, or that does not resonate with me. So one of the things I love is leveraging your zone of genius. And you talked about how you can tap into your creativity and amplify it.

Monica: 15:21

Yes.

Zohra: 15:22

And I'm a content creator, and in my role, I am seeing a benefit. I am an AI advocate. As much as I am fearful of what our roles will transform into, you're right. If I don't ride the wave, I'm gonna get across the bite regardless. So I might as well ride the wave and see where it takes me. And I've definitely seen some great outcomes, some great experience. At the same time, when I've been told that the AI will kind of take away your job over time as I've been using it for the past three years, I feel less severe about it. Right? However, I was thinking about it. You mentioned, you know, just as an example, you mentioned that if you have RFPs and you were doing three RFPs, in my world, there are a certain number of documentation deliverables that I have to deliver. And I've definitely benefited as I partner with AI. That said, I'm hearing of organizations that are saying we want your department to be productive by 30% in 2026 or by this number, whatever that number is, that X percentage. I feel this whole AI adoption, that is where it's kind of falling apart. Because productivity, one, uh is it the number of documents that you deliver? How are we continuing to measure the quality of that? Those are questions that I'm asking of myself. There are two things that I have experienced. One is that the more I do with AI, the more I'm starting to feel a burnout. Because sometimes a creative process requires time and patience. And here we are starting to focus on the numbers. So that pressure is starting to build up. And I keep reading, AI is gonna make your job easy, or you're gonna have a four-day work week. I know there is a lot of euphoria around that because I do tap into that positive narrative as well, and I think we have to look at the positives too. But my question to you is have you run into this scenario with the organizations that you work with where they're picking out this, in my opinion, meaningless metrics and their employees.

Monica: 17:35

I mean, it's that age-old conundrum of qualitative, quantitative, right? And sometimes, you know, how do you start to measure that? But it's also kind of, you know, some of these deep ingrained condition beliefs that we have, whether they're cultural, whether they're societal, whether they're organizational from the organization and other things, where effort equals success, right? Or high productivity or like whatever that is equals success. And we're having to rewrite some of those equations that we've have ingrained in our brains for decades and decades, that it's really impact equals success when you're leveraging AI, or quality equals success. Because what you really need to do, like you said, is that the generative AI, where it's generating content for you or generating lots of output for you, the danger is that you really have to have a human or an expert or somebody come in and apply that discernment and that judgment and that the expertise that you bring to differentiate between the work slop and the kind of generic output that it can put out. And so that's the interesting piece is that I tell people is that you can leverage artificial intelligence, but you have to layer in your authentic intelligence so that you come out with a superb, kind of unique and a quality output, right? And so that's where the human isn't gonna get replaced. Like you were saying, you're a creative, you're a content generator, right? You're creating all of this content, and AI can create content like nobody's business very, very fast. But the crazy thing is that if you use a particular prompt and your peers use the same exact prompt, you all are probably gonna get a very similar output. The differentiation is your what we like to say, taste, your preference, your expertise, your zone of genius. This is when you start to really go back and forth with the AI to say, nope, this isn't quite what I needed. Here's what I'm looking for. Or here's an example of something that I did that I created, and I want the output to look like this, right? And so this is where you start bringing in your authentic expertise and your authenticity starts to come out in your work. And that's where you start to differentiate yourself from everybody else. Using AI to come up with the same output. And so that's the danger, is where we're starting to see more and more people complaining that, oh, I'm getting work slop from all these people that are using AI. Well, that's where those people are truly lazy, right? It's just like, oh, it did it really fast. And instead of taking the time that they've saved to reinvest it to make a higher quality or better quality output, they're just kind of passing it on. And they're going to get deemed for it, right? And so I tell people, I love a play on words. I'm a big kind of like word nerd or acronym nerd, but AI is your artificial intelligence. You have to treat AI like your artificial intern, your AI, your artificial intern. And just like if you had a live person, physical intern, you would say, Hey, go do this project for me. And they would come back to you and give you this output. You would never take that output and just say, I'm going to pass it on to the client or I'm going to give it to my boss or we're going to, this is the final thing. You coach that intern. You tell that intern, I don't like this. You need to make sure you do this. Did you pull this research? Did you look at the citations? Did it come from credible sources? Make sure that you go back and you need to add this and you need to do that. You have to do the exact same thing with your AI bot, with your tool, with the agentic AI, with whatever it is that you're doing. You have to train it to give you the outputs that you're looking for so that you can have some quality, a quality work or a quality piece that's yours. And so you have to treat it the same way. Just like you kind of groom and grow and coach a live physical intern, you do the same thing with your artificial intern. And that's how you have to really start looking at it so that you can get the kind of the work that you're looking for and the quality that you're looking for. So, yes, there are going to be some push and pulls with these metrics where they say, okay, your KPIs, we expect you to have 30% more productivity. The reality is, is if you use AI correctly and you really do kind of have a concerted effort where teams come together to use AI, and it's not just individual people using AI and not sharing or transferring knowledge of, like, hey, what it is that we need to do, and kind of graduate from this kind of generative AI. I'm working with a bot just back and forth or doing surface level tasks and that's it. But really starting to think about agentic AI, building agents that help you do lots of other things and you connect the dots and you work together to kind of reinvent your workflows, you'll start to see those efficiencies coming through. But that's not going to happen overnight. And it doesn't just happen with a flip of the switch. You really have to sit down as a team and say, let's map out our workflow. Where are all of the inefficiencies in our workflows? How can AI close the gap on those inefficiencies? And then where do we redeploy the time saved or all of those things to really start to amplify the quality and the output of things? And so it's an exercise, right? It's almost like change management. But change management, the old school playbook for change management sometimes used to take a really long time. Well, you could hire the big companies, consulting firms, and come in and pay them tens of millions of dollars to do that. But by the time they roll all this out 18 months, 24 months later, things have changed and it's outdated. So, how do you start doing that at a much faster pace using AI to help you really kind of identify all those efficiencies? And so it's a little bit of a catch-22 of learning how to use the AI to find those efficiencies, but making sure that you come in, the human comes in to bring out the quality and all of those things in it.

Zohra: 23:46

I think I'm fortunate enough to be on a team where we are doing that. And we are having some good things coming out of it.

Monica: 23:55

Right.

Zohra: 23:56

My question was as you have engaged with uh the companies out there, is there a right or wrong way of AI adoption that you think, for example, if there's a company that is pushing out AI adoption, and if you've run into companies that are doing it really well versus companies that are struggling with it and what suggestions you've provided?

Monica: 24:14

Yeah.

Zohra: 24:15

I'm curious because large companies, large organizations, I think are probably they have the resources to do it right, quote unquote. But smaller companies may not.

Monica: 24:25

Yeah, I mean, you make some really good points, but the reality is some of the big companies, quote unquote, who have had the money and the resources to roll out all the AI, even they are the ones who were not seeing the return on investment they thought they were seeing. Because yes, they may be teaching or rolling out the AI, but what was happening is that IT departments and people were coming in and rolling it out, and the managers or the people were waiting for the tech team to say, here's how you use it. And the crazy thing is that the tech teams were saying, I can't tell you how to use the AI. You have to train the AI. Like, I don't know your workflows. I don't know what it is that you do. And so you've got to learn to start to leverage the AI to get the ROIs on it. And so we're starting to learn from a lot of these failed deployments, because for the large majority, AI has become something in the last three years when open AI kind of like made it public for everybody. But AI has been around for years and years. We're talking about more than a decade, in some cases, 20 years ago, in the form of machine learning or all of the automations and the things that were happening, right? But the idea that we weren't really helping people evolve and start to use the AI or explaining to them how they're supposed to, how they're supposed to use it. It's very, very different. And so the right way is that you have to create the psychological safety for people to test and learn with AI, right? It's like with anything of being able to try to adopt things really fast and let them break things and learn from iterations and just this idea of testing and learning. And so, yeah, maybe some of the bigger tech companies are used to this idea of testing and learning, testing and learning. But sometimes you have organizations that are a little bit more industries that are a little more old school that aren't used to that quick of testing and learning. And so some of the organizations where we're seeing the adoption fail is where you have the frozen middle. We call it the frozen middle, where we have senior leadership saying, here's all these AI tools. You need to adopt AI immediately so that we can see an uptick on revenue or an uptick on production or whatever that might be. Then you have a junior talent population who's excited and wants to embrace it because they're a little bit more tech savvy. They're not afraid of trying out new technologies and all of that. But then you have a middle group that are actually probably the line managers or the managers of these, the younger generation, who have a little bit of a fear because for the past decade, 15 years, 20 years, they've had a successful playbook that has helped them be get to where they are and be successful in that way. And now we're telling them that they have to throw out that playbook and reinvent new ways of doing work. And so the adoption level is a little bit where, like, okay, yes, I'm all for technology and innovation, but I don't want to throw out the baby with the bathwater because what if it doesn't work and all of these things. So they're starting to create this environment where it's not giving people the autonomy to do those things. And so companies are having to kind of go back and saying, hey, we want you to test these things out. We want you to break things. There is gonna be a grace period of making mistakes and then course correcting, but we have to do this really, really fast. And so that's where that idea of creating a safe space for people to have permission to say, hey, this is the way that we've done the workflow, but now we have these tools. I'm gonna try something different and it may work or it may not work, and it needs to be okay because maybe we learn good pieces that helped us and other pieces were, oh, the human really has to be involved in this place. The idea, the irony is that typically when things break or fail with AI, it's because we gave too much liberty to the AI and we didn't insert the people where we needed to insert them. And so it's a little bit of uh trying to understand, okay, when it breaks, it's because we needed more human discernment or judgment, or we needed some person to get in there and really be the checks and balance for the AI. We can't just let the AI go go off on its own. And so that's where you realize that, like you said, humans aren't can't get replaced because there are so many things that the human is going to be faster at processing or doing, even if it's tactile, that the AI can't do. And so that's where we have to create this safe space for people to just test and learn and be able to reinvent as they go.

Zohra: 29:05

But I want to reiterate what you've shared as I've jotted my the points that you've given. And I think they're amazing. Psychological safety that you talked about is super important because that has been my personal experience. When I know my company trusts me enough to break and they're not looking for a success story, but rather what learnings have I had from this interaction and experimentation, it creates that psychological safety. The second thing that you talked about, the testing and learning iteratively. Yes. I'm all about the growth mindset. And a company, in my opinion, needs to have that if they want their AI deployment to succeed. The other thing that you mentioned was autonomy and grace period. And these are also things that I have anecdotally experienced in my current role. Then you also talked about this disconnect between the middle, the frozen middle, and then the junior talent. But how this is pushed down by the top tier is super important. And for all that to happen, the psychological safety, testing and learning, it needs to trickle down. So there's practically a radical rethink here. Yes. Yes, right. I love how you have you contextualized this for me because it's hard for for me, being an individual contributor, to think how do I engage? So these are some things that employees can rather ask. So I'm kind of flipping that argument and saying if a company needs to succeed, then employees need to maybe ask for that. Picard.

Monica: 30:34

Yes, right. Exactly. Exactly. And and that's what you said. Like most of us, many of us are individual contributors, but we need to also transfer the knowledge because you may be testing and learning and iterating and saying, hey, this prompt worked really well, or I replaced my workflow with X, Y, and Z. And where we're seeing some disconnect is where you may have an individual contributor gaining lots of efficiencies in terms of they're 30% more productive, but it's not cascading to the rest of the team because there are some teams who their adoption or their learning of it is a lot slower. So it's like, how do you make sure that you're pulling teams together? And for those people who are learning to MacGyver their way through it or learning to kind of like figure out new ways, how do you transfer that knowledge? And how do you get them to start documenting those things so that the broader kind of, you know, instead of the this micro, kind of like this micro environment and getting all the benefits, but then how do you kind of multiply it so that the macro organization is actually starting to see the compounding effect of that? And so we are seeing, and one of the, you know, some of the surveys are saying that the individuals who are adopting AI are also reporting a level of increased satisfaction in their work because they're finding where sometimes at the end of the day, like sometimes they'll tell you it's like really only 20% of your effort is really valued, where you have to spend 80% of your time doing all of this grunt work and kind of work. But this is where AI comes in, where it can take care of that majority of that work that might burn you out, right? You know, when you start playing in your zone of genius, you can go all night. You get into a flow and you just feel really good and you are like doing, you just feel happier when you're in in flow or when you are kind of in your zone of genius. Where we get burned out is where we have to do all of this administrative, monotonous tasks or doing all of like gathering all of the data and things like that. And that is where AI, bots, GPTs, agentic tools can do that in spades, right? And so this is where we're starting to see people realizing the capacity that it's opening up for them. But the key is where do you reinvest that capacity or that time to do more work, right? And in some cases, companies, if they're not really helping individuals figure out where to redeploy that capacity or redeploy that time, the people will then they'll use that time for themselves. In some cases, maybe it is gaining, giving back them some time for some work-life balance, which is great, but there is some where you can redeploy some of that capacity to the quality or the quantitative kind of um results. So, how do you start balancing that out where people are starting to feel more satisfied, but aren't just taking the time saved and the energy saved and wasting it? So, how do you actually strategically redeploy that time and energy, some into themselves, but then some back into the work so that they can level up or amplify the quality?

Zohra: 33:41

Right. I was wondering how often would I be wasting the time? Yeah. I don't think that's gonna happen because if I have the opportunity to experiment, to automate and create quality output, I become a more driven person. Yeah. And it also kind of creates a sense of loyalty to the company. Not that I I would want anybody to become slave to a company, but it's a partnership. And the more you're trusting of your employee, the more the employee is willing to invest. Exactly. It's a win-win on both sides.

Monica: 34:14

Yeah, it's a win-win. And what we are seeing is that AI can be kind of this level playing field in a sense, too, where it's giving you some of your time back. You're still able to do quality work, but you can also start pivoting into areas and reinventing yourself into areas of expertise or areas that you may have had some passion about that in the past you may not have been able to because you just didn't have proximity to the expert or to somebody that could teach you that. Where now you can learn really, really quickly with AI that those types of tools where you're kind of learning as you go. And I see it as, and because maybe I'm a very, I tend to learn, I'm a very visual learner, but I'm also a very kinesthetic learner. And so I used to remember that sometimes I would be sitting in my science class, like biology or microbiology, and the lectures would just like drive me crazy. But then you get me in the lab, and it was like everything would click and all of those things, and I could stay in the lab for hours and hours, but just sitting there listening to lecture would kind of like bore me. And so with AI, there are so many ways now that you can learn where you're kind of engaging with the tool and you and it's helping you, and you can ask a question, and there's also this sense of safety with various different tools where you don't feel, oh my goodness, Zora's gonna judge me because I'm asking a stupid question. You can ask the AI a question and it spits out the answer, or it says, Oh, that's a really great perspective, or that's a great question, or no, that's not gonna impact anything, you know, or no, that's not the way you should think about it, right? So it's one of those things where it's starting to create a safe space for people to test and learn, and you have the autonomy to kind of ask the dumb questions. And sometimes it'll sit there, I'll be like, I don't know. I don't know. So you explain to me like an expert, you're the expert, but explain to me like a fifth grader what this really means. And so then you can start asking questions and saying, okay, well, what is the, you know, what is the opposite? What do the naysayers say? And where are my blind spots? So those are the things where you can really start to learn very quickly and be able to pivot in your career or pivot in a way or start working on things that you have always been curious about, right? You can now quench that curiosity that you have with these tools, and it makes you a better person, a better employee, and like you said, more satisfied and more loyal because you're getting to really get your hands dirty in things that you love to do.

Zohra: 36:49

Absolutely. What a coincidence. Uh, case in point, this afternoon, actually, literally before I got on chat with you, I was in a similar situation where I was trying to understand our whole DevOps pipeline. What is used on the back end, and how do I ask, I was running into some technical issue and I was I wasn't sure what language I should be using with my dev team. Like you said, I didn't want to sound dumb. And so I asked the AI tool that we use, how do I approach this? So, first explain to me what's being used on the back end, and because we have a corporate controlled environment, I could safely ask that question. And then we had this conversation. So I used it for brainstorming to quickly educate me. And then when I reached out to my developer, I was able to ask a question that was not out of like the Wild West. I wasn't just releasing something out there. I came at it with some knowledge. Yes, it I think it saved me on the back and forth, not only me, but also for the developer. We all want answers.

Monica: 37:51

Yeah. But exactly what you said, case in point, you saved time. I did. In many ways. Behaviorally, you probably might have sat there before having access to this tool to be, you know, you might be procrastinating asking the question because you're like, oh, I don't want to feel dumb. I don't want them to get like, I don't want them to judge me. I don't want them to say, oh my God, I don't want to work with her because it takes forever to kind of understand what she needs. But now, with a couple of questions, you know, 15, 20 minutes of really kind of digging in there and getting the answer for yourself, now you're able to give them some product requirements or speak their language a little bit to kind of help that conversation go faster to where it's just like, oh yeah, she knew exactly what she wanted. She gave me the specs. I was able to give that right back to her. That's where the time savings comes, where if you kind of think about the old way, may have taken you all, like you said, quite a bit of back and forth and you losing half a day because, oh, I sent him an email, I'm waiting for him to come back. He's wondering, oh, you didn't give me enough information, all of this kind of things. And so that's the beauty of where you can start finding efficiencies, but it also gives you some confidence. Like you said, you felt more confident going and asking these questions because it's almost kind of like the educated consumer, the educated client, where you know enough to be able to guide the conversation in the direction you need it to go.

Zohra: 39:15

And you gave some great tips on how AI adoption can be done the right way. Now that was from the macro to the micro level. How do I tap into the zone of genius? One was use your artificial intelligence as your artificial intern, coach it, give it your creative spin, train for the output you want. As I was thinking deeper, and the case in point that we just talked about, where I was able to educate myself quickly. In that instance, I realized that, and with everything that you've been sharing, it doesn't efficiency doesn't have to be for the big things. It can be in these the micro instances that we encounter because those sometimes end up spinning or going out of control. Control. I think I've kind of tried to bring this conversation back together. What are your thoughts on what I have sort of tried to summarize here?

Monica: 40:08

No, I mean, I think you definitely were on point on some of the things of pointing out of like, it's not going to be this magic kind of like, oh, I used AI and I got 30% more productivity. It's going to be the compounding effect of these, like you said, micro instances where you are finding micro efficiencies in those things that you've done, right? And so little by little, it's going to be getting kind of smoother for you, where all of a sudden you're seeing that compounding effect of leveraging the tools in different ways. But it's all testing and learning of figuring out how you might do it differently. Where you're talking about the zone of genius, that's another important thing, though, because it's one of these things where the questions about what's your zone of genius or your zone of genius sound very innocuous, like, oh yeah, that's totally, I get it. Like, but if I were to say, Zora, what is your zone of genius? What's your elevator pitch? What are you good at? What can AI amplify for you? That's really hard to answer. And so people have to gift themselves the time to do that reflective work, to understand where are the areas that I bring the most value? What are the things, where does my expertise, my zone of genius, bring high value to the organization, to my team, to this workflow, to this process? And then how do I leverage AI to afford me more time to apply that high-level work or that high-level effort in those places? And that's where we start to, when we come in with working with companies and we partner with companies, we focus on people, processes, and tools. But what companies rolled out all the tools, they really stopped, didn't pay it a lot of attention. And you would be surprised how many companies don't really have documented all of their processes, right? It's kind of like the nature of the work, this is the way that they do it. There's kind of like spirit of letter of the law, spirit of the law. And they might might have like, yes, an operating manual, but it's evolved so much that it's not accurate. And so, really kind of starting to focus on the people to say, how do you help people first behaviorally reinvent themselves and see themselves as more of an agentic human, leveraging AI to afford them to be able to use their zone of genius or apply their high value expertise more and kind of help release some of the capacity stealing tasks that they have been doing? And so we come in in a kind of like this 90-day sprint to help people do some of this reflective work, leveraging our agentic tool. We have Flippy, the kind of like your agentic change agent, somebody who's helping you change, help you answer all of these questions of like, what do you love to do? What do people come to you for? Where is your zone of genius? Where do you lose yourself in your work? Walk me through a day in your work life. Where do we see opportunities for AI to help you offload some of those things that drain you so that you can do more of the things that energize you? And so those are the things where you first have to think and get the person to shift their mindset, to flip their mindset, to say, AI is not going to replace me. AI is actually going to amplify the work that I do. And then once the person starts to understand, like you said, starting to see the value and not be as scared of the AI anymore, then you can pull together them along with their team to say, okay, let's map out your workflows and let's reinvent your workflows to start thinking about where are all of those microefficiencies that we can find that we can then cobble together and you can start to see that you are getting 30% more production now from your team and all of those things. But it's an exercise, it's a process. And we are leveraging AI and agentic AI, generative AI to help people go through that journey faster than the old school way of change management. And so after you come kind of do your workflows, we call that the Flip Lab. We take you into the Flip Lab to start getting you to think about what are those workflows. We then also have our flip factory, which is our group of engineers that can actually help build the agents, like enterprise agents that'll help your team continue doing work. Because contrary to what a lot of people believe, just using the bots and the generative AI tools and things, those are very helpful. But it's really the agentic tools when you start plugging processes together and then inserting people where you need to, that you start to see the true efficiencies and the productivity and the ROI from the AI come out. And so it's a process, but it's a journey that you've got to go through so that teams and organizations and you as individuals can reinvent the way that you do work.

Zohra: 45:05

I love how you explained what Flippy does. Yeah. Uh I I want to get to know Flippy, where it gives me the opportunity to get reflective and to really introspect, kind of uh ruminate on what value do I bring.

Monica: 45:20

Yeah.

Zohra: 45:21

That sounds amazing. Have you seen any sort of patterns emerge as you've done these evaluations?

Monica: 45:27

Yeah. So we created a proprietary assessment. We call it the flip factor. And really, organizations and leaders, we have them take this assessment so that they can kind of start to understand, are they ready? Like this readiness, like just reinvention readiness to really start to shift the way that they think. And so a lot of the times we might think like, oh yes, like I'm using AI, but what we're finding is that they're using them for very surface level activities, like, oh, it's helping me sort out my emails, or it's helping me draft my emails, or it's helping me, it's helping me summarize meetings in my like virtual meetings and things like that. That's all great. And yes, it's creating some efficiencies, but it's not adding to the bottom line. Like, what is your real job? Like, are you client-facing? Are you building products? Are you content creating? Are you what are you doing for the organization? And how are you actually applying it to those workflows to make you faster and better? And so they take the assessment and they start to understand that there's probably lots of other areas that they can be using these tools to become much more efficient, but also much more productive for towards the bottom line or the KPIs that they are there to meet. The interesting other one is that we give this to leaders to say, how ready are your people? So that we can also help them see the gap analysis where they have a perception that their people are ready. So they deployed all these tools, and then they start to see that their people in the assessment are saying they're not really ready or they're not using the tools the way they should. And so we kind of see the gap in perception. And then we can help them close that gap by helping their people become more to flip mindset of really saying, okay, how am I going to flip my work on its head and start to use AI in different ways? So that's what we're seeing is the trends more of people having these aha moments of like, I could be applying machines or AI or technology in a very different way to get the work done. And so we're starting to see more and more of that, where people are working with Flippy and having some of these aha moments. I mean, the goal is right now, you know, Flippy is you're kind of engaging back and forth. You have to be proactive with Flippy. And we're in the process of like evolving Flippy, where it becomes kind of a are you familiar with Grammarly where you have like your this extension and it kind of just goes along and it's more in context training? Flippy will be doing the same thing of like, hey, I see you're working on this task. Have you considered using this to make it faster? Or you could do this, or so really kind of helping people find efficiencies where if you were working with a team or two brains are better than one, kind of were like, oh, hey, here's how I do it. And this is a faster way of just learning how to maybe shift the way that you're working in your day-to-day. And so we're seeing more trends where people are having those aha moments, right? Especially in the behavioral change piece, where, like you said, having those reflective moments, sometimes it's really hard where when it's just like, I don't know what my zone of genius is, or I don't know how to articulate where my high value strengths are or what I'm doing. You know, those are really hard questions. And sometimes when it's the old school pen to paper, or you're working with a coach, or you're working with someone who it takes you a longer time to kind of like articulate that or to get it out, or to whether it's fear of being judged or any of those things, we're finding that people are moving along that behavioral journey much faster with Flippy, because Flippy's this non-judgmental change agent that's just kind of helping you synthesize what's in your head into something coherent that you're like, ah, that's it. I can put my finger on it now. So we're starting to see more people using it in a really productive way, but also in a very kind of personal growth way that is much more welcoming or feel is safer, right? The psychological safety is there where it's like, I can divulge all of this to Flippy, this trusted person, and he's not gonna go tell the next person. Uh so Flippy is almost like a coach. He can be a coach in those instances, but then also the change agent that's helping you be more efficient. Okay.

Zohra: 49:44

One question that I think I would like to end on. When you do these assessments using Flippy, has there been a reluctance on the individual contributors taking that assessment? Because I can imagine the C-level execs pushing, you know, they're able to roll that out and say, okay, we're going to do this assessment, but have you faced this resistance from individual contributors who feel that they might be exposed?

Monica: 50:07

I can't think of any examples offhand because it's a self-rated assessment. And the questions are a little bit on a scale of one to five. I'm using AI daily for more than one task or two tasks and things like that. So it's just kind of getting a pulse of how familiar, how much you're using it, those types of things. But it asks several different questions around are you leveraging it? How are you using it? How ready are you? How comfortable are you, those types of things. And it's a self-assessment. But the other thing, too, is we always share with the individual is that these individual results aren't going to be shared with leadership on an individual basis. It's an aggregate score that we then give the leadership to say, here's how the group as a whole have reported. You do get an individual report for yourself so that you can see kind of like the group index. Like, here's my team, and as a group, here's our readiness score. And we have different tiers like curiosity, mastery, all of those types of things. And we kind of fell in this, like we're curious about AI, but we really haven't become masters of it. And so, but here's my score. You know, I scored actually on awareness where I'm beyond curiosity, I'm more aware of how to start using AI. And then I'm going to get to a mastery level. So you get your individual score and you can kind of assess where you are compared to your peers. And then as you go through the program, we kind of do some what we call microassessments to help you see that trend line of growth so that it kind of helps you be more motivated to kind of keep growing in that way.

Zohra: 51:42

That's awesome. I love the whole idea and the concept that we are your company's trying to break down these silos because silos exist no matter what we say. Absolutely. The fact that you you can bridge departments to individual contributors to C level XX. I mean, overall, I love that approach. And even just bridging me to myself.

Monica: 52:05

Yeah. Exactly. I love what you just said, bridging you to yourself, because sometimes we don't gift ourselves the time to do that reflective work, to kind of really say, who am I going to be or how am I going to work differently with AI? And you might have seen it, like you said, as an adversary of like AI is going to take over my role, instead of saying, This is going to be my partner and this is going to be my secret weapon to actually do better, right? And so I think that's where we're getting people to flip their mindset of how do you become people squared? And so that's what we say become an agentic human and you will become people squared, where you have more capacity and you feel more satisfied and you're leveraging your zone of genius.

Zohra: 52:49

This has been such a fantastic conversation, Monica. Thank you for taking me down this path. I went completely off script.

Monica: 52:57

This was an awesome. And you know, the last time you and I spoke, I mean, it was just a very easy conversation, but an engaging and exciting conversation. And so I'm more than happy to continue the conversation in the future, but I always enjoy my time with you.

Zohra: 53:11

Thank you so much. I really appreciate this. Any last additions that you would like to add before just a yeah, I may have missed?

Monica: 53:19

Feel free to reach out and connect with me on LinkedIn. It's the Monica Marquez on LinkedIn. But I also have a newsletter. It's a weekly newsletter that goes out. It's called III AI. And it's really kind of a five-minute read of like different tools that you might experiment with, you know, maybe aha moments that I've had, really kind of giving you prompts and things where that I say you can steal these prompts and use them on your own, but just getting people comfortable with the different ways of leveraging AI in your in your day-to-day.

Zohra: 53:51

Uh, I would love to include these links in my show notes. Sure. When I'm ready to publish, I will definitely have those included. And this again has been a fantastic conversation, Monica. Thank you so much for educating me and sharing this interesting perspective on how your company is approaching it.

Monica: 54:09

I've loved it. Awesome. Well, thank you again so much for giving me the time and space on your platform.

Zohra: 54:14

Listen to Inside Techcom on your favorite lab and follow me on LinkedIn or visit me at www.inside techcom.show. Catch you soon on another episode. Thank you for listening. Bye bye.