AI Inclusion: Amplifying Diverse Voices in Workplace Transformation - Monica Marquez

Monica Marquez, workplace AI strategist and founder of Flipwork Inc., shares insights on how organizations can adapt to AI-driven changes in the workplace through inclusive practices and human-centered transformation. She explains why companies struggle with AI adoption despite significant investments, pointing to outdated training methods and the need for alignment between identity, mindset, and environment.

• AI is changing jobs faster than people can change how they work
• Bias-free AI is unrealistic – the goal should be making bias visible and accountable
• Diverse perspectives in AI training data lead to more inclusive systems
• Cultural norms and beliefs can create barriers to AI adoption, especially for marginalized groups
• Management needs to create cultures where AI experimentation is encouraged
• Leaders should identify workflows where AI can provide significant leverage
• The goal isn't to do the same things faster but to reimagine processes entirely
• Adaptability is now the "golden skill" in an era of constant workplace change
• "If you don't use AI, someone who leverages AI will replace you"

Don't wait - start using AI today, even in small ways. Ask better questions about what data is being used and what assumptions are baked into the outputs. Focus on workflows - reimagine your work so AI can help you achieve more, not just do the same things faster. Check out our newsletter "The Flip" at gettheflip.com for weekly insights on AI adoption and workplace transformation.

Chapters

• 0:00 Episode Introduction with Monica Marquez
• 3:57 Why AI Inclusion Matters
• 9:50 The Reality of Bias in AI
• 15:43 AI Adoption Challenges in Organizations
• 22:24 Cultural Barriers to AI Implementation
• 29:48 Management Solutions for AI Integration
• 34:08 Final Thoughts and Call to Action

Transcript

Stephen King
And we're back yet again. This is Stephen King. Today, I am rejoined by new incongruent. How do we call you? What do we call these? It's Lovell. What do we call us? Now? The incongruent you can call me.

Lovell
You can call me Mr Incongruent.

Stephen King
Mr Incongruent Menzes. So there we go. You've done two out of the first six, which is fantastic. Thank you very much for being with us, Lovell, good to be speaking to you today.

Lovell
So today we spoke to Monica Marquez. We had an incredible conversation with her. She is a workplace AI strategist, a leadership learning and inclusion expert and a serial entrepreneur, and she is the founder of Flipwork Inc. Which is the only enterprise-wide AI workforce transformation system that ensures Fortune 500 companies adapt quickly to the disruptive change in AI.

Stephen King
That's a very good advertising blooper. We talked a lot about inclusion. We talked a lot about getting people involved. I mean not just inclusion in the sense of workplace inclusion, it was inclusion of data, inclusion of stories, inclusion of information into the training models, and I thought that was very interesting. And also the complications if you are in one of these fortune 50 or 500 companies. You have so many people to bring on board.

Lovell
The change management exercise is immense right absolutely, and I loved her transparency about the fact that bias does exist in all sorts of ai tools and models and in the podcast podcast we discuss her views on this bias and the importance of inclusion and what the next steps would be for companies, big or small.

Stephen King
Absolutely super Well. Thank you again, Lovell, for joining us, and we're ready to go. Here we go, we're ready to go.

Lovell Menezes
Hi, I am Lovell and welcome to a new episode of The Incongruent. And today we are welcoming Monica Marquez. Monica is a workplace AI strategist, leadership, learning and inclusion expert and serial entrepreneur. She is the founder of Flipwork Inc, the only enterprise-wide AI workforce transformation system that ensures Fortune 500 companies adapt quickly to disruptive change. Flipwork helps employees at every level adopt AI successfully by transforming how people see, think, and work so they can keep pace with rapid workplace change. She also publishes IIII, a weekly newsletter that gives leaders five minutes of clear, actionable guidance on the thriving in the age of AI. Monica brings more than 20 years of executive experience spanning talent, learning, and inclusion at Google, EY, Bank of America and Goldman Sachs. At Goldman Sachs, she pioneered the Return Ship and New Directions programs, setting the industry benchmark for helping experienced professionals re-enter the workforce. At Google, she co-founded the Product Inclusion Council to shape more inclusive product design. She is also the co-founder of Beyond Barriers and has hosted more than 300 podcast episodes spotlighting transformative leadership journeys.

Recognized among entrepreneur magazines, 100 Women of Influence and ALPFA's most powerful Latinas in 2024, Monica continues to champion equity, innovation and leadership in every arena. So welcome to the podcast, Monica, and please tell us a bit about yourself and what led you to found Flipwork.

Monica Marquez
Awesome. Thank you so much, Lovell. I appreciate the intro. A little bit about myself. I am the founder of Flipwork Inc. It's a workplace AI company that's helping organizations really adapt to disruptive change, right? And mainly, what we're really, the problem we're trying to solve is that AI is changing jobs faster than people can change how they work and how they change themselves. And unfortunately, we're seeing now more and more of the research and reporting that most companies are still relying on outdated training and change management models that are slow, costly, and they're really disconnected from the daily workflows. And the result is that we have leaders who feel unprepared and employees who are disengaged and transformation efforts are failing to stick. 

Even though companies are trying to adopt AI and rolling them out. so Flipwork is intended, you we've created that to solve that problem. We help employees reimagine how they work so they can adapt quickly and continuously. And the deeper challenge isn't just about skills. It's really aligning that identity, the mindset and the environment so that change becomes sustainable and scalable. Right. And so really getting where the companies themselves are bringing in tools with a purpose. What is the purpose of the AI tools we're bringing in? What are the outcomes and outputs that we want? And have that clear transparency with the employees so the employee understands how they're supposed to start leveraging. So just by way of my expertise and just history, my own career history is where I spent more than 20 years as a corporate executive at several of blue chip companies where I helped build programs focused on people transformation and leadership and along the way, really understanding how people need to adapt to change. And so today my focus is on helping people at every level of a company adopt AI in a very practical human centered way. What does this mean? Like who do I need to be? How do I leverage AI in order to amplify the work that I do? And so that is really in a nutshell what my goal and mission is to do.

Lovell Menezes
Thanks for that Monica, that was quite interesting that you talk about how you want to implement AI at every single level of a company and I think that's absolutely important and it's great that you believe that there's emphasis to employees regardless of whether they are at the executive level or at a lower level such as an intern or someone at an entry level position. So why do you think it's important? to think about inclusion when we speak about AI, especially when the first thing on everyone's mind is efficiency.

Monica Marquez
That is a really great point. know, I, know, better part of my career, I did spend in the areas of, you know, diversity, equity and inclusion. And, know, given the current environment where people are shifting of, you know, really how do you, how do you make sure that AI isn't widening the digital divide? And, you know, there's two sides of the camp that they feel like AI will level the playing field and they're, you know,

That one side of campus saying that the other side is believing that it is going to widen that digital divide only because of the data being input into the algorithms. And so when you really think about, can AI or anything be bias-free? I really do feel like bias-free AI is unrealistic. And part of that is because

AI reflects the data that it's trained on and the choices made by its developers, right? If the data is incomplete, outdated, skewed, the system itself will carry that bias forward. And bias can also come from those hidden assumptions, right? The training data, like who's the typical customer or what is, know, and that's important because we all have a limited frame of reference and we each have that, right? We each carry these unconscious biases. And if we are training,

A system or building a tool, we are putting our own assumptions into that. And so what we need to really focus on, you what's more important is, you know, people, you know, more people from across industries, backgrounds are actively engaging with AI. The more diverse the inputs, the better the systems become. And, you know, many of you.

Maybe familiar with some of the main platforms, right? Like you have OpenAI, ChatGPT, you have Gemini, you have Copilot, all of these different tools. And then there's some things like in ChatGPT where you can say, you can use my information to train the model and things like that. A lot of people are clicking it because they're afraid about privacy and all this kind of things. But what they're also doing is limiting the inputs going into the AI.
 
And so really it's the more that we can train it ourselves or add to the content that the AI is learning from, the more inclusive it's going to become. so, you know, and that's some of the, also leads to some of the fears of, know, people really adopting AI because they're afraid of the bias. So we can go into more about that where you yourself, can start to make sure that you are questioning or prompting the AI to make sure that it is getting an inclusive answer, the output is inclusive. So there's lots of things there. And there are days where I can see things where I'm like, okay, this is where AI is, the diversity of the developers definitely you can see is lacking. And in there and some things where I feel like...

You know, it's a shared responsibility for all of us, right? It's definitely, developers need to design responsibly. Regulators need to set guardrails. think ultimately though, companies that deploy AI systems are accountable for their employees, customers, and users. And so you can't outsource that responsibility or put that responsibility on someone else to have, to give you an unbiased tool. Part of it is that we have to look for those biases and figure out how do we course correct or how do we make sure that we are not going in blindly, you know, taking the outputs for as they come out.

Stephen King
Yeah, I understand exactly what you're coming from there. In the education arena, there is a very interesting form of this phenomenon. The Department for Education here has warned the UK colleges and schools that because the students are using these American or Chinese AI systems the answers that they are receiving are based on American or Chinese curricula. And therefore, without training or having our own British system, if you like, then we are at risk of our education system being colonized. I don't know if you have any thoughts on that.

Monica Marquez
Yes. That is a really interesting point. And you're right, because there are certain countries who have really adopted or taken AI and kind of running with it and others. It's that whole idea, like you're saying, we have to disrupt ourselves before we get disrupted. so you can't, the more the latency can cause problems if you aren't kind of moving forward. And part of that is what we're seeing too with a lot of the marginalized groups as well, where they aren't adopting to AI because of certain limiting beliefs or, you know, for fear of the unknown and they're getting left behind. And so it's, it's a really valuable point of how do you make sure that you are, like you said, taking a model and making sure that you're training it, and feeding it all of the, you know, the information, that is respective to a country, a culture, a community, a population, because you are right. The outputs, if someone is leveraging a system that has been trained in the US, or like you said, in China or somewhere, those are the outputs that are gonna come out. you can see, like you said, naturally, there's gonna be an assimilation where that unique perspective is lost. And so how do you make sure that every culture is visible and accounted for and in inputting that. And so that's where I feel like it's a shared responsibility of if not you, then who, right? And so we can't rely on other developers or other people trying to infuse that because again, they may be limited in those perspectives or their own perspectives may be tainting their assumptions of what that your perspective should be, et cetera.

I think it's a real, real problem. it's, it's a little bit like, like I've always told, know, when I was thinking about individuals in, you know, just more cultural competency is that, you know, when you go into an organization and you start kind of drinking the Kool-Aid, you start to assimilate and you feel like you start losing yourself. And I used to, you know, I always tell people you have to learn to acculturate, you know, acculturate to an organization's culture.

Or like just when you're traveling, when in Rome do as Romans do, but you still are core to like, are your roots? Don't forget your roots. And so I think that's part of it is making sure that we are, that everybody is taking part or finding responsibility and training the tools in the system so that we don't lose our culture, our own kind of unique perspectives and ultimately end up assimilating to something that doesn't feel like us.

Stephen King
So do you think that each company will eventually take its own flavor? mean, I've seen in the advertising industry, are protecting their brands information. So they're building their own localized LLM models. But do think there's going to be like a voice of Kellogg's or a voice of McDonald's where they have their own AI with its own Ronald McDonald AI? Is that what you predict?

Monica Marquez
No, I predict more of, you know, individuals like what we're, what we're seeing is that many of these larger platforms are coming to organizations with the enterprise model. That's kind of a closed environment and the closed environment, like you said, is safeguarding their, you know, proprietary information and data and all of that. and, training that. I do think like more of the bigger, you're going to have like, micro, you know, micro environments inside a macro environment. And, you know, there are certain things that are going to be safeguarded, but yes, I do like, do foresee, organizations probably keeping a very tight hold on it, which we've seen already some of the organizations that we partner with, even, you know, even with, with flip work of making sure that our system is, is the security and the privacy is really tight and that, you know, only there invite their people will have access to that information. So I don't think it's going to be any different than any tech company or any SaaS platform or something that was going into enterprise organizations that they were building their own. It was more the securities and systems placed around it so that information doesn't get out.

Lovell Menezes
I like the fact that we all can collectively agree that AI can have a bias. And I'm one of the people who has selected the option where, know, chat GPT can use the information that I've given it in the past. And I believe it is very, very useful, at least on an individual level. Whenever I ask any question, now it, you know, For example, right now I'm in my job hunting phase. So if I ask it to write a cover letter for a specific job application, it already knows my information. So I don't have to spend time giving it a prompt. It already knows a lot of information about myself. And I personally have found it very useful. But as you've said, it is very important that AI has a lot of diverse inputs. And you spoke a lot about the bias. And you mentioned that when a company uses AI, they do a lot of things to safeguard themselves so that even though they are using AI, information remains confidential. But at the end of the day, they're still using a model that's created and generated by a large company. So do you think that AI can ever be bias-free?

Because as Steve mentioned as well, every country is trying to create their own type of AI. China has their own, the US, India, and other countries. So do you think that we can ever reach a point where AI is bias-free, or is the better goal to make the bias visible and accountable?

Monica Marquez
Yeah, I mean, that's a really great question. I don't think that bias-free is realistic. think because at the end of the day, it's humans who are building these products. And we all know that every human has built-in biases, regardless of who you are, where you come from, your belief systems, cultural beliefs. We all have. those unconscious biases that we have been conditioned in us from when we've grown up, depending on the environment or who we are. And so it's really thinking about, you know, how do you make sure that we can identify the bias, right? Of really, how do we make sure that there's visibility or accountability of the systems, you know, or, you know, always the disclaimers of you know, we need to know where the risks are, right? How do we monitor those risks? How do we correct them? And just like companies audit finances, you know, they should audit AI for fairness and the transparency will start building that trust, right? And so it's really making sure that we create those checks and balances, but that we also disrupt some of that, you know, that cognitive bias that we even have as we are kind of whether we're involved in building products with AI and things like that. so learning also to really leverage AI as this, I like to call AI kind of my artificial intern.

Kind of like when you have an intern, you trust them to do some of the work, but you are always going to check that work to make sure that it is right or that the perspective or that it is representative of what you're wanting to put out there. I think that you have to treat AI kind of like that artificial intern or that artificial teammate that's giving you information that you are then kind of bias checking yourself and making sure you're not taking the output for face value.

Monica Marquez
And so those are the things where our own unique perspective is really going to help mold the outputs from AI the way that you want them as well. And so for us to be able to say that we would be able to create a bias-free AI, I think is really unrealistic. I don't think we will ever see it just because we are human and we are the ones honestly creating the tool and putting the bias in there. Even if it's unconsciously putting the bias in there.

Stephen King 

So you mentioned a word I triggered. I'm looking for my pen to write it down so I can follow up later. The word audit. Audit your AI. And I really like the idea because I see a lot of the academics are testing the eyes on all different kinds of criteria right now and see which one's better. And it made me think, whatever comes up. You work with such large organizations. They all have stock market and quarterly reviews. How long is it going to be before the use of AI and the proliferation is going to be within these quarterly reports. Is it happening already? Because shareholders should be concerned, right? I'm concerned having just heard from you for a minutes and that just triggered my thought.

Monica Marquez
Yeah. No, I mean, I agree. think everybody's kind of in that learning mode, right? I think if you think back to the days when internet was just introduced or when we weren't using those types of things, I mean, I see sometimes AI, especially in academia, right? Because I do have my background as well in academia, where I remember the days where calculators are frowned upon, right? And so I feel like we're going through another evolution like that of revolution, a technology revolution of where there's the, you know, the calculator at the time was like, no, you shouldn't be using the calculator. But now I don't think I see anybody calculating anything by longhand. It's all your leveraging tools and technology. And I think we're very in early onset with AI where I don't know that we're gonna see it in reports, like you said, in quarterly reports and everything in the near future, but I do think that there are many like you who are thinking about how do we make sure that we are auditing checks and balance? But I think companies are just now figuring it out. I I have lots of conversations with senior leaders at some of the big companies.

Early August, I was at a conference and I was speaking with a very senior leader at Microsoft and they were talking about even at Microsoft, you know, they rolled out copilot and all of these things where, you know, people can leverage it to, you know, really enhance their productivity and, know, in the workplace, but the, the usage, the, the, guess user, you know, adoption of those tools was less than 50%. And so, you know, it's one of those things where now it's the individual, right? And that's where, that's where, that's really the genesis of where, you know, we, you know, my team and I really started thinking about Flipwork, right? How do you, you know, Flipwork makes kind of the, the, the people operating system for employees, for companies, right? It really helps everyone at work lead and think in new ways so they can keep up with rapid change in the workplace. And so what we really focus on is the kind of the identity, the behaviors, like how do you change those behaviors and how do you work differently? So how do you see yourself, right? How do you see yourself as a valuable individual able to grow in the world of AI? How do you build confidence and the belief that you can learn new skills?

And adapt, right? And I really do think that adaptability is the golden skillset now, now because of the constant change that AI is causing. And then how do you actually re-imagine your workflows, creating like routines and tools and team habits that really help the new way of work stick. So the, really do tell people that the old success book to the old playbook for success is obsolete.

You have to basically create a new playbook for success. And part of this is it's a bit scary, right? When you think about identity. And this is where I see the, unfortunately, the marginalized groups sometimes getting left behind a little more because of our conditioned cultural beliefs, right? So, you know, I can speak as a Latina, a female Latina who was always taught to put your head down, work really hard. You have to work twice as hard to get half as far sometimes. And sometimes you don't trust the system where what used to take me three days to do something, if I use AI, it takes me 30 minutes. Well, what does that mean for hard work? Like if I'm not doing really hard work, is it really valuable? Am I being a value add? And so you're having to get people to unlearn some of these limiting beliefs of the kind of like amount of time.

Equals success now that AI is cutting it in half. And so then they end up fearing and not wanting to use it and saying, I'm going to do it the old way. But now they're getting left behind by individuals who are leveraging AI and getting it done in 30 minutes. So some of those cultural norms are really, think what's going to play out and widen the digital divide, which is really around identity and mindset.

Stephen King
Yeah. And if you have half the company using AI and half the company not using AI, what does the manager do? Because you've got half the company finishing their work and going home at 12 o'clock on a Friday and the rest of them sweating and having to have, I mean, that will have a significant impact on productivity because I play rugby and if you have the line, bit dogleg we call it, the bowl falls.

Monica Marquez
Yeah.

Stephen King
It doesn't. So what do you think the management do? You've got to have a stick and a carrot, but what can we do in the current situation?

Monica Marquez
Yes. Yes.

Well, that what we're, what we're kind of really helping leaders and managers, you know, kind of like direct managers, frontline managers is really helping them create a culture. What experimenting with AI is encouraged, right? employees need to start small, get comfortable, and then expand and really kind of adopting boldly, in terms of really figuring out what is the, what are the core KPIs that your team is, you know, really trying to accomplish and how do you leverage AI for those processes or those workflows that are taking up a lot of time, right? And so getting them to think about how do we leverage AI in the right way. And so I think what companies have made the mistake of doing is they're rolling out all of these technologies, platforms, tools that people can use, but they're not really taking the time to teach or help their people learn how to use them and leverage them, right? And so, you know, for instance, just a couple of different kind of examples of, you know, let's talk about maybe the recruiting side of the house instead of our spending manually screening resumes, AI can pre-screen candidates and generate tailored interview guides, et cetera. But again, making sure that you are improving fairness by saying, you know, what is it that we're actually like looking for? Product development, right? Instead of long research cycles, AI can analyze customer data and market trends and generate prototypes much faster than we could do it manually. And so really getting managers and others to kind of sit down and think about where might we gain more leverage? Where in the process or our workflow can we gain more leverage? And so that's the problem is I don't think that, you know, leaders are allowing themselves to do it because at the same time it's like trying to fly the plane while you're fixing the engine, right? And so, because you still are currently having to keep your day-to-day going, how do you take a few people on the side or how do you take a few of these projects and try to improve the workflows and not losing ground on your current KPIs? And so I think that's part of it where they're learning how to try to juggle that where you're trying to do, fly the plane while you're trying to fix the engine.

Lovell Menezes
I like that you said that everyone in the company should, or if not everyone, most people in the company should experiment with the AI. It's a great point. From my personal experience, when I first started using AI, it's only when I experimented, gave it all crazy questions, I saw the true potential of it. And you spoke about different cult... cultural norms and how one person may view AI differently compared to another person. how would you ensure that everyone views AI as something that does not hold any bias, something that's here to help them out? Because you said you see AI as an intern. So how would an ordinary employee at a company view it the same way as you do?

Monica Marquez
Yeah, I mean, I think part of it is sharing within the reality that AI is not here to replace you, but someone who leverages AI will, you know, if you're not, if you aren't leveraging AI, someone who leverages AI will replace you. And so that is really getting them to understand that, you know, it's one of these things, but it's not a matter of if, but when. And the new mantra really is survival of the fastest, no longer survival of fittest, right? Who is going to be the fastest to adopt and really starting to find easy entry points that, know, what, what are some of the things that you do that really you feel like, my God, this is just, you know, not a waste of time, but this is where most of your time is being spent that you were getting burned out. And so use AI to like prep for meetings or tailor talking points, right? Drafting follow-up emails, starting small to get under, to understand what AI can do for you.

And then really start to think about for your teams, When you're ready to go beyond the basics of really leveraging AI to do some of the busy work that you can then start focusing more on strategy and the creativity, really the human things, That AI cannot do for you. Then you need to pull teams together to start imagining your processes, right? In order to exceed your KPIs.

For example, customer insights that shift from quarterly reports to AI driven real time analysis, opposed to gathering all of these feedback requests or feedback reports from customers, takes you an entire quarter to analyze them. AI could analyze them for you in a day and you're having daily reports of what people are having problems with or customer insights and things where you're able to iterate and innovate on a daily basis opposed to quarterly basis.

And so that means leaders are going to have to make better decisions, you know, with faster and greater, you know, they'll be able to make better decisions with faster and greater impact. And so really getting people to just kind of get used to it, right. As like, you know, really starting to, leverage it as their, you know, checks and balance and really testing themselves of, know what, maybe if I need to AB test, I'm going to do this solo the old way.

Monica Marquez
And now I'm going to use AI to do it and see how the outputs are and how might I tweak AI to get the same outputs that I trust the old way, right? And so it's a little bit of testing and learning. I mean, it's that old adage that you see in technology spaces. If you have to test and learn and normalize it, normalize the fact that AI is, you know, if you don't use AI, then you're doing it wrong.

Stephen King
Wow, well, that's that's a big, that's a big statement. But I think it'll be proven. I think we're coming to the end of our time now, I'm afraid, Monica, I've really enjoyed the conversation. Is there any other point that you would like to raise? there any sort of call to action you'd like any of the listeners if they want to find out more? How would what would they how would they reach you? Or how would they find out a bit more? Well, you have this

Monica Marquez
Yeah, certainly. You know, I'm on LinkedIn. So just looking me up, Monica Marquez on LinkedIn. But we also have a newsletter, a weekly newsletter that goes out really kind of sharing some of these, what are limiting beliefs? What is a new empowering belief you can embrace? And our newsletter is called The Flip. So my old newsletter, IIAI, has been rebranded. It's called The Flip. Like, how do you flip your mindset?

From this kind of disempowering belief of what AI can do for you to the more empowering. And it's called The Flip. And you can get it at gettheflip.com. And it's a weekly newsletter that'll give you tips and tricks and really the trends and some of the research of what's happening in the world of AI and change management in the workplace to help people really learn to adopt themselves as quickly as possible.

I'll leave people with three things, right? Don't wait, start using AI today, even in very small ways, and ask better questions, right? You know, what data is being used? What assumptions are baked into the outputs that the AI is giving you? And, you know, how can you pull in your unique perspective to make sure that the outputs reflect you and focus on workflows, right? How can you reimagine your work so AI can help you achieve more?

Not just do the same things faster, right? Like how can you achieve more? How can you make those outputs even better opposed to the status quo as, you know, doing it the old way?

Stephen King
Brilliant, you made me think, I'm gonna go back, there's a cartoon, our animation called Wallace and Gromit, where they have this sort of robotic system where when you wake up in the morning, it sort of serves him coffee and the robots are like, that's what I'm thinking for work. But it might be in a more business professional sense. Lovell, would you like to close us down?

Lovell Menezes
Okay, Monica, once again, thanks so much for sharing your wonderful insight here with us and taking your time out for this conversation as well. And for everyone listening, if you want to dive deeper into these ideas, be sure to check out the incongruent for more episodes packed with stories and strategies from transformative leaders like Monica. And thanks for tuning in. We'll catch you next time.