Innovating Leadership:
Co-Creating Our Future

Resources:

Our host Maureen Metcalf posts a newsletter every week on LinkedIn. You can subscribe here.

Maureen’s latest book is Innovative Leadership & Followership in the Age of AI. You’ll find details about it at https://bit.ly/LeaderInAI, or check out the Kindle version at https://amzn.to/44buVz8. The audiobook version is now available at  https://amzn.to/4dTCleZ.

Her other 10 books are available on Amazon here.

Other episodes you’ll enjoy:

– AI, Your Story, & the Professional Identity Crisis with Christopher Washington

– Leading When We’ve Stopped Thinking: AI’s Red Flag with Srini Koushik

– AI at Work: The Human Side of Tech Transformation with Neil Sahota

Guest(s):

Guest(s) Bio:

Maria Angel Ferrero is an innovation and responsible AI strategist, entrepreneur, and academic. She is the founder and CEO of Makia Labs, where she works with organizations to design AI-driven innovation ecosystems that integrate technology, people, and purpose. She is also a PhD, researcher, and professor of Innovation and Entrepreneurship at the University of Montpellier, with a background in design thinking and user-centered innovation.

Ferrero’s work focuses on helping leaders move beyond AI experimentation to measurable impact, emphasizing governance, human-centered adoption, and ethical AI integration.

In addition to her advisory work, she creates educational content on AI and innovation across platforms, including LinkedIn and social media, where she shares practical guidance on applying AI in professional and academic contexts.

Transcript
(auto-generated)

[00:00:00]  

Maureen: Welcome to Innovating Leadership Co-Creating our Future. I’m Maureen Metcalf, your host and founder, and CEO of the Innovative Leadership Institute. Through the institute and our think Tank, we work with senior leaders navigating complexity.  

So today I’m joined by Christopher Washington, our think tank president and Maria Angel, a responsible AI strategist and innovation leader and the CEO of Makia Labs. Maria works with organizations to design AI driven innovation ecosystems that integrate technology, people and purpose. So Maria and Christopher both welcome. We are delighted to have [00:01:00] you. 

Christopher: Today’s conversation, what I’m hoping for is that we move beyond the hype associated with AI. What does it take to design a responsible human-centered AI innovation ecosystem that leaders can scale with confidence? 

And so Maria, you described your work as helping organizations build AI driven innovation ecosystems. And how is that different from simply, adopting AI tools? 

Maria: The difference is that today most organizations think that investing in tools is what they need and that’s enough. But tools don’t change organizations; systems do. So what is important is to actually think how we can integrate AI in the innovation strategy of organizations. So thinking when and why do we need AI and how to use it. Leaders have to think about how they can develop the skills of [00:02:00] their colleagues so they can actually adopt these AI tools. And so people feel safe with experimenting with these tools and integrate it in the innovation process. 

Christopher: You point out this idea of experimentation, but one might think, I buy AI tools and then I innovate with them, or I experiment with them, I should say, but what’s different from an organization that sort of takes that approach of buying the AI tools to experiment with them, from those that systematically innovate with the tools. 

Maria: The difference is starting with what are we trying to solve, right? Where is it that we actually need AI? Because most of the mistakes that we are making today and organizations are making today is thinking that AI tools will completely solve manager’s problem; that they will completely reduce cost and replace people. But it’s actually [00:03:00] thinking, where is it that I can add value with AI? Like with any other technological tool, first is understanding what problem we want to solve and what kind of value and impact we want to create once we adopt this tool.  

Maureen: You developed the MIA framework in response to a recurring challenge. What were you seeing across organizations that told you that a new approach was needed? 

Maria: It was the gap between the investment that was being made by organizations in AI tools and the actual use from the collaborators and actually measuring the impact it had in their processes, in their operations, and of course in their competitive advantage. 

So that gap between, a lot of investment being made and money going to AI tools and the gap of actually being able to measure what those tools were [00:04:00] giving to the organization was what make me jump into this framework. And I actually tried to blend design thinking, which is actually user center innovation method to integrate AI in that process. 

And that was what I wanted to do, was to be able to integrate the user first to adopt AI in the organization. 

Maureen: Can you give an example so our listeners can see what you saw through your eyes? 

Maria: A very simple example, and I think that we can see it in many organizations is deciding, for example, to buy ChatGPT or Claude and companies buying subscriptions for their organizations, let’s say ChatGPT. So it costs at least $20 per user per month. So roughly if you have 10 people in your organization, [00:05:00] well, that becomes a budget that you have to pay. But the reality is that many of those who were given a subscription don’t really use ChatGPT, don’t know how to use it. Some of them prefer Claude or prefer Perplexity because there are so many tools in the market. So they still use what they know, right? 

So why should I use ChatGPT? Okay, I have the pro version, but I don’t know how to use it. I already have invested in developing skills to use Claude or to use Perplexity. So changing to another tool seems that it needs more effort from my side. So companies were still paying for those subscriptions and people were using other tools because they already knew how to use them. 

Also there was no guidelines from the leaders on what are the practices or the best practices for our [00:06:00] organizations in terms of AI use? What type of data should I be sharing? What is not supposed to be shared? How can I organize my documents and all the conversations I have based, for example, on projects on ChatGPT? All these things were simply thought that people will know how to do it and how to use them without training and without a central strategy. 

Maureen: So it sounds like lack of guardrails lack of capability. And I’m also making an assumption that for people using systems not necessarily paid for by the organization, they may have been using free subscriptions and sharing data that is proprietary to companies. 

Maria: Yes. And even if, because so many of us also use, AI tools for our personal lives because let’s face [00:07:00] it, we have a lot of things to do. So if AI can help us do some of those things, uh, why not use it? So many people are also paying for their personal subscriptions, so it is kind of trying to use 10 tools when you already know how to use one. 

So I think that the issue is of course there is lack of skills, but there is also lack of governance from the company. And governance means not only who is responsible for the risk and for the impact of AI, but also giving the guidelines on what is acceptable, what is not acceptable, and making sure that we have surveillance of what data, for example, is going out to these tools and who is accessing this data and how we are analyzing this data. 

Maureen: And surveillance [00:08:00] is an interesting word some people have an allergy to and for folks trying to ensure compliance, you certainly need to be paying attention to what’s going on to manage risk. 

Maria: Yeah, so probably, surveillance might not be the right word. Maybe human in the loop is the right word. There is need of surveillance, but it’s more than just controlling what is happening. It is more having someone that is responsible to make sure that how we are interacting with these tools is acceptable, meaning ethical, responsible in terms of what kind of data we are sharing, but also how much we are using it. Because again, using AI also means, using some of our most precious resources, which is energy and which is water and so on. So it’s understanding also that it’s not just [00:09:00] limitless use of tool just because it’s fun and it’s hype and everyone is doing it. It’s about also knowing when we should use it and how to use it responsibly. So the human in the loop is more than just controlling, is actually getting a step over, which we take back the ownership and the responsibility of the outcomes and what we can create with AI. 

Maureen: when you mention responsible, I had a conversation the other day and it’s the first time I’ve had this specific conversation about, a leader saying, AI makes my work easier, but it’s not good for the planet. So I’m struggling. Should I be using it or not? And in what ways? So I’m assuming. That’s not the only person having that concern, that part of [00:10:00] the ethical use is the planetary impact. 

Maria: Some of the main challenges of any technology is that they also have this dark side and an impact on society and on environment. They not only come with positive things, it also comes with negative impact. So that’s when thinking about the intentionality: why I am using this tool and what impact I’m looking for is what will help you reduce that dark side of the tool. Any innovation comes with an impact, negative impact. So when we use that innovation, we have to think about, okay, how can I reduce that, so that means that I probably don’t need to use it for everything. 

If for example, I’m using I don’t know ChatGPT for writing an email, do [00:11:00] I actually need to interact five times to write one email when I was okay with writing it by myself the first time? 

Christopher: Yeah. 

Maria: So these are things that, because we have access to it, we think that we have no limits. 

And I think that training on not just the skills, but also the ethical side of AI tools is important in organizations. And since I am former professor, I think it’s also really important to start earlier in the school. Because this is where they start, people start using AI. 

Christopher: For those that have of us who are very interested in using it responsibly and sensibly and have desires to create innovation with AI, you, you talk about this AI innovation ecosystem. What I find most fascinating is that you begin with this idea that you have to map mindsets. 

And this is something that [00:12:00] ILI does quite a bit of work related to. And I think a lot of people skip the step. And so I wonder why do you think, mapping the mindsets is so critical for those of us interested in creating an innovation ecosystem and what happens if we ignore doing that work? 

Maria: mapping mindsets: it’s actually trying to understand where people are at the moment before you can adopt any tool. It’s like the innovation adoption. you need to know if people are ready for it or not. And it’s not just the technological readiness is actually also the emotional readiness. 

Are people trusting these tools because trust is the first thing that we need before we can actually use something, an innovation. So in this case, AI; are they willing to change the tool they are using today for that, is this tool actually going to give them [00:13:00] more time for do the things that they like the most or that they enjoy, for example, because it’s not just about doing, things faster or improving performance is about also enjoying the job that you do. So if we are going to say that AI is going to replace the things that you love to do in your work, then why should we do it? Mapping the mindset is actually understanding where people at the moment to understand what are the strategies, the incentives that we have to put in place in order to make adoption actually happen. And the risk of not mapping those mindsets is actually not adopting anything. So we usually go and overlook this phase because it doesn’t give us results right away, right? It takes time. It’s [00:14:00] intangible. But if we don’t do it, then we cannot ensure adoption at the end. 

Christopher: I really appreciate your attention to how people feel about AI. I imagine some folks adopt a tool and they just wanna stick with that one, you know? But nevertheless, let’s say people are interested in adopting AI are amenable to it. Have you seen instances where AI tools or the AI process has really unlocked new ways of thinking for people, ways of thinking, new mindsets? Not just faster execution, but really has had a developmental impact on people. 

Maria: sure. So, one of the, of the first ways that people use AI is for content generation, right? Writing things or doing a slide deck for a presentation or this kind of things. 

I think that one [00:15:00] way that AI, helps in, in thinking differently is actually that it becomes a thinking buddy who is not just there to execute and write an article for you or create these slides, but also to give you some interesting ideas that you might not think of it right away. Because we have a cognitive limit, right? We have our ways, we’ve been doing slides the same way as we are used to. 

And then this AI can help you think about and look for information that will help you inspire and get inspiration from. So that’s one of the examples, I can think of but also, it can help you find solutions by giving you new perspectives. So sometimes, for example, when we are prototyping we don’t have right away the users in front of us to give us feedback. 

So the the great way about AI is that it can [00:16:00] take the role of whoever we like, right? So they can a act as personas and give us feedbacks based on. Whatever personality we’d give. And so we get a view that we didn’t explore because we didn’t have that person in front of us. And sometimes that test of the persona comes really late in the process that we have to go back again to think about a better solution. 

So I think AI there has a really good potential to help us go like, change perspectives and also take, um, like an upper view of the problem and what solutions are available. 

Christopher: That’s fascinating. I can see how that it will impact an individual’s way of thinking. And when I think about innovation, I think about this sort of collective thought process that we have to engage in together. And you’ve described [00:17:00] innovation as a continuous loop, not a linear, a rollout. 

And, I’m wondering, how can leaders institutionalize a mindset, a collective mindset inside a larger organization around innovation as a continuous loop? 

Maria: well, I think that’s probably something that should happen. And it’s happening in the organizations that innovate all the time before AI… 

Christopher: yes. 

Maria: …is actually institutionalizing that mindset and not just thinking about innovation one time only. So sometimes organizations say, “Okay, let’s do a workshop, an innovation workshop,” and they do it once and they think ” That’s it. We did it.” And it’s more about this trying to put in place rhythm, within collaborators of being able to experiment. So not being afraid of making mistakes. [00:18:00] Of failing. That is one of the most important parts because many managers says, “I want my company to be innovative and I want my collaborators to be innovative.” 

But then the first mistake they make, they are: “Why did you do that? And you are not supposed to experiment with that.” So how do you want innovation to happen if experimentation is not possible? So I think it’s that making experiments happen, being okay with some kind of mistakes and failure and the risk that comes with it. 

And also thinking about, again, innovation is continuous, is not just something that happened one time and then every five years we decide to innovate again. It’s something that should happen every time. Methods like Scrum or agile management are already a good method to adopt in an [00:19:00] organization where you actually are testing all the time, learning about the results of those tests, adapting to whatever these learnings tell you and scaling to something different or bigger. 

Maureen: Christopher, can you tell us a little bit about your innovation lab, because I think this dovetails really nicely with what Maria’s talking about. 

Christopher: There’s very much an alignment there because what she’s talking about is, innovation has to happen within the context of support a culture: that supports innovation and methods that, that people can follow. And what I’m focusing on is how do we engage in sensing, sensemaking, execution, uh, through maybe experimentation, and then assessing what we do and following into, again, a sort of continuous loop… a cycle of knowing what’s changing in the world, knowing [00:20:00] ourselves really well, determining how we collectively understand those factors, where the opportunities and challenges exist that we must address and how we put together, whether it’s some methodology like Scrum or Agile or some other method, an approach to execute well and to follow up and give feedback. 

So I think there’s a tremendous alignment, Maureen. 

Maureen: Can you give a story or an example of how that plays out? Especially the idea of sense making, because we don’t talk about that as leadership teams very often. 

Christopher: And sense making is about coming to a general idea about what we are sensing, what that means to our organization. And I often think about leaders who do a great job of framing, using powerful metaphors that we are in a protective stage right now. We are cocooning, we are protecting ourselves [00:21:00] from and defending ourselves from what’s changing out in the world around us. However, it’s time for us to undergo a metamorphosis. We need to take flight. The conditions have changed, the world has changed around us. We can leverage these tools and approaches to have a more effective response to our world. But, we have to do it together. And so I often talk about William Bridges’ idea of transition narratives that help organizations make sense, but also think about themselves and what is changing in the world. 

So, William Bridges talks about dealing with endings. He talks about having a clear sense of purpose, a plan of action, a part for people to play, and a picture of the outcome. Leaders who can describe those things often help people make those transitions to adopt new technologies, for example. 

Maureen: So the cocooning could be, we’re doing a lot of AI experimentation internally, and the next step to take flight would be looking at [00:22:00] personas, asking the questions, using AI to accelerate our ability, then to make the changes rather than me creating, doing the design thinking, but then having to go out and hire a research firm after I’ve built the thing. 

Christopher: It could be, we’re even seeing cocooning at a heavier level where faculty are just saying, “I don’t wanna use AI. I want people to write their papers out. I’m going to penalize people who’ve, if there’s evidence of use of AI, because I’m not good at using the tool and that has not been my historic definition of what academic quality represents.” 

That could be a form of cocooning. And, alternatively, organizations that are embracing the tool because they know employers will require these fresh graduates to be competent and literate in its use. And so there may be faculty who decide, “I’m gonna adopt these tools in a [00:23:00] responsible way,” in much the way that Maria’s describing. 

So responsible, ethical, and intentional so that our graduates are not neglected, but are prepared for the world that they’re gonna face.  

Maureen: So Maria, you’ve made a strong claim that most AI failures are human, not technical. What does this mean for leadership, pace of thinking and priorities? Because once we exit the cocooning stage, I think a lot of people are rushing. is, as for everything, an appropriate pace, especially if we’re trying to make systemic changes beyond the experimentation phase. 

Maria: Companies are going straight to the question, which model should we implement without thinking from the beginning who is involved? For example, what are the problems that we are solving? Do we have the skills? Do we [00:24:00] need (to) go and look for skills before we can make it happen? 

And will this affect trust, for example, or work quality or decision making? So not thinking about all these things, the first thing we are going to blame, is IT, so the tool doesn’t work? They sold us dreams and it is not working. But in reality what happened is that people were not involved from the beginning. 

So no understanding whatsoever about people’s needs and the skill they have to use these tools, the fears they have of using the tools, the kind of trust they need to build before they can use it. Roles were unclear so no one knew who was responsible of doing what. Usually there is this IT guy that is also now responsible of AI tools and they think that because they have [00:25:00] a degree in informatics, that’s it. That’s all we need. He can solve everything. And the thing is that we need someone also that is owner of the method of this strategy of when we are using AI and what for, and that’s the person, the champion of AI that every organization should have, and knowing who else is involved and what is the role of everyone is important also for adoption. 

So I think that’s, when I say most of the problem come from a human side is not because humans are not capable, it is because the human component was not taken in consideration from the beginning. And guess what? Our humans are the ones who are going to be using it and are going to be impact by the use of it or not use of it. 

So they should be involved from the beginning, [00:26:00] otherwise it’s not going to happen. 

Maureen: So systemically then, starting with an AI strategy, what does it enable and what do we not use it for? Second, being governance and guardrails, how do we ensure that we do the things we say we’re gonna do the leadership mindsets, preparing the data foundation, and then engaging our humans early and regularly to build trust. 

So training, exposure to the systems. That’s where the experimentation comes in. And then adding in the measurement systems, what’s working, not working, and refining, because this is certainly an unknown area. 

Christopher: Maureen, I’m curious about when you think about this whole idea of an ecosystem how do you build one that’s sustainable? When you take all those things into [00:27:00] account. If we were to think in system terms, what are some of the essential components of a sustainable AI-driven ecosystem? 

Maria: The first thing is, again, human in the loop. The fact of having always a human or a few. Humans in the loop every time, making sure that the interactions that we are having with AI are responsible. 

That the outputs that we are getting and therefore using for whatever kind of job we are doing are ethical and respond to our values and the strategy of the business. And it’s not just the fact of making great content or great things with AI that we let them be. human in the loop, that’s the very first thing. 

Second thing will be thinking about the strategy first. So what is it? Where is it that we want to go in terms of [00:28:00] company, in terms of strategy and how AI is helping us to get there? And probably the third thing is feedback all the time. So trying to experiment, get feedback, and improve what we are doing with AI. So that means also measuring impact. So are we actually getting our KPIs are actually improving? Are we actually getting closer to our strategy or not? And where is it that we can improve and should we still invest in this AI if we are not getting the impact that we expected. 

Christopher: Yeah. That cost of AI has to be related to what you describe as KPIs. The performance that you’re anticipating. That are we more efficient? Are we more effective? Are we more productive, more profitable? Have we reduced cost in one area by increasing the cost of [00:29:00] this AI? All of those factors have improved quality or customer satisfaction. 

What is, what are we driving to here and how do we measure? That seems to be a really important part and where humans can play a big role in defining the value of this AI use. 

Maria: That’s why it’s so important from the beginning to have that strategy in mind. It’s like, in any other implementation of a tool, first you need to measure without the tool so that you can actually improve. 

Christopher: That’s right. 

Maria: That’s one of the biggest mistakes is that we want AI to help us do better, but we don’t know how we do right now, so how can we know if we’re doing better? 

Christopher: Yeah, it seems like you have to map the system and its performance today, then think about the use of the AI tools. Implement, measure the effects of that on the system if you’ve made [00:30:00] improvements. I think that’s a really a strong approach to this work. We’ve been talking about this notion of governance throughout the interview and I’m just curious to know, what sort of governance structures or leadership roles you see as important in sustaining a sort of AI innovation ecosystem. 

Maria: What you need first is someone like an AI champion or referent in the company that is responsible, that has a lot of knowledge and skills, and it’s continually improving their skills and knowledge on the tools and on the strategy that can guide all the, the people in the organization on how to use it, why we are using it and when you to use it and all those things. 

But it’s not necessarily a technical role. ‘Cause I think that’s really important to separate the technical role from the strategical role. [00:31:00] Knowing the, the techniques the technical part of AI is important, but. It’s more important to understand where is it that we want to go with AI for organization. 

The second thing we’ll have to think about that cross-functional ownership. So again thinking about how. AI is going to be used by different units or departments in the organization and making sure there is someone from each department that is leading that AI strategy. 

And that can not only help. The group and the unit, but also that all the information can go up so that person can know when they need for example, a different tool or if there is a need of a specific skill or a training that should happen and so on. And finally, I think that the [00:32:00] most important is probably the executive ownership and sponsorship. 

So again, if managers want their organization to be innovative, but if we are not given the space to experiment and the mindset of being able to fail and making mistakes without losing your job because of it, then we cannot expect the organization to be innovative. So I think the most important part also is that the sponsorship from the leadership where we say, it’s okay that you experiment with AI, let’s see what happen and be able to accept that there is also a risk from experimenting. 

And it’s not just because the person using it is not capable that it fail, but it can be just [00:33:00] that it’s part of the experiment. 

Christopher: You’re in uh, Paris, France. I’m in the United States. I just wonder if you see these governing principles. As universal. I know many companies function all around the world, but I wonder 

if you had thoughts about that. 

Maria: It’s universal. I think that one the effects of COVID is that today, companies are still very, rooted to their nation, where they are implanted. We have this global view and AI also makes this possible. But that problematic is the same everywhere. 

And I’m going to give you an example. So I think that maybe it’s only that we have different degrees of adoption, like with any other innovation, but we are all having the same… we are all asking the same question right now, and you see it whether in the US where you guys have, [00:34:00] I think that probably 80% of AI tools are built there. 

Talking about biggest ones, and here in France there is a lot of investment going on. In terms of AI deployment, but also building tools and solutions. And then you have countries like Colombia, which is my native country. 

Maria: So in Colombia, for example the use of AI started, uh, with the personal use, and I think that we are at the same level as US and France, but it’s only starting now to be adopted in organizations. I had recently a talk with someone from my family that runs a business in Colombia in the logistic operations area. 

And the thing is that we all share the same question, which is when I can start using AI. And the thing is that most leaders ask them, “Okay, where should I put my money? Where should I invest?” And they think that that question is the only thing they need to start working with AI and adopting. 

But the issue is, I think the same globally how can we use AI, adopt AI so that we can actually focus on our core business and not replace or stop thinking that AI is going to actually replace people and we, we should stop thinking that we can make cuts in terms of human resources just because we implement AI; we should start thinking that AI is going to be kind of extension of our workforce rather than a replacement of our workforce. So it’s more like, “Okay, how can we integrate AI in our workflows and how can people in our organization can see AI as something that will help them do their job better and enjoy more what they are doing rather than  fear they will be replaced by it. 

Christopher: If you could leave our leaders, our audience, with one mindset shift about AI and innovation, uh, what would it be? 

Maria: Think more of the problem rather than the solution. Um, that means that we have to take more time on understanding the problem, what we that we want to solve, and using a framework that help us see all the different elements of that problem before we can think about solutions. 

So when the solution comes, we can actually check those cases and say, okay, this solution works. 

Christopher: Thank you. 

Maureen: Beautiful. So I want to close with our signature question. What’s one leadership choice you believe senior leaders or boards should make in the next 30 days, and what system or practice must be in place so that that choice can stick? 

Maria: It should be about, um, defining that governance system before anything else. So identifying the right people in the organization that can lead that change, and is not usually the leader. Because again, it’s not just about the person in the top of the company that has the impact to adopt, to make people adopt, but the people inside that are impacted by the AI tools in the work that have the best place to actually engage the other people in the organization. 

So my advice will be identify those people, those AI champions inside the organization and make them understand what is the strategy that we want to pursue, and start giving them access to knowledge, to training so that they can actually engage the whole organization in this transformation. 

Christopher: Maria, I wanna personally thank you for bringing clarity, rigor, and leadership relevance to this conversation about AI and innovation and your work is clearly, based on our conversation, aligns with and supports what we’re exploring through the ILI think tank. 

Maria: Well, thank you very much for inviting me. It was really a pleasure and I’m really happy that we made this happen, so thank you for all the knowledge also that you’re giving to this audience. 

Maureen: So Maria, how would people find you? 

Maria: So you can find me through LinkedIn. I share a lot of my general experience as the CEO of Makia Labs, uh, on this AI strategy and, and how to adopt them. So you can search me on LinkedIn, on Maria Angel and I also am very present on TikTok and Instagram as the hybrid professor, uh, where I share, uh, some tips on how to use AI tools mainly for work and studies. 

Maureen: Thank you. And Christopher, you are prolific right now. 

Christopher: Well, you can find me on LinkedIn, like Maria, Dr. Maria Angel, at Christopher Washington PhD. And you can search for me on uh, forbes -dot-com. Christopher Washington, Forbes, nonprofit. 

Maureen: And to our listeners, thank you for joining us. This is one of our premier think tank conversations. Please like and share us so that others will have the advantage of learning from Christopher and Maria. 

Maria: Thank you.