[0:00] Simmi: Okay, welcome, everyone. Thank you for joining us to the second episode of the Majlis. Let me start by introducing myself. I am Simi Dixit. I've spent last 20 years shaping how organizations in Middle East think about people, from designing reward frameworks in PwC and as part of my consulting stint, to building the people analytics function at Majid Al Futtaim, and leading HR strategy at Schneider Electric.
[0:34] Simmi: I've been part of these complex workforce decisions over these years. Today, I'm setting up my own practice to build all of this experience directly to the leaders who need it most. And I'm joined by Dr. Regis Chasse, who's currently chief learning officer at CLO Advisors, working daily with CHROs and CLOs on AI transformation.
[1:01] Simmi: So Regis, welcome to the Majlis. Before we start, quick housekeeping. Please drop in your questions in the Q&A box as we go. We'll take last 10 to 15 minutes to go through the questions and answer some of those. Also, you can check in the checkbox in your registration confirmation if you would like to receive a summary and highlights email from us.
[1:29] Simmi: Okay. So today's conversation is one I'm sure sitting with every organization. It's the gap that is between AI ambition and the actual transformation, and what leadership has to do with closing it. So Regis, let's begin by you telling us about your journey that brought you to this conversation.
[1:53] Dr. Regis: Thank you, Simmi, for having me and good morning, good afternoon, good evening to everyone online.
[2:02] Dr. Regis: Yeah, so my background — I come from twenty years with Capgemini, where I was co-leading the Capgemini University for a long time. And there I was exposed to how to run enterprise digital transformation at scale. So you will see that transformation is a theme that we'll address during this webinar.
[2:36] Dr. Regis: Then... And that's where I met you. I was in Dubai, in the UAE, at Majid Al Futtaim, where I was the dean of The Leadership Institute over there.
[2:48] Dr. Regis: And I was really part of a transformation initiative around making sure that Majid Al Futtaim — which had grown through brick-and-mortar tradition operating movie theaters, operating grocery stores — had suddenly to face the likes of Amazon starting to target the GCC market.
[3:22] Dr. Regis: And that was really a move of survival to transform Majid Al Futtaim digitally and to enable and unleash data and analytics, which Amazon had tons of, but that we didn't know so much at that point in time.
[3:42] Dr. Regis: So there I've learned really how to leverage that leadership institute — some might call it the learning function — to support that transformation and working hand-in-hand with the rest of the streams and the governance around that transformation.
[4:02] Dr. Regis: Again, we will touch on that a bit later. And then my latest major piece of work was with Heidrick & Struggles, where I led the leadership development practice globally and I have done there quite a bit of research on the future of the enterprise and the future of leadership.
[4:25] Dr. Regis: And that was actually an extensive research, really. It spanned over two years and it was validated with three hundred and fifty organizations across the world. And the conclusion of that research was that as AI and technology are more and more embedded within organizations — the future of organizations is actually becoming more human.
[5:02] Dr. Regis: So — wow — this is quite counterintuitive, but you see all those signs through the research that I've done, but also you see some of the trends at the World Economic Forum, this is discussed. You see also what are those skills that are consumed on LinkedIn by organizations besides the AI awareness or sometimes the use of AI.
[5:33] Dr. Regis: Besides that, really the human skills are the premium right now. That's what makes a difference. And the reason for that is because as AI and tech are automating transactional activities within the organizations, humans are asked to do what is more human — such as collaboration, creativity, critical thinking, empathy.
[6:03] Simmi: Empathy.
[6:03] Dr. Regis: Yes. So humans are asked to become more human within the future of the organization. And how do you do that? What kind of leadership do you need to unleash the full human potential of your human workforce, which of course will be augmented with AI? So this is really the thesis of this webinar.
[6:31] Dr. Regis: When we said the human side of AI is really the fact that the organization, the enterprise is becoming more human while asking humans to become more human, and what kind of leadership does it take to unleash that full potential?
[6:51] Simmi: That's very interesting way to introduce our topic, Regis, as well.
[6:57] Simmi: I think the whole thread of your experience from Capgemini to MAF to Heidrick and Struggles is exactly what makes this conversation so interesting for the next 45 minutes. Okay, so let's go deep dive in, and let me introduce the structure of the conversation today. We are gonna go through it through three questions together.
[7:22] Simmi: The first one, which is the first section of our conversation is why. So why are there few organizations which are seeing real transformation from their AI investments and others are not seeing it? They have the technology, their dashboards look good, but what is it that is gonna need it? That is our second question.
[7:47] Simmi: What? What are those specific leadership capabilities that will make human and AI collaboration work in today's world? And the third and the last section of our conversation is gonna be how. And I think, Regis, you can really help our audience here. How do you build those capabilities at scale? And I'm sure it's not a training program, but it has to be a system that sticks.
[8:15] Simmi: So looking forward, let's start. Going to the first section, I will pose the question to you, Regis. So we've already brought the topic in. There are many organizations, especially in Middle East, I'm seeing such huge investments happening for AI. There is a whole AI ministry we have in UAE now.
[8:43] Simmi: So the ambition is real, the budgets are significant, but yet the real transformation that we expect is not happening. So Regis, why? What is this gap between adoption and transformation that is existing now? Over to you.
[9:05] Dr. Regis: Very good question, and unfortunately a question that is not often that well-articulated.
[9:13] Dr. Regis: Really here what is important is to draw a contrast between understanding AI, using AI, and creating with AI. And by the way, this framework is not from me. I was three weeks ago at Arizona State University, discussing the future of higher education and that was quite fascinating to see how ASU, which is quite innovative in the way it modernizes higher ed, using that framework of understand AI, use AI, create with AI.
[9:53] Simmi: Create with AI. Very interesting.
[9:55] Dr. Regis: Yeah, yeah. So I let it sink a little bit because it took me a little while to really comprehend the nuances. But really, understanding AI is the awareness level where you understand a little bit of what it can do, a little bit of the risks.
[10:17] Dr. Regis: You understand also a little bit of the limitations and what it cannot do. Very intellectual, very cognitive. The use of AI is really more — what I would say ninety percent of us are doing — is using AI on a daily basis, prompting and interacting. I know that you are quite a friend of Claude.
[10:45] Simmi: I am — Claude, ChatGPT, all of them.
[10:48] Dr. Regis: And others may be friends of Microsoft Copilot or GPT, exactly. So the use of AI really — more on an individual basis — to improve daily productivity, to help us validate sometimes some of the decisions. Do we miss anything in the decisions that we are making?
[11:13] Dr. Regis: And in preparation for meetings... Some call it the AI coach, but that's really more of an individual interaction. But it is application of AI, and it is using AI for individual outcomes. Now, the next dimension is to create a new environment with AI, to create new workflows with AI, to position the role of humans within a workflow — what does the AI do, and what must the human do?
[12:00] Dr. Regis: And we will talk a little bit about accountability. We will talk about a little bit about trust and judgment. But really creating with AI equates to transforming those workflows, transforming the organization, in a future where you have a true human/AI collaboration.
[12:28] Dr. Regis: So let me take maybe an example of what that means for a specific process that we all know, which is performance management. All of us go through these evaluation cycles on an annual basis typically, and often it is articulated around the self-evaluation that the employee performs, and then the manager reviews that self-evaluation and provides some feedback, and ultimately there is a dialogue and calibration across the organization...
[13:11] Dr. Regis: ...to have a performance rating. This is typically the process that is taking place. Some more modern organizations may have a more progressive approach to performance management, but that's typically the cycle. Now, if we start using AI, which is happening today in that process, then the employee may use GPT or Claude to better articulate the self-evaluation.
[13:46] Dr. Regis: The manager may draft the feedback in a maybe more polished way, the way it is articulated and documented. The manager and the employee may prepare for the dialogue for the performance sitting with AI and rehearse it before the meeting.
[14:11] Simmi: Honestly, Regis, I did that. In Schneider Electric, I had an AI coach, Nada, and I prepared my year-end conversation with Nada wherein, you know, I was prepared to have some difficult conversation on two or three KPIs. So I did my argument and everything with the AI coach before I went into the conversation.
[14:34] Simmi: So yes, AI definitely...
[14:36] Dr. Regis: Exactly, did the work. So this is an example of sticking to the existing process and using AI to improve the outcomes, but not fundamentally rethinking about the performance process. Now imagine that world where you have agentic AI implemented within the organization.
[15:11] Dr. Regis: Agentic AI is making sure that workflows are implemented throughout the organization, integrated across business units and the entire organization. And so imagine a world where actually an AI agent is collecting, based on the objectives that have been set up initially with the manager...
[15:39] Dr. Regis: AI agents are collecting the data points across the emails that you receive — testimonials throughout the year, for instance, the dashboards that you report on on a monthly basis. Imagine that an agent is collecting all that evidence that will support your performance evaluation in the next six months or at the end of the year.
[16:04] Dr. Regis: That is already something that is changing a little bit of how you think about performance management and especially as an employer and even as an employee. When you do that, you know the recency effect — what has been done in the last month counts more than what was done in the rest of the 11 months, right?
[16:28] Dr. Regis: And having this type of more objective continuous collection of data is an example of really changing the way this works. But more importantly than that is you position the employee — both the employee and the manager — to review that data that has been collected, validate whether that data is relevant or not for those objectives.
[16:56] Dr. Regis: Eventually you might want to challenge it. Eventually when there is a red flag that is coming through throughout the year, the manager may be triggered to have a meeting with the employee to understand what was the situation, was there an incident, and how can we improve moving forward.
[17:21] Dr. Regis: Suddenly the legwork of collecting data is offloaded to AI, and the human is really focusing on the value of those activities that were performed, on the judgment of the performance. And ultimately the manager needs to own that final rating that is provided.
[17:51] Dr. Regis: Of course there is a calibration process that can be also AI-enabled. But you see how here with this very simple example, there is use of AI, and then there is create a new process with AI.
[18:03] Simmi: It's a redesign of how we take decisions maybe. So what I am understanding is — and a very interesting and relatable example, Regis — the real challenge is not the technology or AI coming, it's the system around the technology and how ready we are to redesign it.
[18:24] Simmi: Rather than being scared of that — AI is gonna replace me — how about I become more human to see how to use AI and in this case of performance management, make the process more objective rather than subjective, and actually get some biases out of it. Fair enough. In fact, there has been a research done this year by Deloitte in 2026 — Human Capital Trends — and they've actually put a number to it, where they're saying that organizations that take a technology-focused approach to AI are 1.6 times more likely not to realize the expected returns compared to organizations that will take a more human-centered approach.
[19:14] Simmi: So I think the competitive advantage is no more the technology, it is the human edge. So I'm very happy we're having this conversation. Great. So let's move to our next section then. We defined the why — we need to move from pure AI adoption to AI transformation.
[19:38] Simmi: But then what is it? What is that human aspect that we have to build in, or the leadership gap that we have to make it happen for us to realize those transformations in the organization? So walk us through, Regis, how do we make it more human in the organizations, and what does leadership need to do?
[20:08] Dr. Regis: So there are several organizations that are being somewhat successful at embedding AI in the way they think about their future and redefining themselves and the role of humans and the role of AI within the organization.
[20:31] Dr. Regis: And if I synthesize what I've seen, there are four leadership principles that really help drive those transformations across the organization, and those principles are dealing primarily with the human side of the organization. We will talk about trust, we will talk about judgment, we will talk about accountability, and we will talk about learning agility.
[21:07] Dr. Regis: Those are the four principles that really leaders need to pay special attention to make sure that as organizations are embarking on that transformation process, humans are not left behind, but humans are part of that transformation as well. So let me start with trust.
[21:42] Dr. Regis: I don't remember the exact source, but there are many data points that show that actually AI creates fear — fear for employees that AI will take their job, fear for people who don't have a job competing against AI. So trust is really a critical element that needs to be nurtured and created within organizations to be able to bring humans as part of this transformation and make sure that we unleash that full human potential that I was talking about earlier.
[22:31] Dr. Regis: And human potential while adopting AI, of course. So trust is about creating confidence about the intent. Why do we want to use AI? It is about creating confidence in the processes that we're putting in place enabled with AI, confidence in the data that we are leveraging with AI, and confidence around the governance of AI.
[23:12] Dr. Regis: Those are the few elements that are really important within the organization to create confidence so that humans can really trust the movement, transparently understand what is going on. And in the example of performance management that I took earlier — if we move towards more the AI-transformed version of performance management — then here it's all about explaining to both employees and managers why are we moving towards this type of process?
[23:49] Dr. Regis: What is the purpose of it? What are the benefits of it? I talked about the recency effect that suddenly goes away. Remember when you have performance reviews and you send to three or four colleagues of the employee you have to evaluate to gather the feedback — and actually those colleagues, they interacted with the employee six months ago, and they don't necessarily remember vividly.
[24:33] Dr. Regis: So all that is an example of benefits that needs to be explained to both the employee and the managers, but also explaining their duties — explaining that they are the ones that have to challenge that data, and we will come back to challenging.
[24:57] Dr. Regis: That they are the ones that need to own the decisions. AI will not replace them. So really if we don't have that trust, I'd like to say that we are playing a compliance game. We will show that we are using AI, but we will not really trust it and amplify the effect of it.
[25:24] Dr. Regis: If there is that trust, then really we can start experimenting and gradually improving how all this works.
[25:41] Dr. Regis: (to Simmi) I cannot hear you, Simmi.
[25:47] Simmi: Sorry, I went on mute. I'm saying what I'm gathering from here is it's not about adoption on paper. It's not about a communication campaign. It is the leader's daily behavior of building that trust and for the team to really see the value with the example.
[26:09] Simmi: You explained it so beautifully. And then the adoption will actually start getting those benefits, and the transformation will begin. Very interesting. So definitely trust — for those who are taking notes, let's take notes — trust is our very important dimension.
[26:29] Simmi: In fact, for GCC, we work on trust. In general, the business is done on trust and relationships, but now this is a new angle to the whole trust. The leaders have to build trust in their team if they want AI to really get the transformation in. So let's move to the next one, Regis. You said judgment. What do we mean by that, and how does it matter now more than any time before?
[27:00] Dr. Regis: So what I like to position first is it's very easy when we use AI to be guilty of cognitive laziness, I call it. The recommendations come through. They look pretty good. Let's forward that to the next step of the process. Did I challenge those outcomes? Did I really understand the implications and took the time to understand the implications of those data points that are coming through? We all know that generative AI is not perfect.
[27:51] Dr. Regis: Accuracy is one of the challenges with generative AI. And it is very important that humans understand that they have a role to play in judging what comes out of the machine. If we take the performance management example again —
[28:18] Dr. Regis: AI will gather all that evidence throughout the year. But the manager still has to validate whether that evidence is relevant for the objectives that the AI has aligned them to. The manager still has to interpret that data on how it contributes to the overall objective.
[28:46] Dr. Regis: Also, what data is missing. That's great, I have all this data, but actually there is something also missing in that picture. So it is all about critical thinking, all about judgment of what is generated by AI. It's knowing when to rely on the evidence. It's also knowing when to question.
[29:17] Dr. Regis: And that's why human judgment is so important and should be an elevated duty of humans in an AI-enabled organization.
[29:32] Simmi: It's like the AI code, you know. You don't use what AI is giving you. You use your judgment. In fact, a very large point in my professional life — I worked in rewards and compensation, and now with all the AI coming in, the data, the numbers, the benchmarking is all happening by AI.
[29:56] Simmi: But I agree with you, and I would extend the logic of performance management into reward ranges, where the AI is giving the number, okay? But it's my judgment as a leader to guide or advise what is that number for this individual or for this role and for this organization. It cannot be just a data point — the context of it, the judgment of it, has to be applied and overlaid before any decision is taken.
[30:22] Simmi: So I completely agree, and it can lead to a big failure or damage in an organization if judgment is not applied. And then we will say the investments on AI are not coming. So let's move to our next one — accountability?
[30:53] Dr. Regis: So here accountability is all about — in the same line of thinking as judgment — it's all about not hiding behind AI or not hiding behind a process. At the end of the day, the human is still the one that needs to take ownership of the decisions that are being made.
[31:26] Dr. Regis: When you think about transformation, this is a big part of the design of the new workflows that you are designing with AI. Where does the human need to be there as a gatekeeper — in terms of checking the quality of the deliverable, the quality of the data — in terms of understanding the consequences of the decisions?
[31:56] Dr. Regis: So it's important for humans within the organization to own those outcomes. AI may provide recommendations, may provide all the elements to make the decision, but ultimately somebody has to own that decision so that there is ultimately an accountable organization.
[32:21] Dr. Regis: So if we take the performance management example — that's where accountability must be explicit. Managers shouldn't hide behind the data that is collected and the recommended text that generative AI will generate for them, nicely polished, et cetera.
[32:47] Dr. Regis: But at the end of the day — is it fair? That's the judgment part. Do I understand the consequences that this will have on the individual? Do I understand the consequences that it will have on the team? Do I understand the consequences that it will have on the overall organization and ultimately the value created?
[33:10] Dr. Regis: HR must govern the process. Yes, AI structures the evidence. But most importantly, the manager must take ownership of interpreting that data that was provided to them, and must own the evaluation outcome.
[33:37] Simmi: Yes. So you develop the trust in the team so that they use AI, you use your judgment, and then whatever decisions you're taking, you feel like you are accountable for them.
[33:50] Simmi: So the judgment and accountability balance creates the impact, I believe. Sure, Regis, we can keep moving to the last one — I guess the fourth — learning agility.
[34:06] Dr. Regis: Yeah. So here, that's very interesting. I'm part of a program with Drucker School of Management on agentic AI and the future of organization and leadership.
[34:18] Dr. Regis: And that's fascinating to see how the Bay Area in San Francisco — where a lot of AI development is taking place — there is such a sense of urgency because on a daily basis, AI is improving, evolving. You saw what happened recently with Anthropic and the release of what was released and then the next morning it was no longer available.
[34:52] Dr. Regis: So there is that sense of accelerated pace of change on one side. So things will keep changing. And also as always, when we have business initiatives and transformation initiatives, we will not nail it the first time.
[35:17] Dr. Regis: We will have to implement some feedback loops into the way we implement those new processes that we talked about, and continuously improve — also with the improvement that the technology will bring us through AI, but also the way we're embedding those processes within the organization.
[35:40] Dr. Regis: And maybe for instance for the performance management example, we realize that the way the managers are articulating the objectives upfront impacts greatly the way the AI agents are capturing the data throughout the year. Also, we realize if we review that process, maybe we realize that there are some specific data points around collaboration or conflict resolution that are not captured with the system.
[36:15] Dr. Regis: So how do we embed that into the system? Is it a human duty to embed that type of data into the evaluation? Or is it an AI agent that can really capture that? So in an AI-enabled performance cycle, learning is not about training about the system.
[36:45] Dr. Regis: Learning agility is about reflecting on how effective that new system that has been put in place is and taking lessons from it, and learning from those lessons to improve the process. And that, over time, becomes a learning for the organization.
[37:10] Simmi: Yeah. And I think just being agile and open...
[37:15] Simmi: If you remember, you spoke about Majid Al Futtaim in the beginning in your introduction. When the whole digital transformation was happening, there were operating companies and functions where the data maturity was progressing so well, and there were functions which didn't know what to do and they were constantly complaining.
[37:37] Simmi: And I guess the difference there — overlaying what you just explained — was the leadership. The leaders who were curious, the leaders who wanted to learn, who asked questions in public, who wanted to see the dashboards, who wanted to spend their critical time on those things were the ones where the organization progressed with them, while the others went in the loophole of complaining and "Why are we doing this?
[38:05] Simmi: It's not leading anywhere." So I'm fully relating to everything you said, the four aspects. And I will repeat here for the audience: trust, judgment, accountability, and learning agility are the four most important leadership traits in an AI-enabled world to make it more human.
[38:33] Simmi: We're a little behind time, Regis, but I don't want to miss this important section. But maybe we'll cover it quickly now. So before we go into how, we spoke about what is more important, but I would also want to ask you — what is less important in leadership now? So what can senior leaders let go of while we bring these four aspects on the forefront?
[39:02] Dr. Regis: Yeah. So clearly what is more and more important is human-centered leadership — everything I discussed was all about human-centered leadership. And what is becoming less of a differentiator as a leader or a critical success factor for a leader is to be the holder of the knowledge and the expertise.
[39:34] Dr. Regis: We all know those experts that became leaders without having necessarily leadership skills, but they had the expertise to justify being in charge, right? But now the expertise, the knowledge is broadly available to everyone within the organization. So being the expert in the room is becoming less and less of a power within the organization since everybody has access to that expertise.
[40:08] Dr. Regis: The other bit is controlling. If you want to control every aspect of the organization, then you cannot implement AI, and agentic AI in particular. So controlling information flows is something that would be less focused on, and more looking at the quality of the deliverables and how those can be improved is really what should be focused on.
[40:39] Dr. Regis: Less on information flow and more on the quality of the outcomes. So those are the two main elements that create conditions for leaders if they are — accepting to be less of the expert, accepting to be less controlling of the information flow and also more human-centered — then they create the conditions for better trust, judgment, human accountability and so on.
[41:07] Simmi: Yeah. And now this is the new operating system for leadership. Amazing. Great. Last section of our conversation — and I'm conscious because I would like to cover some questions as well. The how. The leadership expertise or the capability that we have spoken has to become an enterprise system.
[41:33] Simmi: So how do we do it? I'm sure it's not the leadership development programs because we have all seen leaders go do the programs and then they come back and the work continues to happen the same way. So Regis, the question to you is what is that design principle of making this operating system — the four traits that we've spoken about — as a DNA of the organization?
[42:02] Simmi: How do we do that?
[42:06] Dr. Regis: Yeah. So I think you gave a bit of the answer in the question. Definitely the answer is not to only send leaders to a three-day class session or even online session and this is it. Not at all. So what I bring from those three experiences I mentioned is that for those massive transformation programs, one of the streams — typically you have people, process, technology — on the people side, that's where you have to have a stream around leadership, around championing that transformation and around also redefining what type of leadership we need to carry forward that transformation.
[43:12] Dr. Regis: And we just touched on what are those principles that are changing. So it's about making sure that the system, the environment of the leader is evolving at the same pace as the leader mindset is shifting.
[43:34] Dr. Regis: The worst case scenario, which we have seen so many times — you send somebody to a course. The course was exceptional. They have the smile up to the ears — super motivated, super engaged. They try to implement what they have learned, let's say empowerment. They've learned that through empowerment within the team, the engagement of each team member would be so much better.
[44:09] Dr. Regis: So that leader is trying to implement empowerment within the remit of that team, but the environment is not created for that. The systems are not created for that empowerment. The manager of that leader is not up to speed on this new view of leadership.
[44:33] Dr. Regis: So the environment has to evolve at the same time as the leader's mindset evolves. That's why leadership development needs to be embedded within the broader transformation initiative and needs to be at the table — often through the CHRO or the chief people officer — to govern that transformation. So when we talk about the how, the takeaway is that...
[45:20] Dr. Regis: The takeaway is that it is the system that has to change. That includes leadership, and it's not leadership in solo that needs to change, isolated from the transformation.
[45:32] Simmi: Yes. The system changes will scale the transformation. Great. Thank you, Regis. I'll summarize the three aspects that we covered, and then probably we go on to take one or two questions before we end this webinar.
[45:53] Simmi: I can see some questions are coming in. For those of you, we will try to answer them. If we are not able to do it within the timeframe, we will send them across to you later and we will answer them for sure. Okay. So three things we covered. The why: if the organization has an AI investment, then they need to do a parallel investment on leadership capability as well — and that's the why of AI transformation.
[46:30] Simmi: Otherwise, if you only keep focusing on technology — we spoke about Deloitte Research — the human angle will get missed and the transformation will underdeliver. Then we spoke about what — the four capabilities — and we had a detailed discussion of the most valuable capabilities for next four to five years: trust, judgment, accountability and learning agility.
[46:56] Simmi: So we need to now — if we don't need to hire for expertise, we need to hire for these skills, and these are no longer soft skills in the context of today's world. And finally, the how — which is building an enterprise system, not sending your leadership for a program, connecting the capability to the strategy, and then embedding the learning as a workflow so that there is a real behavioral change that happens in the work.
[47:31] Simmi: And those are my takeaways, Regis. Any final comments from you before we go on to Q&A?
[47:40] Dr. Regis: No. I like the way you framed it. I like especially the last sentence — to make sure that whatever we do within leadership development is absolutely supporting the strategy of the organization.
[48:00] Dr. Regis: And I talked about mindsets, you talked about behaviors. In order to change behaviors you need to change mindset — so linked to the strategy of the organization and the transformation initiatives, what are those mindsets that need to change in order to change leadership behaviors?
[48:16] Simmi: Those behaviors. Yes, and that's the cascade. Very good. Great. I'm gonna start with Q&A now. I'll first take the question that came to us before the webinar, and then I will start looking into the comments. So Regis, the question is directed towards you, of course.
[48:38] Simmi: You know, the whole discussion we had — this is how the question is framed: What does that mean for CLOs or the learning leaders? Because the design for a CLO sitting in a room could be a completely different position if we are saying it's not about the learning programs.
[48:59] Simmi: So what would be your advice to them?
[49:04] Dr. Regis: Exactly what we just covered. That final line — really making sure that it is not about reaching so many leaders within the organization with a program, but it is really about working hand-in-hand with the rest of the transformation team to make sure that whatever leadership mindset shifts need to happen are being done in sync with the rest of the transformation.
[49:43] Dr. Regis: It could be geographical synchronization, it could be functional synchronization, depending on the use cases that are being implemented. But you want this type of totally aligned impact through the organization. The other bit is — it's not about discussing the cost of the training, but rather looking at what are those mindsets that are necessary to make this use case successful and the value that it creates.
[50:28] Dr. Regis: So suddenly the CLO becomes a contributor to the new value creation. Whatever the business case for that use case is — it could be efficiency, it could be a better top line — the CLO needs to speak the language of the business and use that value dimension created to justify the investments that will support that value creation.
[50:59] Simmi: Yeah, and those huge AI investments being made have to be coupled with leadership development or everything we spoke —
[51:08] Dr. Regis: Yeah, yeah, that's part of the people, process, technology — others add even data and others add even a fifth dimension redesign. But that's all about those streams moving in parallel.
[51:25] Simmi: Very good. I quickly went through the questions. I'm gonna start from the last one here and then go up the line. This is a question from Akash Yadav. Thank you for your question. "AI just handed every operator the expert knowledge that used to sit with senior leaders. So if the thing that made someone senior was knowing more than everybody else, what makes somebody senior now?"
[52:01] Dr. Regis: Yeah. I mean, that's exactly what we covered. Now leaders are more human-centered. Their focus should be really to unleash the full potential of that human and AI collaboration through creating that trust, judgment, accountability and learning agility.
[52:32] Dr. Regis: As part of the study I've done for Heidrick — and I can put also Satya Nadella from Microsoft — saying it's no longer the era of knowing, it's the era of learning. And leaders, you see that Heidrick & Struggles was a search firm recruiting executives, and one of the requirements anymore is learning agility.
[53:08] Dr. Regis: People have to be able to adapt and learn as fast as possible. That is becoming the premium. Expertise — you can surround yourself with expertise — but leaders need to focus on driving that transformation.
[53:26] Simmi: Yes. Very good. As leaders should take responsibility in enabling AI in a team — what about intentional human plus AI collaboration at a team level? I think we spoke a lot about at a leadership level. The question is — how does it get translated at a team level?
[53:56] Dr. Regis: So that is similar to taking a use case, like the performance management process, and really reflecting on where can AI add value, where do we need humans within that workflow?
[54:22] Dr. Regis: What should be the handover, the gates between the two? Who validates the outputs? Who owns the decision? Those questions you can ask regarding a specific use case, but you can ask also regarding the way a team functions. For instance, in team effectiveness — do you create an AI team member who is always with you during the meetings, who is always bringing you the latest data of the organization whenever relevant, either when you are at your desk or during the team meetings?
[55:00] Dr. Regis: Those questions will vary by industry, will vary by functional areas, of course. And each team has to decide what is the right answer to those questions.
[55:10] Simmi: Yes. I think it's about embedding it on a day-to-day basis at each level of the organization. Of course leadership is the most important because they set the tone of the organization.
[55:41] Simmi: But as you articulated it, Regis, for the team level — making it more real, getting an AI team member, referencing it in everything, every discussion — will start driving the transformation.
[55:59] Dr. Regis: And deciding when human must be in the loop. That is what will create trust as well.
[56:09] Simmi: Yes. I think that would be the last question we take. We just have two minutes to go, so I would end the webinar now with a one-liner from me, which is a famous — it's not something new — the future is human-led, powered by AI. So let's focus on the human part of it and not too much getting scared from the AI part of it — that would be my one liner.
[56:38] Simmi: Regis, if you would like to add something, otherwise we will close the webinar. Thank you so much for everybody joining.
[56:48] Dr. Regis: Yeah. No, thank you very much, Simi, for the opportunity. I wanted to just mention that if people have questions, this is what I do on a daily basis, so they can reach out to me via email as a follow-up to this session.
[57:04] Simmi: Very good. Great. Thank you. Thank you, Regis, again for your time and for all the expertise you brought to The Majlis, and thank you, everybody. We will be in touch with you post the session. Thank you all. Bye-bye.
[57:17] Dr. Regis: Bye, everyone.
Simmi Dixit Senior Director Middle East · Wisemonk
Dr. Regis Chasse Chief Learning Officer · CLO Advisors