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Incoming Airbus Chair Amparo Moraleda on the governance challenges of AI

AI is reshaping leadership, governance and decision-making at the highest levels. Here’s what leaders and boards need to consider as they navigate the transition.

September 1, 2026

Amparo Moraleda has witnessed several IT revolutions throughout her career, “and it’s never been about the technology,” she says. “It’s always been about purpose, governance and how agile we are at changing our operating models.”

Moraleda, an IESE alumna, is a respected board member at several leading global companies and has been named the first woman to chair Airbus, effective October 2026. She has decades of experience leading organizations through major transformations and has consistently advocated for business ethics and long-term thinking in governance.

In this edited interview taken from IESE’s podcast, “Leading in the Age of AI,” Moraleda discusses how AI is reshaping leadership, governance and decision-making at the highest levels.

How will organizational processes need to change for AI?

One thing we see, as companies have started using AI to automate certain routine processes, is that it’s not as easy as it seems. A lot of new challenges emerge for people in the process. Can you speak to this point?

I think the dream of every organization, especially in B2C, is to achieve a zero-friction customer experience, so that every interaction is easy, lean and seamless. Yet, time and again, we have seen that what technology makes easier for individuals can make things more complex for companies.

We saw this with APIs, or application programming interfaces. The intention was to simply add a mobile-ready layer that would make services easier to use, but it created considerable complexity behind the scenes because many companies’ IT systems were not fit for purpose. Significant additional investment was needed to prepare their infrastructure for this new environment.

With AI, we are facing a similar challenge, so we need to be very deliberate about why we are implementing it. What is the purpose? How will our processes, workflows and IT infrastructure need to change to properly benefit from it? And most important, how can we enable the organization to embrace those changes?

I was recently with a team that was developing AI agents to take over certain tasks, and they spoke about “onboarding agents” in a similar way that they used to speak about onboarding employees. How do you onboard AI agents within an organization? What happens when you eventually have a department in which agents handle most activities and a human is needed only to address exceptions or situations that have not been predefined within the system?

Significant change is coming to our working environments. Reengineering organizational processes and managing cultural change — the usual success factors in any major digital transformation — will become increasingly important.

What does that mean for the company culture?

This transformation will require a concerted effort to keep everybody on board. What concerns people is not necessarily change itself, but what that change will mean for them. People are afraid of losing things that matter to them.

Therefore, you need to set the tone from the top, supported by a clearly defined strategy. However, if middle managers and frontline leaders cannot visualize a brighter future, both for the organization and for themselves, then you will not achieve the pace of change you need.

Much of change management comes down to good communication, with empathy and authenticity playing a vital role.

Here is David Bowie, speaking to the BBC in 1999 about the internet:

“I think the potential of what the internet is going to do to society, both good and bad, is unimaginable. I think we’re actually on the cusp of something exhilarating and terrifying. It’s not just a tool. It’s an alien life form. (By that, I’m talking about something more profound than it being simply a different delivery system.) The actual context and the state of content is going to be so different from anything we can really envisage at the moment, where the interplay between the user and the provider will be so simpatico, it’s going to crush our ideas of what mediums are all about.”

How does this sound like what we are experiencing today with AI?

For industrial companies and businesses with long investment cycles, AI is less disruptive, at least in the short term. For services firms, B2C businesses, and companies with large volumes of customer interactions, it is likely to be hugely disruptive. Particularly in the services sector, AI significantly lowers the entry barriers for new players.

In the large corporate environments in which I work, it has never been only about the technology. The real challenge is how you integrate new technology into legacy systems, processes and tools, and the pace at which you are able to do so.

For executive and non-executive directors alike, the challenge is to understand the technology well enough to assess its potential contribution to value creation and, equally important, to value destruction. Failing to understand the impact of AI on our respective sectors could represent an existential risk for our organizations. It must, therefore, be a core responsibility of both executive and non-executive directors.

Can you elaborate on why there’s more to it than technology?

When I was at engineering school, I was taught how to write code. Today, with AI, the need to write code manually has disappeared in many contexts, potentially creating significant savings for organizations.

Does it matter that I may no longer need to know how to code? Did it matter when trains replaced horse-drawn carriages? Did society lose something essential when fewer people needed the skills required to drive a carriage?

Consider another example: core banking systems. Employees working in bank branches used to close the books on their daily transactions manually, recording them on sheets of paper that they then sent to the central office. With the emergence of centralized core banking systems connecting all branch operations and transactions, employees no longer needed to perform this task manually, nor did they need to understand precisely how the new system worked.

Today, AI can help organizations interpret legacy code, recovering the business logic embedded in systems developed decades ago and modernizing those systems more efficiently. It is not that knowledge is disappearing. Rather, in many cases, it is being recovered and transferred into a different kind of repository from which it can be retrieved, reexamined and improved whenever needed.

We are in a period of transition: We know that the old world is disappearing, but we do not yet know exactly what the new one will look like. Leading through this kind of ambiguity makes people feel uncomfortable, uneasy and insecure, particularly at the lower levels of an organization.

That is why I say there is more to this transformation than technology. The challenge is how we absorb the changes, redeploy people whose existing skills are becoming less relevant, and support them in developing new capabilities. How do we manage this transition? That is the challenge we currently face.

How well are companies implementing AI?

39 %

of companies using AI reported it was improving their bottom lines

Although a McKinsey survey found 88% of companies were using AI regularly in at least one business function, a much lower percentage were seeing business results, and most had yet to achieve anything of scale. To some extent, this is not surprising: Even with computers and the internet, and also with Industry 4.0 or the Internet of Things, it takes time between piloting a new technology and then experiencing broader operational improvements at significant scale.

How can we show leadership during this period of transition?

That is a good question. Scaling innovation is always a challenge. It is one thing to capture unstructured data and use it to take an isolated action. Integrating that capability into your daily operations and processes is a very different challenge.

Organizations are also using AI in many different ways, so we are still trying to determine how best to measure its impact.

What are the appropriate KPIs for AI?
How does it affect the top line?
What is its impact on CapEx and OpEx?
How does it contribute not only to direct cost savings, but also to cost avoidance — for example, by allowing an organization to continue growing without increasing its workforce proportionately?

Revenue growth, cost savings, cost avoidance: measuring these three dimensions sounds like a good start to me.

Boards themselves are also being challenged and disrupted by AI, including those of us with technical backgrounds. We need to guide the organization through these journeys with limited experience, because it is still rare. Directors typically reach the boardroom after long executive careers. Many began working during the PC era or the early internet days. Very few are cloud native. That’s an acknowledgement that technology moves very quickly!

The crucial thing, for both executive and non-executive directors, is to ask the right questions. Board members do not need to be domain experts in AI, but they do need to frame the issue properly in terms of what it means for the business, and ensure that the appropriate governance is in place.

Boards must also understand the provenance and integrity of the data on which AI-assisted decisions are based. Without reliable data and sound processes, even sophisticated systems can produce misleading recommendations.

You also need to understand where your blind spots are. In such a complex world, when making a significant strategic or capital-allocation decision, the board as a whole must bring together a sufficiently broad range of expertise and perspectives to identify and address potential blind spots.

So, what are boards doing?

AI has different implications for each company. Some boards have chosen to bring in younger AI specialists. The challenge with this approach is that their expertise may be most directly relevant when the discussion focuses specifically on AI, but they may have less experience contributing to the much broader range of issues that a board must address.

For this reason, we are organizing training sessions on AI, focused not primarily on the technical details, but on how AI could disrupt the business model, operating processes and competitive landscape. We are learning to reimagine our business models through the lens of AI.

For Airbus, the challenges are around process certification, since ensuring safety is the overarching principle behind automation, from engineering and design to automation of the production system. In the aerospace industry, processes and components must meet strict certification requirements. Introducing AI into safety-critical or certified environments may require the relevant processes, systems or components to undergo additional validation and recertification. That takes time.

For Vodafone, where I also serve on the board, one key question is how AI can strengthen — or potentially threaten — the resilience and cybersecurity of critical infrastructure. For banks, it is about remaining compliant with regulation, protecting client data, and how to improve customer experience and intimacy.

The impact of AI on jobs

Without naming any company specifically — say you’re a CEO of a big multinational with 100,000 employees worldwide — how many people do you think will work for your company in 10 years’ time?

That is difficult to predict, partly because of the economics involved. When you calculate the full cost of automating a task with an AI agent — including data use, processing power, cloud capacity and energy consumption — and compare it with the cost of having that task performed by an employee, we are finding that using an agent can sometimes be more expensive.

We do not yet have a sufficiently detailed understanding of the cost of the full AI stack. Although computing costs are likely to decline over time, the economics of deploying AI agents at scale remain uncertain. In the short term, certain roles have already proven to be more cost-effective when performed by people than by agents.

I would also highlight trust as a key currency for the future. Even a system that is 99% accurate will create little value if employees, customers or regulators do not trust it. Mistrust in the system can quickly translate into mistrust of the organization deploying it.

That means you need to understand how the data has been curated, how the algorithms have been developed, and what assumptions or potential biases might be embedded in them.

Organizations must also invest in reskilling and retraining their employees. As they develop and deploy AI systems, they need to ensure that sound judgment and objective critical thinking are nurtured throughout the organization.

Airplanes operate on autopilot much of the time, but pilots must still be able to take over when the autopilot disengages because it is no longer receiving reliable sensor data. Likewise, organizations will need people who can intervene and manage exceptions.

People must retain the judgment required to assess whether a system’s recommendation is appropriate, particularly in exceptional or unforeseen circumstances. And judgment is not artificial — it is human.

MORE INFO: Amparo Moraleda was in conversation with IESE Prof. Philip Moscoso as part of the podcast “Leading in the Age of AI.” Watch and listen to the full podcast (40 minutes).

This interview is included in IESE Business School Insight online magazine No. 173 (Sept.-Dec. 2026).