Today’s marketers are expected to do more than ever – drive growth, engage customers, and even market to agents, algorithms, and machines. But in this rush to modernize, something essential has been lost: the soul of marketing.

The latest report from the ÎÚŃ»´«Ă˝ Research Institute, From complexity to clarity: How CMOs can reclaim marketing to build competitive edge, the third in the CMO playbook series, explores how marketing leaders can reclaim their craft – reconnecting with storytelling, customer centricity, and operational excellence – while navigating the realities of AI, fragmented tech stacks, and rising expectations. The report is based on insights from a survey of 1,500 respondents from the marketing function (director-level and above) from 15 countries, and 29 in-depth interviews with leading CMOs and marketing leaders.

Key findings include:

• AI is not delivering its potential – with only 7% of marketers strongly agreeing it has improved performance. While the tools are powerful, fractured visions, lack of foundational readiness, and unclear roadmaps are limiting AI’s impact on marketing outcomes.
• Martech ownership and influence are blurring – 39% of marketing leaders say Gen AI and agentic AI initiatives are funded by the marketing function while 55% says they are funded by the IT function
• CMOs are constrained by ineffectiveness – only 15% of marketing leaders completely agree their current set up enables them to do high value work
• The key to growth is engagement that converts – only 18% of organizations completely agree that they successfully personalise customer interactions to boost outcomes.

Reclaiming marketing is essential reading for CMOs, chief brand officers, chief customer officers, and experience leaders driving marketing transformation. It also offers valuable insights for CEOs, growth and sales leaders, and technology heads supporting marketing modernization, as well as consultants and advisors specializing in marketing strategy, digital transformation, and AI adoption. To reclaim marketing, organizations must:

• Rebuild marketing around customer-centric storytelling and brand purpose
• Use AI and automation not just for operational efficiency, but to support storytelling, customer connection, and measurable marketing impact
• Streamline tech stacks and align tools with strategic outcomes
• Empower teams with clear roadmaps, cross-functional collaboration, and upskilling strategies
• Redefine marketing’s role as a growth engine and architect of customer experience, not just a support function.

Download the ÎÚŃ»´«Ă˝ Research Institute’s latest CMO Playbook to:

• Learn how CMOs are meeting rising expectations despite limited resources – and what strategies can help you do the same.
• Uncover why only 7% of marketers agree that they are unlocking real value from AI, and learn how to overcome the barriers holding the rest back.
• Explore actionable strategies to elevate marketing’s influence and reclaim its strategic seat at the leadership table.

Becoming quantum-safe

Michele Mosca, CEO, evolutionQ

Michele Mosca is CEO and co-founder of evolutionQ, a cybersecurity company that pioneered quantum risk management and the Basejump^TM software product suite that enables scalable cryptographic resilience. Prior to co-founding evolutionQ, Michele co-founded the Institute for Quantum Computing while being a Professor of Mathematics at the University of Waterloo, Canada. He is a founding member of the Perimeter Institute for Theoretical Physics and his work on quantum computing and quantum-safe cryptography is widely cited.

Can you begin by telling us about your journey into the quantum-safe cryptography space?

I’ve been working at the intersection of cryptography and quantum computing since the 1990s – before the two were overtly connected. Over the past decade, I’ve shifted toward commercialization. Initially through services and, over the past five years, by building out a product company to address the need for cryptographic modernization, including quantum readiness and overall readiness for a cryptographic zero-day.

How would you describe the current state of awareness around quantum-safe cryptography?

Awareness has definitely increased, owing in part to organizations such as Google and IBM. But we’re still not where we need to be. Most people see this as a “one-problem-one-solution” situation. What they need to understand is that the quantum threat is just a visible example of the things that could go wrong with our cryptographic foundations. We don’t know the limits of quantum computing and, with AI accelerating, it’s becoming even more difficult to predict future vulnerabilities.

There are two waves of awareness. First, we must recognize that there is a threat. Second, and more profoundly, we have to accept that it’s not going to go away. On the contrary, code-breaking threats will keep evolving, and our systems must be resilient by design. Just as we moved from passwords to multi-factor authentication, our key exchange and cryptographic practices must also become layered and agile. But agility alone is not enough. If your infrastructure is hijacked and money is stolen, you must be resilient.

The quantum threat is just a visible example of the things that could go wrong with our cryptographic foundations. We don’t know the limits of quantum computing.

Why do you think there is an urgency to address the quantum threat?

A few key risks drive urgency. ‍Firstly, transitioning to quantum-safe cryptographic infrastructure is a complex, multi-year process. Organizations that underestimate this challenge risk rushed, poorly executed migrations that could leave critical systems exposed and lead to prolonged operational disruption. Or they might be too late, and systemic quantum-enabled attacks start before they are ready. There’s no free lunch: every unit of crypto-procrastination translates either into a unit of catastrophic risk or a unit of rushed migration risk.

Another risk is already becoming a reality: “harvest now, decrypt later” attacks. ‍Although a cryptographically relevant quantum computer does not yet exist, malicious actors are collecting encrypted data with the intent to decrypt it once quantum computers become powerful enough to do so. If organizations fail to implement quantum-safe cryptographic strategies proactively, sensitive communications, financial transactions, and classified data may be at immediate risk.

And then, as regulators, partners, and other stakeholders push for quantum-readiness, there is compliance risk and the risk of simply not keeping up with the needs of your key stakeholders.

There’s no free lunch: every unit of crypto-procrastination translates either into a unit of catastrophic risk or a unit of rushed migration risk.

What are the main challenges in scaling and commercializing quantum-safe solutions?

Interestingly, the technical challenges while tough, are manageable. The harder part is getting timelines aligned across the ecosystem. Everyone from vendors to customers must commit to securing their systems by a certain date.

But some are still lagging, and we can’t cater to the lowest common denominator anymore. It’s time to separate the wheat from the chaff and improve our vendor ecosystem quality.

Another key issue is the lack of a clear mandate. If regulators and customers demanded resilience and set clear expectations, it would accelerate adoption. But too many are still debating when “Q-Day” will be, rather than acknowledging the urgency. That question was valid 10 years ago, but now it’s outdated. Today, we need to focus on getting this done. The threat is already too close for comfort.

How do you create a sense of urgency around this threat?

Organizations need to understand that the quantum threat isn’t far off in the future. It’s already affecting them today, as in the “harvest now, decrypt later” threat. They must also consider the time required for a proper migration to quantum-safe technology.

This will quickly pivot from “doesn’t matter” to “you better have it done.” Adequate preparation will be a real business differentiator. One investor told me, “It’s a dollar to get ready before left of boom, and hundreds of millions right of boom.” That captures the stakes.

One of the major obstacles is self-imposed. A lot of this is driven by cool technological tactics that are unconnected with business objectives. The real goals are business continuity, resilience, trust, and risk reduction.

Adequate preparation will be a real business differentiator.

Why is the industry’s focus on crypto inventory slowing progress, and what is the correct approach?

People are embarking on the gargantuan task of inventorying their cryptography but can’t remember why they are doing it. They must use it to understand and mitigate business risk. Some even say, “I can’t do my risk assessment yet because I haven’t done my inventory.” That’s missing the point.

I’ll give an example. Someone from Ericsson showed one slide in Toronto recently, it explained how 5G works and said: “The biggest threat is firmware updates.” Boom. In 30 seconds, there’s your biggest quantum risk. They didn’t spend years scanning software just to produce massive data tables.

When cleaning your house, you don’t need to dust every chandelier before you deal with the corpses in the dining room. Inventory is a part of mature crypto management, but don’t let it stall your risk assessments. Act on the most obvious risks.

Have you heard of any post-migration concerns around latency, performance, or compatibility with legacy systems?

Around 80% of the time, you’ll be fine. Even on a phone. But what if you’re in an internet of things (IoT) scenario or other constrained environments? Then, it becomes a problem. And you better find out in advance. If you need lightweight PQC and it doesn’t exist, then what?

Some experienced applied cryptographers are realizing that, most of the time, PQC is the answer. Just upgrade your PKI [public key infrastructure] to post-quantum PKI and you’re good. But, in a few cases, we’re seeing situations where PKI might be overkill. Here, we should revisit assumptions.

There are use cases where we did PKI because that’s what we knew. But, in controlled, exclusive systems, it’s worth asking why we’re still using PKI. It’s slow, consumes energy, and is vulnerable to cryptanalysis. In these cases, maybe it’s time to leverage symmetric key solutions, which are faster and more secure in the long term.

What would be your advice to a company beginning its post-quantum journey?

First, do a very quick, high-level business risk assessment. It doesn’t have to be complete. Just make a start.

Second, start engaging with the ecosystem. Figure out who’s going to evolve to be part of the solution and who you’ll have to replace. Start pilots and proofs of concept. Ideally, some members of your existing ecosystem step up. But not all will.

You might light a fire under some of them to get them to improve. But you’ll need to find alternatives to others, either because they lack the business will or the technical ability to get where you need to be. So, start right away. Don’t try to eat the elephant all at once. Begin doing some rapid mitigations, so you can learn quickly.

Do a very quick, high-level business risk assessment. It doesn’t have to be complete. Just make a start.
Second, start engaging with the ecosystem.

Perspective from ÎÚŃ»´«Ă˝

Pascal Brier, Group Chief Innovation Officer, ÎÚŃ»´«Ă˝

Pascal Brier is Group Chief Innovation Officer and a member of the Global Executive Committee at ÎÚŃ»´«Ă˝. Since 2021, he has led the Group’s global Technology, Innovation & Ventures agenda, overseeing how emerging technologies are identified, explored, and applied across industries. Under his leadership, the company helps organizations harness technological progress to create new forms of business value and positive impact for society.

From technologies to systems

When the Gutenberg Bible was printed around 1455 – a masterpiece of clarity and beauty – the world changed forever. Knowledge could finally be reproduced and shared at scale. Gutenberg’s genius lay not in inventing something new, but in combining existing elements: movable metal type, oil-based ink, and a screw-press adapted from wine presses. His work marked a new era through convergence, not isolation.

What was exceptional in the fifteenth century has now become the norm. We have entered an era where innovation no longer happens within technologies, but between them. AI learns to reason, robots to collaborate, and energy systems to think. Each breakthrough is impressive, but true power emerges when they interact: intelligence meets embodiment, computation meets physics, digital meets biological.

The new frontier lies in orchestrating these convergences. How do we make technologies work together coherently, safely, and responsibly? How do we design interfaces – technical, human, and ethical – that enable this intelligence to operate across systems?

Progress now depends on connecting what we have already invented.

We have entered an era where innovation no longer happens within technologies, but between them.

The new logic of innovation – From mastery to orchestration

In the twentieth century, industrial success was mainly built on the mastery of a single technology. Companies specialized and scaled around one domain: electricity, computing, telecommunications, materials, and so on. Innovation was linear: invention led to engineering, engineering led to production, which led to distribution. Competitive advantage came from depth of expertise and control over a well-defined value chain.

Today, that logic no longer holds. In the twenty-first century, value lies in connection. Disruptive innovations now emerge from the interplay: when AI meets robotics to create autonomous systems; when biology merges with computing to enable precision health and sustainable manufacturing; when new materials meet advanced energy systems to accelerate decarbonization. The frontier is now a fluid, cross-disciplinary network.

This shift transforms how organizations must think and operate. It moves innovation from R&D silos to open ecosystems, from patents to partnerships, from vertical integration to horizontal collaboration. Companies shaping the next decade will orchestrate across boundaries, linking technologies, industries, and expertise into coherent systems.

Convergence also reshapes interdependence. Industries blend into shared value networks. Humans and machines evolve toward hybrid collaboration. Public and private sectors co-architect common infrastructure – from digital platforms and shared data spaces to interconnected energy and mobility systems. Innovation, once the prerogative of a few, is now a collective act.

This transformation also challenges how public research is organized. Most institutions still reflect a logic of specialization, with labs structured by discipline. Yet today’s problems rarely fit such boundaries. Designing next-generation prosthetics, for instance, requires collaboration across medicine, engineering, acoustics, computer vision, and robotics.

Convergence calls for research that breaks silos, fosters multidisciplinary collaboration, and aligns inquiry with real-world complexity.

Convergence in practice

Three arenas illustrate how technologies now evolve in partnership.

First, AI and quantum computing: two revolutions increasingly intertwined. Quantum offers a new way to process information; AI provides reasoning to navigate that complexity. Together, they open new frontiers in materials science, logistics, and drug discovery. AI designs efficient quantum algorithms; quantum models accelerate AI training and optimization. This mutual reinforcement is not just computational, it signals the beginning of a new intelligence infrastructure that will redefine how we simulate, predict, and decide.

Second, humanoid robotics, driven by AI, spatial intelligence, and advanced materials. What we are witnessing, particularly in China, is a rapid migration of knowledge from adjacent industries (notably drones, autonomous vehicles, and consumer electronics) toward robotics. Companies are repurposing their expertise in sensors, batteries, and vision models to create robots that can perceive, adapt, and operate in real-world environments. The result is not only a new generation of machines, but a new industrial fabric: robots are now assembling robots.

Finally, we see convergence in energy transition. The interplay between electric vehicles, battery innovation, and solar technologies is reshaping both mobility and infrastructure. Advances in one domain trigger leaps in another: better batteries enable cheaper solar storage; AI-optimized grids stabilize renewable production; circular materials science extends the lifespan of components. What were once three distinct industries are increasingly one ecosystem.

Robots are now assembling robots.

Converging away from sovereignty

Converging technologies reveal a deeper truth about technology sovereignty. As nations and companies seek control over critical technologies (chips, models, energy systems), they discover profound interdependence. The more we strive for independence, the more we uncover our interdependencies. Quantum breakthroughs rely on global semiconductor supply chains; AI depends on semiconductors, shared data, and open science; cloud relies on complex infrastructures, routers; clean energy transitions hinge on rare materials mined globally.

True sovereignty in the age of convergence will not come from isolation, nor from collaboration alone. It will depend on mastering, securing, and strategically integrating technologies that underpin interdependence. Resilience means developing know-how, talent, and infrastructure to contribute meaningfully to shared systems. Sovereignty must be redefined as the power to choose and limit dependencies: to collaborate from strength, grounded in mastery and trusted partnerships.

Leadership in a converging world

If convergence defines innovation’s new logic, it also redefines leadership. The innovation model emerging today is collaborative by design. It demands not just technical mastery, but the ability to translate complexity into direction.

Leadership is no longer about commanding technologies; it is about orchestrating relationships between them.

• From silos to ecosystems. Innovation is networked. No single company – however large – can own the full stack of capabilities required to compete in a convergent world. The most successful organizations are those that cultivate open ecosystems: partnerships with startups, academic institutions, and even competitors. Their advantage lies not in exclusivity, but in connectivity, in the speed with which they can combine technologies and scale new ideas across domains.
• From speed to coherence. For years, innovation was measured by velocity: how fast a company could move from prototype to product. But when technologies converge, the challenge shifts from speed to synchronization. Progress in AI is meaningless if it outpaces progress in energy efficiency or cybersecurity. The race is not just to move fast, but also to move in harmony. Leadership, therefore, becomes an exercise in alignment, ensuring that strategy, talent, and technology evolve in concert.
• From invention to intention. Convergence multiplies potential – and consequences. As systems grow more intelligent and autonomous, the boundaries between technical choice and ethical responsibility blur. The question is no longer whether we can build it, but should we, and under what conditions. Responsible innovation is not a constraint; it is the foundation of long-term trust.

Ultimately, leaders who will shape this century are those who understand that innovation is no longer a solitary act of creation, but a continuous act of connection – balancing ambition with responsibility, and curiosity with coherence.

As technologies converge, progress will come not from mastering individual tools, but from engineering the relationships that connect them: between data and matter, intelligence and energy, humans and machines. This is the new frontier of leadership: not simply to innovate faster, but to build coherence out of complexity, to shape ecosystems where intelligence, purpose, and impact evolve together.

We are entering the age of intelligent systems and autonomy, where AI reasons, quantum computes, robots act, and energy networks self-optimize. Our task now is not only to harness these systems, but to give them direction: to align intelligence with intention, power with responsibility, and progress with planetary limits. The true measure of innovation will not be the sophistication of our technologies, but our ability to make them work in concert, creating prosperity that endures, and intelligence that serves humanity.

As technologies converge, progress will come not from mastering individual tools, but from engineering the relationships that connect them.

When IT meets AI

Mike Crisafulli, Senior Vice President and Chief Information Officer, Connectivity & Platforms at Comcast

Mike Crisafulli, Senior Vice President and Chief Information Officer, Connectivity & Platforms at Comcast, where he leads the design and operation of the company’s digital core to deliver seamless connectivity and exceptional experiences for customers and employees. This entails leading large-scale digital transformation of web, mobile, and desktop products and platforms. Before his current role, Mike served as Senior Vice President of Residential Services and SVP of Product and Platform Services Development at Comcast, where he oversaw strategic planning and delivery of systems impacting the entire customer lifecycle. Mike holds a bachelor’s degree in Information Systems from George Mason University and an MBA from the University of North Carolina at Chapel Hill. Outside of work, he is a dedicated community volunteer, serving as an emergency medical technician and firefighter for over 25 years.

How has the role of IT at Comcast changed, and which business drivers have pushed that transformation?

The big tech moves – starting with cloud and microservices, and now AI – have all been about helping us go faster and be more efficient in delivering capabilities across all our lines of business. IT has become primarily an enabler of speed and efficiency.

At the same time, business pressures have increased dramatically. The advent of 5G and fixed wireless broadband created new competitors in connectivity. We operate now in a hyper-competitive environment, where we must do more with less. For IT, that translates into a relentless drive for efficiency, agility, and quality. There’s constant pressure to launch new products faster and continuously improve the customer experience [CX], while optimizing existing operations.

Our technology function has evolved from back-office support arm to critical strategic partner. We adopt modern tech not for its own sake, but to support business requirements, whether that’s launching new streaming experiences or improving broadband reliability. Major external changes (such as wireless broadband competition and streaming) have pushed agility and efficiency in IT, aligned tightly with business strategy.

Our technology function has evolved from back-office support arm to critical strategic partner.

Which new technologies or capabilities will matter most to you and to Comcast in the next two years?

Without a doubt, AI – especially generative and agentic AI – is front and center. The pace of change in AI over the last year has been astounding.  Over the next 24 months, I see these AI capabilities making the biggest impact, both in the products and services we deliver to customers, and in how we operate internally.

We’re exploring AI to enhance user experiences and automate more complex functions (network optimization, personalized content recommendations, and so on). But where I’m most excited is applying AI internally to our software engineering and IT operations. There is enormous potential to reduce complexity in our legacy systems, automate routine tasks, and even generate code or test cases using AI. We have other priorities, such as evolving our cloud infrastructure and bolstering cybersecurity, but AI is the new frontier.

The pace of change in AI over the last year has been astounding.

Staying ahead of the curve

How do you keep up with the rapid pace of technological development?

Personally, I try to absorb information continuously. For example, I listen to tech podcasts that focus on emerging trends. More importantly, we have a large organization full of curious, passionate people, and a lot of knowledge flows in through them.

We’ve built mechanisms into our operating model to foster continuous learning and innovation and create a grassroots organic flow of knowledge. For instance, we run an internal event called “Knowledgefest,” a dedicated day of learning, where employees present to thousands of their peers about cool innovations or lessons learned across a variety of technical tracks. We also have a longstanding tradition of hosting “Lab Week” – essentially a hackathon week – a few times a year, where employees can experiment with new ideas or technologies.

Before Lab Week kicks off, employees start to submit ideas, and teams form around the most promising ones. We also offer up challenges connected to broad strategic priorities (say, CX improvements, entertainment or network innovation). During the hackathon, teams work intensively on their projects, and at the end we hold a science-fair-style demo day. We identify the best projects, which are then developed, potentially into production. Some of our most impactful new solutions have come out of Lab Week. And it’s not limited to traditional IT folk. We often have cross-functional participation from business units, which brings in diverse perspectives – and gives employees an opportunity to network and connect with colleagues from other teams.

AI and the future of software engineering

Where are you seeing the biggest impact and greatest operational value of the application of AI and Gen AI?

The biggest AI-driven transformation is in software engineering and IT. We recently used Gen AI to reimagine a core domain of our work: the software development lifecycle. This arose from my team’s own experimentation. Over the past few months, we built out a new Gen AI-powered developer workflow, essentially a Gen AI-based SDLC [software development lifecycle]. About 700 of our engineers use this AI-augmented development workflow in their day-to-day work, and it has fundamentally changed how they operate. They’re now interacting with AI tools that can generate code snippets, create test cases, draft user stories from plain English requirements, perform impact analysis, and so on. This toolset accelerates the upfront phases of development, from initial requirements to a ready-to-code solution design. Ultimately, we will roll it out to many software engineers across the organization.

There are definitely AI pilots in other parts of Comcast but many of those are still at proof-of-concept or small-scale trials. We also have a couple of major customer-facing AI initiatives in the pipeline. But the forefront of AI adoption today is really in improving our internal software development processes: developer productivity and output quality.

About 700 of our engineers use this AI-augmented development workflow in their day-to-day work.

If we project forward – to the year 2030, say – how much of IT work will be AI-driven versus human‑driven?

I’m pretty bullish. Based on what we’ve seen just in the last six to 12 months, I think in five years we could see on the order of 80% of software development tasks being automated or AI-generated in some way.

We don’t have end-to-end automation but there’s already a lot of human-assisted automation happening in generating code, testing, deployment.

Developers today spend a considerable part of their time coding, and the rest on requirements, design, test cases, impact analysis, user story generation. So, when we started automating the upfront parts, it’s already had a huge impact – and when you add in the rest of the lifecycle, like automated testing and auto-deployment, it’s hard to imagine less than 80% of it being AI-driven within five years.

I think in five years we could see on the order of 80% of software development tasks being automated or AI-generated in some way.

With AI coming into play, how do you ensure your teams remain innovative and that the IT–business partnership thrives?

At Comcast, IT doesn’t operate in a vacuum. I’ve always pushed for a tight partnership with our business stakeholders. With the emergence of Gen AI, that partnership is becoming critical. To develop AI use cases and scale them, you need deep integration of technical and business expertise.

That said, we’re still learning which are the best models of collaboration in an AI-driven context. One thing we learned from our internal AI rollout is that change management is huge. Rolling out AI-powered tools is the easy part – we’ve needed to put a lot more energy and focus into change management and helping teams adapt to new ways of working.

Introducing AI isn’t just a technical exercise. It changes how people do their jobs. Now, if I extend that lesson to the broader business, the change management challenge is even bigger.

For our most strategic AI initiatives, we’ve started creating cross-functional “AI pods.” In these AI pods, product owners, business analysts, and engineers are all part of one agile team. It’s like forming a mini startup within the company, focusing on a specific business problem and using AI as an accelerator. We’re piloting it on a couple of high-priority projects. But, already, it’s promising. We have business stakeholders working with developers, and even using the AI tools together to define a solution. This brings a shared understanding and much faster iteration. That real-time collaboration is powerful.

Different areas across Comcast have different maturity in product ownership. Historically, some platforms didn’t even have formal product owners on the business side. In our customer-facing digital experiences, we have UX designers and business leads deeply involved, whereas some back-end systems were more IT-driven. So, we will need to vary our approach to rolling out this new integrated model. But broadly, I see AI acting as a catalyst for closer IT–business integration. To get the most out of these AI tools, we have to rethink roles and break down silos that have existed for decades.

We need to educate our business partners about what AI can and cannot do, so they can ideate with us. In some companies, I’ve even heard of product managers or business analysts using AI themselves to better communicate their ideas and test its feasibility. That blurring of lines is interesting and can be positive. The more tech-fluency on the business side, the better. It means everyone speaks the same language, at least to some extent.

I believe this “one team” approach will yield a whole new level of partnership. To succeed, AI relies on tech and business working in concert. Culturally, we are fostering curiosity, continuous learning, and listening closely to the business on what will move the needle for them. It’s an exciting evolution in how we work together.

I see AI acting as a catalyst for closer IT–business integration.

Balancing speed of progress and avoidance of technical debt

How do you balance delivering new features quickly with the need to manage technical debt and maintain stable platforms?

This is a classic dilemma for any large IT organization. The advent of AI cuts both ways here. On one hand, AI will let us deploy new features faster. But, if we’re not careful, that could exacerbate technical debt, because we might spin up new services rapidly without the usual constraints, potentially increasing the complexity of our estate. For example, we don’t want to launch 100 new microservices powered by AI and forget to retire the 50 old ones they were meant to replace.

On the other hand, I see a huge opportunity to use AI to tackle technical debt. Imagine AI tools that can analyze legacy codebases and propose simplifications or even automatically refactor code into more modern languages/frameworks. Or AI-assisted testing that makes it easier and safer to decommission old systems. So, I’m optimistic that we can apply the same AI power to “cleaning up” as we do to building new things. In the best case, AI helps us simultaneously accelerate feature delivery and the retirement of obsolete stuff.

It still comes down to discipline and prioritization. We need to bake platform simplification into our roadmap, even as we speed up features. The goal is a balanced approach: using AI both to accelerate and simplify. But it requires conscious effort: governance to ensure we’re decommissioning as fast as we’re adding, and maybe even dedicating some AI capacity to hunting down inefficiencies in our architecture.

I’m optimistic that we can apply the same AI power to “cleaning up” as we do to building new things.

How do you prevent teams from being overwhelmed by the pace of change?

It does feel like the tech is moving faster than many teams can absorb, on the business side as well as the IT side.

We don’t want teams so paralyzed by new options that they stop experimenting. We give them an open environment to try out new tools and ideas (within reason), so they stay engaged with the latest technology. That’s the whole idea behind Lab Weeks: create safe spaces to play with what’s new. But there’s always a focus on business outcomes. We have to prioritize the problems we’re trying to solve.

This ties into the classic build-vs-buy and portfolio management discussions. In the past, you might buy a technology solution and expect it to serve you for two to three years. Now, something new might emerge in six months that upends that assumption. So, we have to stay nimble. We’re trying to keep a hybrid approach in our tech stack, rather than locking ourselves into one vendor or architecture, which might be outdated in a year. We modularize where we can, so if a better component comes along, we can swap it in. And we re-evaluate our portfolio priorities every quarter, or even more frequently.

In practice, it becomes a cycle: keep experimenting, while delivering incremental value. We want to be aware of what’s around the corner, without that distracting us from what’s possible today. There’s so much we can do with the tools at hand, even if they’re not perfect or the very latest, that can give us 80% of the benefit we’re looking for. Let’s deliver something tangible, get value, and then we can iterate when the next improvement comes. It’s a balance of staying adaptable without losing focus on execution.

We also put great emphasis on an adaptive mindset. You hear clichés like, “Today is the slowest rate of change you’ll experience going forward.” Well, it’s true. We have to internalize that. For example, I tell my leaders: we might spend four months implementing a solution and then a new technology makes part of it obsolete. And that’s okay. We delivered value for those four months, and now we adapt again. The old mindset of “set a three-year plan and stick to it” doesn’t fully work in this environment. Instead, we plan in smaller chunks, deliver in smaller increments, and be ready to pivot when needed.

This is a big cultural shift, especially in a large enterprise like ours that traditionally valued predictability and long-term roadmaps. We’re retraining ourselves to think more like, “What value can we deliver in the next month or quarter with what we know now?” and then iterate. It’s an agile mentality taken to the next level due to the extreme pace of change. We still have an overall strategy, but we’re fluid in how we get there.

In short, to prevent overwhelm, we narrow focus to what matters (business value) and cultivate an adaptive culture. Encourage the team to try new things, but also to accept that not every new thing will stick. Celebrate quick wins and learning, not just big, long-term projects. The goal is that our people don’t fear the change but see it as exciting – as long as we’re delivering outcomes along the way.

We want to be aware of what’s around the corner, without that distracting us from what’s possible today.

How will automation and AI change your approach to IT governance and oversight?

I think AI is going to reshape governance significantly. A lot of governance today is about policy enforcement, approvals, auditing what people do – tasks that could be automated. If we apply generative and agentic AI to these areas, we can imagine things like automated policy definition and real-time compliance monitoring. I see governance shifting to be much more about strategic oversight of the AI, rather than humans doing all the checking. For example, you might have AI agents that handle certain approvals or keep audit trails. Our job is to audit the auditor. We’ll need robust traceability of exactly what the AI did, what decisions it made, and why.

We’re working on an orchestration layer for AI agents. Think of it as a management framework for AI “employees.” In many respects, we’re going to treat those agents as we would human team members. That means assigning roles, monitoring performance, setting up controls and logs for everything they do. So, as AI takes over routine governance tasks, humans will focus on meta-governance: designing the policies, reviewing exceptions, and guiding the AI. It’s a shift from doing the work to overseeing the work. And because AI can give more visibility into processes than we’ve ever had (through logs, analytics, etc.), we might actually get more transparency and accuracy.

As AI takes over routine governance tasks, humans will focus on meta-governance: designing the policies, reviewing exceptions, and guiding the AI.

What is your biggest takeaway from this transformation journey?

We’re all learning. We’re at various stages across domains. I’m sticking to this: change the way we work. Don’t just automate something that’s already broken. Prepare your workforce for change and being adaptive and keep your eye on outcomes.

Much of it is change management. Much of it is new tech, governance, and privacy. For our consumers, our ways of thinking need to change as much as the tech.

That’s the key. Otherwise, we’re just “AI-ing” what exists today – maybe faster, maybe more efficient but if we don’t have the talent to reimagine what’s possible, especially for our products and consumers, it’s all for nothing. That’s why we’re focused on talent alignment, change management, and adaptability as much as the tech.

Reimagine, don’t just replicate. Invest in your people and change management, and stay laser-focused on the outcomes you want. It’s a daunting, but truly exciting time. I’m confident that, by keeping those principles in mind, we’ll navigate whatever the future holds.

Reimagine, don’t just replicate.

Conversation between Aiman Ezzat, CEO ÎÚŃ»´«Ă˝, and Christel Heydemann, CEO Orange

Christel Heydemann began her career in 1999 at Alcatel, where she held various management positions. In 2011, she was promoted to Director of Human Resources and Transformation and member of the Executive Committee.

She joined Schneider Electric in 2014. In 2017, she became Executive Director France Operations of Schneider Electric and member of Schneider Electric’s Executive Committee. In 2021, she was named Director Europe Operations, a position she held until the beginning of 2022 when she became Chief Executive Officer of the Orange Group, having served as a member of its Board of Directors since 2017.

Christel is a graduate of École Polytechnique and École Nationale des Ponts et Chaussées, an Officer of the French Order of Merit and a Knight of the French Legion of Honor.

Unlock large-scale growth with cloud-powered AI agents

The financial services (FS) industry is on the verge of a remarkable transformation. Cloud platforms now do far more than support infrastructure: they help orchestrate innovation. And when paired with AI agents – autonomous systems that can reason, learn, and act – financial institutions can rethink how they operate, innovate, and engage with customers.

While most firms across the industry have adopted some version of AI, agentic AI in FS is still nascent – with only 10% of organizations implementing AI agents at scale. Continuing adoption is crucial to future success: cloud-based AI agents can help unlock new business opportunities by driving efficiency, enabling next-gen operating models, delivering improved customer experiences, streamlining product development, and expanding access to underserved markets.

The World Cloud Report – Financial Services 2026 draws on learnings from two primary sources. We polled 1,100 leaders of FS firms across seven sectors – retail banking, wealth management, payments, capital markets, P&C insurance, life insurance, and health insurance – spanning 14 markets in the Americas, Europe, and Asia-Pacific. The report also includes insights from more than 40 focused interviews with cloud leaders from banks, insurance companies, and hyperscalers around the globe.

In today’s rapidly evolving digital economy, the integration of cloud modernization and AI in FS has become a competitive differentiator. To capture new business and revenue growth, ÎÚŃ»´«Ă˝â€™s World Cloud Report – Financial Services 2026 recommends that industry players lean into the following three strategies:

• Move from offshoring to cloud shoring to allow for location-agnostic, continuous delivery and increased focus on smarter, more resilient, and socially responsible services.
• Harmonize human expertise with intelligent agents to create a blended “cyborg workforce” where humans are augmented with appropriate technical capabilities – including across key frontline processes and client services.
• Leverage cloud-based AI agents to drive intelligent automation and greater CX personalization, by expanding offerings and speeding up delivery.

FS firms that can quickly build a strategic roadmap aligning business goals and operations, governance, and culture with these evolving technologies – and then continue to innovate over time – will be well positioned for continuing success.

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