Q1. To begin, could you briefly outline your professional journey and how your experience leading large-scale IT and organizational transformations has shaped your perspective on technology-driven value creation?

My professional journey spans more than 30 years and has been characterised by movement across different types of organisations and roles. I started my career in a system integrator as a Java programmer, working very close to technology delivery. Over time, I progressively moved into managerial and executive roles, eventually becoming Executive Director at IBM, where I held responsibility for live business and customer outcomes.

Along this path, I worked in senior roles at Accenture, where I focused heavily on IT strategy, infrastructure services, IT operating models, and large-scale transformation programs, particularly in the Financial Services sector. I also had experience with other major technology providers, which allowed me to further broaden my understanding of both services-led and product-led organisations.

In parallel, I’ve worked with clients across multiple industries, including banking, telecom, and industrial companies. This exposure to different sectors gave me the opportunity to observe how technology transformation is approached in very different business contexts.

I consider this diversity a strong foundation because it allows me to see technology transformation from multiple perspectives: from the supply side, including system integrators, technology vendors, and service providers, and from the client side, understanding how enterprises in different industries adopt and operationalise technology. This combination has shaped my view that technology-driven value is not created by technology alone, but by how it is integrated into organisations, processes, and operating models.

 

Q2. From an IT strategy and advisory standpoint, how do you assess the market size and segmentation for hybrid cloud, AI-led transformation, and IT operating model redesign, and which segments are demonstrating the most sustained enterprise demand?

The first important point is that these three areas are very closely connected. While they have different histories and maturity levels, they should not be seen as completely separate segments.

IT operating model transformation has existed since the early days of corporate IT, more than fifty years ago. Hybrid cloud transformation became a major topic roughly ten years ago, even though some elements existed earlier. AI-led transformation, on the other hand, is very recent.

Despite these differences, the three dimensions strongly influence one another. Today, IT operating model transformation is often driven by cloud initiatives and increasingly by AI-led transformation. In many cases, cloud investments are accelerated specifically to enable AI use cases.  For this reason, I see them as interconnected dimensions within a wider transformation journey rather than isolated subjects.

From a market perspective, all three represent very significant opportunities. We are talking about opportunities in the range of hundreds of billions of dollars over the coming years. Analysts can provide more precise figures, but at a high level the scale of opportunity is very clear.

Hybrid cloud transformation is already a well-established and stable path. It is ongoing and will continue for at least the next five to ten years. AI transformation is emerging rapidly, but it is still less stabilised. While cloud transformation outcomes are now well understood, AI projections are still largely based on expected potential rather than long-term, large-scale enterprise data. As real implementations increase, these projections may evolve.

IT operating model transformation will continue to follow both cloud and AI adoption. For cloud, the required operating model changes are now quite well defined. With AI, there is still discussion and experimentation, but it is clear that the transformation will be deep, as AI becomes embedded and pervasive across enterprise processes.

 

Q3. What are the strongest forces currently driving enterprise investment in cloud and AI transformation, and where do complexity, skills gaps, or operating-model inertia most often dilute expected outcomes?

One of the strongest drivers of investment is competitiveness. AI is a disruptive technology that will impact Enterprises across several dimensions, and has the potential to fundamentally change how companies operate. Enterprises do not want to miss this shift, so they are investing to remain competitive within their markets.

This competitiveness spans several dimensions, including process efficiency, higher levels of automation, faster execution, and improved decision-making. While many of these benefits still need to be fully implemented and scaled, the perceived impact is already significant.

A second major driver is cost optimisation. Many technology shifts promise to simplify operations and reduce execution costs. AI, in particular, is expected to lower the cost of running many processes, which makes it very attractive from an economic standpoint.

A third driver comes from the infrastructure side. Large technology providers are making massive investments in AI-focused data centres. AI requires powerful and specialised infrastructure, and the scale of investment in this area over the past year has been very high. This infrastructure build-out itself is a major driver of overall spending.

On the other hand, several factors can dilute expected outcomes.

Regulation is one source of complexity. AI is still new, and regulatory frameworks are evolving. There is not yet full clarity on how AI will be regulated across different regions, which can slow down large-scale adoption.

Skills are another critical challenge. There is a shortage of AI skills, both on the implementation side and on the business side. Even junior AI roles command very high salaries, reflecting how scarce these skills are. Education systems will need time to scale and provide the required expertise, similar to what happened during previous technology waves such as the internet.

Skills challenges also affect end users. While Generative AI tools may appear simple to use due to their conversational nature, their impact on daily work and processes can be complex. Employees need training to understand how AI impact their roles, responsibilities, and workflow. This training must extend across the organisation.

Finally, operating model complexity plays a major role. AI capabilities will increasingly be embedded directly into core processes rather than added as a complement to existing tools. As AI becomes pervasive, applications and workflows will change significantly, requiring deep operating model redesign. This complexity is often underestimated.

 

Q4. Based on your experience building customer success functions, what distinguishes organizations that translate technology adoption into measurable business value from those that stall after initial implementation?

In many cases, customer success is directly linked to the first implementation. Especially in the software world, the primary objective of a customer success function is to ensure that the client achieves a successful initial deployment of what they have purchased.

It often happens that clients buy software with the intention of testing it or exploring its potential. However, due to shifting priorities or other factors, the software may remain unused, sometimes without even a first implementation. For this reason, the first responsibility of customer success is simply to make sure the implementation actually happens.

The key difference between organisations that succeed and those that stall lies in focus. Companies with strong customer success functions do not focus only on internal metrics such as sales, revenue, or contract value. They also place a strong and explicit focus on client outcomes.

This means that already during the sales phase, they work to understand the real business value the solution is meant to deliver. They do not stop at the sale; they stay engaged to ensure that what was sold is implemented properly and delivers tangible business value.

They are also structured to resolve issues quickly and escalate when necessary. This focus on client outcomes builds long-term relationships and drives sustained commitment rather than one-off transactions.

 

Q5. In a crowded ecosystem of cloud, AI, and digital transformation providers, what non-obvious differentiation factors most influence long-term client commitment?

This question is closely linked to to the previous. Long-term commitment depends on ensuring that what the client purchases actually achieves its objectives, and this often involves a customer success function.

For example, if you sell a security solution, you need to understand why it is valuable for the organisation as a whole, how it supports the specific business unit adopting it, and how it benefits the individuals responsible for using and managing it.

When providers align themselves with these different levels of client objectives, they build trust. This goes beyond a transactional relationship and creates a more collaborative partnership.

A deep understanding of client objectives, combined with a genuine focus on delivering value at multiple levels, is what most often differentiates providers and drives long-term commitment.

 

Q6. How should enterprises balance innovation speed with resilience, security, and regulatory compliance, and where do operating-model decisions most often create hidden long-term risk?

Most large enterprises already have operating models and processes in place to manage innovation. On paper, they typically know how to balance innovation speed with resilience, security, and regulatory compliance.

They often have innovation teams, dedicated budgets, and structured processes to experiment with new technologies, run proofs of concept, and evaluate risks. Risk management, security, and resilience functions are usually involved.

The main challenge, however, is not the model itself but execution. What makes the real difference is people and culture.

Blocks often arise not because processes are missing, but because individuals or groups are reluctant to take the next step. This makes culture and leadership more important than process design.

Hidden long-term risks emerge when organisations fail to execute and miss major technology shifts. There are many examples: in retail banking, the shift from branch-based models to online and mobile channels; in telecom, the impact of voice over IP. Companies that miss these shifts become less competitive and may ultimately be displaced by more aggressive players or new entrants.

 

Q7. If you were advising investors or senior management today, what signals would you prioritise to distinguish durable value creation from transformation programs with fragile ROI?

The starting point is to clearly understand which technology transformation trends are relevant to your business and how they apply to your specific context.

A proper investment case must go beyond technology. It should include use cases, operating model transformation, process changes, training needs, and organisational impact. This is a complex exercise, but it provides essential direction.

In parallel, organisations should run initial implementations or pilots to validate the assumptions behind the investment case. If both the economic rationale and early execution are positive, then scaling makes sense.

Not all technologies deliver the returns initially promised. I have seen cases where expectations were not met, especially when innovative technologies are bent to the rules they are meant to disrupt. It is critical to perform a deep analysis to understand what represents a real investment opportunity for your specific organisation, leveraging the real strengths of the new technologies and giving them the opportunity to express their potential beyond the business as usual rules.

Strong strategic, operational, and governance signals—grounded in realistic economics and validated execution—are what distinguish durable value creation from transformation programs with fragile ROI. And finally, the right people that aren't afraid to test uncharted waters.