Over the past five years, investment in cloud, analytics, and automation has surged, with global digital‑transformation spending forecast to reach US$3.4 trillion by 2026 as organizations modernize legacy systems and seek new productivity gains. However, translating those investments into measurable business gains remains elusive; fewer than one in five companies say their digital initiatives have delivered sustained performance improvement.
Companies are under pressure not just to deploy new systems but to prove that each decision strengthens long-term competitiveness. The distinction between meaningful transformation and incremental tinkering has become one of the defining tests of corporate leadership. “People think this new trinket is going to solve all their problems. It does not work like that,” says Christophe Derdeyn, Managing Director of Icon Cloud Solutions and co‑founder of delaware Southeast Asia. “You have to look at the end‑to‑end business process and the outcome you want before you invest.” Over the years, Derdeyn has helped both global enterprises and early‑stage companies align technology strategy with tangible business results.
Start with a Growth Matrix, Not a Procurement Request
Derdeyn argues that a strategic IT investment begins with a full organizational assessment. He recommends building a “matrix of opportunities” that maps every initiative against complexity, cost, timelines, and business impact. That matrix makes a critical distinction between back office optimization, which improves the bottom line, and growth initiatives, which expand markets and customer value.
“If you want business growth, then you really need to focus on customer centricity,” he says. That can mean improving after-sales service, enriching digital experiences, or accelerating the way products are brought to market. The point is to select the projects that help a company serve customers better, not simply run the same processes cheaper. This portfolio view also helps finance leaders see why some quick wins should sit beside longer-horizon programs. Smaller projects produce fast ROI and prove the model. Larger programs, such as building an enterprise-wide data foundation, may take six to twelve months before value appears, yet they unlock future use cases that cannot be measured at the outset. “If the basics are right, if your entire data ecosystem is better aligned, then you can actually run larger engagements and make them measurable,” Derdeyn says.
Building Better Insights with AI and Data Strategy
Artificial intelligence and advanced analytics are the most immediate accelerators of growth because they make insights faster to obtain. In the past, analytics required teams to connect multiple data sources, harmonize fields, build dashboards, and test outputs. “With AI, it becomes a lot easier to bring these different data streams together and to unlock the insights you want,” he says, quick to point out that most organizations still face the “garbage in, garbage out” dilemma. Without a strong data strategy and consistent master data across systems, AI will only speed up bad answers.
Companies benefit from starting small. Rather than trying to reinvent the entire enterprise with AI, it’s advisable to pick a specific use case, automate what can be automated, and let the business gain confidence. While only a small percentage of AI projects deliver the hoped-for outcome currently, the next five years will see AI create a “massive difference” in measurable business value. The companies that will benefit are the ones that build internal capability now.
One Version of the Truth
While leading large-scale transformation programs across Asia, Derdeyn saw firsthand how the value of IT investments erodes when implementations diverge from one market to another. Problems arise when local preferences alter the core design. Once that happens, every new IT initiative becomes harder to roll out and harder to measure. “You need to make sure you have one version of the truth,” he says. That means similar or identical core systems, well integrated, with an end-to-end view of operations. For multinationals seeking to scale, this means starting with consolidation: roughly 80% of processes, such as finance, can be standardized across markets, while the remaining 20% should be localized for regulatory or reporting requirements.
The Human Cost of Innovation
The true driver of successful transformation depends on an honest recognition of how change affects people on the ground. Leaders must anticipate the strain on staff time, morale, and productivity, and treat these as strategic considerations. “The immediate costs are often borne by employees,” he says. “They’re asked to innovate while still managing day‑to‑day operations, and that split focus is rarely sustainable.”
He advises leaders to use the opportunity matrix not just to track financial impact but also to plan for the time, focus, and workforce commitment each initiative demands. “This idea that employees do it part-time next to their day‑to‑day responsibilities is something that does not really work very well,” he adds. In the most successful programs he has seen, business people are pulled out of operations and seconded full‑time to transformation projects. “Their involvement must be treated as half of the total investment, equal to the spend on systems and consultants. Without that level of commitment, change management becomes lip service and the outcome falls short.”
Sustainable digital transformation is less about the technology and more about building the organizational capacity to absorb change. It is this perspective that continues to guide his approach to business transformation and IT innovation.
To learn more about business transformation and AI‑driven growth, connect with Christophe Derdeyn on LinkedIn and visit his website.
