Core platforms as a brake on innovation: why CIOs must choose again

A single system that could scale e-commerce, data, and transactions right alongside an organization's ambitions. That was once the promise that convinced many CIOs to invest heavily in a core platform. Bit by bit, however, that platform turned into a system that was difficult to upgrade and maintain How should a CIO deal with a platform that increasingly became the bottleneck to move quickly on the possibilities of new (AI) technology?

The British theoretical physicist Stephen Hawking once said: "Intelligence is the ability to adapt to change." Choosing a new digital platform sets the stage for the room and time-to-market your organization will have to respond to new customer needs and new technology in the future.

That was once an important reason to choose a decisive core platform that could directly support and scale all your requirements. Unfortunately, that platform's flexibility gradually diminished, while something new kept getting added: a new channel, an exception, an integration, a piece of custom code.

Each of these adjustments was defensible on its own. Together, they produced a landscape that became increasingly difficult to change. Now, in an era when AI demands speed, accessible data, and modular architecture, the question arises whether this foundation still helps the organization move forward or is actually holding it back. In conversations with CIOs and CTOs, a new question is emerging: not how to optimize further, but whether the current platform still fits the organization's future.

When complexity grows faster than the business

The patterns emerge gradually, but the signals are clear. Organizations see their operating costs rising while teams grow just to maintain relatively standard functionality. At the same time, even small changes take longer and longer, and a dependency develops on a limited number of experts.

What began as customization to create flexibility has grown into a landscape where every change is complex and costly. The underlying dynamic is an unwelcome one: systems that were once designed to scale increasingly scale complexity instead.

AI exposes the weak spots

Where this problem used to mainly affect cost and speed, an additional factor is now in play: AI. Many organizations are discovering that their platform is not just slow, but also poorly prepared for AI applications. Data is fragmented, of inconsistent quality, not available in real time, or hard to access. Integrating AI models or agentic systems therefore demands a disproportionate amount of effort. The implication is direct: organizations with an inflexible platform are falling behind in their ability to apply AI effectively.

The core question for CIOs

This shifts the discussion to a more fundamental level. Does the platform support the organization's ambitions, or does it actually limit them? The answer determines whether optimization still makes sense, or whether structural change is necessary.

From optimizing to redesigning

Organizations that take this question seriously typically go through three steps: insight, choice, and execution.

1. Insight: where does the real problem lie?

An effective analysis looks not only at technology, but also at data and the organization itself. It concerns the ratio between cost and value, the speed of delivery, and the extent to which the organization depends on specific knowledge. At the same time, it is becoming increasingly clear that data quality and suitability for real-time use and AI applications are decisive factors.

2. Choice: which route fits the future?

Based on this analysis, three directions typically emerge. Organizations can choose to simplify by reducing complexity and standardizing. Another option is replatforming, migrating to a more modern technological foundation. In more far-reaching situations, organizations choose to rebuild their platform entirely as a new, modular whole. In practice, organizations that explicitly steer toward modular architectures and standard components prove better able to restore speed and scalability. Combined with data- and AI-driven applications in particular, this approach offers more control.

3. Execution: renewing without standing still

The biggest challenge lies in execution. How do you renew a platform without disrupting the business? In practice, organizations rely on a number of proven approaches. Components are replaced in phases according to the so-called strangler pattern, while new functionality is built in parallel alongside the existing platform. At the same time, APIs create a clear separation between old and new, allowing migrations to take place in a controlled way.

This approach makes it possible to keep developing continuously while the foundation is being renewed. At the same time, it creates an architecture in which new functionality, such as AI, can be integrated faster and more easily.

Start where the impact is

Successful organizations rarely opt for an all-encompassing approach. Instead, they start in a targeted way, at the points where the impact is greatest. These are often components with high costs or limited flexibility, but also domains where innovation is being blocked or where AI can add value directly.

Think, for example, of personalization in digital channels, improved search functionality, or automating customer interactions. Starting here quickly produces visible results, which helps build support within the organization.

Both technology and organization must change

A recurring insight is that platform problems are rarely purely technical. The way organizations work also plays an important role. Decision-making often runs through complex dependencies, critical knowledge sits with a small number of individuals, and a great deal of time goes into coordination rather than execution.

That is why platform renewal also requires organizational change. It is about clear ownership of systems and data, teams that are responsible end to end, and a way of working that reduces dependency on individual experts. Without these changes, complexity will persist, regardless of the technology chosen.

From platform to foundation for AI

The organizations leading the way treat platform renewal not as an IT project, but as a strategic reorientation. The goal is not just a more stable system, but a foundation that puts data at the center, is built modularly, and can quickly support new capabilities. This makes it possible to actually deploy AI applications — including more advanced and agentic systems — at scale. It calls for different design choices than in the past, with less custom-built code and greater use of standard and managed services.

Conclusion: not a question of optimization, but a question of choice

For many CIOs, the conclusion is clear. The question surrounding core platforms is no longer purely one of optimization, but one of choice. Do you keep investing in a complex foundation, or do you opt for a targeted reorientation? Organizations that recognize the impact of growing complexity on their ability to act can respond by reshaping their core platforms. In doing so, they create the basis to move faster, respond better to change, and put AI to genuine use.