―From Modernization to Value Creation－ Transforming the Digital Core in the Age of Generative AI

Over the past two decades, rapid advances in digital technologies have fundamentally transformed the very nature of business. Starting from on-premises systems centered on core infrastructure and intranets, the technological foundation has expanded to include mobile, cloud, blockchain, big data, and AI. Since 2023, the emergence of generative AI and agentic AI has accelerated this transformation, pushing companies to evolve at unprecedented speed and scale.

Corporate transformation has similarly matured, progressing from initial efforts focused on operational efficiency to comprehensive enterprise-wide digital transformation, which entails redesigning organizational structures, decision-making processes, and even business models.

At the heart of this evolution lies IT modernization, whose importance continues to grow. More than a simple technology upgrade, IT modernization demands rebuilding digital foundations to be flexible, agile, and scalable, while driving changes across business processes, workforce capabilities, and corporate culture. Crucially, this modernization must advance toward a value-creation platform befitting the generative AI era.

Furthermore, in response to pressing challenges such as accelerating development cycles and talent shortages, leveraging generative and agentic AI has become a strategic pillar of IT modernization. By applying AI to system development and process automation, organizations can achieve high-quality transformation despite constrained resources.

The timeline below (Table 1) summarizes this progression of technological innovation and corporate transformation. Building on this momentum, this paper explores the reconstruction of enterprise foundations and the creation of new value in the generative AI era. It aims to serve as a guide for forward-looking strategic planning in today’s rapidly evolving landscape.

Digital technologies now permeate every aspect of business, making digital transformation (DX) a central pillar of corporate strategy. Enterprise technology is estimated to account for up to 71% of the value derived from business transformation(1). In particular, the emergence of generative AI has fundamentally altered the speed and depth of transformation now expected from enterprises. However, many organizations still rely on outdated IT environments, which act as a brake on transformation. To drive meaningful change, companies must start by fundamentally rethinking and modernizing their digital foundations—what we call IT modernization.

Technical debt(2), often embodied in legacy systems, leads to rising maintenance costs, sluggish innovation, slow response to change, and poor integration with emerging technologies like generative AI. These factors create "invisible costs" that undermine the returns on transformation investments—turning what was once an IT issue into a core challenge for business competitiveness.

Traditional IT modernization efforts have largely focused on technical migration—updating code and infrastructure. But this alone is no longer sufficient to unlock agility and innovation. What is now required is a holistic reinvention, encompassing not just technology, but also development methods, architectures, processes, organizational structures, and talent. Deploying generative AI without being AI-ready can backfire, resulting in lower productivity(3).

In this context, the use of generative AI in IT modernization itself is gaining attention. Leading global enterprises—such as Morgan Stanley(4), Toyota Systems & Fujitsu(5), a major bank & Accenture(6), and a major automotive manufacturer & IBM(7)—are leveraging generative AI for code conversion, automated documentation, and test support. These efforts have led to significant reductions in manual work and accelerated transformation timelines (see Table 2).

The use of generative AI is also expanding beyond finance and manufacturing into the public sector, including local governments(8). Furthermore, the emergence of multi-agent autonomous systems, or agentic AI, is beginning to redefine what’s possible. These intelligent agents are expected to serve as powerful enablers of faster, cheaper, and more effective modernization. Proof-of-concept trials have shown that generative AI alone can reduce modernization timelines by around 20%, while agentic AI can cut them by up to 50%, dramatically boosting speed and productivity(9).

That said, IT modernization must not become an end in itself, narrowly focused on code translation or technical productivity. The true objective is to build a foundation—one that supports sustained enterprise transformation and unlocks long-term value creation in the AI-driven era.

As generative AI enters full-scale adoption, enterprise digital transformation is shifting into a new phase. Traditional DX initiatives (DX 1.0) have primarily focused on digitalizing and automating business processes to improve operational efficiency.
Technologies such as RPA, cloud computing, and data analytics — along with rule-based or machine learning-driven AI — have played central roles in these efforts.

In contrast, DX 2.0, driven by generative and agentic AI, demands more fundamental transformation. It requires organizations to re-examine their business models, organizational structures, and even their core purpose and sources of competitive advantage.

To clarify this shift, Table 3 outlines the key differences between DX 1.0 and DX 2.0 across multiple dimensions. Notably, the transition is not only technological, but also organizational and strategic. In DX 2.0, AI plays a central role in decision-making and creative processes, working alongside people — or in some cases, autonomously — to drive company-wide transformation. The impact reaches beyond operations, influencing corporate values and the very nature of how businesses compete and create value.

In light of this shift, the role of IT modernization must also be redefined. It is no longer sufficient to simply replace legacy systems or upgrade infrastructure. Organizations must now build a next-generation foundation — one that is agile, scalable, data-driven, and capable of supporting continuous innovation and strategic value creation in the AI era.

To help organizations assess their current position and future direction, The author proposes a two-axis matrix (Figure 1) that maps technology maturity (Legacy IT to Modernized IT) against DX maturity (from DX 1.0 to DX 2.0). Today, many companies still find themselves in the lower-left or upper-left quadrants. To compete in the generative AI era, they must aim for the top-right — where modernized infrastructure and strategic, AI-driven transformation are fully aligned.

Generative AI is not just another emerging technology — it is a force that challenges the foundation of enterprise strategy. For this reason, organizations must shift from a defensive approach to IT, to a proactive, value-centric rethinking of their enterprise foundations. This is the only way to sustain relevance, agility, and growth in the era ahead

Fujitsu acknowledges that traditional, isolated system integration methods are inadequate for tackling the growing complexity of management challenges, which have become increasingly difficult to address using conventional, siloed approaches. In response, Fujitsu emphasizes the importance of enabling organizations to continuously evolve their business and operations by actively leveraging data and AI. To support this transformation, Fujitsu proposes the Enterprise Agentic Foundation—an autonomous enterprise platform designed to maximize the value of data and AI through agent-driven business transformation, IT modernization, and integrated security across all operations(10).

Building on this proposition, the author contends that in any industry where IT modernization is imperative, evolving toward an AI-native enterprise—one that places AI not merely as a tool, but as a core driver of value creation—requires a redefinition of the integrated platforms that support cross-functional and knowledge-intensive activities. To that end, the author proposes a four-layer architectural framework that moves beyond the traditional IT-centric IaaS/PaaS/SaaS model, integrating technology, knowledge, operations, and orchestration in an organic and cohesive manner.