From now until the end of the year, every project at FPT Japan will be assessed using the AI-First Index, a composite measure of the level of AI adoption.
The index has been selected as a company-level KPI for FPT Japan to monitor progress, evaluate implementation results, and drive AI into real project operations rather than limiting it to training or awareness-building activities.
The AI-First Index is calculated using a formula centered on two pillars, Business Impact and People & Talent, together with a Technology bonus.
The Business Impact pillar includes the AI Revenue Ratio, Project Transformation, and ODC Transformation. People & Talent includes AI Leadership Density, AI Augmentation Engineers (AAE), and specialized AI certifications. The Technology bonus covers AI implementation and adoption, as well as contributions to intellectual property and solutions.
FPT Japan is rolling out the AI-First Index calculation method to individual units to create a constructive performance mechanism and increase each unit's contribution to common KPIs.
In the short term, projects will become familiar with AI tools and adopt weekly monitoring and reporting. In the medium term, AI will be integrated into actual workflows, with productivity gains measured against a target of 10% to 15%. In the long term, the use of AI in projects is expected to become the new normal, embedded by default in everyday ways of working rather than treated as an exception.
According to an FPT Japan representative, implementing the AI-First Index not only aligns with the Corporation's pivotal transformation stage, but also promotes practical AI adoption in projects, encourages healthy competition among units, and sustains FPT's competitiveness throughout the AI transformation.
FPT recently researched and launched CASAN, a five-level AI-Native Capability Framework, to help organizations and enterprises standardize their AI transformation roadmaps. Its five tightly connected levels are Curious, Augmented, Standard, Automated, and Native.
At the Curious level, an organization or individual is still exploring and experimenting with AI for isolated needs such as writing, translation, summarization, drafting, or creating slides. Many enterprises are currently at this stage: they recognize AI's usefulness, discuss it frequently, and experiment with it, but have not yet established a formal mechanism for bringing AI into operations safely and strategically.
At the Augmented level, AI becomes a productivity-enhancing tool. Employees receive official AI accounts, and small teams begin using AI for programming, testing, document review, customer support, and other tasks. However, this level can also create an illusion of productivity. An individual or team may work much faster, but if the organization's end-to-end workflow remains unchanged, bottlenecks persist in the links that have not yet been transformed.
The Standard level marks the transition from fragmented productivity gains to organizational capability. AI is no longer an isolated tool used by only a few teams. It is standardized across data, processes, roles, responsibilities, and governance mechanisms. Enterprises need to know what data may be used, which systems each agent may access, who provides approval, who is accountable, which use cases have the greatest impact, and what risks they entail.
At the Automated level, AI systems and agents are authorized to execute selected workflows within a controlled scope. The human role does not disappear, but shifts from completing each individual task to designing, supervising, approving, and governing. Organizations must clearly determine which activities can be entrusted to AI, which must remain under human control, where the boundaries of delegation lie, and how risks will be managed.
The highest level, Native, describes an enterprise designed around an AI core. At this stage, AI is no longer an additional layer placed over existing systems. It becomes the organization's fundamental operating platform.