富士フイルムBI、複合機保守にAI導入 修理時間を半減

Fujifilm Business Innovation announced on July 16, 2026, that it will equip its maintenance systems for office multifunction printers and related equipment with artificial intelligence. The new system is designed to assist field technicians by automatically recommending required parts and repair procedures based on historical service data.

When a technician is dispatched to a customer site in response to an equipment malfunction, they input the machine model and details of the problem into the system. The AI then analyzes past response records and suggests the parts needed, the expected repair steps, and relevant instructions from service manuals. Previously, technicians relied heavily on their own experience and judgment to diagnose and resolve issues. This often led to situations where the problem was not fully resolved on the first visit, requiring multiple trips to the same customer and consuming time in manual document lookup.

Trials conducted in Hong Kong and Australia demonstrated measurable improvement. The time required to identify the root cause of equipment failures dropped to less than half of the pre-deployment baseline. The company expects the system to reduce repair time, prevent rework, and standardize maintenance quality across its service teams.

Rollout begins with deployment across Asia in July 2026, followed by expansion to Japan and Western markets around October 2026.

Fujifilm Business Innovation's move to embed AI in equipment maintenance reflects a broader trend of applying machine learning to field service operations. Historically, the maintenance of office equipment has relied heavily on individual technician expertise and experience, creating variability in response times and quality. By anchoring decisions to historical data and standardized recommendations, the company aims to reduce rework and speed first-contact resolution—a metric of critical importance in commercial service businesses where downtime directly affects customer productivity.

The trial results in Hong Kong and Australia provide concrete evidence of the value proposition: halving the diagnostic time is a significant operational gain. This early validation likely informed the company's decision to move to broader rollout across Asia first, suggesting either a focus on markets where the installed base is large or where technician expertise gaps are most pronounced, before expanding to Japan and Western regions by October.

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