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Fujitsu Causal AI
From Data to Decision
Stop relying on intuition because AI
recommendations are difficult to trust, and the
expertise required for rigorous analysis is scarce
and too expensive.
Fujitsu Causal AI delivers statistically grounded
recommendations in minutes—identifying the
actions that truly drive your KPIs.
Contact us for a free trial or learn more.
Would you defend an AI recommendation
in the boardroom?
People readily accept AI recommendations
for personal decisions.
As decisions become more consequential
and affect others, trust quickly erodes
unless the reasoning can be explained
and defended.
Fujitsu Causal AI brings data science-level rigor to every decision.
E.g., Commercial activities, Pricing, Inventory, Staffing, Process settings, etc.
Here’s how:
Contact us for a free trial or learn more.
How you want to work
Get Started In Munities
From free trial to real deployment
For first users, join our Causal Bootcamp, including expert review & validation of customer data
Use Case
Cross Industry
Causal Analysis of ESG Survey Data,
Causal Analysis of ESG and Materials
Cross Industry
Analysis of Group Company Engagement Surveys
Biotechnology
Analysis of the causal relationship between genetics and lifestyle habits
Retail
Analysis of Management and Financial Data with Countermeasures
Manufacturing
Technical verification for yield improvement in optical semiconductor manufacturing
Automotive
Analysis of wellness values and behavior change for healthcare service planning using In-vehicle vital data
Food Retail
Distribute personalized coupons using PoS data and causal-based customer grouping
Manufacturing
Optimize brewing procedures using process parameters to achieve target beer characteristics
Fujitsu Corporate Wide Project
Quantify financial and non-financial data and apply it to drive a continuous financial performance improvement cycle
Case Study
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Deloitte Tohmatsu x Fujitsu: Turning ESG Disclosure into Corporate Value
Press Release, May 29, 2026
As ESG and sustainability reporting requirements continue to grow, companies face increasing challenges in understanding evolving evaluation criteria, benchmarking competitors, and developing effective disclosure strategies.
Fujitsu developed an AI-powered platform that analyzes ESG evaluation criteria and disclosure data from more than 1,000 listed companies in Japan. The solution helps organizations visualize their disclosure status, identify strengths and gaps, and understand how they differentiate from peers.
By enabling more strategic and evidence-based disclosure decisions, the platform supports stronger ESG evaluations, improved investor communication, and ultimately long-term corporate value creation.
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Kyoto University x Hirosaki University x Fujitsu: Reliable Causal Analysis with Limited Data
Press Release, March 6, 2025 (Available in Japanese only)
Fujitsu combined its Causal AI with the Hirosaki Health Checkup Causal Network, a trusted causal knowledge base built from 20 years of health data and approximately 3,000 variables.
By leveraging existing causal knowledge, the technology can derive more reliable causal relationships even when only limited data is available. In a sleep health study, the approach eliminated implausible causal relationships and identified sleep duration and sleep quality as more direct drivers of insomnia.
The result is more trustworthy decision support for healthcare and health management initiatives.
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GenQuest x Fujitsu: Uncovering Hidden Drivers of Health Outcomes
Press Release, October 9, 2025
Using genetic and lifestyle data from approximately 4,000 individuals, Fujitsu Causal AI analyzed the complex relationships between genetic traits, food preferences, lifestyle habits, and BMI.
By combining causal discovery with existing medical knowledge, the analysis uncovered hidden drivers that traditional correlation-based approaches often miss. The study revealed how factors such as drinking frequency, taste preferences, family medical history, and lifestyle characteristics influence health outcomes.
These insights can be used to generate personalized, evidence-based recommendations that support healthier behaviors and more effective preventive interventions.
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Atmonia × Fujitsu: Accelerating Discovery of Sustainable Ammonia Catalysts with AI
Press Release, April 13, 2022
Ammonia is a promising carbon-free energy carrier, but conventional production methods remain heavily dependent on fossil fuels and generate significant CO2 emissions.
Fujitsu and Atmonia combined high-performance computing (HPC), quantum chemistry simulations, and AI technology to accelerate the discovery of new catalyst materials for sustainable ammonia production. By efficiently exploring a large design space of catalyst candidates, the approach reduced the time required for catalyst selection and optimization while improving the efficiency of materials discovery.
The project supports the development of clean ammonia production technologies and contributes to the transition toward a carbon-neutral future.
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Tokyo Medical and Dental University × Fujitsu: Discovering Hidden Drivers of Drug Resistance with AI
Press Release, March 7, 2022
Drug resistance remains one of the greatest challenges in cancer treatment, yet identifying the underlying causal mechanisms across thousands of genes is extremely difficult.
Fujitsu combined its Wide Learning AI technology with the supercomputer to analyze more than 20,000 genes and over one quadrillion potential gene interactions. This enabled researchers to perform a comprehensive causal search across the entire human genome within a single day.
The analysis uncovered a previously unknown causal mechanism associated with resistance to lung cancer therapies. These insights have the potential to accelerate drug discovery, improve patient stratification, and support the development of more personalized cancer treatments.
Fujitsu Causal AI
Materials
Contact us for a free trial or learn more.