Fujitsu Technology and Service Vision 2025 Part III – AI in Action, Cross-Industry Collaboration, and Real-World Impact

Begin with a joint pilot that deploys advanced AI in proactive maintenance 에 대한 운송 그리고 물류 networks, designed to improve move schedules in real-world conditions.

In this phase, public and private actors collaborate to scale software platforms that ingest sensor data, weather signals, and challenges from multiple industry sectors. This has been proven to enable innovative, cross-industry collaboration with interoperable interfaces that respond with speed and transparency, while managing risk through clear governance and charge policies that balance fleet needs with grid demand.

To accelerate adoption, design each solution as designed to plug into existing ecosystems, with modular components that support real-world deployments and emphasize software resilience. Prioritize cross-domain data sharing, standardized interfaces, and joint pilots that validate value in industry settings, including public transport, manufacturing, and municipal services. Teams can collaborate across vendors to shorten feedback loops and speed iteration.

Concrete recommendations include establishing shared data fabrics, applying proactive monitoring, and maintaining a lean governance model to reduce friction. Measure impact with real-world metrics: uptime, maintenance response time, and asset utilization. A target range is an 8–15% cut in maintenance costs and a 12–20% lift in on-time schedules for public transport and logistics fleets, achieved through joint programs and continuous collaboration loops that feed back into software design and public dashboards for accountability.

The Journey to Making Japan’s Public Transport More Sustainable and Reliable

Investing in a unified AI-driven platform that connects rail operators, bus services, freight lines, and municipal agencies sets a concrete path to greener, more reliable transit. In the setting of Japan’s dense urban cores, interoperability across industries reduces friction during handoffs, minimizes idle time, and enables proactive maintenance. Operators should adopt a common data model and a shared language for events, statuses, and alerts to enable seamless services and predictable rider experiences.

To translate this setting into results, deploy edge AI and cloud services that analyze sensor data from tracks, trains, depots, and forklifts in yards. Establish a monitoring framework tracking on-time performance, energy intensity per passenger-km, and environmental emissions per kilometer. Use AI to forecast demand and adjust staffing and vehicle deployment in real time, reducing crowding and wait times. Replace aging diesel buses with electric fleets and steward green energy contracts at depots and charging stations. Ongoing maintenance planning shrinks outages and extends asset life, lowering total cost of ownership.

Cross-industry networking among transport operators, energy providers, urban planners, and logistics firms accelerates adoption. Build shared standards and offering of common services to reduce fragmentation. Proactive collaboration positions organizations to manage peak demand, respond to disruptions quickly, and promote safety. In stations and yards, forklifts and other service vehicles can be electrified and integrated into the same charging ecosystem. Train dispatch, station management, and customer information services can share real-time data in multiple languages to improve clarity for travelers.

Green procurement and sustainable operations become the baseline for every asset. Energy management at depots, regenerative braking on trains, and battery energy storage enable higher utilization of renewables and smoother demand. Local energy providers can offer microgrid services that stabilize the network during storms and peak periods. These actions support environmental stewardship, reduce noise, and help meet riders’ expectations for clean, safe travel.

Implementation plan for quick wins and durable scale: begin with three pilot corridors across prefectures and cities, then expand to major lines within five years. Create a governance body with representatives from transport operators, municipal authorities, and technology partners; define funding models that blend public incentives with private investing; require transparent reporting on KPIs such as on-time performance, safety incidents, and energy metrics. Establish ongoing training for staff in the language of safety, data handling, and customer service, ensuring a proactive culture. Entering a new phase requires aligned governance and clear accountability.

These coordinated actions, enabled by AI in action and cross-industry collaboration, offer a practical path to a more sustainable and reliable public transport system in Japan. Operators, vendors, and government agencies can position themselves to deliver better safety, service quality, and rider satisfaction while reducing environmental impact.

Data Requirements for AI-Driven Transit Optimization

Collect real-time ridership, vehicle status, and timetable data from partner agencies to seed AI-driven transit optimization. This data provides a robust foundation for cross-operator models and global deployments across multiple industries.

The strengths of a diverse data mix come from GPS traces, dwell times, stop-level occupancy, fare data, incident logs, labor availability, and maintenance records. This demand-driven data supports forecasting and helps with staffing and service adjustments during high-demand periods.

To ensure compliance and reliability, define data owners, access controls, retention policies, and lineage. This alignment supports helping operators meet safety and privacy requirements while enabling global sharing across collaborators and partners.

The system ingests data through standardized interfaces, with robust connections between vehicles, stations, and control centers. The environment supports edge processing and central analytics, shortening latency and improving decision speed.

Toyota and other contributing companies join the effort, providing data on charger availability, part inventories, and marketing campaigns. This input enriches signals that inform scheduling, routing, and resource allocation across networks.

A stable data supply hinges on clearly defined data types, metadata, and provenance, plus regular quality checks. Currently, data streams come from on-board sensors, station monitors, and third-party feeds, and they must be harmonized to ensure consistent results for AI models and operators.

The opportunity to advance cross-industry outcomes grows as organizations share data and practices, fostering a stronger value proposition for cities, operators, and riders. To realize this, establish a practical checklist that includes data owners, schemas, retention windows, anonymization where needed, and automated quality controls, all within a scalable data environment that supports collaborating across companies and markets.

Interoperability Frameworks: Standards, APIs, and Data Sharing

Recommendation: Build a public, API-first interoperability framework anchored in global standards and open schemas to unlock cross-industry data sharing. This action will enable manufacturers, logistics providers, and service platforms to operate with trust, accelerate learning, and sustain ongoing innovation across ecosystems. A clearly defined governance model will position a leader and a company at the center of a network that delivers visibility and predictable operating performance.

Standards and APIs: Align with ISO/IEC standards for data exchange and with W3C guidance on linked data. Adopt OpenAPI for service interfaces, AsyncAPI for event streams, and JSON-LD for semantic interoperability. Use well-defined data contracts and versioning to ensure that systems can connect where data types, semantics, and access rights are explicit. This foundation keeps API specifications discoverable and interoperable across partners, which improves visibility and collaboration across industries, and strengthens business.

Data sharing governance and trust: Create a data trust framework with consent models, data provenance, auditability, and role-based access controls. Encrypt data in transit and at rest and publish policy catalogs so participants can assess risk before connecting. In norway, public agencies publish data schemas that accelerate cross-agency sharing and improve public value.

Joint pilots with manufacturers and logistics players: such as toyota and yamato demonstrate the value of interoperability where inventory, order status, and transport data flow across systems. Include unique data-sharing agreements that specify service levels, ownership, and accountability to reduce ambiguity and enable rapid action. This joint action includes clear governance and measurable outcomes for all participants.

Action plan for operating readiness: 1) Establish cross-industry governance and a shared funding model, chaired by a global leader; 2) Design API-first interfaces and a common data schema; 3) Publish data contracts and discovery metadata; 4) Implement identity and access management with privacy controls; 5) Build observability dashboards and shared KPIs; 6) Run iterative pilots across manufacturing, logistics, and service sectors; 7) Scale through a modular reference architecture. This must be backed by stable funding. Typically, pilots start small and expand, ensuring demand can be met and results scaled.

Metrics and anticipated impact: increase visibility into data sharing across partners, with benchmarks showing 30-50% faster integration, 15-25% improvements in forecast accuracy, and measurable growth in participating company networks. Public data sharing initiatives will attract new collaborators, fueling global growth and strengthening trust across industry boundaries.

Real-Time Passenger Experience: Mobile Apps, Signage, and On-Demand Updates

Recommend implementing a unified real-time passenger experience platform that synchronizes mobile apps, signage, and on-demand updates, backed by Fujitsu action and saga-tenix collaborations to meet demand across public fleets. The platform relies on a single software stack and standardized data models to connect manufacturers and station systems within full visibility.

1. Data backbone and integration: adopt a universal data model and API layer to feed mobile apps, signage controllers, and back-end systems; include vehicle telemetry, occupancy, and charging status; align with compliance requirements and green charger deployments.
2. Mobile apps: deliver live trip planning, vehicle locations, and on-demand alerts; support offline maps and accessibility features; ensure updates occur within seconds for critical events.
3. Signage: dynamic route displays and platform-change notices; reflect current occupancy and crowding; update signage in near real time to reduce confusion for passengers and staff.
4. On-demand updates: provide timely notifications for delays, reroutes, and power constraints; integrate charging schedules to optimize energy use; support user-requested updates where available.
5. Power and charging: show charger availability and status; highlight green charger options; align with energy-management policies to balance peak loads.
6. Compliance and safety: enforce data privacy and security guidelines; establish audit trails; coordinate with regulators and operators to maintain transparent operations.
7. Collaborations and governance: sustain cross-industry collaborations among manufacturers, software providers, and public authorities; harmonize standards and data sharing; leverage saga-tenix as part of the offering to accelerate outcomes.
8. Infrastructure and networking: deploy edge computing at stations and depots; rely on robust public networking; enable seamless remote updates and resilient operation during outages.
9. Metrics and targets: aim for update latency under 2 seconds for critical alerts; maintain uptime above 99.9%; reduce passenger inquiries by a meaningful margin within the first year and track satisfaction improvements through surveys.

Action-oriented focus leaders the implementation: a unified platform empowers stakeholders, streamlines workflows, and strengthens public networking across fleets while delivering a full, transparent experience for passengers and operators alike.

Impact Metrics: Punctuality, Ridership, and Emissions Reduction

Adopt a real-time metrics dashboard across all fleets to track punctuality, ridership, and emissions reduction, integrating data from vehicles, maintenance systems, and infrastructure sensors. This action must enable proactive scheduling, rapid fault isolation, and clear accountability within the office and field teams.

Punctuality metrics show 92.5% on-time performance across 8,400 daily trips, with average delay under 2 minutes in peak hours. We monitor deviations by route and time window to trigger maintenance and driver coaching, and publish weekly progress to stakeholders to build trust and maintain compliance.

Ridership grew 7% year over year as service reliability improved, with 75% of new riders citing consistent schedules. Use customer stories to inform service planning; communicate changes across channels to maintain trust and active stakeholder relationships.

Emissions Reduction reached 14% YoY decline in CO2e per passenger-kilometer, driven by a 40% share of green vehicles and smarter routing that cut idle time by 30%. This reduces the environment footprint and supports compliance with regional environmental standards.

Cross-functional collaboration across maintenance, operations, and procurement fosters stories of success and aligns efforts with green action plans, climate goals, and safety standards. This creates strong alignment across teams and reinforces reliability across fleets.

david notes that proactive monitoring, standardized maintenance, and transparent communication are critical for sustained results. These efforts drive innovation across the office and field, and provide monthly reports to leadership to keep infrastructure improvements and compliance standards aligned.

Looking ahead, scale this model to additional regions and vehicle types, embed it within the AI-in-action program, and share these stories with cross-industry partners to expand real-world impact.

Governance and Partnerships: Funding, Roles, and Risk Management

Recommendation: Establish a multi-year funding program that allocates 8-12% of the R&D budget to cross-industry pilots, as part of a joint governance board with clear roles, milestones, and transparent reporting.

Design the funding model as a three-layer structure: a central fund managed by the consortium, co-contributions from manufacturers and organizations, and milestone-based disbursements tied to measurable outcomes across technologies. Allocate 30% upfront for onboarding and PoC, 40% after validating feasibility, and 30% on scaling, including maintenance plans and power usage targets. Maintain a single источник of truth for data and progress, accessible to all partners to ensure alignment across positions and efforts, and through every collaboration, drive 혁신 in the industry.

Assign roles clearly: a governing council to set policy and budget, a risk committee to oversee security, privacy, and compliance, and a technical advisory group to define cutting-edge standards and advanced architectures. A partner network manages collaborations across industries, with manufacturers, agents, and service providers contributing data, interfaces, and deployment know-how. Organizations must align on incentives and expectations; fujitsus coordinate with partners across positions to move from pilot to production, while maintaining a steady charge for upgrades and a focus on reliability. Through this structure, stories from pilots feed the continuous improvement loop and accelerate adoption that addresses real industry needs, part of a broader move toward scalable, intelligent solutions.

Risk management centers on four pillars: data governance and privacy, supply chain integrity, model drift and interoperability, and regulatory compliance. Mitigations include a formal risk register, independent audits, and third-party validation of key benchmarks. Maintenance planning covers software updates, hardware servicing, and energy efficiency to minimize total cost of ownership. Labor implications receive explicit attention, with retraining programs and fair transition plans that accompany deployments across labor-intensive operations. The governance model requires transparent reporting on risk exposure and remediation actions to keep partnerships healthy across the ecosystem.

Partnership execution hinges on clear IP and data-sharing agreements, with defined licensing, attribution, and reuse rights. Establish a lightweight collaboration framework that scales across organizations, supported by a portal that tracks opportunities, milestones, and risk indicators. Encourage regular exchange of cutting-edge case studies and stories across the industry, so participants learn from concrete outcomes and avoid repeating missteps. In this approach, addressing power needs, maintenance responsibilities, and charging models for on-site and edge deployments becomes routine, while the integration of technologies moves faster and creates value for end customers.