- All Foundries
- 商品管理と取引
- 顧客とマーケティング戦略
- デジタルエンジニアリング
- ステーション・スタジオ
- サプライチェーン＆インダストリー5.0
- サステナビリティ
- Physical AI
- Next-Gen Infrastructure
From Assisted to AI-First Care: Building the Self-Healing Customer Experience
Customers expect continuity across channels, fast resolution and help before they need to repeat context. Agentic AI, real-time analytics and contextual knowledge can automate structured interactions, equip human agents for complexity and turn every contact into feedback for the next one.
An AI-first care model brings AI agents, human expertise, knowledge and analytics into one system to make service faster, more proactive and more consistent across channels.
Use specialized virtual agents to complete structured journeys, access core systems and maintain continuity across channels.
Provide one contextual desktop with real-time guidance, knowledge, summaries and next-best actions for complex interactions.
Preserve context across voice, chat, app and assisted channels so customers can move without starting again.
Use interaction analytics, network insight and predictive signals to identify needs and act before service issues escalate.
NTTデータのサポート体制は？
Prioritize high-volume and high-friction demand, simplify the process and define the right balance of self-service, AI and human support.
Connect customer data, knowledge, interaction history, network insight and real-time analytics into one decision layer.
Use specialized agents for structured interactions with clear identity, permissions, confidence thresholds and safe handoff.
Bring guidance, search, summaries, workflows and recommendations into a unified desktop for faster, higher-quality resolution.
Monitor outcomes in real time and use every interaction to improve content, routing, models, journeys and operational decisions.
AI handles structured demand and continuous orchestration, while people focus on exceptions, judgment and higher-value experiences.
Lower cost-to-serve, faster resolution, more effective agents and a customer experience that becomes more proactive over time.
Automate structured demand and direct customers to the most effective channel without losing continuity.
Use contextual knowledge and integrated actions to reduce handling time, transfers and repeated explanations.
Remove repetitive work and give people the insight and guidance needed for complex cases.
Turn analytics and reinforced learning into earlier action, better journeys and continuous service improvement.
The opportunity is to combine autonomous resolution, contextual human support and real-time feedback into one service model that improves experience and efficiency together.
