All Foundries

商品管理と取引

データに基づくインサイトと、セキュアで機動性の高いプラットフォームにより、グローバルな商取引を支えるトレーディングエコシステムを変革します。

顧客とマーケティング戦略

パーソナライズされたつながりある体験を通じて顧客エンゲージメントを再構築し、信頼と長期的な価値を築きます。

デジタルエンジニアリング

アイデアをスケーラブルなデジタルソリューションへと転換し、デザイン・テクノロジー・エンジニアリングの力で加速させます。

ステーション・スタジオ

デジタルイノベーションでモビリティハブを再定義し、移動中の人々と企業に、よりスマートでサステナブルな体験を創出します。

サプライチェーン＆インダストリー5.0

変化を予測し、目的を持って効率性を実現する、強靭で知的な供給ネットワークを構築します。

サステナビリティ

責任あるイノベーションを推進し、組織がネットゼロ目標を達成し、将来の世代に良い影響をもたらすよう応援します。

Physical AI

Physical AI gives machines, robots and physical operations the ability to perceive their environment, decide what matters and act in real time. Factories, plants, stores and infrastructure become systems able to sense conditions, learn from them and improve on their own, closing the gap between the physical and the digital world.

Next-Gen Infrastructure

From networks as infrastructure to connected platforms that enable critical, autonomous and intelligent operations.

3% more invoices per person in an automated Procure-to-Pay cycle

Purchasing teams spent hours on each invoice, and the cycle had gaps in control. NTT DATA reviewed operations, tools and the organizational model, then built a roadmap with four automation scenarios. Each person in the process now handles 43% more invoices, and the process runs with 34% fewer full-time employees.

3% more invoices per person in an automated Procure-to-Pay cycle

43% more invoices per person in an automated Procure-to-Pay cycle

お問い合わせ

Utility operations team reviewing inspection evidence from a field drone and direct asset checks before deciding the next operational action.

報告書

公益事業

Physical AI will create an accountable action layer for Utilities

7 Oct 2026

When utility infrastructure starts to act, every decision needs clear boundaries

Machine action needs an accountable operating layer

Physical AI can give Utilities a new operating layer that perceives conditions, prepares decisions and acts across dispersed assets. The executive question is not how many robots a Utility can deploy. It is which systems may act, within what limits, using which evidence and under whose accountability.

Current evidence supports a staged path. Inspection, maintenance and work preparation are scaling faster than real-time control because their outputs remain visible, reviewable and recoverable. The proposed accountable action layer connects field evidence to asset, work and control workflows, while keeping authority bounded and the decision record traceable.

At a glance

Four signals show why utility leaders should scale accountable action before expanding machine authority.

Trust falls as authority rises

An indicative IEA survey of 25 operators found wide AI use at 70% for maintenance and 23% for real-time operations.

Complexity accumulates before autonomy

NESO documented a heavily manual, 47-step scheduling process in one completed AI adviser project, not as a sector benchmark.

Evidence must reach the workflow

An NTT DATA case linked multi-sensor inspection to operational follow-up across about 100 km of pipeline. It does not establish universal ROI.

Governance is already on the clock

EU rules for certain high-risk AI uses, including critical infrastructure, apply from 2 December 2027. Each use case still requires legal classification.

What leaders should do

Define bounded action domains, human accountability and evidence before expanding machine authority.

Scale machine action without losing human accountability

Download “Physical AI Will Create an Accountable Action Layer for Utilities” and explore the evidence, the Accountable Action Ladder and the decisions shaping utility autonomy through 2028.

Download the full prespective

Download the full report

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