Agentic AI at Work: Scaling Efficient, Sustainable Operations

Agentic AI for Operations: Scale efficiency and sustainability at the same time

Operations leaders need solutions that improve uptime, lower cost, and cut waste-without adding complexity. Agentic AI helps you do that. It makes decisions and takes actions autonomously across workflows, so improvements stick and scale.

In a point of view developed with Microsoft, we outline how agentic AI reduces waste, optimizes processes, and embeds sustainability across day-to-day operations. The outcome: leaner processes, faster reporting, and real-time visibility into emissions and resource use.

What agentic AI actually does in Ops

Unlike traditional AI that only predicts or recommends, agentic AI plans, decides, and executes within guardrails. It learns across interactions and adjusts tactics based on the objective you set-fill rate, OEE, energy intensity, SLA adherence, you name it.

Deployed end to end, it coordinates tasks between systems, triggers workflows, and keeps humans in the loop where it matters. Think of it as a digital operations analyst that never sleeps and always documents the why.

Where it moves the needle first

• Dynamic inventory and replenishment: Rebalance stock using live demand, supplier risk, and service-level targets to cut working capital and stockouts.
• Production scheduling and OEE: Auto-resolve bottlenecks, resequence jobs, and route around constraints to improve throughput and reduce changeover losses.
• Energy optimization: Tune setpoints, shift loads, and schedule energy-intensive runs when rates and grid carbon intensity are lower.
• Maintenance and asset care: Trigger autonomous work orders from anomaly detection to reduce unplanned downtime and spare parts waste.
• Logistics and routing: Replan routes in real time to hit OTIF targets while shrinking miles traveled and empty runs.
• Automated sustainability reporting: Map activity data to emissions factors, reconcile gaps, and generate auditable reports aligned to the GHG Protocol.

Sustainability, built into daily operations

Agentic AI doesn't just track sustainability-it operationalizes it. By allocating resources more intelligently, it reduces scrap, idle time, and energy waste while maintaining service levels.

Expect tighter control of Scope 1-3 data, faster reporting cycles, and real-time insights to act on. The result is better governance and stronger social responsibility with measurable impact.

Data and integration essentials

• Unified data layer: Orders, inventory, production, asset telemetry, logistics, and supplier data joined to emissions factors and cost drivers.
• Event-driven architecture: Streaming signals (e.g., sensor data, ETA updates) to trigger agent actions in near real time.
• System hooks: Secure read/write connectors into ERP, MES, WMS, TMS, EAM, and sustainability systems for closed-loop execution.
• Observability: Audit logs, decision traces, data lineage, and performance dashboards tied to business KPIs.

Governance and risk controls that Ops teams trust

• Clear objectives and guardrails: Define allowed actions, thresholds, and escalation paths. High-stakes decisions require human approval.
• Policy and compliance: Role-based access, segregation of duties, data minimization, and retention aligned to regulations.
• Testing and monitoring: Scenario testing before go-live, drift detection, bias checks, and rollback plans.
• Cost and carbon controls: Track compute use, schedule workloads for lower-carbon windows, and set budgets with alerts.

90-day starter plan for Operations

• Weeks 1-2: Pick two high-value use cases tied to hard metrics (e.g., -10% energy per unit, -15% expedites, -20% reporting cycle time). Map decisions, data, and systems.
• Weeks 3-6: Build a sandbox with synthetic and historical data. Stand up agents with read-only access. Validate decisions against baselines.
• Weeks 7-10: Integrate write access for low-risk actions. Add human-in-the-loop gates. Prove value in one plant or region.
• Weeks 11-12: Formalize playbook, controls, and training. Scale to the next site and automate reporting.

Metrics that prove it's working

• Operations: OEE, OTIF, schedule stability, changeover time, forecast bias, working capital.
• Sustainability: Energy per unit, scrap rate, water use, Scope 1-3 accuracy, and audit-ready reporting time.
• Financials: Cost to serve, logistics cost per shipment, maintenance cost per asset, avoided expedites.

Partnerships matter

Agentic AI scales faster with the right platform and ecosystem. Solutions built with Microsoft's stack integrate cleanly into ERP, supply chain, and sustainability tools while meeting enterprise security and compliance needs.

Pair that with a clear human-AI framework and you get consistent delivery: faster execution, fewer errors, and sustainability embedded in everyday decisions.

Meet our experts

Joana Santos
Commercial Strategy Lead - Azure AI Platform (incl. Agent Service & Agent Factory) at Microsoft EMEA
Joana leads the commercial go-to-market strategy for Azure AI Platform across Europe, Middle East, and Africa. With over 11 years at Microsoft, an INSEAD MBA, and a consulting background, she drives impact at scale. A strong advocate for agentic AI, Joana champions solutions that reduce environmental footprint, optimize resources, and accelerate sustainable innovation.

Khurram Zaki
Commercial Strategy Lead - EMEA for Dynamics 365 (Agentic ERP, Sustainability Manager) at Microsoft
Khurram leads the commercial strategy for Dynamics 365 across EMEA, bringing AI, agentic automation, and analytics to finance, supply chain, and commerce. He promotes embedding ESG priorities into core processes using the latest ERP and agentic capabilities to run more responsibly and efficiently. His 18-year Microsoft journey spans business applications, technical, and product roles.

Mark Oost
Vice President, AI, Analytics & Agents Group Offer Leader, Capgemini
Mark previously served as CTO of AI and Analytics at Sogeti Global, building the AI portfolio and strategy. He has led data science teams and worked with clients worldwide, applying AI, deep learning, and machine learning to solve complex operational challenges.

Aurélie Lustenberger
Vice President Global Sustainability, Sustainable Future Performance Lead, Capgemini Invent
Aurélie embeds data and AI at the core of sustainable transformation. She designs operating models, prioritizes high-impact use cases, and delivers roadmaps that link ESG goals with business performance. Her work spans ESG data foundations, industrialized AI and GenAI, and data-driven sustainability across strategy, operations, and supply chains.

Christopher Scheefer
Vice President, Global Data & AI Sustainability Lead, Intelligent Industry, Gen AI Ambassador, Capgemini
Christopher is a recognized thought leader in sustainability advisory and data and analytics. He focuses on AI-driven transformation at scale, integrating climate tech and energy transition into corporate value chains to build resilient, purpose-led growth.

Next step

If you're ready to skill up your team for agentic automation in operations, explore practical learning paths by role at Complete AI Training.