Agentic AI in Action—Industry-Specific Use Cases and Implementation Roadmaps

Introduction: The Next Leap in AI Transformation

The evolution from generative AI to agentic AI marks a pivotal shift in how organizations leverage artificial intelligence. While generative AI has rapidly delivered value through content creation, chatbots, and automation, agentic AI introduces a new paradigm: autonomous systems that not only generate information but also act on it—executing complex workflows, making decisions, and orchestrating business processes with minimal human intervention. This page explores how agentic AI is being piloted and implemented across key industries, the technical and organizational requirements for success, and a practical roadmap to help organizations assess readiness and move from proof of concept to production.

What Is Agentic AI?

Agentic AI refers to systems designed to autonomously pursue complex goals, adapt to changing environments, and execute multi-step processes across integrated enterprise systems. Unlike generative AI, which produces content or suggestions for human action, agentic AI can independently analyze data, make decisions, and trigger actions—often coordinating with other AI agents and business platforms. This leap in capability promises transformative value but also introduces new challenges in integration, governance, and change management.

Industry-Specific Use Cases: Agentic AI in Action

Retail: Automated Supply Chain and Dynamic Pricing

Use Case: An AI agent monitors real-time sales data, supply chain disruptions, and local consumer demand to automatically adjust pricing and restocking schedules for each store location.

Impact: Retailers lose over $1 trillion annually due to stockouts and overstocking. A dynamic agentic AI system can optimize margins, reduce waste, and boost revenue by 5–15% per retailer, translating to billions in new profit across the industry.

Technical Requirements:

Integration with point-of-sale, inventory, and supplier systems
Real-time data streaming and event-driven architecture
AI models for demand forecasting and price optimization
Security and compliance modules for sensitive data

Financial Services: Proactive Fraud Detection and Personalized Lending

Use Case: A real-time financial assistant monitors customer transactions, predicts cash flow issues, and proactively recommends personalized loan options—auto-filling applications and checking risk factors.

Impact: Banks face high default rates and low customer engagement. Agentic AI can increase loan approval efficiency, drive revenue through personalized offers, and reduce risk, potentially adding billions in new lending opportunities while lowering operational costs.

Technical Requirements:

Secure integration with core banking, CRM, and risk management systems
Machine learning for anomaly detection and credit scoring
Automated document processing and regulatory compliance checks
Human-in-the-loop oversight for high-stakes decisions

Healthcare: Prior Authorization Automation and Clinical Workflow Support

Use Case: AI agents automate prior authorization approvals for insurance claims by reading patient history, validating medical necessity, and auto-filling forms for providers and payers.

Impact: Prior authorizations delay treatments and cost billions annually. Automating this process can reduce administrative costs by 50%, improve patient outcomes, and create a multi-billion-dollar AI-driven insurance automation market.

Technical Requirements:

Deep integration with EHRs, payer systems, and regulatory databases
NLP for unstructured medical data extraction
Federated learning for privacy-preserving model training
Blockchain-based audit trails for compliance

Energy: Automated Carbon Credit Trading and Compliance

Use Case: An AI agent automates carbon credit trading by analyzing real-time emissions data, verifying compliance reports, and recommending optimal credit purchases or sales.

Impact: The carbon credit market is expected to reach $100 billion by 2030. Automating compliance and trading with AI unlocks liquidity, prevents regulatory fines, and creates new revenue streams for energy companies.

Technical Requirements:

Integration with IoT sensors, emissions databases, and trading platforms
Real-time analytics and event-driven triggers
AI-driven policy enforcement and reporting
Security and regulatory compliance modules

Technical and Organizational Requirements for Agentic AI

Implementing agentic AI is not just a technology upgrade—it’s a business transformation. Success depends on:

Systems Integration: Agentic AI requires seamless, real-time connectivity across enterprise platforms (ERP, CRM, SCM, EHR, etc.). Legacy systems and data silos must be addressed to enable autonomous workflows.
Data Maturity: High-quality, accessible, and well-governed data is essential. Organizations must invest in data strategy, governance, and enrichment to fuel AI agents.
Security and Compliance: Autonomous agents must operate within robust security frameworks, with audit trails, access controls, and compliance checks built in.
Human-in-the-Loop Governance: Even as AI agents take on more responsibility, human oversight remains critical—especially for high-stakes or regulated decisions.
Change Management: Agentic AI changes how work gets done. Upskilling, new roles (such as AI engineers and orchestrators), and clear communication are vital to adoption and trust.

Agentic AI Maturity Checklist

Before embarking on an agentic AI journey, organizations should assess their readiness across these dimensions:

Interoperability: Are your core systems (ERP, CRM, SCM, EHR, etc.) API-enabled and capable of real-time integration?
Data Quality: Is your data accurate, accessible, and well-governed?
Cloud and Infrastructure: Are you equipped for scalable, event-driven AI operations?
Security and Compliance: Do you have robust controls, audit logging, and regulatory frameworks in place?
Human Oversight: Is there a clear process for human review and intervention?
Change Management: Are your teams prepared for new workflows, roles, and responsibilities?

Roadmap: From Proof of Concept to Production

Discovery and Assessment
- Audit current workflows, data, and systems for integration opportunities and bottlenecks
- Identify high-value, low-risk use cases for agentic AI pilots
- Engage stakeholders across business, IT, compliance, and operations
Proof of Concept (PoC)
- Build a limited-scope agentic AI workflow in a controlled environment
- Integrate with select systems and data sources
- Validate technical feasibility, business value, and user experience
Pilot and Iteration
- Expand the PoC to a broader user group or additional workflow steps
- Monitor performance, accuracy, and compliance
- Collect feedback and refine the agentic workflow
Production Deployment
- Scale integration across enterprise systems
- Implement robust security, monitoring, and governance
- Train staff and establish new roles for AI oversight and orchestration
Continuous Optimization
- Monitor for model drift, performance, and compliance
- Iterate on workflows, expand to new use cases, and optimize for ROI
- Foster a culture of learning and adaptation as AI capabilities evolve

Integration Considerations

Start with Internal, Non-Customer-Facing Workflows: Early agentic AI success often comes from automating internal processes (e.g., supply chain, claims processing) before moving to customer-facing applications.
Leverage Third-Party Agentic Platforms for Standardized Tasks: For non-core functions, third-party agentic AI tools can deliver quick wins without deep integration.
Invest in Custom Agents for High-Value, Differentiating Workflows: Proprietary agentic AI is best reserved for workflows that are essential to your business model and require deep integration and customization.

Conclusion: The Path Forward

Agentic AI is not a distant future—it is already reshaping how leading organizations operate, compete, and deliver value. The journey from generative to agentic AI is complex, requiring not just new technology but new ways of working, thinking, and governing. By focusing on high-impact use cases, building robust integration and governance foundations, and investing in people and change management, organizations can unlock the next wave of AI-driven transformation.

Ready to assess your agentic AI readiness or pilot your first workflow? Connect with Publicis Sapient’s AI transformation experts to start your journey.