Agentic AI in Action: Industry-Specific Use Cases and Roadmaps

The Next Evolution of AI: From Generative to Agentic

Artificial intelligence is rapidly evolving, and the next frontier is agentic AI—systems that don’t just generate content or insights, but autonomously execute complex workflows, make decisions, and adapt in real time. While generative AI has already transformed industries by automating content creation and enhancing productivity, agentic AI promises to fundamentally reshape business execution by connecting insights to action, eliminating bottlenecks, and unlocking new levels of efficiency and innovation.

But what does this look like in practice? How are leading organizations in retail, financial services, and healthcare deploying agentic AI today? And what roadmaps can industry leaders follow to move from experimentation to enterprise-scale transformation? Let’s explore the concrete, sector-specific applications of agentic AI and outline actionable steps for adoption.

What Is Agentic AI?

Agentic AI refers to autonomous systems—often composed of multiple collaborating AI agents—that can perceive context, make decisions, and execute multi-step tasks with minimal human intervention. Unlike traditional automation or even generative AI, which typically requires a human to act on its outputs, agentic AI can:

Set its own goals and break down complex tasks
Integrate with enterprise systems (ERP, CRM, SCM, EHR, etc.)
Make real-time decisions and adapt to changing data
Operate with built-in compliance, security, and ethical guardrails

This leap in capability means agentic AI can move beyond isolated use cases to orchestrate end-to-end business processes, delivering measurable value across industries.

Retail: Dynamic Pricing, Inventory, and Personalized Experiences

Use Case: Dynamic Pricing and Inventory Optimization

Retailers face razor-thin margins and volatile demand. Agentic AI can autonomously monitor real-time sales, supply chain disruptions, and local consumer trends to adjust pricing and restocking schedules for each store location. For example, an AI agent might detect a surge in demand for a product due to a viral social media trend, cross-reference inventory levels, and automatically trigger restocking or price adjustments—without human intervention.

Benefits:

Reduces stockouts and overstocking, minimizing lost sales and waste
Optimizes margins and boosts revenue by 5–15% per retailer
Frees up staff to focus on customer engagement and innovation

Use Case: Personalized Content and Conversational Commerce

Agentic AI can power hyper-personalized marketing campaigns, product recommendations, and conversational shopping assistants. By integrating with customer data platforms, these agents can tailor offers, generate dynamic content, and even guide customers through the entire purchase journey via chat or voice interfaces.

Roadmap for Retail Leaders:

Data Foundation: Cleanse and unify customer and inventory data across channels.
Pilot Micro-Experiments: Start with targeted agentic AI pilots (e.g., dynamic pricing for a product line or region).
Integrate with Core Systems: Ensure seamless connectivity with POS, ERP, and e-commerce platforms.
Scale and Govern: Expand successful pilots, implement human-in-the-loop oversight, and establish ethical guardrails.

Financial Services: Real-Time Risk, Personalized Advice, and Automation

Use Case: Real-Time Risk Assessment and Proactive Financial Assistance

Agentic AI can monitor customer transactions, predict cash flow issues, and proactively recommend personalized loan options or financial products. For example, an AI agent could detect unusual spending patterns, assess risk in real time, and automatically initiate fraud checks or offer tailored financial advice.

Benefits:

Increases loan approval efficiency and customer engagement
Reduces fraud and operational costs
Enables 24/7, always-on financial support

Use Case: Intelligent Process Automation (Robo-Ops)

Beyond customer-facing applications, agentic AI can automate back-office operations—such as onboarding, compliance checks, and claims processing—by integrating with multiple internal systems and making decisions based on real-time data.

Roadmap for Financial Services Leaders:

Assess Data and Integration Readiness: Map out data flows and integration points across legacy and modern systems.
Start with Low-Risk, High-Volume Processes: Automate routine tasks (e.g., KYC, transaction monitoring) with agentic AI pilots.
Implement Human Oversight: Use human-in-the-loop frameworks for critical decisions.
Expand to Personalized Services: Gradually introduce agentic AI for customer-facing advisory and risk management.

Healthcare: Automating Patient Intake, Claims, and Clinical Workflows

Use Case: Automated Patient Intake and Claims Processing

Healthcare organizations are burdened by administrative complexity and data silos. Agentic AI can automate patient intake by extracting information from unstructured documents, verifying insurance eligibility, and initiating claims—all while ensuring compliance with privacy regulations.

Benefits:

Reduces administrative workload and errors
Accelerates patient onboarding and claims resolution
Improves patient and provider satisfaction

Use Case: Clinical Decision Support and Workflow Orchestration

Agentic AI can assist clinicians by summarizing patient histories, flagging potential risks, and coordinating care across departments. For example, an AI agent could monitor EHR data, identify patients at risk of readmission, and trigger follow-up actions automatically.

Roadmap for Healthcare Leaders:

Prioritize Data Interoperability: Invest in EHR integration and data standardization (e.g., FHIR/HL7).
Pilot Administrative Automation: Start with claims or intake processes to demonstrate ROI.
Expand to Clinical Workflows: Introduce agentic AI for care coordination and decision support, with strong compliance and audit trails.
Establish Governance: Implement privacy, security, and ethical frameworks to ensure trust and regulatory compliance.

Building Your Agentic AI Roadmap: Key Considerations

Integration Is Everything: Agentic AI’s power lies in its ability to connect and act across systems. Assess your current tech stack for interoperability and invest in APIs, event-driven architectures, and cloud-native platforms.
Start Small, Scale Fast: Begin with focused pilots in high-impact, low-risk areas. Validate value, then expand to more complex workflows.
Human-in-the-Loop: Maintain oversight, especially for high-stakes decisions. Design workflows where humans can intervene, audit, and continuously improve AI performance.
Security and Ethics: Build in data privacy, compliance, and ethical guardrails from the start. Use audit logging, access controls, and bias detection to ensure responsible AI use.
Upskill Your Workforce: Invest in training employees to work alongside AI agents, focusing on oversight, quality control, and creative problem-solving.

Why Publicis Sapient?

With decades of digital transformation expertise and a proven track record in AI integration, Publicis Sapient partners with organizations to design, build, and scale agentic AI solutions tailored to industry-specific needs. Our proprietary platforms, such as Sapient Slingshot, accelerate system integration and workflow automation, while our human-centered approach ensures that technology augments—not replaces—human brilliance.

Whether you’re in retail, financial services, healthcare, or beyond, the future of business is agentic. The organizations that act now—experimenting, integrating, and scaling agentic AI—will define the next era of industry leadership.

Ready to put agentic AI to work in your industry? Let’s connect.