Agentic AI in Action: Industry-Specific Use Cases and Implementation Challenges

Agentic AI is rapidly emerging as the next frontier in digital business transformation, promising to move organizations beyond the capabilities of generative AI by enabling autonomous, goal-driven systems that can execute complex workflows with minimal human intervention. While the potential is vast, the journey from pilot to production is fraught with unique challenges—especially when it comes to integrating agentic AI into the fabric of industry-specific operations. Here, we explore how agentic AI is being piloted and deployed in retail, automotive, and healthcare, highlighting concrete use cases, integration hurdles, and lessons learned from early adopters.

What Sets Agentic AI Apart?

Unlike generative AI, which excels at creating content and providing information, agentic AI is designed to take action. These systems can break down high-level objectives into actionable steps, interface with external systems, and execute tasks autonomously. This leap from information provider to workflow orchestrator is not just a technical upgrade—it’s a paradigm shift in how businesses operate, automate, and deliver value.

Retail: Dynamic Supply Chain Optimization

Use Case: Real-Time Inventory and Pricing Agents

Retailers face a perennial challenge: balancing inventory to avoid both stockouts and overstocking, which together cost the industry over $1 trillion annually. Agentic AI offers a solution by deploying autonomous agents that monitor real-time sales data, supply chain disruptions, and local consumer demand. These agents can automatically adjust pricing and restocking schedules for individual store locations, optimizing margins and reducing waste.

Value Realized: Early pilots have shown that dynamic AI-driven inventory and pricing can boost revenue by 5–15% per retailer, translating to billions in new profit across the sector.

Implementation Challenges

Expert Insight: Hilding Anderson, Retail Strategy Lead at Publicis Sapient, notes, “The biggest opportunity is not just in automating tasks, but in orchestrating end-to-end experiences that connect digital and physical retail. Agentic AI is the catalyst for this next wave of transformation, but only if organizations invest in integration and data readiness.”

Automotive: Predictive Maintenance and Connected Experiences

Use Case: Autonomous Predictive Maintenance

In the automotive sector, agentic AI is being piloted to monitor vehicle sensor data, predict mechanical failures before they occur, and automatically optimize delivery routes based on real-time traffic and weather. These agents can schedule maintenance, order parts, and even communicate with drivers or fleet managers to minimize downtime.

Value Realized: Transportation companies leveraging agentic AI for predictive maintenance have seen maintenance costs drop by up to 30% and fuel costs by 10–15%, resulting in significant industry-wide savings and improved delivery efficiency.

Implementation Challenges

Expert Insight: AJ Dalal, Senior Managing Director of Data Strategy at Publicis Sapient, emphasizes, “The real challenge isn’t just technical—it’s about creating a culture of collaboration across OEMs, dealers, and partners. Agentic AI thrives when data flows freely and stakeholders are aligned on shared outcomes.”

Healthcare: Administrative Automation and Patient Journey Orchestration

Use Case: Automated Prior Authorization and Patient Registration

Healthcare is notorious for administrative bottlenecks that drain resources and frustrate patients. Agentic AI is being deployed to automate prior authorization approvals for insurance claims, read patient histories, validate medical necessity, and auto-fill forms for providers and payers. In clinical settings, agents can register participants for trials, ensure post-discharge care, and summarize patient records for physicians.

Value Realized: Automating these processes can reduce administrative costs by up to 50%, improve patient outcomes, and free up clinicians to focus on care rather than paperwork.

Implementation Challenges

Expert Insight: Publicis Sapient’s healthcare leaders stress the importance of federated learning and blockchain-based audit trails to ensure both privacy and accountability in agentic AI deployments.

Lessons from Early Adopters: Integration, Data, and Change Management

Across industries, the most successful agentic AI pilots share three common threads:

  1. Integration First: Agentic AI is only as powerful as the systems it can access. Early adopters prioritize seamless integration across platforms, often modernizing legacy systems as a prerequisite.
  2. Data Readiness: Clean, well-governed data is non-negotiable. Organizations invest in data quality, governance, and real-time access to ensure AI agents make sound decisions.
  3. Human Oversight: Even as AI agents gain autonomy, human-in-the-loop frameworks remain essential for oversight, exception handling, and continuous improvement.

The Road Ahead: Practical Guidance for Leaders

Agentic AI is not a plug-and-play solution. It demands a strategic approach that balances ambition with pragmatism:

As Andy Maskin, Director of AI Creative Technology at Publicis Sapient, puts it: “Agentic AI will soon be the default way business gets done. The winners will be those who solve the integration puzzle first and focus on real, measurable outcomes—not just hype.”

Conclusion

Agentic AI is already reshaping how industries operate, from retail supply chains to automotive maintenance and healthcare administration. The path to value is clear: prioritize integration, invest in data readiness, and manage change with a human-centered approach. By learning from early adopters and focusing on practical, high-impact use cases, organizations can unlock the transformative potential of agentic AI—turning autonomous action into sustainable business advantage.