Why agentic AI is poised to transform multiple industries

Generative AI (GenAI) is entering a new phase with the emergence of agentic AI, which promises to transform how organizations operate by enabling autonomous decision-making and task execution capabilities.

Current state of GenAI adoption: Despite widespread enthusiasm and planned investments in GenAI technology, many organizations are struggling to realize its full potential.

• A comprehensive study by NTT DATA reveals that while 97% of CEOs anticipate material impact from GenAI, only 43% of C-suite executives are fully satisfied with their current solutions
• Nearly all surveyed organizations (99%) plan to increase their GenAI investments
• About 95% of organizations acknowledge that the technology is fostering increased creativity and innovation

Understanding agentic AI: Agentic AI represents a significant evolution beyond traditional GenAI capabilities, introducing autonomous decision-making and execution abilities.

• Unlike basic question-and-answer systems, agentic AI can independently execute tasks within workflows
• The technology can analyze situations in real-time and make autonomous decisions
• This enhanced capability enables complete transformation of business processes and workflows

Practical applications: Several industries are already beginning to implement agentic AI solutions in meaningful ways.

• Autonomous vehicles utilize agentic AI for real-time decision-making and navigation
• Healthcare providers are implementing AI-powered diagnostic tools for medical image analysis and disease detection
• Insurance companies are developing AI agents to manage end-to-end customer service workflows, including data updates and complex task completion

Implementation framework: NTT DATA outlines five critical factors for successful agentic AI deployment.

• Integration must be comprehensive, combining agentic AI with other technologies and services
• Strategic alignment between AI initiatives and business objectives is essential, though currently only achieved by 49% of organizations
• Data readiness and governance should be implemented through a staged transformation process
• Mature AIOps systems are necessary for model maintenance, establishing guardrails, and managing workflow feedback
• Strategic partnerships with third-party service providers can accelerate innovation and reduce implementation risks

Looking ahead: The trajectory of agentic AI adoption signals both opportunities and challenges for organizations.

• Organizations should begin identifying specific use cases that align with their business objectives
• Preparation of resources and infrastructure will be crucial for successful implementation
• Partnership selection will play a vital role in ensuring successful smart-agent deployments
• Early adoption and strategic planning will be key factors in gaining competitive advantages as the technology matures