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Why agentic AI is poised to transform multiple industries
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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
Agentic AI: Why this emerging technology will revolutionise multiple sectors

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