by For tech-driven businesses, developments in generative AI (GenAI) are creating new opportunities for innovation and growth. However, while the GenAI boom is real and optimism is high, many organizations are still struggling to see measurable results from their AI investments.
According to NTT DATA’s Global GenAI Report, only 43% of C-suite executives strongly believe that current GenAI solutions are meeting expectations. Yet, an overwhelming 97% of CEOs expect significant business impacts, and 99% plan to increase their investment in GenAI. These numbers highlight a gap between potential and current value.
The arrival of agentic AI is set to change that. Unlike conventional AI, which primarily responds to queries or offers predictions, agentic AI can act autonomously, make real-time decisions, and execute tasks independently. This ability allows businesses to unlock new value and fundamentally transform operations and employee workflows.
What Makes Agentic AI Different
Agentic AI represents a step beyond traditional GenAI. While current AI is mostly reactive, agentic AI is proactive and capable of independent decision-making. It can integrate into workflows, act on data insights, and complete complex tasks without constant human supervision.
NTT DATA research shows that 95% of organizations believe GenAI encourages greater creativity and innovation. Agentic AI takes this further, enabling autonomous action, continuous learning, and execution of operational tasks. This technology isn’t just about answering questions—it’s about thinking, deciding, and acting to create real business impact.
Real-World Applications
Agentic AI is beginning to show tangible benefits across multiple sectors:
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Healthcare: Hospitals and clinics are using agentic AI for diagnostics, medical imaging, and treatment recommendations. AI can analyze large datasets, detect anomalies, and suggest interventions with minimal human oversight.
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Finance: Automated trading systems leverage agentic AI to monitor markets, execute trades, and optimize portfolios in real time, improving speed, accuracy, and decision-making efficiency.
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Insurance and Customer Service: AI can manage entire customer engagement processes, from processing claims to updating client information. Agentic AI handles tasks independently, ensuring consistent service and data accuracy.
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Autonomous Vehicles: Self-driving cars use agentic AI to navigate complex environments, make real-time driving decisions, and ensure passenger safety.
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Scientific Research: Agentic AI supports complex simulations, predictive modeling, and experimental automation, accelerating research and innovation.
In each case, agentic AI enhances operational efficiency, reduces human workload, and enables more scalable and intelligent workflows.
Key Success Factors for Agentic AI
NTT DATA identifies five essential factors for successfully implementing agentic AI:
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Integration: Agentic AI must be part of a comprehensive technology stack. On its own, it cannot deliver transformative benefits. Combining it with complementary tools and processes ensures maximum efficiency and impact.
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Alignment with Strategy: AI should be integrated into the organization’s business strategy. Nearly half of companies have yet to align GenAI with their overall business plans. Successful adoption requires viewing AI as a strategic asset, not just a technical experiment.
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Data Readiness and Governance: High-quality, secure, and structured data is essential. Organizations should adopt gradual data transformation strategies to ensure reliable and scalable AI outcomes.
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Operational Oversight: Mature AI operations (AIOps) are necessary to monitor agentic models, prevent drift, and maintain performance. Human oversight is critical, especially when AI makes autonomous decisions. Feedback loops and boundaries help ensure safe and effective deployment.
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Partnerships and Ecosystems: Collaborating with AI vendors and technology partners accelerates development and reduces risks. Access to specialized expertise and innovative tools helps organizations deploy agentic AI efficiently and gain long-term competitive advantages.
Preparing for Adoption
Organizations should treat agentic AI adoption as a strategic journey rather than a one-time project. Key steps include:
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Identify High-Impact Use Cases: Focus on processes where AI can provide immediate value or reduce operational risk.
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Ensure Data Quality: Clean, structured, and secure datasets are crucial for accurate AI performance.
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Implement Oversight: Set up monitoring, feedback, and control mechanisms to supervise AI decisions.
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Leverage Partnerships: Work with vendors and technology partners to access expertise and resources.
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Train Teams: Educate employees on AI capabilities, workflow integration, and ethical considerations.
These steps help organizations minimize risks and maximize the benefits of agentic AI.
Unlocking Long-Term Value
Agentic AI is more than a tool—it is a catalyst for organizational transformation. By automating repetitive tasks, analyzing large datasets, and making autonomous decisions, AI allows businesses to focus on strategic priorities, innovation, and customer engagement.
Industries such as healthcare, finance, automotive, and scientific research are likely to see the fastest adoption and benefits. Hospitals can enhance diagnostics, financial firms can execute trades with speed and accuracy, and autonomous vehicles can navigate safely in dynamic environments. Across sectors, the key to success lies in strategic integration, human oversight, and robust data governance.
The Future of Agentic AI
Although adoption is still in its early stages, the momentum behind agentic AI is growing rapidly. Companies that act now—by developing use cases, preparing data, and forming strategic partnerships—will gain a competitive edge. Early adoption fosters organizational learning, as employees gain experience in supervising AI processes and integrating insights into operations.
In the coming years, agentic AI is expected to become a central component of enterprise strategy, driving efficiency, innovation, and revenue growth. Organizations that prepare today will be positioned as leaders tomorrow, leveraging autonomous intelligence to achieve operational excellence.
