What Is Agentic AI and Is Your Data Ready for It?

Artificial intelligence is evolving at an unprecedented pace, moving beyond static models and simple automation towards systems capable of independent reasoning, planning, and action. Two critical concepts driving this evolution are agentic AI, a class of AI focused on building autonomous systems that learn from mistakes and complete complex tasks with little human interaction, and AI-ready Data, which is cleaned, structured, validated, and enriched data suitable for direct use in AI applications.  

While agentic AI represents a significant leap forward in AI systems capability, AI-ready Data serves as the indispensable foundation upon which these advanced models are built and sustained. Understanding both, and their deep interdependence, is crucial for navigating the future of technology and business innovation. 

Key Takeaways 

Agentic AI Is Changing the Way Businesses Use Artificial Intelligence 

AI-Ready Data Is Essential for Effective Agentic AI Systems 

Agentic AI Combined with High Quality Data Unlocks Endless Business Potential 

Strategic Implementation Requires Robust Data Management and Ethical Oversight 

Understanding Agentic AI

Artificial intelligence has long promised to revolutionise the way we live and work. While earlier iterations focused on specific tasks, like data analysis or pattern recognition, and more recent advancements have captivated the world with their ability to create human-like content, agentic AI represents a distinct paradigm shift. It moves beyond merely processing information or generating outputs to actively pursuing goals and making decisions in dynamic environments. 

What Exactly Is Agentic AI?

Agentic AI refers to artificial intelligence systems designed to operate with a high degree of autonomy, capable of reasoning, planning, acting, and reflecting on its own performance with limited human supervision. Unlike traditional AI models, agentic AI is a more proactive and self-directed form of artificial intelligence. While a generative AI model might complete simple tasks like answering a question or writing an essay, an agentic AI system would identify a problem, devise a strategy to solve it, activate tools to execute the necessary steps to complete the task, and evaluate the outcome, adjusting its approach as needed. 

Here’s how to differentiate agentic AI from other forms of AI:

• AI vs. Agentic AI: General AI encompasses any machine exhibiting intelligence. Agentic AI is a specific, advanced subset focused on autonomous, goal-driven action. 
• Generative AI (GenAI) vs. Agentic AI: GenAI excels at creating new content (text, images, code), while agentic AI only leverages generative AI models as its "brain" for reasoning and understanding. It also incorporates memory and connects with external tools to complete multi-step processes that drive real-world productivity—all without external intervention. 
• Machine Learning (ML) vs. Agentic AI: Agentic AI vs. Machine Learning: Machine learning is designed to identify patterns and make predictions from historical data, but it’s inherently passive—requiring external systems or humans to act on its outputs. Agentic AI goes further by autonomously planning, reasoning, and executing actions to achieve defined goals.
• Chatbot vs. Agentic AI: Chatbots are typically rules-based or powered by large language models (LLMs) to engage in conversational interactions. They are dependent upon user input. While agentic AI might interact via a conversational interface, it goes beyond mere conversation to perform tasks, make decisions, and execute actions to achieve a defined objective. 

How Do Agentic AI Systems Work?

The ability of agentic AI systems to operate autonomously stems from a combination of sophisticated capabilities that mimic aspects of human problem solving. This enables the AI model to bridge the gap between understanding and action, transforming insights into tangible outcomes. 

Here’s how it works: 

• Planning: Agentic AI divides complex objectives into achievable steps, optimising the sequence and resource allocation for effective execution.
• Memory: These systems utilise memory to retain past interactions and experiences, informing better decisions in future scenarios.
• Tools: Agentic AI dynamically accesses various external tools and APIs to accomplish tasks beyond its inherent abilities.
• Reflection/Self-Correction: After taking action, these agents review outcomes and adapt strategies based on results.
• Autonomy: The hallmark of agentic AI is its ability to act independently, making decisions and carrying out actions with minimal human input once a goal is defined.

What Are the Advantages of Agentic AI? 

Agentic AI systems can help enterprises achieve unprecedented levels of efficiency, productivity, and innovation. 

Advantages include: 

• Enhanced Efficiency and Productivity: With minimal need for intervention, teams can focus on more creative, strategic, and high-value activities, boosting overall organisational productivity. 
• Optimised Operations: Agentic AI can continuously monitor and optimise operational processes in real-time. From supply chain management to resource allocation, these systems can identify inefficiencies, predict potential issues, and implement corrective actions, cutting costs and improving business performance. 
• Accelerated Innovation: Agentic AI can help speed up innovation by automating research, data analysis, and experimental design. It allows organisations to rapidly explore solutions, test them, and bring new products to market faster. 
• Superior Decision-Making: Agentic AI systems can process and analyse vast amounts of data, identify patterns, and generate insights that inform more robust and data-driven decision-making. Their ability to learn and adapt means their decision-making capabilities continuously improve, leading to more effective strategies and outcomes. 
• Unlocking New Business Models: Enable entirely new services and business models that were previously unfeasible through the power of agentic AI.  
• Orchestrating Agents to Automate Workflows: Use Model Context Protocol (MCP) to let diverse agents communicate and share context, enabling end-to-end workflow automation without manual intervention.

How Is Agentic AI Used in the Real World? 

The potential applications of agentic AI are endless and promise to automate complex processes, enhance decision-making, and unlock new levels of efficiency. Here are some real-world examples of agentic AI systems in action: 

• Automated Customer Service and Support: Beyond simple chatbots, agentic AI can resolve issues end-to-end by handling multi-step customer inquiries, diagnosing technical issues, accessing customer records, initiating refunds, or even proactively offering solutions based on predictive analysis. 
• Complex Workflow Automation: In business operations, agentic AI can manage intricate workflows, such as supply chain optimisation, by monitoring inventory levels, predicting demand fluctuations, automatically placing orders, tracking shipments, and even re-routing logistics to mitigate disruptions. 
• Financial Analysis: Agentic AI systems can monitor market data in real-time, identify opportunities, and manage portfolios, all while tracking changes in the market and adjusting risk parameters. 
• Sales and Marketing Applications: Agents can streamline sales by automatically qualifying leads, sending personalised outreach emails, and scheduling follow-ups with prospects. In marketing, agents can analyse customer data, segment audiences, and generate personalised content to drive campaign performance.

AI-Ready Data: The Foundation for Effective Agentic AI Systems

The full potential of agentic AI hinges entirely on one critical prerequisite: AI-ready data. As Dun & Bradstreet’s Chief Data & Analytics Officer, Gary Kotovets, told CIO, “Having clean and quality data is the most important part of the job. You want to ensure you don’t have the ‘garbage in, garbage out’ kind of scenario.”1

Just as humans require accurate and timely information to make informed decisions, Agentic AI systems rely on high-quality, well-structured, and accessible data to function effectively. Without this key foundational element, even the most advanced AI models will struggle to deliver reliable results. 

What Does “AI-Ready Data” Mean? 

AI-ready data is data that has been meticulously prepared, structured, and governed in a way that makes it immediately usable, reliable, and effective for training, validating, and operating AI models. It's not just about having data; it's about having data that is optimised for consumption by AI systems. This ensures that AI models can accurately interpret the data, learn meaningful patterns, and make sound predictions or decisions without being hampered by inconsistencies, errors, or irrelevance. 

Why AI-Ready Data Matters for AI Models

Data quality and readiness directly correlate with the performance, accuracy, and reliability of AI models. But for agentic AI systems the stakes are even higher. 

• Impact on Model Performance: An AI model trained on poor quality data will produce flawed outputs, undermining its utility and trustworthiness. High-quality, AI-ready data ensures that models learn from representative and accurate information, leading to superior performance. 
• Accuracy and Reliability: An AI agent's decisions will be compromised if the underlying data is unreliable, potentially leading to incorrect actions, operational failures, or suboptimal outcomes. AI-ready data provides the necessary foundation for accurate predictions and reliable operations. 
• Fairness and Ethics: Biases present in the training data can be amplified by AI models, leading to unfair or discriminatory outcomes. AI-ready data identifies and mitigates such biases, ensuring that AI systems operate ethically and equitably. 
• Efficiency of Development: AI-ready data allows scientists and machine learning engineers to spend less time on data cleaning and preparation and more time on model development, experimentation, and deployment—accelerating the entire AI lifecycle. 
• Real-Time Decision-Making: Agentic AI systems require real-time data for dynamic decision-making. AI-ready data ensures that information is consistently up to date, enabling the AI to respond effectively, even in changing conditions. 

AI-ready data is more than just a technical requirement; it’s a strategic imperative for leveraging the full potential of artificial intelligence. 

The 8 Key Principles of AI-Ready Data

Achieving AI-ready data involves adhering to eight fundamental principles that ensure data assets are optimised for AI consumption. While the specific implementation may vary, these principles form a robust framework for data readiness. 

1. Quality and Accuracy: Data must be clean, consistent, and error-free. 
2. Completeness: Incomplete data can lead to biased models or prevent AI from learning critical relationships. 
3. Relevance: Irrelevant data can introduce noise, increase computational overhead, and dilute the model's ability to identify meaningful patterns. 
4. Accessibility and Discoverability: Data should be discoverable, understandable, and easily accessible to AI systems, as well as data scientists. 
5. Consistency and Standardisation: Data should adhere to uniform formats, definitions, and structures across different data assets. 
6. Governance and Security: Strong data governance frameworks are crucial to ensure ethical use and build trust. 
7. Timeliness: For many AI applications, especially those involving real-time decision-making or predictive analytics, data must always be kept up to date. 
8. Volume and Variety: AI models often benefit from large and diverse data sources, providing a more comprehensive view along with the ability to learn from richer context. 

The Role of Data Management in AI Readiness

Effective data management is the bedrock upon which AI readiness is built. A comprehensive data management strategy helps ensure that your data is trustworthy, accessible, and ready to perform when it’s called upon by agentic AI systems. 

Here’s how robust data management contributes to developing AI-ready data: 

• Data Integration: Unified data delivers a 360 degree view of your business, helps eliminate inconsistencies introduced by siloed data, and delivers data you can trust to run autonomous agentic AI systems. 
• Data Quality Management: Data quality is essential for ensuring trusted AI output.  
• Data Governance: Establishing clear policies, processes, and responsibilities for managing data assets ensures that the data fueling your AI agents was handled ethically, securely, and in compliance with regulations. 
• Data Security and Privacy: Ensuring robust security and adherence to privacy regulations are a top priorities for AI initiatives, especially when dealing with personal or proprietary information.  
• Metadata Management: Comprehensive metadata (data about data) helps keep data discoverable, understandable, and usable for agentic AI systems. 
• Data Architecture and Infrastructure: Designing scalable, resilient, and performant data architectures (e.g., data lakes, data warehouses, data meshes) is essential to support the storage, processing, and retrieval needs of AI models, particularly for large-scale and real-time applications. 

Challenges & Considerations of Agentic AI and AI-Ready Data 

While the potential is immense, agentic AI comes with its own set of challenges and critical considerations. 

• Data Privacy and Ethical AI: The autonomous nature of agentic AI, combined with its reliance on extensive data, amplifies concerns around data privacy, security, and ethical use. At Dun & Bradstreet, we follow 11 core principles to guide how we design, build, and use AI to ensure our AI solutions are built to do good—for people, for companies, and for the future. 
• Scalability of Data Infrastructure: As AI initiatives grow, the volume, velocity, and variety of data required can quickly overwhelm existing data infrastructure. Through the power of Dun & Bradstreet’s Data Cloud, we deliver the AI-ready data to fuel agentic AI and drive growth at any stage in the business lifecycle. 
• Complexity of Integrating Diverse Data Sources: Achieving AI-ready data often means integrating information from a multitude of disparate systems, each with its own format and structure. Dun & Bradstreet simplifies the consolidation of information from various systems, apps, and tools, so our clients can focus their efforts on getting results, not wrestling with tech. 
• Ensuring Robust Governance for Autonomous Systems: The autonomous actions of agentic AI require a strong governance framework. Our governance practices ensure that data is handled the right way, from how it’s collected to how it’s shared, and we perform over 60B quality checks each month to deliver the pristine data our clients depend on.   
• The Need for Human Oversight and Collaboration: Despite their autonomy, human oversight is still crucial for setting strategic goals, interpreting complex outcomes, intervening in unforeseen circumstances, and continuously refining data management and the AI learning process. That’s why we our data advisors, with decades of experience and a strong global presence, are trained support every part of a company’s data journey, from helping them integrate our AI-ready data into their existing systems to making sense of complex information. We deliver the support organisations need to drive business growth. 

Next Steps

Agentic AI systems, with their capacity for autonomous reasoning, planning, and action, promise to revolutionise how businesses operate, driving unprecedented levels of efficiency, innovation, and personalised experiences. However, realising this potential is entirely contingent upon high-quality, well-governed, and easily accessible AI-ready data. 

At Dun & Bradstreet, our patented process for collecting and enhancing data, along with rigorous stewardship, ensures the kind of accuracy, reliability, and trust that AI systems need to run complex, end-to-end processes with minimal human intervention. Learn more about our Five Pillars of Data Excellence and how we deliver AI-ready data to companies large and small across the globe.