The excitement to hop onto the agentic AI journey is evident and justifiably so. AI agents possess goal-oriented intelligence, enabling them to deliver human-like decision-making capabilities. The productivity gain AI agents promise is unprecedented.

As a result, companies of all sizes are exploring the deployment of Agentic AI to leapfrog innovation. According to a leading analyst firm, by 2028, “33% of enterprise software applications will include agentic AI.”¹

However, autonomous AI deployments are not insulated from risks. In fact, they amplify it. Before organizations can scale agentic workflows across the enterprise with confidence, they need foundational work to achieve AI readiness. The eBook, “Master Agentic AI with AI-Powered Data Management,” details how to close the readiness gap by strengthening data management.

Technology leaders are approaching investments in AI agents with caution. Nearly 80% of leaders indicate they don’t always trust agentic AI systems.²

The autonomous nature of AI agents, combined with their ability to make decisions and take actions independently, amplifies several risks. Limited human oversight on agentic workflows may quickly result in unpredictable, unintended and undesirable outcomes.

Ninety percent of data leaders cite data reliability as a barrier to scaling GenAI initiatives from pilots to production.³

In this blog, we explore some of the key considerations for organizations as they rewire their data strategies to build trust in agent-driven operations.

The decisions AI agents make are based on underlying data. This introduces essential data and AI governance considerations that organizations must address from the beginning. Governing data feeding AI models minimizes the chances of agents being trained or operated on incorrect, incomplete, biased or unprotected data.

As CDOs and data leaders lead their organizations into the agentic AI era, they must consider several critical factors that reduce the susceptibility of agents to suboptimal data.

Balance autonomy with control. Autonomy is essential for AI agents to drive the desired impact. However, it is equally important to implement control measures that complement the dynamic and adaptive nature of agentic AI. Excessive independence for agents can result in unpredictable or unwarranted actions. On the other hand, imposing too much control can restrict flexibility, hinder innovation and diminish system efficiency. Finding the right balance between autonomy and control, therefore, becomes crucial.

Build trust in AI. Understanding how AI makes decisions is key for stakeholders to build trust. Without clear visibility, AI systems will continue to be perceived as unpredictable. Insights into areas such as what data is used, what processes are involved, what policies are applied, what the quality of training data is and how the agents are connected are fundamental to instilling confidence in the use of AI.

AI transformation is human-centric. AI agents are intended to augment human intelligence, not replace it. They can understand goals, define paths of action and handle execution without needing intervention while elevating human roles to focus on higher-level problem-solving. It is essential that agentic AI systems are designed with the purpose, flexibility and challenges of the workforce in mind.

Ethical and moral boundaries for AI. It is essential to realize that AI systems are not inherently equipped to align with an organization's values, ethics or responsibilities. They operate solely on data inputs and programmed objectives. However, they can be trained to operate within a compass of ethical and moral values. Reducing biases in data and provisioning safeguards around how data is leveraged helps ensure the responsible use of AI agents.

Imagine a large retail company operating with an AI agent-driven supply chain management system. The agent is designed to manage inventory, process orders autonomously and set up logistics to optimize costs and meet customer demand in real time. However, any vulnerabilities in the data pipeline can lead to cascading failure. Lengthy delays in data availability or inconsistent, outdated sales data can lead the AI agent to develop erroneous forecasts and misguided order decisions, which can disrupt logistics.

The result: The company faces operational chaos, customer dissatisfaction and financial losses, all stemming from the inability to fuel AI agents with trustworthy data.

Regardless of the use case or industry, agentic AI relies on the accuracy of data for driving reliable outcomes. While nearly 60% of organizations consider data management critical for harnessing the full potential of AI, less than 20% of organizations report high maturity in any aspect of data readiness.⁴  Organizations must bridge this significant divide between the aspiration and the reality of successfully adopting agents.

Aligning data management foundations to support agentic architecture is a priority for companies globally. Eighty-six percent of data leaders expected the level of investments in data management to increase in 2025.⁵

Reinforcing data management with AI-powered and agent-based capabilities helps ensure that accurate, complete and trustworthy data is available, allowing agents to make informed decisions and take effective actions with minimal human oversight.

In the eBook ‘Master Agentic AI with AI-Powered Data Management, ’ we outline key challenges data leaders need to solve for effective agentic AI. Download the eBook, and explore how companies can accelerate AI-readiness with agent-driven data management.
 

_{¹https://www.gartner.com/en/articles/intelligent-agent-in-ai
²https://www.blueprism.com/resources/white-papers/agentic-and-gen-ai-2025-global-enterprise-ai-survey/
³https://www.informatica.com/lp/cdo-insights-2025_5039.html
⁴https://www.capgemini.com/wp-content/uploads/2025/07/Final-Web-Version-Report-AI-Agents.pdf
⁵https://www.informatica.com/lp/cdo-insights-2025_5039.html}