7 Strategic Steps to Leverage AI Agents with Unstructured Data

Set the Stage for AI Agent Excellence

Data stands as a vital strategic asset that enhances efficiency, drives innovation, and secures competitive advantage. However, many organizations grapple with chaotic and fragmented data management, making it challenging to harness this invaluable resource for effective decision-making and AI agent deployment.

The AI Agent Data Dilemma

Organizations recognize the transformative potential of AI agents to drive business value and innovation. AI agents can automate processes, provide insights, and enhance decision-making. Yet, challenges such as fragmented unstructured data, inadequate data governance, and infrastructure uncertainties persist.

The Path to Empowering AI Agents with Data

Chief Data and Analytics Officers must focus on establishing a solid data foundation to fully leverage AI agents. This approach ensures data is organized, high-quality, and readily accessible across the organization. 

Here are seven strategic steps to unlock the full potential of AI agents:

1 | Establish a Data Architecture Designed for AI Agents

A robust data architecture ensures that your systems can support AI agents effectively. It allows for seamless data integration and retrieval, crucial for AI agents that need real-time access to diverse datasets.

• Integrate Structured and Unstructured Data: Ensure comprehensive capture and integration of all relevant data types.
• Design Data Pipelines for AI Agents: Create efficient and clear data pathways to facilitate real-time data access and processing by AI agents.
• Scalable Architecture: Implement systems that can grow alongside expanding AI agent deployments.

2 | Prepare Data to Work with AI Agents

Data preparation is a critical step in the AI journey, providing AI agents with clean, structured, and reliable datasets for optimal performance.

• Assess and Cleanse Data: Identify gaps, cleanse the data, and ensure consistent data formats across the organization.
• Automate Data Processing: Utilize machine learning tools to streamline data validation and integration, ensuring AI agents work with the most accurate data possible.

3 | Implement Governance with AI Agents in Mind

Strong governance ensures data security, reliability, and compliance—key factors for trustworthy AI agent operations.

• Establish Data Ownership: Define clear roles and responsibilities for data that AI agents will use.
• Introduce Governance Policies for AI Agents: Develop policies to safeguard the data used and processed by AI agents, ensuring regulatory compliance.
• Secure Data Environment: Monitor and protect the data infrastructure supporting AI agents.

4 | Deploy AI Agents to Drive Business Value

Effective deployment translates AI agents from theoretical models into tools driving real business value.

• Align Agent Functions with Business Goals: Tailor AI agents to support specific business objectives, ensuring direct, measurable impacts.
• Integrate Agents Within Workflows: Ensure AI agents are embedded within company processes with security in mind.
• Continuous Testing and Validation: Verify AI agent outputs for consistent performance and adjust as necessary. 

5 | Scale AI Agent Operations

Expand AI agent capabilities throughout your organization like a factory—streamlined and repeatable processes enhance efficiency and effectiveness.

• Standardize AI Agent Development: Create processes to ensure rapid and consistent development of AI agent capabilities.
• Continuous Improvement: Ensure AI agents continuously learn and adapt, bringing incremental value across business functions.

6 | Ongoing Monitoring for AI Agents

Ongoing governance ensures AI agents maintain performance, security, and trustworthiness.

• Continuous Monitoring and Management: Regularly check AI agent performance, employing adaptive processes as needed.
• Compliance with Standards: Ensure AI agents adhere to regulatory and ethical guidelines at all times.

7 | Addressing AI Agent Hesitancy

Overcome hesitancy regarding AI agents through strategic approaches and infrastructure enhancements.

• Educate Stakeholders on AI Agents: Highlight practical benefits and ROI of AI agent deployment.
• Build Strategic Business Cases: Align AI agent initiatives with business goals and objectives.
• Showcase Quick Wins: Demonstrate early successes to boost confidence and support for AI agent initiatives.

Build data products that rely on agile management systems, elevated data quality, and solid operational foundations. We’ll help you establish federated data ownership practices and data models optimized for specific domains and lines of business.

Anticipate and adapt to the wide-ranging impacts AI can have on your data and organization, including budgets and data controls, secure data practices, and cloud-native architectures.

Harness the power of data ethically and responsibly with trusted data principles and governance models for managing risk.

Create a consumer lifecycle approach that incorporates self-service models, AI assistants and agents, and builds a foundation for enterprise insights.

Operate and manage your data infrastructure with integrated frameworks that support access to a broad range of data sources and make analytics faster with less friction.