AI agents and the need for AI-ready data: Building the foundation for digital workers

AI agents and the need for AI-ready data: Building the foundation for digital workers

AI agents, often referred to as digital workers, represent a new wave of autonomous systems that are increasingly designed to operate independently. These agents can be trained much like human workers, assuming roles such as accountants, sales assistants, customer service agents, or IT service desk operators. By training them with specific skills and domain knowledge, organizations can deploy a digital workforce capable of executing tasks with minimal human oversight.

However, while the promise of digital workers is immense—driving efficiency, productivity, and cost savings—there's a critical step that is often overlooked in the rush to deploy these agents: making your organization's data AI-ready.

Training digital workers: Mimicking human training

Training AI agents is not unlike training human employees. We can teach digital workers the same way we teach human workers—by equipping them with specific skills, assigning roles, and testing their abilities before putting them into production environments.

Just as you'd train an accountant or customer service agent on company processes, regulations, and customer service protocols, digital workers can be trained in similar ways. Moreover, because they learn from conversations, data, and interactions, they can continually improve and adapt to new information, increasing their efficiency and effectiveness over time.

This brings us to a future where digital workers not only work alongside humans but also collaborate with other digital agents to manage complex workflows. These hybrid teams of digital and human workers will redefine how business processes are managed, with digital workers handling routine and repetitive tasks, and humans focusing on strategic and creative problem-solving.

Agent platforms: The rise of low-code agentic frameworks

As enterprises begin deploying AI agents across different functions, the need for robust agentic platforms becomes crucial. Much like the evolution of robotic process automation (RPA) platforms (e.g., UiPath), platforms for building and managing AI agents are rapidly emerging. Giants like Google, Microsoft, AWS, and Apple are already at the forefront, and we are seeing innovative startups making it easier to build and deploy agents quickly.

These platforms often leverage large language models (LLMs), typically trained on vast public datasets, but they also provide the tools for enterprises to customize agents with their proprietary knowledge. Existing software-as-a-service (SaaS) platforms are embedding agents into their products, and this trend is expected to grow, leading to a future where AI-driven agents are ubiquitous in enterprise environments.

The role of data in AI agent performance

At the heart of this transformation lies data. While AI platforms offer off-the-shelf capabilities, they don't always address the most critical aspect—centralizing and preparing organizational data. For AI agents to perform optimally, they must be fueled by high-quality, trustworthy data, relevant to the enterprise's context. This is where many organizations face a significant challenge.

While public datasets power generic LLMs, enterprise-specific applications require fine-tuning models with internal data. Emerging techniques like retrieval-augmented generation (RAG) and knowledge graph-based RAG can help these agents adapt to an organization's specific needs. These techniques allow for real-time retrieval of relevant information to inform agent decision-making, making the agent more precise and contextually aware.

AI-ready data: The key to effective AI agents

For AI agents to truly drive value, organizations must first focus on making their data AI-ready. This involves more than just collecting data—it requires ensuring that the data is relevant, well-structured, and free from bias. Achieving this requires a robust data strategy, one that focuses on:

1. Centralizing data: Ensuring that all data sources across the enterprise are collected into a single source of truth
2. Data cleaning and normalization: Preparing the data for analysis, removing inconsistencies, and making it easy for AI agents to understand and learn from it
3. Creating trustworthy datasets: Data must be relevant to the task at hand and free from bias. For instance, deploying a digital worker in a finance department will require highly accurate and secure financial data.
4. Ensuring data governance: Implementing policies to manage data privacy, security, and compliance is critical, especially as AI agents interact with sensitive information
5. Monitoring data lineage and integrity: Tracking the origin, movement, and transformation of data, while maintaining integrity with systems like Master Data Management (MDM)

Without AI-ready data, even the most sophisticated agentic frameworks will struggle to deliver the promised benefits. But enterprises don’t need to “boil the ocean” to get started. You can begin small by focusing on areas where your data is already rich and complete—deploying agents in those specific functions to generate quick wins.

A phased approach to AI agent deployment

Our research, surveying over 1,400 enterprises, found that only 16% of organizations have a comprehensive AI data strategy in place. This signals a huge opportunity for those who can master the data challenge. By adopting a phased approach to AI agent deployment—starting with focused, high-impact areas—you can:

• Learn from early deployments
• Fine-tune your data strategies
• Scale agent usage across more complex functions

As AI agent platforms continue to evolve, the organizations that invest in AI-ready data today will be the ones best positioned to unlock the full potential of autonomous digital workers.

Data is the new currency for AI agents

The future of business will be shaped by the seamless collaboration between digital and human workers. AI agents will play a key role in this transformation, but their success will depend on how well enterprises manage their data. As organizations invest in agent platforms, they must also invest in creating AI-ready data—centralized, clean, and governed—to fuel these digital workers.

By focusing on getting your data house in order, you can ensure that AI agents become a competitive advantage, driving growth, innovation, and operational efficiency. The businesses that master this balance between platform and data will lead the way in the AI-driven future.

Visit our website for more information on how Iron Mountain InSight^® Digital Experience Platform (DXP) can help get your organization’s data AI-ready.