Enterprise interest in AI agents is soaring, and for good reason. After years of automation efforts and rising pressure to deliver AI success, CIOs are looking for smarter, more scalable ways to accelerate decision-making, reduce operational drag, and unlock value from their data.

At the centre of this shift are AI agents; autonomous, task-driven systems designed to assist with decision-making, content processing, and real-time responses. Their potential to streamline knowledge work, accelerate decisions and reduce operational friction is undeniable. But the reality of enterprise AI adoption is nuanced.

To unlock value from enterprise AI applications, CIOs must critically assess whether their organisation has the foundations in place, starting with the data.

What are AI agents and why do they matter?

At their simplest, AI agents are software entities that act on instructions and inputs to carry out a task or goal. In the context of enterprise artificial intelligence, these agents are becoming increasingly sophisticated, moving beyond simple automation to support complex knowledge work and decision-making.

Examples include:

  • Virtual assistants capable of retrieving policy documents or responding to queries
  • Agents that automatically summarise legal or risk documentation
  • Workflow agents that initiate tasks based on pre-defined business signals

According to Gartner, 85% of customer service leaders will explore or pilot customer-facing conversational GenAI in 2025.

While the business case is strong with reduced bottlenecks, improved speed to action, and cost savings, successful implementation requires an intelligent, compliant foundation of enterprise data.

Is your enterprise ready? Use this AI agent checklist

AI agents have the potential to transform productivity across knowledge work. In a recent trial conducted by the UK Government Digital Service, 14,500 civil servants using an AI tool reported saving an average of 26 minutes per day. More than 70% of participants noted reduced time spent on routine tasks and information searches.

However, realising similar gains at scale requires more than deploying an agent. It demands a deliberate strategy, supported by the right data infrastructure and enterprise governance.

AI agents are only as effective (and trustworthy) as the data and policies that guide them. CIOs should ensure the following capabilities are in place before deployment:

  • Federated access to enterprise content, across both structured and unstructured data sources.
  • Contextual understanding of business information and workflows.
  • Robust governance and access controls to protect sensitive data and ensure compliance.
  • Auditability and traceability, enabling outputs to be explained and verified as needed.

AI agents are not standalone capabilities. They operate within the broader enterprise AI platform. Without these capabilities, AI agents risk becoming fragmented, unreliable, or even harmful, particularly in highly regulated environments.

The risk landscape: What keeps CIOs up at night

While the promise of AI agents is compelling, the path to enterprise adoption is fraught with risk. As with any emerging technology, these risks are multifaceted: technical, strategic, operational and reputational.

CIOs must consider the following key concerns when evaluating enterprise AI solutions:

  • Hallucinations and bias: AI agents can produce plausible but incorrect outputs if the underlying data is incomplete, unstructured, or contextually misaligned. When agents draw from ungoverned sources, the risk of biased or misleading outcomes increases.
  • Shadow deployments and tool sprawl: In the absence of clear governance, business units may adopt AI agents independently, bypassing IT oversight. These unsanctioned tools create fragmented risk across the organisation, introduce inconsistent security protocols, and make compliance difficult to monitor.
  • Privacy and security risks: AI agents often require broad data access to perform effectively. Without rigorous controls in place, they may inadvertently expose sensitive or regulated data. This is particularly concerning in environments handling personally identifiable information (PII), protected health data, or legal content.
  • Regulatory scrutiny and liability: Global frameworks such as the EU AI Act, and sector-specific data regulations, are placing increasing emphasis on transparency, accountability, and explainability. CIOs must ensure AI agents can produce auditable outputs and operate within well-defined ethical and legal boundaries.

According to Gartner, over 60% of AI projects will fail to meet business SLAs and be abandoned by 2026, often due to poor data quality or unclear value delivery.

How EncompaaS prepares the enterprise for AI agents

EncompaaS helps enterprises move from AI experimentation to confident, compliant deployment. Its intelligent information management platform delivers the trusted, curated data foundation that AI agents — like Microsoft Copilot—require to deliver accurate, secure, and business-ready outcomes.

EncompaaS creates a unified, AI-optimised data layer across your enterprise, enabling Copilot Agents to access and interact with content—no matter where it resides—without the need for costly migration or manual remediation. This means you can ask smart questions of your data and receive reliable answers instantly.

Key capabilities include:

  • Federated data discovery and access from multiple systems — without moving the data.
  • Automated classification, enrichment and normalisation, turning fragmented content into trusted AI inputs.
  • Integrated governance with access controls, policy enforcement, and auditability.
  • A secure, compliant data foundation, enabling responsible automation and AI-driven decision-making.

EncompaaS enhances the value of your AI agents by ensuring the information they access is correct, relevant, complete, secure, and unbiased.

With EncompaaS Copilot Agents, users can generate accurate, contextual answers from governed enterprise content — without manually curating or preparing the underlying documents. This transforms disorganised data into a strategic asset, accelerating AI adoption while ensuring outcomes are explainable, compliant, and trusted.

Intelligent agents require intelligent infrastructure

AI agents do not succeed in isolation. They depend on quality data, clear governance and the right infrastructure. For CIOs leading AI initiatives, this means embedding a data-first strategy into every phase of AI development and deployment.

EncompaaS supports this by delivering the enterprise AI tools and governance needed to enable safe, scalable adoption, preparing data to support not just the next AI use case, but the entire lifecycle of innovation.

Contact EncompaaS to discover how your organisation can build a trusted, compliant data foundation for AI agents ready to support the next generation of enterprise AI adoption.