Agentic AI and the Autonomous Enterprise: Why Humans Will Stay in the Loop

For GBS leaders under pressure to deliver agility and real-time insights, three converging factors make agentic AI especially timely.

• Explosion of data: both structured and unstructured, across complex ERP and legacy systems.
• Advances in computational power: enabling real-time processing at scale.
• Enterprise AI maturity: organizations have the platforms, models, and skills to begin integrating autonomy.

Data as the Foundation

Autonomy requires data, and poor data remains a critical barrier. Clean, trusted, and well-structured data is essential to prevent agents from making bad decisions. Enterprises are increasingly adopting data platforms, data meshes, and focused domain-specific models alongside large language models (LLMs). Early techniques like retrieval-augmented generation (RAG) highlight the importance of precise, contextual data in reducing bias and hallucinations. The role of the data engineer is, therefore, rising in importance, ensuring robust pipelines and domain-specific data stores to fuel reliable AI systems.

Use Cases Emerging

Agentic AI is already finding applications across the enterprise:

• Finance: autonomous closing processes reduce cycle times from weeks to days.
• Procurement: real-time supply chain monitoring flags risks before disruptions occur.
• HR services: responsive systems address employee needs in real time.
• Customer service: intelligent agents handle Tier 1 interactions, escalating only when necessary.
• Compliance: automated audit trails and transaction monitoring improve transparency.
• R&D: scanning patents and literature uncovers innovation opportunities faster.
  These early pilots illustrate the broad potential and the boundaryless nature of autonomy.

Challenges and Cautions

Autonomy is not without risks. Systems acting without sufficient oversight can amplify errors, introduce bias, or misinterpret context at scale. In sensitive domains like healthcare or finance, consequences could be severe. That’s why most experts argue for human-in-the-loop (HITL) approaches, where humans provide context, empathy, accountability, and ethical checks. The future will likely be hybrid, blending human augmentation with machine autonomy to balance speed, safety, and trust.

Leadership and Governance

GBS leaders face a cultural as much as a technical transformation. Autonomy requires not just systems management but ecosystem leadership – championing innovation, ethics, and trust. Governance must be “AI-native,” embedding transparency and accountability from the start.

The Road Ahead

The path to an autonomous enterprise is not linear. Short-term steps include smart data pipelines and multiple collaborating agents; longer term, broader functions such as finance, supply chain, and R&D could become self-directed with minimal oversight.

Read our new Research Insight Report: Agentic AI in Global Business Services: Unlocking the Next Wave of Transformation