AI Agents in Enterprises: A Game Changer for Data & Analytics

AI agents in the field of Data & Analytics are evolving from a future concept into a concrete lever for greater efficiency, improved decision quality, and faster responsiveness. They understand analytical questions, access data products and business semantics from systems such as SAP Business Data Cloud, autonomously conduct analyses, and deliver precise, transparent recommendations for action. Organizations that establish an AI ready data foundation early on and deploy AI agents strengthen their competitiveness and lay the groundwork for a secure, efficient, and scalable future.

In the following article, Five Questions, Five Answers on AI Agents in Enterprises, Rico Schirmer, Associated Partner and Lead Service Unit SAP Data Analytics, and Li Chen, Manager and Lead Topic AI Analytics at MHP, explain what is already possible today, where the biggest challenges lie, and how companies can use AI agents in a targeted way to transform their data analytics.

1. What can AI agents achieve today in the field of Data & Analytics?

Modern AI agents are now capable of understanding complex analytical questions, independently identifying relevant data sources, performing analyses with both domain and technical expertise, and — while adhering to governance principles such as human-in-the-loop — triggering concrete processes. Advanced agents can even autonomously derive analytical hypotheses from predefined business objectives, continuously validate them, and initiate actions when required.

A practical example is a sales agent: it continuously monitors data products on market sentiment and sales volumes in SAP Business Data Cloud, checks their plausibility, combines them with internal business reports, and automatically generates status reports for controlling or management. If the agent detects negative market sentiment, for example, it identifies deviations or outliers in sales at an early stage and informs the responsible stakeholders with alerts and concrete countermeasures. This makes it possible to measurably reduce risks — with minimal additional time and staffing requirements.

2. What concrete benefits do AI agents offer to teams and decision-makers?

AI agents take over time-consuming tasks along the entire analytical value chain — from data discovery and analysis to reporting. As a result, teams gain more room for creative and strategic activities: engaging with people, asking the right questions, and making well-founded decisions. Analysts and executives receive relevant analytical reports within minutes and around the clock via self-service.

Especially in volatile markets, this creates a decisive advantage: companies can act faster while relying on a solid and consistent data basis. The result is more efficient processes, reduced risks, and noticeable relief for employees across the organization.

3. Where do the biggest challenges arise when introducing AI agents?

In practice, the main challenges usually lie less in the AI agent technology itself and more in data quality, data accessibility, and governance.

AI agents depend on accessible, consistent, and semantically rich data. In many organizations, however, the data landscape has evolved over time and is highly fragmented. Such data silos make it difficult for agents to reliably find relevant information, correctly identify relationships, and deliver high-quality results.

Another challenge is insufficient or inconsistent business semantics. When key figures such as revenue, margin, or planned values are defined differently across departments, contradictory results quickly emerge. A lack of semantic consistency and ontologies reduces the discoverability of KPIs and data products.

Equally critical is a robust governance concept. Without clear authorization rules, transparency, and traceability of agent activities, the risk of uncontrolled or insufficiently secured actions increases.

4. How can companies best overcome these challenges?

The most important step is to build an AI ready data foundation. To achieve this, companies should make data from different source systems and domains available as data products via a Business Data Fabric and enrich them with consistent business semantics. This creates the foundation on which AI agents can operate reliably and at scale.

In an SAP centric architecture, for example, data from SAP and non SAP applications can be integrated into SAP Business Data Cloud, enriched with machine learning using SAP Databricks, and semantically modeled with SAP Datasphere.

On this basis, AI agents access data and interfaces via clearly defined, governance-secured tools. Standards such as the Model Context Protocol support the connection between agents, tools, and data sources and promote scalability and interoperability.

In addition, a well-designed AgentOps model ensures continuous monitoring, further development, and optimization.

5. What first steps should companies take now to successfully introduce AI agents?

The best approach is a gradual one with clear prioritization. A sensible starting point is the development of a semantically rich Business Data Fabric, for example based on SAP Business Data Cloud. Building on this, companies should identify and implement concrete, value adding AI agent use cases in a targeted manner. At the same time, it is important to establish a governance and agent ops model at an early stage.

This enables a secure, scalable transition to agent based data analytics without overburdening teams. Companies thus create the conditions to fully leverage the potential of AI agents and take their competitiveness to a new level.

Further information on the topic: SAP Data Analytics | MHP – A Porsche Company