The transition from passive data reporting to active operational execution is accelerating as artificial intelligence integrates into the core of enterprise analytics. According to new research from Information Services Group (ISG), analytics and business intelligence software are evolving into the foundational layer for AI-enabled workflows that do more than interpret data; they are beginning to execute the actions required to implement decisions. This shift is driven by the rise of agentic AI, which moves the technology beyond simple insight generation toward autonomous coordination. As enterprises seek to bridge the gap between seeing a trend and acting on it, the software landscape is undergoing a fundamental transformation in how data is prepared, analyzed, and ultimately utilized for strategic decision-making.
The Shift Toward Agentic AI and Automated Execution
The 2026 ISG Buyers Guides™ for Analytics indicate that AI and machine learning have already become standard components for generating insights within business intelligence tools. However, the next phase of evolution involves the widespread deployment of AI agents. ISG predicts that by 2028, almost all analytics providers will utilize AI agents to automate and accelerate various analytics processes. This development marks a departure from traditional models where users must manually request analysis; instead, AI is positioning analytics as an automated, continuous source of insights.
This evolution is heavily linked to the emergence of agentic AI, which allows systems to link analysis directly to execution. By coordinating the specific actions necessary to fulfill a decision, these systems aim to reduce the latency between data discovery and operational response. Furthermore, generative AI is addressing the historically time-consuming bottleneck of data preparation. Through self-service tools, enterprises are using generative AI to suggest ways to combine datasets and construct semantic models, theoretically streamlining the path from raw data to actionable intelligence.
Evaluating the Analytics Provider Landscape
As the technology matures, ISG suggests that enterprise buyers must look beyond basic reporting to evaluate how platforms support AI-enabled decision support. Key technical considerations for IT leaders now include data preparation capabilities, embedded analytics, collaboration features, and the depth of integration with existing enterprise systems. Crucially, the research advises organizations to scrutinize provider roadmaps specifically for generative AI, agentic AI, and automated insights, while maintaining a strict focus on governance and transparency to ensure trust in automated decisions.
In the competitive landscape of established providers, Oracle was identified as the top Overall Leader across all ISG Buyers Guides. In the general Analytics guide, Oracle was followed by Domo and IBM, while the AI Analytics guide saw IBM and AWS following Oracle. Sector-specific leadership varied: InterSystems and SAS led in Healthcare Analytics, Microsoft and SAP led in Manufacturing Analytics, and SAP and Infor led in Retail Analytics. For those looking at emerging technologies, Kyvos was named the top Overall Leader in the Analytics Emerging Providers guide, followed by Sigma and Hex.
Key Takeaways
- ISG forecasts that by 2028, nearly all analytics software providers will employ AI agents to automate and accelerate analytics processes.
- Generative AI is being utilized by enterprises to streamline data preparation by suggesting methods for constructing semantic models and combining datasets.
- Oracle was ranked as the top Overall Leader across all ISG Buyers Guides covering established analytics providers.
TechInsyte's Take
In our view, the ISG research highlights a critical pivot point for enterprise IT: the move from "insight-as-a-service" to "action-as-a-service." For years, the primary challenge for CIOs has been the "last mile" of analytics—the gap between a dashboard showing a problem and a human operator executing a solution. The move toward agentic AI suggests that software vendors are attempting to close this gap by embedding execution logic directly into the analytics stack. This signals a significant increase in the complexity of governance requirements. As analytics move from being a tool used by humans to an autonomous agent acting on behalf of the business, the focus for CISOs and IT leaders must shift from data accuracy to "agentic integrity"—ensuring that the automated actions taken by these agents align with corporate policy and risk thresholds.
Questions & Answers
How will the role of AI agents change the traditional analytics workflow by 2028?
According to ISG, the integration of AI agents will shift analytics from a reactive process—where users must manually request specific reports or insights—to an automated, proactive source of insights that can coordinate the actions required to implement decisions.
What specific technical capabilities should enterprises prioritize when selecting new analytics platforms?
Buyers should evaluate fundamental capabilities such as data preparation, collaboration features, and embedded analytics. Additionally, ISG recommends examining provider roadmaps for generative AI, agentic AI, and automated insights, alongside robust governance and transparency frameworks.
Which providers are currently leading in the specialized sectors of Healthcare and Manufacturing analytics?
In the Healthcare Analytics segment, InterSystems and SAS were identified as the leaders following Oracle. In the Manufacturing Analytics segment, Microsoft and SAP were named as the leaders following Oracle.
How is generative AI specifically addressing the data preparation bottleneck in enterprise environments?
Generative AI is being used to provide self-service tools that assist enterprises in streamlining the data preparation process by suggesting ways to combine datasets and construct semantic models.
Source: Businesswire
