Agentic AI and the ability for AI agents to act autonomously within trusted boundaries are reshaping not just the way we interact with data, but the very roles and responsibilities across the data ecosystem.

Rather than simply automating isolated tasks, Agentic AI is poised to redefine how value is created from data by enabling new capabilities, accelerating insights, and expanding the reach of data-driven decision-making.

In this blog, we’ll explore how Agentic AI will disrupt traditional data roles, transform the data lifecycle, and unlock new possibilities that were once impractical.

Rethinking Data Roles in the Era of Agentic AI

Data Engineers: From Builders to Strategists

Data engineers will experience a disruption in many routine tasks with the introduction of AI agents. These tasks include creating ingestion pipelines, authoring new data transformation assets, performing multi-file refactoring, and even automated incident resolution. For instance, AI can already assist in coding ingestion pipelines from various sources and is rapidly being integrated into tools like dbt for authoring, documenting, and testing data transformations. This could lead to a significant enhancement in efficiency, potentially in the higher end of the 20%-80% range.

However, this doesn't mean data engineers will be replaced; instead, their roles will likely shift towards more strategic activities. They will focus more on designing overall data architectures, managing data platforms for performance and governance, building business automations based on data insights, and acting as enablement and support for the insight generation process. The importance of frameworks, tooling, and standards will be enhanced as they provide a consistent foundation for AI to operate effectively. Furthermore, AI can act as a co-pilot in tasks like semantic layer development and maintenance, enhancing their productivity.

Data Scientists: From Model Builders to Orchestrators

Agentic AI will redefine the role of data scientists. While foundational knowledge remains crucial, their focus will shift from solely solving predefined problems to proactively framing complex business challenges. This requires a deeper understanding of business nuances. Data scientists will need to upskill in areas like agentic AI frameworks, reinforcement learning, and multimodal AI. They will increasingly act as orchestrators of agentic AI systems, balancing technical frameworks, business goals, and ethical considerations. Their ability to fine-tune performance across multiple data sources and ensure data transparency and interpretability in multimodal AI will be enhanced in importance. Agentic AI is not expected to replace data scientists but rather elevate their role to a more strategic and coordinating position.

Data Analysts: From Dashboards to Dynamic Insights

Agentic AI offers a significant opportunity to enhance the productivity of data analysts by automating tasks and speeding up the time to generate insights. Their role in ensuring a strong data foundation – by preparing, cleaning, structuring, and augmenting data – becomes even more critical for the effective deployment of AI. AI can also enable data analysts to generate insights faster and potentially reduce reliance on dashboards in some cases.

Business Users: From Requestors to Explorers

AI agents, especially when combined with semantic layers, will disrupt the traditional reliance on technical teams for basic data access for business and non-technical users. Natural language querying, powered by AI and semantic layers, will enable non-technical users to explore data and discover insights through simple questions. This promises to enhance data accessibility and democratize data consumption. AI can also assist in rapidly creating business dashboards through generative AI, enhancing the speed at which business needs can be met.

Reimagining the Data Lifecycle with Agentic AI

In data preparation, AI agents can enhance the process by automating feature engineering, data cleaning, and transformation techniques. Orchestrator Agents can automate the ingestion and preprocessing of raw data, while Specialist Agents can apply AI-driven feature extraction.

Agentic AI will significantly enhance data analysis and insight generation. AI agents can automate self-service analytics, reducing dependency on data teams. They can perform real-time anomaly detection and provide predictive and prescriptive insights for business optimization.

Agentic AI promises to disrupt traditional manual data governance processes by automating access control, policy enforcement, regulatory compliance, and data security. Governance Agents can enforce security policies and access rules in real-time, automate compliance audits, and manage data masking and security policies.

AI agents, particularly Trust Agents, will enhance data trust by automating data quality validation, assigning trust scores to datasets, and ensuring regulatory compliance. AI-enhanced data provenance and lineage tracking will provide full visibility into data transformations.

Preparing for New Agentic AI Capabilities

Agentic AI will enable systems to make day-to-day work decisions autonomously, a significant shift from traditional analytics. Gartner predicts that 15% of such decisions will be made by agentic AI by 2028. The combination of AI and semantic layers will enable more accurate and context-aware natural language querying of data, making data exploration accessible to a wider audience.

AI can act as a co-pilot for data engineers and scientists, assisting in code generation, semantic layer creation, and providing explanations and fixes for errors. Companies can build their own internal AI agents to act as central hubs for knowledge and access across various applications and data stores. These agents could route questions to the appropriate systems and provide unified answers.

AI agents can enable real-time trust scoring of data, ensuring that AI models and BI systems use reliable and validated information. AI-powered tools can enable faster iterations in data modeling, dashboard creation, and overall analytics workflows, reducing technical bottlenecks and accelerating the delivery of insights. Semantic layers, crucial for effective AI interaction with data, help raise and ideally solve ambiguities in business terms and metrics, which LLMs alone struggle with.

The Future of Data Work

Agentic AI, when combined with strong data foundations and semantic models, will fundamentally reshape the data landscape. It will automate what can be automated. It will enhance what must be enhanced. And it will elevate human roles—moving data professionals closer to strategy, orchestration, and innovation.

The organizations that embrace this evolution—not just technologically, but culturally—will unlock faster, more accurate, and more impactful decisions at every level of the business. The age of data-driven enterprises is evolving. The future of work is agentic.

AI agents are poised to significantly affect various data roles and the overall data lifecycle, leading to disruptions, enhancements, and the enablement of new capabilities that were previously impractical.

Impact on Data Roles

  • Data Engineers: AI agents are expected to disrupt many routine tasks. These include creating ingestion pipelines, authoring new data transformation assets, performing multi-file refactoring, and even automated incident resolution. For instance, AI can already assist in coding ingestion pipelines from various sources and is rapidly being integrated into tools like dbt for authoring, documenting, and testing data transformations. This could lead to a significant enhancement in efficiency, potentially in the higher end of the 20%-80% range. However, this doesn't mean data engineers will be replaced; instead, their roles will likely shift towards more strategic activities. They will focus more on designing overall data architectures, managing data platforms for performance and governance, building business automations based on data insights, and acting as enablement and support for the insight generation process. The importance of frameworks, tooling, and standards will be enhanced as they provide a consistent foundation for AI to operate effectively. Furthermore, AI can act as a co-pilot in tasks like semantic layer development and maintenance, enhancing their productivity.

  • Data Scientists: Agentic AI will redefine the role of data scientists. While foundational knowledge remains crucial, their focus will shift from solely solving predefined problems to proactively framing complex business challenges. This requires a deeper understanding of business nuances. Data scientists will need to upskill in areas like agentic AI frameworks, reinforcement learning, and multimodal AI. They will increasingly act as orchestrators of agentic AI systems, balancing technical frameworks, business goals, and ethical considerations. Their ability to fine-tune performance across multiple data sources and ensure data transparency and interpretability in multimodal AI will be enhanced in importance. Data scientists will also play a critical role in ensuring the ethical development and governance of AI models, working alongside ethicists and regulators. Agentic AI is not expected to replace data scientists but rather elevate their role to a more strategic and coordinating position.

  • Data Analysts: Agentic AI offers a significant opportunity to enhance the productivity of data analysts by automating tasks and speeding up the time to generate insights. Their role in ensuring a strong data foundation – by preparing, cleaning, structuring, and augmenting data – becomes even more critical for the effective deployment of AI. AI can also enable data analysts to generate insights faster and potentially reduce their reliance on dashboards in some cases. Business Users/Non-Technical Users: AI agents, especially when combined with semantic layers, will disrupt the traditional reliance on technical teams for basic data access. Natural language querying, powered by AI and semantic layers, will enable non-technical users to explore data and discover insights through simple questions. This promises to enhance data accessibility and democratize data consumption. AI can also assist in rapidly creating business dashboards through generative AI, enhancing the speed at which business needs can be met.

Impact on the Data Lifecycle

  • Data Preparation: AI agents can enhance data preparation by automating feature engineering, data cleaning, and transformation techniques. Orchestrator Agents can automate the ingestion and preprocessing of raw data, while Specialist Agents can apply AI-driven feature extraction. Data Analysis and Insight Generation: Agentic AI will significantly enhance this stage. AI agents can automate self-service analytics, reducing dependency on data teams. They can perform real-time anomaly detection and provide predictive and prescriptive insights for business optimization.
  • Data Governance: Agentic AI promises to disrupt traditional manual data governance processes by automating access control, policy enforcement, regulatory compliance, and data security. Governance Agents can enforce security policies and access rules in real-time, automate compliance audits, and manage data masking and security policies.
  • Data Trust: AI agents, particularly Trust Agents, will enhance data trust by automating data quality validation, assigning trust scores to datasets, and ensuring regulatory compliance. AI-enhanced data provenance and lineage tracking will provide full visibility into data transformations.

New Capabilities Enabled by AI Agents

  • Autonomous Decision-Making: Agentic AI will enable systems to make day-to-day work decisions autonomously, a significant shift from traditional analytics. Gartner predicts that 15% of such decisions will be made by agentic AI by 2028.
  • Enhanced Natural Language Interaction: The combination of AI and semantic layers will enable more accurate and context-aware natural language querying of data, making data exploration accessible to a wider audience.
  • AI Co-pilots for Data Professionals: AI can act as a co-pilot for data engineers and scientists, assisting in code generation, semantic layer creation, and providing explanations and fixes for errors.
  • Internal Company AI Agents: Companies can build their own internal AI agents to act as central hubs for knowledge and access across various applications and data stores. These agents could route questions to the appropriate systems and provide unified answers.
  • Real-time Data Trust Scoring: AI agents can enable real-time trust scoring of data, ensuring that AI models and BI systems use reliable and validated information. Faster Iterations and Reduced Bottlenecks: AI-powered tools can enable faster iterations in data modeling, dashboard creation, and overall analytics workflows, reducing technical bottlenecks and accelerating the delivery of insights.
  • Improved Handling of Ambiguity: Semantic layers, crucial for effective AI interaction with data, help raise and ideally solve ambiguities in business terms and metrics, which LLMs alone struggle with.

Overall, AI agents, particularly when coupled with robust data foundations and semantic layers, will drive a significant evolution in data roles and the data lifecycle. They will automate many existing tasks, enhance efficiency and capabilities, and enable new ways of interacting with and leveraging data that were previously impractical or impossible. Contact sales to learn more about agentic AI.