From passive analytics to agentic AI execution
Agentic AI execution is the use of artificial intelligence agents that not only analyze enterprise data but also make decisions and perform tasks autonomously within operational workflows, closing the gap between insight generation and real-world action across complex business systems. For years, enterprise automation platforms have focused on surfacing dashboards, alerts, and reports. That approach improved visibility but still left humans to rekey findings into separate systems and coordinate the actual work. Agentic AI shifts this pattern by embedding task-oriented agents directly into core platforms, where they can initiate processes, update records, and coordinate workflows in response to changing conditions. This model supports autonomous task execution in areas such as maintenance, construction coordination, and asset operations, while still keeping humans in control of approvals and edge cases. The result is a gradual move from AI as an advisory layer to AI as an operational participant inside the enterprise stack.
Procore’s CDE and Datagrid: AI coworkers in construction
Procore’s connected Common Data Environment (CDE) shows how agentic AI can be built into sector-specific platforms. By combining project data, workflows, BIM models, and asset information in one environment, the CDE supplies the structured context that AI agents need for reliable reasoning and execution. Procore embeds technology from Datagrid, an agentic AI platform it acquired in January 2026, to create what it calls “agentic AI coworkers” that automate construction workflows from design approval through handover. These coworkers help with tasks such as RFI creation, responses, submittal review, and issue resolution directly inside Procore. According to Buro Happold CTO Alain Waha, “We’re on track to reduce construction administration work with respect to RFI creation, response, and submittal review by 50%.” The system can also surface existing answers, spot discrepancies between design and field execution, and keep a defensible audit trail for compliance-heavy projects.
Shell and C3 AI: Predictive maintenance AI at global scale
Shell’s expanded collaboration with C3 AI illustrates agentic AI in industrial reliability. Shell already uses C3 AI Reliability on Microsoft Azure to monitor equipment and support predictive maintenance AI across global operations. The programme, which began in 2018, now tracks more than 13,000 pieces of equipment and has grown from earlier deployments that covered upstream, manufacturing, and integrated gas assets. Under a new multi-year agreement, Shell is moving beyond anomaly detection toward AI agent-based root cause analysis and remediation. The C3 Agentic AI Platform will help diagnose abnormal behaviour and suggest or trigger remedial actions within established maintenance workflows. According to Deloitte, unplanned downtime costs industrial manufacturers about USD 50 billion (approx. RM230 billion) each year, making reliability a high-impact target for autonomous task execution. MarketsandMarkets expects the predictive maintenance market to expand sharply, driven by IIoT, digital twins, and AI-driven models that underpin these types of programmes.
Closing the gap between AI insight and operational change
Both Procore and Shell show how enterprise automation platforms are tackling the long-standing gap between AI insights and operational implementation. In construction, Procore’s CDE links RFIs, submittals, BIM models, and site activity so AI agents can act where the work lives instead of only issuing recommendations. In industrial operations, Shell’s predictive maintenance AI moves from sensors and alerts to agent-driven diagnosis and remediation steps that are embedded in reliability workflows. This model keeps final authority with engineers and project teams but allows AI to handle repetitive, cross-system coordination. It also creates richer audit trails, supporting regulatory compliance and continuous improvement. As more enterprises adopt agentic AI execution, the distinction between analytics, decision-making, and task completion will blur, with AI systems increasingly responsible for orchestrating day-to-day operations while humans focus on complex judgment and strategic direction.
