Innovapptive, the pioneer in Connected Worker Frontline Execution, and Seeq, a global leader in Industrial AI, have announced a strategic partnership designed to bring industrial AI insights directly to the plant floor.
The collaboration, supported by Accenture, debuted at the IQPC Connected Worker Energy Summit, introducing a closed-loop system that turns complex analytical insights into immediate, documented operational action.
Despite billions invested in AI and predictive analytics, industry data shows more than 70% of predictive alerts fail to prompt timely field responses. This “execution gap” leaves organizations unable to fully capitalize on their AI investments.
The Seeq-Innovapptive partnership bridges this divide with a streamlined workflow:
Detection: Seeq’s AI platform identifies operational anomalies in real time.
Activation: Automated triggers push AI-guided work instructions to technicians’ mobile devices via Innovapptive’s platform.
Execution: Frontline teams act on the instructions, while feedback loops validate resolutions and refine AI models.
“Seeq has always been focused on helping the industrial sector accelerate the path from insight to action and removing the silos that hinder progress,” said Lisa Graham, CEO of Seeq. “By partnering with Innovapptive, we are further ensuring that the intelligence generated within Seeq doesn't just sit on a dashboard; it reaches the person best equipped to solve the problem.”
“The frontline worker is the most important sensor in any industrial operation,” said Sundeep Ravande, CEO of Innovapptive. “By connecting Seeq's industrial analytics with our AI-powered mobile workflows, we create a closed-loop execution system that helps energy companies turn insights into action—and action into measurable EBITDA impact.”
Key Benefits Include reduced unplanned downtime through faster anomaly-to-action workflows; enhanced safety with AI-driven work instructions tailored to detected faults; improved data integrity via field feedback loops that enhance Seeq’s machine learning models.
