Davi Almeida

The dawn of AI assistants has revolutionized operations in asset-heavy industries, enabling faster, safer, and more productive workflows. This workshop explores how organizations can achieve transformative efficiency by building team AI capabilities, identifying high-impact opportunities, and bridging data—from corporate process data to operational data—with enterprise decision-making through intelligent AI assistants.

This workshop will provide you with insights to learn how GenAI can reduce workhours in your organization. During the workshop you will build a prioritized roadmap for GenAI deployment in your processes and organization, focusing on solutions that address: high FTE burden, high error cost, frequent exceptions, and availability of quality operational signals.

Discover how to prepare your organization for AI transformation and deploy intelligent assistants to:

• Cut valuable manhours in highly manual processes that require inefficient integrations, extensive data manipulation, and repetitive knowledge work
• Digitize tribal knowledge by capturing experienced workers' insights and integrating them into AI-powered decision workflows that accelerate operations
• Make operational knowledge searchable and actionable, unifying data sources with enterprise-wide systems through robust data governance and contextualization layers
• Meet regulatory requirements through automated reporting, consolidated metrics, and exception-based workflows that reduce manual effort from hours to minutes
• Optimize inspection and maintenance workflows to reduce unplanned downtimes and enable predictive maintenance through quality operational signals
• Accelerate back-office processes by coupling operational data to billing, revenue recognition, and compliance systems with built-in governance

We’ll also explore a real-world case study with ProPetro sharing their transformation journey—demonstrating how they achieved ~50% time savings, reduced operational staff requirements, and improved billing accuracy by building foundational data quality, process redesign, and scalable operating models—now positioning for GenAI enhancement through intelligent copilots for exception handling.