At Autonomous Minds, our mission is to eliminate tools and automate all corporate overhead. However, in our journey, we encountered the productivity paradox—a challenge that arises when AI tools promise efficiency but instead introduce more complexity. We initially attempted to automate overhead functions using AI productivity tools, driven by the hype and impressive social media demos. The reality, however, was far from ideal. In an experiment to automate a Sales Operations Manager role, we ended up with three co-pilots and 17 agents, each performing a single task just adequately. While this approach generated some short-term enthusiasm, it quickly became clear that the solution was:

• Too complex
• Unreliable
• High-maintenance
• Overwhelming for non-technical users

This inefficiency reinforced the productivity paradox, proving that AI tools alone are not the answer to enterprise automation. This problem inspired the inception of Autonomous Minds.

Findings and Requirements for Effective AI Automation

In a previous blogpost, we explored the importance of unified, interconnected data objects as the foundation for automation. However, a critical gap remained—AI tools powered by Generative AI (LLMs) struggle with long-term planning.

While long-term planning at the model layer (incl. hierarchical planning) is crucial in domains like robotics and autonomous driving, we realized that in enterprise AI, it can be achieved outside the model layer. The key is Dynamic Data Models.

What Are Dynamic Data Models?

Dynamic Data Models are representations of business processes and their data objects. Most organizations—whether large enterprises or SMEs—have at least some level of process definition in place. Leveraging this, we expanded our bespoke AI data layer capable of:

1. Ingesting Data from multiple sources (e.g., CRM, ERP, vector databases, documents)
2. Integrating and Connecting Data Objects to form holistic business workflows through relationships
3. Automatically Deriving Dynamic Data Models from structured and unstructured inputs

Using a combination of Machine Learning and Generative AI, we extract information from:

• Enterprise tools (e.g., Salesforce, HubSpot)
• Documents (e.g., PDFs containing process definitions)
• Human input (e.g. text, voice)

Through this approach, we can autonomously build, refine, and execute business processes.

By implementing DynamicData Models, we achieve:

• Autonomous Understanding of Business Processes
  • Extracts process structures, stages, and dependencies across tools and documents
• Long-Term Execution of Recurring Workflows
  • Handles complex business operations over extended periods (e.g., product launches, sales processes, compliance procedures)
• Continuous Process Optimization
  • Learns from execution signals and patterns to suggest improvements and automate process and workflow refinements‍

Advancing AI-Powered Enterprise Automation

The combination of data ingestion, integration, and Dynamic Data Models enables a significant leap toward eliminating tools and automating corporate overhead.

By shifting focus from tool-based AI to data enabled and process-driven AI, Autonomous Minds is pioneering a new era of enterprise automation—where our product doesn’t just assist, but takes ownership of workflows, ensures long-term consistency, and eliminates inefficiencies at scale.

• Dr. Andreas Koeberl