Combining Information & Mechanics To Build Agents That Don’t Get Laid Off

2026-06-20 · Source: High ROI AI · Field: Business & Management — Corporate Strategy & Leadership, Project & Product Management, Artificial Intelligence & Machine Learning · Depth: Intermediate, long

Summary

The article discusses the critical distinction between "knowledge of" and "capable of" for both human professionals and AI agents, arguing that success in the enterprise requires understanding and demonstrating the mechanics of value creation, not just possessing information. It highlights how many AI initiatives and professionals fail to connect their work to tangible business outcomes like revenue growth, leading to a "disconnect in perception" with C-suite leaders. The author recounts a personal layoff experience and a failed consulting pitch to illustrate this gap. The article proposes that AI agents, like people, need both information (description) and mechanics (capabilities) to deliver real work and justify investment. It criticizes the "AI psychosis" where LLMs persuade users of capabilities they don't possess and advocates for a new approach using Structural Causal Models (SCMs) and knowledge graphs to define intents, outcomes, and their connection to value, enabling agents to explain their ROI.

Key takeaway

For AI/ML Directors and consultants aiming to secure executive buy-in for AI initiatives, you must shift from describing technology to demonstrating its causal link to tangible business outcomes. Focus on defining agent intents, measurable outcomes, and their direct contribution to revenue or strategic goals using Structural Causal Models. This approach helps you articulate clear ROI, avoiding the "AI psychosis" trap where perceived capabilities don't translate to reliable enterprise value.

Key insights

Enterprise success for AI and people hinges on demonstrating causal links between work, capabilities, and tangible business outcomes, not just possessing knowledge.

Principles

• Knowledge alone does not deliver outcomes.
• Causality connects work to desired business value.
• AI agents need information and mechanics.

Method

Deploy agents by defining intents, outcomes, and their connection to value creation using Structural Causal Models (SCMs) and knowledge graphs to enable explainable ROI.

In practice

• Evaluate agents for "knowledge of" and "capable of".
• Connect technical work to C-suite desired outcomes.
• Use SCMs to define agent value and explain ROI.

Topics

• AI Agents
• Value Creation
• Structural Causal Models
• Knowledge Graphs
• Enterprise AI
• ROI Measurement

Best for: AI Product Manager, Director of AI/ML, Consultant, Executive

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Editorial summary, takeaway, and curation by AIssential. Original article published by High ROI AI.