The Work That Used To Get You Promoted Is What AI Agents Are Replacing First

2021-11-04 · Source: High ROI AI · Field: Business & Management — Human Resources & Workforce Development, Corporate Strategy & Leadership, Operations & Process Management · Depth: Intermediate, medium

Summary

The article identifies a significant hiring problem where businesses prioritize and reward high-throughput, "80% code" engineers and commodity consulting work, which AI agents are now rapidly automating. Based on an analysis of 217 engineers across four companies, the author found that individuals spending over 80% of their time coding received higher performance reviews and promotions, despite those spending less than 50% of their time coding often contributing to higher-value initiatives. This creates a system where the work most easily automated by AI is also the most visible and rewarded, leading to a loss of critical institutional knowledge related to judgment and strategic thinking. The author argues this represents a fundamental methodology shift, not merely a reskilling challenge, necessitating a re-evaluation of hiring funnels and incentive structures to value "taste and judgment" over raw output.

Key takeaway

For Directors of AI/ML or VPs of Engineering adapting to the AI economy, your current hiring and promotion frameworks likely reward skills AI agents will soon automate. You must redefine what constitutes high-value engineering or consulting work, shifting focus from coding throughput or commodity tasks to "taste and judgment." Prioritize identifying and retaining senior talent capable of articulating value beyond visible output, and rebuild assessment processes to quantify and hire for these critical, often overlooked, capabilities.

Key insights

Businesses reward easily automatable work, overlooking high-value judgment and strategic thinking critical for the AI-driven economy.

Principles

• Visible work often masks true value.
• AI shifts value from throughput to judgment.
• Institutional knowledge is easily lost.

Method

Map workflows task-by-task to identify agent capabilities, limitations, and ethical boundaries. Quantify "taste and judgment" for roles and create assessment mechanisms for hiring.

In practice

• Redefine "10X engineer" for agentic environments.
• Rebuild hiring processes for "taste and judgment."
• Quantify innovation and creativity for budget.

Topics

• AI Agents
• Workflow Automation
• Talent Strategy
• Engineering Leadership
• Consulting Transformation
• Value Definition

Best for: CTO, Director of AI/ML, VP of Engineering/Data, Consultant

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