“If It Works, Don’t Touch It” and Other Excuses That No Longer Hold — Jarroba

2026-07-18 · Source: Towards AI - Medium · Field: Business & Management — Corporate Strategy & Leadership, Human Resources & Workforce Development, Operations & Process Management · Depth: Intermediate, extended

Summary

Corporate inertia, characterized by phrases like "If it works, don't touch it," increasingly hinders AI adoption, despite the falling cost of change and rising cost of stagnation. This resistance fosters "Shadow AI," where employees use unapproved tools, leading to significant risks and missed opportunities. Data shows 49-81% of employees use unsanctioned AI, 66% pay for it themselves, and 25% of organizations lack any AI policy. This lack of visibility increases data breach costs by an average of \$670,000 for high Shadow AI usage and wastes up to 40% of productive potential. The article highlights a "Solow paradox" where AI's 5.4% average time saving is concentrated, with 74% of economic value captured by just 20% of organizations, leading to hidden productivity and employee disengagement.

Key takeaway

For Directors of AI/ML or VPs of Engineering facing organizational resistance to AI adoption, recognize that traditional excuses for inaction now carry higher costs than measured change. Your teams are likely using "Shadow AI," creating unseen risks and lost value. You should advocate for official, safe AI channels by framing proposals in terms of cost reduction and risk mitigation, demonstrating success with small-scale pilots, and using data to support your arguments. This approach mitigates security risks and reveals hidden productivity.

Key insights

Corporate inertia and misaligned incentives drive widespread "Shadow AI," increasing risks and obscuring productivity gains.

Principles

• Prioritize visible risk of changing over invisible risk of not changing.
• Cost of changing has fallen faster than cost of not changing.
• Share upside to incentivize productivity reporting.

Method

Individuals can counter organizational inertia by reframing arguments to cost/risk, using data, demonstrating on a small scale, and protecting their energy and judgment.

In practice

• Frame updates as cost reductions or security gap closures.
• Use data reports to quantify inertia's costs.
• Pilot new approaches in small, low-risk areas.

Topics

• Corporate Inertia
• AI Adoption
• Shadow AI
• Organizational Change
• Productivity Paradox
• Data Governance

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.