Breaking The AI Adoption Paradox

This week, I sat down with the CHRO of a Fortune 100 company, a traditional Midwestern enterprise with deep roots and an even deeper culture of cautious deliberation.

The conversation revealed a paradox many legacy organisations face:

The fear of not understanding AI is the very barrier preventing them from learning how to use it effectively.

“We want to be responsible,” they told me.

“But how do we invest in something our people don’t understand and frankly, neither do we?”

This isn’t a technology problem. It’s a capability building problem masked as a technology problem.

Three Pathways Forward:

1. Start with “AI Apprenticeships,” Not Transformation Programs

Rather than launching an enterprise-wide AI initiative, this company could establish small, cross-functional “AI apprenticeship” teams of 10-15 people from operations, finance, HR, and legal. Give them a constrained problem (e.g., “Reduce invoice processing time by 30%”) and 90 days to experiment with AI tools under expert guidance. The goal isn’t just solving the problem, it’s creating 15 internal evangelists who deeply understand both AI’s capabilities and its limitations. These individuals become the connective tissue between technology and culture.

2. Build an “Ethical AI Council” Before Building AI Solutions

The CHRO’s concern about ethics isn’t a blocker, it’s an asset.

Leading companies are establishing cross-functional AI ethics councils that include legal, HR, IT, customer experience, and frontline employees. This council doesn’t just review AI applications; it actively defines the company’s AI principles and creates decision frameworks.

One Fortune 500 manufacturer I worked with created a simple “AI Ethics Scorecard” consisting of five questions every AI use case must answer before approval. This transformed ethics from a vague concern into a concrete capability, accelerating (not slowing) adoption.

3. Embrace “Bounded Boldness” - Pilot in the Periphery, Not the Core

Traditional companies often make two mistakes: piloting AI in areas too trivial to matter, or deploying it in processes too critical to risk.

The answer is the middle path. Identify high-impact, moderate-risk processes - perhaps candidate screening for non-critical roles, or predictive maintenance for non-safety-critical equipment. These pilots deliver meaningful ROI while keeping organizational risk manageable. Success here builds confidence for larger moves.

The Real Insight

What struck me most was the CHRO’s self-awareness. They recognised that their culture built on reliability, personal relationships, and careful deliberation is both their strength and their challenge in the AI era.

But here’s the reframing: companies don’t need to change their culture to adopt AI. They need to adopt AI in a way that honours their culture.

For this Midwestern enterprise, that means building AI literacy through apprenticeship, establishing ethical guardrails before taking risks, and proving value in contained experiments. It’s not about becoming Silicon Valley. It’s about becoming a better version of themselves—with AI as an enabler, not a disruptor.

The path forward isn’t about moving fast and breaking things. It’s about moving deliberately and building things that last.

What approaches have you seen work for traditional organizations navigating AI adoption? The conversation around ethical and effective AI implementation is just beginning.

Reply here or reach out directly to James Absalom, Chief Commercial Officer - International at ZRG.