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Navigating the In-Between: Strategic Transformation When the Old Model Still Pays the Bills

The most dangerous position in any industry is not obsolescence—it is near-obsolescence. When the existing model still generates sufficient revenue to fund operations but is clearly insufficient to sustain the next decade, executives face a uniquely difficult leadership challenge: transforming at speed without the urgency of crisis, and without the luxury of starting over.

Sep 12, 2026

The Case for Slow: Why Sustainable Learning Transformation Demands Deliberate Friction

The startup-influenced obsession with rapid iteration has migrated from product development into corporate learning strategy, producing organizations that move quickly, adapt constantly, and change very little. Sustainable transformation, the evidence increasingly suggests, requires something that speed-optimized systems are designed to eliminate: deliberate friction, genuine resistance, and the patience to let change consolidate before accelerating. The organizations that have learned this less

Sep 11, 2026

Sacred Practices, Hidden Liabilities: A Diagnostic Guide for Auditing the Assumptions Behind Your Organization's Best Thinking

Every organization carries a collection of practices so deeply embedded in its operations that they are no longer recognized as choices—they are simply treated as the way things are done. This article offers learning leaders and executives a structured diagnostic approach to identifying which of those practices have outlived their original logic, along with real-world examples of industries that unlocked competitive breakthroughs by doing the opposite of what they had always done.

Aug 04, 2026

Personalized by Algorithm, Left Behind by Design: The Hidden Equity Problem in AI-Driven Learning

Artificial intelligence promises to tailor instruction to every learner's unique needs, yet mounting evidence suggests these tools frequently deepen the very disparities they claim to eliminate. A closer examination reveals implementation blind spots, infrastructure inequities, and underestimated human variables that no algorithm has yet been designed to solve.

Jul 11, 2026