Organizations often assume their biggest constraint is a lack of new capabilities. More often, it’s the accumulation of outdated ones. Legacy workflows, entrenched assumptions, and inherited metrics quietly shape decisions long after they’ve ceased to reflect current market realities—especially in environments reshaped by real-time data and AI. This failure to let go, known as organizational forgetting, is both underappreciated and increasingly costly. When companies cling to legacy performance metrics, they distort priorities, reinforcing behaviors that once drove success but now undermine it. When they rely on historical patterns to guide decisions, they misread present conditions. And when past practices remain embedded in systems and processes, they crowd out experimentation and adaptation. AI can play a critical role in breaking this inertia—not just by optimizing existing processes but by exposing where those processes no longer make sense. By surfacing contradictions, stress-testing assumptions, and modeling alternative approaches, AI can help leaders build an objective case for change. Competitiveness, in this context, depends less on what organizations add than on what they are willing—and able—to leave behind.
by Graham Kenny and Ganna Pogrebna
Elena* leads a once-innovative logistics firm we’ve studied that we’ll call Virtal Systems. It’s now struggling to keep pace. “We’re not short of capability,” she explained to us, “we’re weighed down by our own past.” Legacy workflows persist, old assumptions guide decisions, and “the way we’ve always done it” shapes strategy. In a market transformed by real-time data and automation, these habits quietly erode competitiveness.
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