What: BCG's executive coaching lead outlines three steps for balancing board pressure to adopt AI with organisational readiness: treat it as a people problem, focus on two or three high-impact use cases, and redefine success around a 70% experimentation win rate.

Why it is important: It reframes the AI payoff around capabilities that were previously impossible, like faster claims resolution and deeper personalisation, rather than headcount reduction, at a moment when retail AI coverage keeps circling back to job cuts.

A BCG executive coach addresses a common question: how to balance board pressure for AI with teams not yet ready. Both instincts are correct. Waiting has become the riskier choice, but moving fast isn't the same as moving recklessly, and leadership's job is telling the two apart.
Three steps help. First, treat AI as a people challenge rather than a technology one: technology is roughly 10% of the equation and data another 20%, while 70% is organisational and human. Apparent resistance often reflects fear about job security and hard-won expertise, so leaders should make clear AI is meant to extend expertise, not replace it, and give people low-risk ways to build familiarity in daily work.
Second, avoid spreading AI across dozens of initiatives. Point it at two or three problems that matter most, and aim for what the author calls "aperture expansion": capabilities that were previously impossible, rather than treating cost-cutting as the primary goal.
Third, redefine success. AI is statistical, not deterministic; a 70% success rate should count as a win. Failed experiments deserve a blameless postmortem, and pilots should start where being mostly right is useful and a human stays in the loop, avoiding regulatory or safety-critical territory.

IADS Notes: The emphasis on treating AI adoption as a people challenge rather than a technology rollout echoes wider retail coverage. HR Dive, July 2026 found that only 3% of surveyed C-suite and HR leaders felt highly prepared to guide AI adoption, even as deployment outpaces workforce and management readiness. Bain & Company, December 2025 similarly reported that retailers moving AI from pilots to production are finding that leadership engagement and workflow redesign, not the technology itself, determine success. The caution against framing AI primarily as a cost-cutting exercise is reinforced by Forbes, October 2025, which linked Amazon's and Target's job cuts to a broader shift toward redefining productivity around value creation rather than headcount reduction. This aligns with an earlier framework from BCG, November 2023, which argued that generative AI transformation begins with a workforce diagnostic to prioritize high-value use cases before technology deployment.

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