Close the Gap Between AI Ambition and Execution

Your AI strategy won’t deliver results until the people executing it are set up to succeed. Focus on closing the gap between long-term vision and day-to-day reality with these actions.

Diagnose before you prescribe. Start by assessing where your organization truly stands. Identify where teams are aligned, where they’re resistant, and how managers perceive the strategy. Don’t rely on top-level optimism—get a clear, ground-level view before making decisions.

Co-create the playbook. Bring managers into the process early. Involve them in shaping workflows, priorities, and rollout plans. When they help build the roadmap, they’re far more likely to execute it effectively.

Reduce load before adding more. Free up managers’ time before introducing new expectations. Streamline administrative work and create space for learning, experimentation, and team support. Without capacity, even the best tools will stall.

Measure readiness, not just adoption. Go beyond usage metrics. Track confidence, skills, and attitudes. Make readiness a core KPI so you understand whether teams can actually use AI effectively.

Build feedback loops that reward honesty. Create clear channels for managers to share what’s working—and what isn’t. Treat setbacks and cautious assessments as valuable data, not resistance.

缩小AI愿景与执行之间的鸿沟

你的AI战略只有在执行它的人真正具备条件时，才能落地并产生价值。要让长期愿景真正转化为日常成果，关键在于弥合“战略想象”与“一线现实”之间的差距。

首先，在给出方案之前，先诊断真实现状。不要只依赖高层的乐观判断，而要深入一线，了解团队的真实状态：哪些部门已经对齐战略，哪些仍存在抵触，管理者又是如何理解这项转型的。只有建立在真实反馈之上的判断，才不会偏离执行轨道。

其次，让管理者参与共创。不要在完成设计后再“下发执行”，而是在早期就将他们纳入流程设计、工作方式调整与落地路径规划之中。当他们成为方案的共同设计者，而不仅是执行者时，落地的阻力会显著降低。

与此同时，在增加新要求之前，必须先释放他们的负担。如果管理者依然被日常行政事务压得喘不过气，再好的AI工具也难以真正发挥作用。通过简化流程、减少重复性工作，为他们腾出时间去学习、试验并支持团队，是转型成功的前提。

评估方式也需要升级，不应只关注“是否使用”，而要关注“是否具备使用能力”。除了使用率，还应纳入信心、技能水平与态度变化等指标，将“准备度”本身纳入关键绩效评估体系，才能真实判断AI是否被有效吸收。

最后，建立能够鼓励真实反馈的机制。为管理者提供清晰且安全的表达渠道，让他们可以坦诚分享进展、困难与顾虑。无论是挫折还是谨慎的判断，都不应被视为阻力，而应被当作推动系统优化的重要信息来源。