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Performance management, or what I like to call Performance Acceleration, is becoming the front line of the AI and flow of work learning momentum shift. Meta’s decision to grade employees on AI-driven impact from 2026 is not just a tech story; it is an early signal for how companies will fuse performance, skills, learning and day-to-day work into one integrated system of development. In parallel, emerging thinking on the future of learning calls for leaders to redesign work itself so that every task, tool and interaction generates both business value and growth data.
The McKinsey perspective on the future Chief Learning Officer (CLO) argues that learning can no longer live in episodic courses that sit beside real work. Instead, Learning Leaders are urged to build developmental ecosystems in which work is the primary learning experience, with AI providing real-time coaching, feedback and personalization in the flow of tasks.
Meta’s new performance model mirrors this shift by making AI use and AI-enabled outcomes a core expectation in performance reviews, openly tying ratings, promotions and rewards to how effectively employees leverage AI to move key metrics. For 2025, employees are encouraged to showcase AI-fueled wins in self-reviews before AI-driven impact formally becomes measurable in 2026.
Performance reviews as performance acceleration engines
Meta is also overhauling the mechanics of their performance system through tools like an AI Performance Assistant, its internal Meta Mate assistant and even gamified adoption programs such as Level Up. These systems do more than ease paperwork: they normalize AI as a collaborator in reflection, goal setting and feedback, turning review cycles into guided learning experiences rather than purely evaluative rituals.
The CLO playbook points in the same direction, advocating for AI-powered feedback loops embedded in workflows, whether in frontline operations, agile sprint planning, or formal performance evaluations, so that every interaction generates micro coaching moments and structured skills data. When performance tools and workflow systems converge, companies can track skill progression in context, directly linking development to real work outcomes instead of proxies like course completions.
Flow of work learning meets data-rich performance acceleration
Both Meta’s strategy and the CLO vision place data at the center of performance and learning. Meta is using internal dashboards, AI assistants and adoption incentives to measure how employees use AI to boost productivity and build tools, effectively turning AI proficiency into a quantifiable performance 3D picture. In a similar vein, CLOs are urged to get serious about data by instrumenting workflows so that project outcomes, behavioral patterns and practice exercises yield continuous insight into capability building.
The prize is a dynamic skills intelligence system: leaders gain a live view of who can do what, where judgment or technical depth is improving and how interventions AI tools, role redesign, or targeted stretch assignments, are affecting both performance and readiness. Meta’s move to reward exceptional AI-driven impact even before it becomes a formal metric is an example of how incentives can nudge a data-rich learning culture into everyday behavior.
New mandate for leaders
For us leaders, the convergence of Meta style performance design and CLO style work design signals a new mandate: architect performance acceleration systems that are simultaneously measurement frameworks and learning platforms. That means performance acceleration is akin to a continuous, apprentice-like experience supported by AI-fueled dialogue that shapes tasks, skills and ultimately career trajectories in real time.
When done well, this fusion of performance and flow of work learning can increase agility, accelerate reskilling and hardwire innovation into how we all work. The companies that win will be those whose adaptable leaders treat AI-enabled performance acceleration as a design opportunity: designing tools, expectations and metrics so that every review, every sprint and every interaction becomes both a source of value and a moment of growth.
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