What if your next promotion was decided based on how reflective, mindful, or curious you were? Many organizations have implemented values-based competencies to bring a human balance to what is often a clinical, cynical, and purely a results oriented process. Leveraging competencies frameworks helps you more evenly balance the "what" (metrics, goals, etc) of someone's impact with the "how" (behaviors, values, competencies). Ironically, in an AI-enhanced workplace, the measurement of employee value and impact may start to look even-more human.
As AI handles more and more routine tasks, there are quickly new skill demands cropping up, some of which are distinctly human. Technical expertise alone is no longer enough. How-to best work with AI and your human counterparts is quickly becoming mission critical, and how we measure the impact of individuals and the behaviors we value is also quickly evolving.
I developed this framework in collaboration with 3 leaders exploring AI and the future of work and education. Together we are excited to introduce you to our top 10 competencies for the future of work.
Amanda Halle, Fractional People Leader and Advisor: Founder of Mindful Growth Partners, an HR consultancy that specializes in fractional HR leadership, HR and business advisory, coaching, and she's deeply passionate about AI education.
Pascal Vallet, Principal at United Nations International School: Focused on the on-going improvement of the school and optimizing the learning of all students. Provide leadership and management necessary to ensure the success of the school and integrating AI for teacher and student enablement.
Akhila Nagula, Co-founder at The Happy Employees: Akhila is a AI Product Manager passionate about building solutions that create happier, more engaged workplaces. She combines technical expertise with a certification in Organizational Behavior Psychology to design innovative products that improve workplace culture and employee well-being.
This framework is developed with four core principles in mind and should help you to frame performance expectations for employees while offering clarity for how they can develop their skills and thrive in this new era of work. You don't need to use all the competencies together, maybe you swap a couple from your existing framework to keep it fresh and relevant.
The practice of questioning, evaluating, and verifying information before acting on it.
In an age where AI can generate vast amounts of content, the ability to think critically becomes paramount. This competency involves asking "Did you actually verify this?" before accepting AI-generated content, cross-referencing AI outputs with multiple sources when making decisions, and challenging assumptions while exploring alternative explanations.
Critical thinkers in the AI era don't just accept outputs—they interrogate them, understand their limitations, and use human judgment to guide implementation.
Understanding how data works in AI systems and being able to interpret data-driven insights.
As AI systems become more prevalent, employees need to read and interpret charts, trends, and data patterns with confidence. This includes questioning data sources and understanding potential biases, recognizing when sample sizes are too small or data may be unreliable, and translating data insights into actionable business decisions. Certainly not defaulting to believing your AI-buddy is references validated resources.
Data literacy isn't about becoming a data scientist—it's about being a smart consumer of data-driven information.
The ability to effectively communicate with AI systems to get optimal, relevant results.
Working with AI is like working with a new team member that needs training. How you contextualize problems and ensure relevant information is shared is directly linked to the success or failure of the outcome (or output). This emerging skill involves crafting clear, specific instructions that include context and desired outcomes, iterating on prompts to refine AI outputs and improve accuracy, and testing different approaches to find what works best for specific tasks.
As AI tools become standard workplace technology, prompt engineering becomes as essential as email communication once was.
Understanding workflows and identifying where AI can enhance rather than replace human judgment.
Process thinkers map out current processes before introducing AI solutions, identify which parts of work require human insight versus automation opportunities, and design workflows that optimize the human-AI partnership.
This systematic approach prevents the common mistake of applying AI solutions without understanding the underlying work structure.
Leveraging uniquely human creativity to solve complex problems and envision new possibilities.
While AI excels at pattern recognition and optimization, humans remain superior at imaginative thinking, connecting disparate concepts in novel ways, and solving problems that require empathy and cultural understanding. AI can be a helpful thought partner to unlock greater solutoning and creative potential for individuals.
This competency ensures humans remain essential for innovation and complex problem-solving.
Treating AI as a team member that requires clear direction, context, and follow-up.
This competency involves providing comprehensive context when assigning tasks to AI tools, setting clear expectations and success criteria for AI outputs, and reviewing and giving feedback to improve future AI performance. Knowing what and how to delegate tasks, including the size and scope of tasks to AI will become paramount.
As AI becomes more sophisticated, managing it effectively becomes similar to managing human team members.
Being intentional about technology use and maintaining awareness of its impact on thinking.
Mindful employees regularly reflect on when AI is helpful versus when human thinking is needed, notice how AI tools are changing their own thinking patterns, and deliberately practice skills that could atrophy with over-reliance on AI.
This competency prevents the unconscious delegation of critical thinking to machines.
Maintaining intellectual curiosity while having the courage to challenge and explore.
Curious employees experiment with new AI tools to discover potential applications, speak up when AI outputs don't seem right or ethical, and take calculated risks in implementing AI solutions.
This competency drives innovation while maintaining healthy skepticism.
Embracing continuous learning and viewing challenges as opportunities to develop.
This is a competency that we see in many rubrics today and one that is not going away anytime soon. In a rapidly evolving AI landscape, adaptable employees seek out learning opportunities about emerging AI tools and capabilities, view AI-related mistakes as learning experiences rather than failures, and adapt workflows and approaches based on new AI developments. Growing and continuous development is simply not an option at this point. Technology is driving speed of innovation and all employees are confronted with learning new skills and tools on a daily basis.
This competency ensures employees remain relevant as technology evolves.
Understanding the moral implications of AI decisions and maintaining human values.
This competency requires questioning whether AI solutions serve human wellbeing and fairness, considering the broader impact of AI implementations on society, and refusing to implement AI solutions that conflict with core values. This competency also requires an organizational stance and alignment around the company's perspective of AI. Ethics, like values are at some level subjective and organizational perspective is required here before employees are held accountable to a standard set of behaviors that isn't truly and clearly articulated.
Ethical awareness ensures that efficiency gains don't come at the cost of human dignity or societal benefit.
The ability to systematically measure and assess AI system performance to ensure quality, safety, and alignment with intended outcomes.
This competency involves creating structured benchmarks to test whether AI systems are performing as expected, much like unit tests for software. Employees would design scenarios to test AI outputs for correctness, safety, helpfulness, and tone. They establish clear criteria for what "good" looks like from their AI tools and regularly assess whether the systems are meeting those standards.
This new skill is critical for organizations building AI tools ensures that AI tools maintain consistent quality and behave according to organizational standards, rather than assuming AI outputs are always reliable or appropriate for the intended use case. Learn more about this on Lenny's Blog.
Progressive organizations are already adapting new competencies into their performance management and feedback systems. Rather than evaluating employees solely on technical skills, metrics or task completion, they're assessing how well people demonstrate certain types of behaviors including some of those listed above.
Hiring: Look for candidates who demonstrate curiosity about AI, ethical thinking about technology implementation, and the ability to work collaboratively with automated systems. Create a set of questions for each of these competencies or the ones you choose to ask during interviews.
Performance Reviews: Include competency matrix in performance assessments to understand how well employees use AI tools, how they verify AI results or recommendations, and their ability to maintain critical thinking while leveraging technology.
Development Planning: Create learning paths that help employees build these competencies, from prompt engineering workshops to critical thinking training.
Team Dynamics: Foster environments where questioning AI outputs is encouraged, ethical considerations are part of every technology discussion, and human insight is valued alongside technological efficiency.
These competencies represent what humans do best: think critically, ability to act ethically, adapt creatively, and maintain awareness of context and consequence within rapidly changing and dynamic workplaces. As AI handles more routine cognitive tasks, these distinctly human capabilities become our primary source of workplace value.
The employees who will thrive in the AI era aren't those who compete with machines, but those who elevate their uniquely human strengths while using AI to amplify their capabilities. They maintain agency over their work while leveraging technology to achieve better outcomes.
Organizations that recognize and develop these competencies will build workforces that are both more effective and more human. They'll create environments where technology serves people, not the other way around.
The AI revolution isn't replacing human workers—it's redefining what makes them valuable. Technical skills remain important, but they're no longer sufficient. The competencies that will determine career success are those that make us most human: our ability to think critically, act ethically, adapt creatively, and maintain wisdom about when and how to use the powerful tools at our disposal.
Companies implementing this framework aren't just preparing for the future—they're creating workplaces where humans and AI work together to achieve outcomes that neither could accomplish alone. That's not just the future of work—it's the future of human potential.
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