Why Lattice Is Adding AI Fluency to Job Competencies

Most AI fluency frameworks measure the same things: tools used, frequency, tokens consumed. Those metrics show how often someone reaches for AI. They don't show whether that person is producing better work, making smarter decisions, or building things that last. Usage frequency is not the same thing as impact. 

At Lattice, our People + AI focus is grounded in the belief that AI should strengthen human judgment and help people create greater impact, not simply increase activity. That’s why we wanted a more nuanced way to define AI fluency, one that looks beyond usage to how people make decisions, improve their work, and contribute at a higher level. We built our own AI Fluency Framework and incorporated these competencies into every Lattician’s Grow track.

What it is: A company wide-framework to ensure Latticians have the capability to think and work alongside AI. 

What it is not: 

• Mandating when and how people use AI tools.
• Tracking vanity metrics like number of AI tools used, frequency of use, or tokens used.
• A leaderboard or ranking of AI usage, or a precursor to measuring employees based on AI adoption alone.

The competencies below describe the behaviors we expect Latticians to demonstrate as we work alongside AI, not a checklist for using AI more frequently. Success is measured by the impact you create, not by whether or how often you use AI. These behaviors exist to help you make thoughtful decisions about when AI adds value and when human judgment should lead. Many of these are strong behaviors regardless of technology.

5 Core Competencies (All Employees)

The descriptions below live inside each of the AI fluency competency boxes in our Grow Tracks. Each level includes a short summary describing the expected scope of AI fluency based on level. The summaries vary by level but the general descriptions below are the same for everyone.

1. ‍Structural Understanding: Demonstrating a solid understanding of the components of AI, how AI tools create output, and the legal/security guidelines. 
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2. ‍Judgement: Applying critical thinking and personal expertise to determine when, how, and whether to trust, use, and act on AI outputs.
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3. Conscious Delegation: Intentionally deciding what to delegate to AI, what level of autonomy AI should have, and what should remain human-owned, to maximize both quality and impact.
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4. Learning Velocity: Demonstrating the ability and desire to continuously learn new skills and evolve ways of working. If applicable based on role, identifying how AI could improve your existing workflows, and taking action to evolve them. 
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5. Force Multiplier: If applicable based on role, turning AI-driven workflows and expertise into something reusable for others, and building in a way that serves the broader team or organization to work more effectively at scale.

5 Manager/Leader Competencies

The descriptions below are shared across all manager levels. Managers are also expected to be proficient in the AI competencies listed in the IC tracks:

1. Space Creation: Creating psychological safety for the team to experiment and fail with AI, and blocking off time on the calendar for AI upskilling and building.
2. Behavior Modeling: Visibly sharing your own AI experimentation and workflows so the team can learn from you. Showing your team that pressure testing AI is expected, and encouraging your team to adopt healthy skepticism of the output of AI.
3. Process Reinvention: Evolving how the team's work actually gets done, not just encouraging individual tool use.
4. Work Review: Reviewing your team's work to spot patterns and coaching opportunities (versus taking a direct report’s work, dropping it into AI, and handing back AI feedback as your own).
5. Shared Workflows: Defining how your team/function's AI workflows connect to other teams/functions, rather than optimizing in isolation.

How Lattice Will Continue to Support Latticians’ AI Enablement

AI fluency develops through practice. We support Latticians with company-wide learning, role-specific enablement, and manager resources that bring these behaviors into everyday conversations.

How We Are Measuring Impact of This Framework

To measure the impact of this framework, we are focusing on outcomes, not usage. We’re using our engagement survey to discover to what degree Latticians understand how AI applies to their work, feel supported as they build skills, and can focus on higher-impact work. 

How We’re Rolling It Out: 

As we plan the next steps of this rollout, we’re taking a phased approach. We’re starting with suggested conversation topics for managers and their direct reports, then we’re adding AI fluency to performance reviews next year.

We’re sharing this framework in the spirit of learning alongside other HR leaders as we navigate what People + AI looks like in practice. We don’t have all the answers, but we believe progress comes from sharing what we’re learning and keeping people at the center of how work evolves.