riggd
Find your AI
skills gap.
AI is changing the skills that matter at work — but most teams don't know where they stand. The gap between what your team can do today and what AI-augmented work demands is growing fast, and the teams that close it first will have a serious competitive edge.
This free assessment analyzes your team's current capabilities, identifies the specific AI skills you're missing, and delivers a prioritized action plan for closing the gap. Answer a few questions and get your personalized report in about 2 minutes.
What is an AI skills gap?
An AI skills gap is the difference between the AI capabilities your team currently has and the ones they need to work effectively alongside AI tools. This includes everything from basic AI literacy — understanding what AI can and can't do — to hands-on skills like prompt engineering, AI-assisted workflow design, and evaluating AI outputs for accuracy. Most organizations have a wider gap than they realize because AI capabilities are advancing faster than training programs.
How do I assess my team's AI readiness?
Start by mapping your team's current workflows and identifying where AI tools could add value. Then evaluate each team member's comfort level with AI — do they use AI tools daily, occasionally, or not at all? Look at both technical skills (can they write effective prompts, evaluate AI outputs, integrate APIs?) and strategic skills (can they identify automation opportunities, design AI-augmented processes?). Our assessment automates this evaluation and gives you a structured readiness score.
What AI skills should I learn in 2026?
The highest-value AI skills right now are prompt engineering and AI collaboration (getting consistently good results from AI tools), AI-augmented decision-making (knowing when to trust AI outputs and when to override them), workflow automation design (identifying and building AI-powered processes), and AI output evaluation (catching errors, hallucinations, and bias). Technical roles should add AI agent architecture and API integration. Every role benefits from understanding AI capabilities and limitations.
How do I upskill my team for AI?
The most effective approach is learning by doing, not classroom training. Start by giving your team access to AI tools and dedicated time to experiment with them on real work tasks. Pair AI-proficient team members with beginners. Run weekly "AI wins" sessions where people share how they used AI to save time or improve quality. Focus on practical skills — prompt writing, output evaluation, workflow integration — rather than theoretical AI knowledge. Build a shared library of effective prompts and workflows specific to your domain.
What's the difference between AI literacy and AI proficiency?
AI literacy means understanding what AI is, what it can do, and where its limitations are — enough to have informed conversations about AI strategy and evaluate vendor claims. AI proficiency means you can actively use AI tools to improve your work: writing effective prompts, building AI-augmented workflows, evaluating and correcting AI outputs, and identifying new automation opportunities. Most teams need universal literacy and selective proficiency — everyone should understand AI, but not everyone needs to build with it.