All Level Plans
A complete reference for every AI readiness level. Each plan includes priorities, quick wins, risks to watch for, dimension-specific guidance, and recommended resources.
← Take the AssessmentYou're at the start of your AI journey — and that's exactly the right place to begin.
Most people are still figuring out where AI fits into their work. The fact that you're here, taking stock honestly, puts you ahead of the curve. Your most valuable next step is to build familiarity through small, low-risk exploration — not to become an expert overnight. As the roadmap reminds us, 'the long-term risk of AI is not a lack of capability by the tools, but a failure to have the human capability and institutional knowledge to use them effectively.' Building that capability starts here.
Try one AI tool with a real task
Choose a broadly capable AI tool like ChatGPT, Claude, or Gemini — all have free tiers. Pick a simple, low-stakes task like summarizing a document, drafting an email, or brainstorming ideas. Use only non-sensitive, publicly available information. The goal is experience, not perfection.
Complete a basic AI orientation
Find a recent introductory tutorial for the AI tool you chose — most major platforms offer free, self-paced getting-started guides. Look for materials published in the last 90 days, as AI develops quickly and older resources go stale fast. Focus on understanding what the tool does well and where it falls short.
Identify one recurring task AI could help with
Look at your weekly routine and find one task that is repetitive, time-consuming, or tedious — something you do often enough that even a small improvement adds up. This becomes your first candidate for AI experimentation. Don't try to transform your workflow; just find one place where AI might make your work a little easier.
Don't wait until you feel 'ready' to start — the best way to learn AI is by using it. Start small, stay curious, and remember: the only failure is the one you don't learn from.
Start by trying one broadly capable AI tool — ChatGPT, Claude, or Gemini all have free tiers. Look for the platform's most recent getting-started tutorial. Given how quickly AI develops, seek materials published in the last 90 days to ensure what you learn reflects current capabilities.
It's completely normal to feel uncertain — most people are still figuring this out, and admitting that is a strength, not a weakness. Start with a low-stakes task using only non-sensitive, public information. There's no pressure to get it right the first time. As the roadmap reminds us, 'the only failure is the one you don't learn from.'
Start with a simple prompt structure: describe what you need, give the AI some context about your situation, and specify the format you want back. Even this basic approach will dramatically improve your results compared to vague, one-line prompts. You can build sophistication from there.
Start with one simple rule: never use AI-generated content without reading it through at least once. AI can be confidently wrong, so developing the habit of review is your most important first step. When in doubt about whether something is accurate, verify it independently. Think 'trust, but verify.'
Pick one task you do every week and try using AI to help with it — even if the first attempt isn't perfect. The goal is to build the habit of reaching for AI when it might help. Start with non-sensitive, routine work and gradually expand from there as your comfort grows.
You've started exploring — now build consistency through deliberate practice.
You have enough exposure to see AI's potential, but inconsistent use won't build lasting skill. This phase is about moving from occasional curiosity to regular, structured practice. Think of it like learning any new capability — repetition and reflection are what turn experimentation into real confidence. AI augments human work; your role is to develop the judgment and skill to direct that augmentation effectively.
Improve one prompt you use regularly
Take a prompt that gave you mediocre results and iterate on it at least three times. Add context, specify the format you want, or break the task into smaller steps. Notice how small changes to your input dramatically affect the output. This is the foundation of practical AI skill.
Apply AI to a real work task and document the result
Choose a task from your actual work — not a test or experiment — and use AI to help complete it. Then take five minutes to write down what you tried, what worked, and what you would do differently. This practice of reflection is how experimentation becomes learning.
Establish a regular AI practice rhythm
Commit to using AI for at least one task per week for the next month. Set a recurring reminder if needed. The goal is not perfection but consistency — building the habit of reaching for AI when it can help and developing judgment about when it can't.
Avoid the trap of shallow, inconsistent use. Using AI once a month won't build real skill any more than going to the gym once a month builds fitness. Commit to regular practice — even 15 minutes a week adds up to meaningful capability over time.
Set aside 15 minutes each week to explore what's new in AI for your field. Follow one or two trusted sources — not to become an expert, but to stay aware of how the landscape is shifting. Awareness compounds over time and will sharpen your judgment about which developments actually matter for your work.
Your curiosity is your greatest asset right now. Lean into it by giving yourself permission to experiment without needing perfect results. Create your own personal 'sandbox' — a space where you practice with dummy data or personal tasks to build comfort at your own pace, without any risk.
Focus on the art of iteration. When a prompt doesn't give you what you need, don't start over — refine it. Add more context, break the task into steps, or ask the AI to explain its reasoning. Small adjustments often lead to significantly better output, and this iterative mindset is the core skill of effective AI use.
Practice asking yourself 'What might be wrong with this?' before accepting any AI output. Look for unsupported claims, one-sided perspectives, and information that seems too convenient or too confident. This critical lens will sharpen with practice and protect you from AI's known blind spots.
Choose one recurring workflow and commit to using AI for it consistently for two weeks. Track what works and what doesn't. Consistency is how experimentation becomes capability — sporadic use won't build the muscle memory you need to develop real proficiency.
You're consistently capable — now refine your approach and expand your influence.
AI is already a meaningful part of how you work. Your challenge now is to move from effective use to intentional mastery: building personal systems, refining your judgment about when and how to use AI, and helping others get started. Your experience is valuable not just to you but to everyone around you who is earlier in their journey. As the roadmap puts it: 'All Teach, All Learn' — everyone has something to share and something to discover.
Build your personal AI toolkit
Document your most effective prompts, preferred tools, and go-to workflows. Create a simple personal reference — even a notes file — that captures what works for you. This becomes your foundation for consistent, high-quality AI use and something you can share with others.
Identify where AI saves you the most time
Review your past month and identify the two or three tasks where AI made the biggest difference. Double down on these — refine your approach, document your process, and look for ways to make these workflows even more efficient. Focus creates mastery.
Help one colleague get started with AI
Find someone on your team who is earlier in their AI journey and offer to show them one technique that has been valuable to you. This isn't about formal training — it's about peer support. Visible examples of AI helping real work are the most powerful way to build interest and reduce hesitation.
Expertise without sharing creates silos. Your value multiplies when others learn from you. Don't hoard what you know — the strongest AI practitioners are also the most generous teachers.
You have a solid understanding of the AI landscape. Consider sharing what you've learned with colleagues who are just getting started — your perspective helps others navigate the noise and focus on what actually matters. A quick summary of 'tools worth trying' could be genuinely valuable to your team.
Your confidence gives you a real advantage. Use it to try more ambitious AI applications and to model the mindset of experimentation for those around you who may be more hesitant. When people see that experimentation is safe and valued, they're more likely to try it themselves.
You're ready to build a personal prompt library — a collection of your most effective templates that you can reuse and share. Consider experimenting with more advanced techniques like role-based prompting, multi-step workflows, or asking the AI to critique its own output before finalizing.
Your critical eye is essential and increasingly rare. Consider sharing your review process with colleagues — how you evaluate AI output, what red flags you look for, and when you choose not to use AI at all. These judgment calls are hard to learn from a tutorial but easy to learn from a colleague who models them.
Document your most effective AI-integrated workflows so you can refine and share them. Look for patterns — which types of tasks does AI help you most with? That insight will help you identify new opportunities and will be invaluable to colleagues who are looking for practical starting points.
You're leading the way — now focus on impact beyond your own work.
You've built genuine AI capability: strong skills, good judgment, and consistent integration into your work. The opportunity ahead is to shift from personal productivity to broader influence — helping shape how your team or organization approaches AI. As the roadmap reminds us, 'the long-term risk of AI is not a lack of capability by the tools, but a failure to have the human capability and institutional knowledge to use them effectively.' You're in a position to help close that gap for the people around you.
Identify one process that could be redesigned with AI
Look beyond individual tasks to entire workflows. Where could AI fundamentally change how work gets done — not just speed up existing steps but enable a better approach entirely? Think about what you could now accomplish that was previously out of reach, and draft a brief proposal for a small pilot.
Mentor someone earlier in their AI journey
Identify a colleague who is curious but hasn't built momentum yet. Offer regular check-ins — even 15 minutes biweekly — to answer questions, share what's worked for you, and provide encouragement. Your lived experience is more valuable than any training course, and mentoring is how human capability scales.
Propose or contribute to an AI learning community
Consider starting a 'mini book club' — a recurring one-hour session where your team reads and discusses a recent AI article together. Or propose an AI Innovation Moment in team meetings: a brief, rotating spotlight where someone shares something useful they've learned. These small rituals build the culture of continuous learning that sustains AI adoption long-term.
Don't rest on your current capability. AI is evolving rapidly, and the skills that make you effective today will need constant refreshing. Stay curious, keep experimenting, and remember: the strongest position is one of continuous learning, not arrived expertise.
Your awareness is a genuine asset to those around you. Look for opportunities to help your team stay current — even an occasional 'AI Innovation Moment' in team meetings where you briefly share something useful can make a real difference. As AI capabilities evolve, your ability to translate what's new into what's relevant keeps everyone moving forward.
Your comfort with AI positions you to be the kind of learner others look to for reassurance. When you share both your successes and your struggles openly, you make it safer for everyone around you to engage. That psychological safety is what turns individual confidence into team-wide capability.
Your practical skill is strong. Consider documenting your most effective workflows and sharing them as templates others can learn from. Your expertise becomes even more valuable when it's accessible to your team — and teaching often reveals new insights about your own practice.
You understand both the 'can we' and the 'should we' of AI usage — a distinction the roadmap emphasizes as critical for responsible adoption. You're well-positioned to help establish responsible AI practices for your team — not rigid rules, but shared principles that guide thoughtful use.
Your consistent integration of AI into daily work sets an example for everyone around you. Look for opportunities to redesign entire processes — not just individual tasks — with AI in mind. And consider helping colleagues build their own AI habits by sharing not just what you do, but how you got started and what you learned along the way.