Written by the AgileFever editorial team. Facts checked on November 13, 2026 against the official pages of the organizations named in this post. AgileFever sells training for some of the topics covered here, and we say so wherever it applies. Statements about how AI systems behave come from model-maker documentation and the OWASP list for LLM applications, opened on that date. Hours, weeks and projects are our own design. This post names no course providers and ranks nothing.
Most people who want an AI learning plan share one constraint: a job, and about five hours a week to give it. Roadmaps written for engineers do not fit that, and course lists do not tell you what to do on Tuesday. This plan is built for non-engineers and mixed-background professionals. It runs 12 weeks, and each week has a goal, activities, a deliverable and a check you can run on yourself.
If you are an engineer who wants a build-heavy path, read our free LLM learning path instead. It runs about 108 hours over 16 weeks and starts with Python and APIs. This plan is shorter, starts with judgment rather than code, and schedules safety and company policy as a week of its own.
An AI learning plan in four phases and 60 hours
Twelve weeks at five hours a week is 60 hours. We split them by what each phase has to produce, not evenly.
What AI systems do and do not do, how to prompt with context, and how to read output with suspicion. You finish with a tested prompt template and a review log.
Data basics, one automation that runs on five inputs, and answers drawn from your own documents with the source named.
A score instead of a feeling, then privacy, safety and your company’s rules. You finish with an evaluation sheet and a one-page usage rule.
A small agent workflow with human approval, a two-week portfolio project, and a review of everything you made.
Total: 60 hours (12 weeks x 5 hours).
Hours are this plan’s own allocation, not published figures.
The last phase gets the most hours because a project shows what you can actually do. An AI learning plan that stops at week 8 leaves you with notes and no proof.
The 12-week AI learning plan, week by week
Use your own work for every activity: real tasks, real documents, real tables. If a file is confidential, read your company’s rules before you paste it anywhere, even before week 8.
Phase 1: understand (weeks 1 to 3)
| Week | Goal | Activities | Deliverable | Self-check |
|---|---|---|---|---|
| 1 | Explain what an AI system does and where it fails | Ask an AI tool five questions whose answers you know. Note each answer that is wrong, vague or unsupported. | A one-page note: three things AI does well, three it does not, each with an example | Can you tell a colleague in two minutes why a fluent answer can still be wrong? |
| 2 | Prompting and context: get a usable first answer | Rewrite one work task as a prompt with context, output format and three examples. Run it on five inputs. | A prompt template with three examples, tested on five inputs | Could a colleague with no background do the task from your prompt alone? |
| 3 | Read AI output critically | Collect ten outputs on your own work. Check each claim against a source. Tag errors: wrong fact, invented source, missing context, wrong tone. | A review log with ten rows | Did you find errors? None in ten outputs means your checking is too light. |
Phase 2: build (weeks 4 to 6)
| Week | Goal | Activities | Deliverable | Self-check |
|---|---|---|---|---|
| 4 | Data basics and spreadsheets: know what an AI task needs | Take one work table. Fix headers, duplicates and blanks. Ask AI to summarise it, then recompute three figures yourself. | A clean table plus three totals you checked by hand | Do the AI’s totals match yours? If not, can you say why? |
| 5 | A first automation: one weekly task, input to output | Pick a task you repeat. Write its steps, put them in a prompt template and fix the output format. Run it on five inputs. | A written workflow and five logged runs | Was the output format right on all five runs without you editing the prompt? |
| 6 | Retrieval and documents: answers with the source named | Gather 10 to 30 documents you may use. Ask for exact quotes first, then the answer. Write ten questions, two the documents cannot answer. | A question sheet with answer, quote and your verdict for each | For the two unanswerable questions, did the tool say it did not know? |
Phase 3: check (weeks 7 and 8)
| Week | Goal | Activities | Deliverable | Self-check |
|---|---|---|---|---|
| 7 | Evaluation by sampling: replace “feels better” with a score | Take 30 outputs from your week 5 workflow. Write pass criteria first, mark each pass or fail, change one thing and rerun the same 30. | An evaluation sheet with a before score and an after score | Did you write the pass criteria before you read the outputs? |
| 8 | Safety, privacy and company policy | Read your company’s AI or data policy, or find who owns it. List what data never goes into an AI tool. Plant an instruction inside a week 6 document and see whether the tool obeys it. | A one-page usage rule for yourself or your team | Could a new teammate tell from your page what never goes into an AI tool? |
Phase 4: apply (weeks 9 to 12)
| Week | Goal | Activities | Deliverable | Self-check |
|---|---|---|---|---|
| 9 | A small agent workflow with a person in the loop | Pick a task with three to five steps. List the actions the AI may take, set a step limit, mark steps needing your approval. Run it by hand five times. | A workflow spec and a five-run log | Does any step send, delete or pay without your approval? |
| 10 | Portfolio project: choose and build | Choose one problem your team has. Reuse your template, documents or workflow. | A first version that runs end to end | Can you name the person who would use it and what they do today instead? |
| 11 | Portfolio project: test and document | Run your week 7 method on the project. Write down the problem, the method, the score and the limits. | A write-up with evaluation numbers and a list of known limits | Could someone else repeat the result from your write-up alone? |
| 12 | Review | Reread your eleven deliverables. Rerun week 1 questions and the week 7 test. List three gaps. | A one-page review with before and after on weeks 1 and 7 | Can you say what you still cannot do? |
Two tracks: business and builder
This AI learning plan is the same for everyone in weeks 1 to 3, 7, 8, 11 and 12. Weeks 4, 5, 6, 9 and 10 change with your role. Pick the track by what you will do after week 12.
| Week | Business or manager track (no code) | Technical or builder track (some code) |
|---|---|---|
| 4 | Clean a table with filters and pivots, check AI summaries by hand | Load the same table with a script, clean it and compute the totals in code |
| 5 | A prompt template and checklist, run by hand or in a no-code tool your company allows | A short script that sends inputs to a model and saves outputs in a fixed format |
| 6 | Use a document-grounded assistant your company approves, with quote-first prompts | Split documents, search them, pass the best passages to the model, print the source |
| 9 | Design the workflow on paper with approval points and run each step manually | A loop with two tools, a step limit and a log of every call |
| 10 | A decision memo or a team proposal backed by your evaluation numbers | A small tool with a test set and a README |
If you have never coded and want the builder track, add a beginner Python course first. Our guide to the 6-hour Python for AI course has a five-minute test for whether you can skip it. The course is on its program page. Its six hours sit on top of the 60: call that week 0.
A 5-hour weekly template
Small blocks suit most working adults, because a missed evening costs 60 minutes, not a weekend.
| Block | Minutes | What you do |
|---|---|---|
| 1. Learn | 60 | Read or watch the material. Write three questions. |
| 2. Try | 60 | Test those questions on your own work material. |
| 3. Build | 60 | Start the deliverable. |
| 4. Finish | 90 | Finish the deliverable. Fix the weakest part first. |
| 5. Self-check | 30 | Answer the week’s self-check in writing. Log what you would do differently. |
| Total | 300 | 5 hours |
Do the self-check on paper, because a check you only think about always passes. When a week goes wrong, repeat it. Do not squeeze two weeks into one: each deliverable feeds the next.
What the primary documentation says, and where this plan uses it
Four statements from model-maker documentation and the security community shaped the plan. We opened each on November 13, 2026.
- Define success before you tune (week 7). Anthropic’s prompt engineering overview says to have clear success criteria, a way to test against them and a first draft before you improve a prompt. Hence week 7.
- Say what you need and show examples (week 2). Anthropic’s prompting guide recommends showing your prompt to a colleague with minimal context, and including 3 to 5 examples. That is the week 2 check.
- Allow “I don’t know” and ask for quotes (weeks 3 and 6). Anthropic’s guidance on reducing hallucinations suggests permitting the model to admit uncertainty, extracting word-for-word quotes first when documents are long, and verifying claims with citations. It also says these techniques reduce hallucinations but do not eliminate them, so you still check.
- Know the risks (week 8). OWASP’s 2025 list of the ten risks for LLM applications includes prompt injection (LLM01), sensitive information disclosure (LLM02), excessive agency (LLM06) and misinformation (LLM09). Its guidance on both prompt injection and excessive agency includes requiring human approval for high-risk actions, which is the week 9 rule.
Anthropic’s evaluation guidance favours automated grading over many cases once you have a system. You have none yet, so week 7 uses hand-marked samples. That is our adaptation.
Checkpoint projects and what “done” means
A project is done when a stranger could check it. Use these criteria, not a feeling.
| Week | Project | Done when |
|---|---|---|
| 5 | One automation | It runs on five different inputs and the output format is right every time without editing the prompt |
| 6 | Document question sheet | Ten questions answered with a quote and a verdict; the two unanswerable questions return “not in the documents” |
| 7 | Evaluation sheet | Thirty cases marked against criteria written first; scores recorded before and after one change |
| 9 | Agent workflow spec | A step limit, a list of allowed actions, human approval on every step that sends, changes or deletes, and five logged runs |
| 11 | Portfolio write-up | Problem, method, evaluation numbers and limits; someone else can repeat the result from it |
A worked example of evaluation by sampling
Suppose your week 5 workflow turns meeting notes into action items. You write one rule (every action item needs an owner and a date that appear in the notes) and mark 30 outputs pass or fail. Illustrative result: 21 pass, which is 70%. You change one thing, telling the tool to leave the owner blank when the notes name nobody, and rerun the same 30. Now 26 pass, which is 87%.
| Run | Passes out of 30 | Pass rate | What changed |
|---|---|---|---|
| Before | 21 | 70% | Nothing |
| After | 26 | 87% | Added: leave the owner blank if the notes name nobody |
Illustrative example with invented numbers, not real data.
Change one thing per run, so you know which change did it, and read the failures, because they show what to fix next. Thirty cases is a small sample, so a gap of one or two passes may be noise.
Choosing among the best AI courses for beginners: a checklist with no names
You can follow this AI learning plan with free material alone. If you want a course, run this checklist on any of the best AI courses for beginners you are considering. We name no providers, because the right course depends on your plan, not on a ranking.
| Check | Ask | Warning sign |
|---|---|---|
| Syllabus fit | Does it cover this plan’s topics, and in what order? | A long module list with no hours per module |
| Hands-on work | How many hours are practice, and what do you hand in? | Mostly video, quizzes as the only output |
| Feedback | Who reviews your work, how fast, and in what form? | “Community support” and no named reviewer |
| Time zone | Are live sessions at times you can attend, and are they recorded? | Only one session time, in a time zone that costs you sleep |
| Certificate meaning | Who issues it, what earns it, and does an outside body examine it? | An attendance certificate sold as a qualification |
| Refund terms | What is refundable, until when, and what counts as having started? | Terms you can only read after paying |
Two AgileFever courses fit the early weeks, and we sell both. The AI Blueprint is a 4-hour course in 7 modules with no code and no prerequisites, per its program page as read on November 13, 2026. It covers weeks 1 to 3 at overview level and does not replace the deliverables. Python for AI is the 6-hour course behind the builder track. For a build-heavy program, read whether the Gen AI BootCamp suits complete beginners and its module-by-module curriculum against the checklist above.
To judge syllabi, read our syllabus checklist for LLM courses. For what credentials prove and do not prove, see LLM certification: which credentials exist. If you lead a team and want everyone on one path, AI training for product teams covers what to teach first, second and last.
Questions people ask
Can I learn AI in 12 weeks with 5 hours a week?
You can finish this AI learning plan: 60 hours that end in a portfolio project. It gets you to using AI tools with checks, reading output critically and building one small workflow. It does not make you an engineer or a researcher. Use week 12 to choose what the next 60 hours cover.
Do I need to code to follow an AI study plan for beginners?
No. The business track has no code: spreadsheets, prompt templates and tools your company approves. The builder track adds scripts from week 4. If you have never coded, take a beginner Python course first and count it as week 0.
What are the best AI courses for beginners?
The one that fits your syllabus, gives hands-on feedback, runs at a time you can attend and has refund terms you accept. Run the checklist above on every option. A beginner course should state its prerequisites and what you hand in, not only its topics.
What should I do if I miss a week?
Repeat the week and move every later week back by one. Do not squeeze two weeks into one, because each deliverable feeds the next. If you fall behind, drop extra activities and keep the deliverable and self-check, which are what show progress.
Is 60 hours enough for an AI learning roadmap?
It is enough for the deliverables in this AI learning plan, which is a foundation. Our free path for engineers is about 108 hours over 16 weeks and assumes you code. Neither number is a published standard. Both are our estimates, built from each plan’s tasks.
