Here are the 9 AI skills with the highest income potential (last one pays the most):

1. Prompt Engineering ($50–$100/hr)

→ Start with context: “You’re a SaaS copywriter for founders on LinkedIn.”
→ Upload or share data: case studies, brand guidelines, examples.
→ Give a clear task: “Write 3 email sequences to convert free users to paid.”
→ Specify the format: bullets, tables, scripts, code blocks.
→ Test and refine: adjust tone, length, and audience targeting.

2. AI-Assisted Software Development ($100–$200/hr)

→ Ask GPT to break down coding tasks step-by-step like a tutor.
→ Identify common problems in niches (e.g., scheduling, reporting).
→ Build prototypes quickly with Replit, Retool, or Bubble.
→ Package and sell the finished product to multiple clients.
→ Keep iterating—turn user feedback into updates.

3. AI Design ($100–$200/hr)

→ Generate initial ideas with Midjourney or Firefly.
→ Edit and refine images: remove backgrounds, upscale, color-correct.
→ Build website or app mockups using Figma and Relume.
→ Deliver final assets in ready-to-use formats for clients.
→ Upsell style guides and templates for recurring income.


4. AI Video Editing ($100–$200/hr)

→ Feed long-form content into OpusClip or Descript to auto-cut highlights.
→ Add avatars or voiceovers with Synthesia or ElevenLabs.
→ Insert B-roll and captions automatically with AI search tools.
→ Export and package shorts for TikTok, YouTube, LinkedIn.
→ Offer monthly editing packages to creators and brands.

5. AI Writing ($100–$200/hr)

→ Pull transcripts and notes with Otter.ai http://otter.ai/ or Whisper.
→ Use GPT to turn raw ideas into articles, threads, scripts, or ads.
→ Fine-tune tone and style by training a custom GPT on client content.
→ Repurpose one piece across multiple platforms (blog → email → LinkedIn).
→ Add human editing for polish and higher fees.

6. AI Content Marketing ($200–$300/hr)

→ Map out a content strategy with clear goals and platforms.
→ Generate blogs, graphics, and videos using AI tools.
→ Repurpose content across LinkedIn, TikTok, YouTube Shorts, newsletters.
→ Automate posting and tracking with Taplio, FeedHive, or Buffer.
→ Offer analytics and growth reports to show ROI.

7. No-Code AI Automation ($300–$400/hr)

→ Audit a business’s tasks to find repeatable workflows.
→ Identify revenue-impact tasks (follow-ups, lead nurture, reports).
→ Build automations using Zapier, Make, or n8n.
→ Test workflows and document them for the client.
→ Charge for setup plus ongoing monitoring and updates.

8. AI Data Analysis ($300–$400/hr)

→ Collect raw data from CRMs, sheets, or APIs.
→ Clean and organize it automatically using AI or scripts.
→ Enrich data with sources like LinkedIn, Clearbit, or Apollo.
→ Extract patterns and insights with ChatGPT ADA, Tableau, or PowerBI.
→ Present findings in dashboards or concise executive reports.

9/ No-Code AI Agent Development ($400–$500/hr)

→ Define the agent’s job (sales assistant, support rep, recruiter).
→ Upload client data, FAQs, chat logs, and product docs for training.
→ Build and deploy using LangChain, Flowise, CrewAI.
→ Monitor performance and fine-tune prompts or workflows.
→ Offer maintenance retainers for updates and scaling.

The next 12 months → AI gold rush.

Pick 1 skill → charge $50–$500/hr.

Learn 3–5 → you’ll out-earn devs, marketers, and sales pros.

Want to scale your business? 
Join my free 5-day crash course below 🎁

natiakurdadze.com/crashcourse

Top AI Skills for High Income

Here are 5 industries desperately paying for AI automation in 2025:

1/ Coaching & Consulting

→ Automate client onboarding with Tally + Zapier
→ Repurpose recorded content into short videos via OpusClip
→ Handle payments & invoice follow-ups via Stripe + Make .com 
→ Build RAG chatbot trained on client session videos
→ Track client engagement & progress automatically in Notion

2/ Recruitment & Staffing

→ Build candidate pipelines with Apollo + Instantly
→ Automate follow-ups for interviews via HubSpot sequences
→ Track placements in Airtable dashboards
→ Run automated reference checks via APIs
→ Auto-score & rank candidates → feed into hiring manager reports

3/ Digital Marketing & Creative Agencies

→ Collect client intake via Typeform → push to ClickUp for tasks
→ Track resource allocation & time via Harvest + Make
→ Automate invoice collection via QuickBooks + Zapier
→ Generate proposals & reminders via PandaDoc + Slack
→ Auto-schedule social content + report campaign metrics

4/ High-Touch SaaS

→ Route leads & qualify via HubSpot workflows
→ Personalize outbound cold campaigns with Clay + Apollo
→ Automate demo booking via Calendly + Make
→ Build AI ticket triage for support (Freshdesk + GPT agents)
→ Auto-assign tasks & reminders to sales reps → track KPIs

5/ Financial Services

→ Automate client onboarding with DocuSign + Notion
→ Run compliance checks via GPT-powered agents
→ Personalize client communications via ActiveCampaign
→ Organize files & assets automatically in Google Drive + Make
→ Track account health & alerts automatically → dashboard for advisors

→ Pick one industry
→ Identify 2–3 high-value automations
→ Build & test workflows with N8N/Make + AI tools
→ Package as a recurring service ($5K+/month)
→ Sell to 2–3 clients → refine → scale

The demand is real. The tools exist. Start now.

Want to scale your business? Join my free 5-day crash course below 🎁

natiakurdadze.com/crashcourse

Industries Embracing AI

Don't Sell N8N Workflows, Sell AI Infrastructure. 

Here's How:

Why raw workflows don’t scale:

→ A 100-node Make .com or N8N workflow collapses in client work.
→ Every workflow is bespoke → edits and bug fixes.
→ Clients often learn they could pay a freelancer $500 to replicate much of it.
→ Result: high effort, low margins, unhappy clients.

Sell the result, not the stack.

Clients don’t care if you used Claude or OpenAI. They don’t care if the backend runs on N8N, Python, or duct tape.

They only care about outcomes they can measure:

→ Did this system get me more qualified leads?
→ Did this system save me 50 hours/month?
→ Can I actually use it?

Backend tech is a commodity.

Sell your workflow as a front-end experience.

Comprehensive dashboards → live metrics on leads, calls, revenue
Client portals → simple controls to tweak campaigns/settings
White glove delivery → you run the system, they watch outcomes roll in

Sell workflow alone = $5K one-time project

Package with dashboards + reporting = $25K+ upfront or $10K/month retainer

Example: Lead Gen System

❌ Wrong pitch → “I’ll build you a complex workflow with AI scraping + email sequencing.”

✅ Right pitch → “Here’s your dashboard where you’ll track every lead and booked call.”

Dashboard shows:

→ # of prospects sourced
→ # of emails sent
→ Open/reply rates
→ Warm leads generated
→ Calls booked

Don’t sell to small businesses (<25 employees):

→ $5K might be their biggest spend ever.
→ They move slow and second-guess every detail.
→ They lack internal ops teams → too much hand-holding.

Instead target mid-market companies ($100K+ MRR):

→ Already spending $50K–$100K/month on sales + marketing.
→ Have budget, urgency, and tolerance for experimentation.
→ Care more about speed and results than line-item costs.

How to sell it (Lead Gen framing) :

Baseline → “Where are you now?” (e.g. $2M ARR, 25 sales reps, 100 demos/month).

Target → “Where do you want to be?” (e.g. $10M ARR in 36 months).

Math gap → “To get there, you’ll need 500 demos/month, 100 more calls/week.”

Bridge → Show your system as the only repeatable way to close that gap fast.

Proof → “Here’s a dashboard from another client showing 112 calls booked in 3 weeks.”

Say: “You need 5x more demos to hit $10M ARR. Here’s the system + dashboard that generates those demos every week.”

Tech stack for backend:

N8N / Make.com http://make.com/ → orchestration of APIs + automations
Python → when you hit limits, custom scrapers, DB pipelines, AI logic
OpenRouter → central hub for any LLMs you want (Claude, GPT-4.1, etc.)

Tech stack for interface:

Lovable / Retool → instant dashboards + CRUD apps
Notion / Airtable → lightweight reporting + collaborative interface
Carrd / Webflow → packaging 1-pager offers with live embeds

How to gather trust and proof:

Pilot first → Run one system free or at cost for a limited period.

Focus on quick wins → Booked calls, warm leads, or direct revenue.

Document everything → Screenshot dashboards, log before/after metrics.

Package results into a story → “In 3 weeks, this dashboard booked 17 calls → 4 converted → $ X pipeline.

Selling AI Outcomes

Here are 7 game-changing tactics to level up your Cursor skills:

→ Sketch UI in Figma, Excalidraw, or even paper; define layout, navigation, key states
→ Turn sketches into working HTML/CSS with v0, Uizard, or TeleportHQ
→ Create a “feature doc” listing goals, expected inputs/outputs, and edge cases
→ Import code into Cursor; prompt: “Refactor this UI according to the doc, keep component names consistent”
→ Version control: push to GitHub and tag each iteration before asking Cursor to modify

2. Use Cursor Directory

→ Browse cursor .directory https://cursor.directory/ and find rulesets for your stack (Next.js, React, Python, Tailwind)
→ Copy ruleset to cursor-rules file at project root
→ Layer multiple rulesets for frontend + backend + testing; use Cursor prompt: “Follow all rules from cursor-rules while coding this feature”
→ Regularly update rulesets from community contributions
→ Track errors that violate rules; add them to a custom-rules.md file to enforce best practices

3. Tag the Docs

→ Collect official API docs (Stripe, Supabase, Firebase) in a folder inside Cursor
→ Create a “prompt template”: “Reference docs in [folder] before generating code”
→ Highlight critical sections (auth flows, API calls, validation rules) for Cursor to prioritize
→ Use inline comments in Cursor: “Follow this exact method signature from docs”
→ Regularly refresh docs to avoid outdated code

4. When Cursor gets stuck

→ Copy failed code snippet, error logs, and description of expected behavior
→ Prompt Claude/GPT/other LLM: “Analyze this code, explain why it failed, and rewrite it to meet the goal”
→ Log the solution in a lessons.md file for future reference
→ Re-run the fixed code in Cursor with a prompt: “Refactor previous code using the lessons from lessons.md”
→ Repeat until Cursor outputs clean, tested code

5. Use Cursor to learn

→ Prompt Cursor: “Explain this code line by line in plain English”
→ Request multiple approaches: “Show 3 ways to implement this function, compare pros and cons”
→ Ask Cursor to annotate with performance notes: “Add Big O complexity to each solution”
→ Highlight best practices inline for team onboarding
→ Store explanations in a Notion/Markdown doc for repeated learning

6. Duplicate working patterns

→ Identify reusable features (auth, dashboard, payments)
→ Prompt Cursor: “Replicate this component for [new feature], maintain style + validation rules”
→ Save patterns in a “library folder” with meta-comments: “Copyable template for future features”
→ For backend, duplicate endpoints and update parameter names using prompts
→ Test each copied pattern before integrating

7. Start from templates, not zero

→ Download starter kits from GitHub (Next.js, Tailwind, SaaS boilerplates, Stripe payments)
→ Import into Cursor: prompt “Refactor and optimize this template according to project doc”
→ Tag template components (auth, dashboard, forms) in Cursor so it can reuse them efficiently
→ Combine with Cursor Directory rules and tagged docs
→ Customize incrementally; test each module before moving to the next

For 10x development, fewer bugs and fully reusable workflows:

→ Plan visually, define goals, and create a feature doc
→ Enforce rules with Cursor Directory and custom rules
→ Tag official docs for accuracy and reliability
→ Handle stuck code via external LLMs and log fixes
→ Learn through annotations and comparisons
→ Duplicate working patterns to scale fast
→ Start from tested templates for speed and efficiency

Level Up Cursor Skills

How to use AI to write SEO content (without getting caught by Google) 🍿

1. Go to AnswerThePublic com and type you target keyword

2. Click on one of the questions, and it will fetch the top 3 Google search results.

3. Now install the free chrome extension "Detailed"

4. Go back to the top 3 articles, click on the extension, and save the outline with the list of headings from the articles.

4. Now copy these headings and open o3-Mini and use this prompt:

I need your help to draft a "deep dive article" for  [YOUR NICHE] space.

The article is written in a [punchy writing style]

Here's an example of a past edition:

[INSERT EXAMPLE OF PAST ARTICLES YOU WROTE YOURSELF]

Now, I want you to write a "deep dive article" for [YOUR BRAND] with the tone of the example, and roughly following the template i provided you.

The article should be about [INSERT TOPIC]

(Here's a rough outline of what I want to include in the newsletter:)

[INSERT HEADINGS FROM PREVIOUS STEP]

5. After getting the output, do another round of prompting. 

[Use H1, H2, H3, and H4, and include FAQs (insert the related questions from AnswerThePublic)]

6. Now go to GPT store and select "Humanize AI"

7. Recheck whether the text has been humanized for free using Undetectable .AI

Want to scale your business?
Join my free 5-day crash course below 🎁

http://natiakurdadze.com/crashcourse

AI for SEO Content Success

If I was starting to learn AI agents today: 

This would be the first video I’d watch.

Google Opal just became a huge headache for n8n and Lovable!

You can build, edit, and share mini AI apps using natural language.

Have you tried it?