Getting Started in AI Consulting: We Answer the Most Common Questions

The questions come up in every conversation about AI consulting. "Do I need to code?" "What tools should I learn?" "Am I technical enough?"

After launching over 50 AI Consultant businesses at Stack, we decided to cut through the noise and answer what people actually need to know about starting an AI consulting business in 2025.

The reality is both simpler and more nuanced than most expect. You don't need a computer science degree, but you do need to understand how business problems connect to technical solutions. You don't need to master every tool, but you need fluency with a focused few. You don't need years of consulting experience, but you need to deliver real value from day one.

The questions we’ll answer in today’s article are:

1. What technical background do I need? Spoiler: Your existing professional experience is probably more valuable than you think.

2. How technical do I need to be? Does an AI consultant need to know how to code? We'll separate the myths from reality about technical requirements.

3. What tools does an AI consultant need to know? We'll map out the essential toolkit and explain why less is more.

4. Which industries are hiring AI consultants? We'll break down where the real opportunities are right now.

5. How much can I expect to make as an AI consultant? Real numbers, not hype—from your first projects to established practice.

By the end of this article, you'll know whether AI consulting aligns with your skills, goals, and situation. More importantly, you'll understand exactly what it takes to get started.

What Technical Background Do I Need?

Here's what surprises most people: your existing professional background is probably more valuable than any technical certification. Technical expertise helps, but isn’t a must have.

The beauty in LLMs and the new wave of AI tools is that they all use natural language as the main interface. The things they do best is to code and the things they do worst is industry specific deep expertise. Technical knowledge is a nice to have, since the LLMs help you get it done.

The AI consulting opportunity exists because businesses need translators—people who understand both their operational reality and what's possible with AI. That intersection is surprisingly rare. If you spent five years in healthcare administration, you understand HIPAA compliance, insurance billing, and patient flow in ways a pure technologist never could. If you managed operations at an e-commerce company, you know inventory bottlenecks, customer service pain points, and seasonal planning challenges intimately.

This domain expertise becomes your competitive advantage. While other consultants fumble with industry jargon or miss critical compliance requirements, you speak the language fluently. You know which problems actually matter and which are just annoyances. You understand the politics, the constraints, and the unwritten rules that determine whether a solution actually gets adopted.

The technical knowledge you need is learnable. The industry insight you already have is not.

Think about it this way: a developer with ten years of experience might build you a technically perfect automation. But if they don't understand that your sales team will revolt if you change their commission calculation process, or that your compliance officer needs audit trails for every automated decision, that perfect solution becomes expensive shelfware.

Your background—whether in finance, marketing, operations, legal, or any other field—gives you the context to build solutions that actually work in the real world. That's worth more than knowing how to code.

How Technical Do I Need to Be? Do AI consultants need to know how to code?

The honest answer: less technical than you think, but more systematic than you're probably comfortable with initially.

Clients need someone who can translate their messy, human problems into clear, systematic solutions. They don't need you to understand transformer architecture or write custom APIs from scratch. They need you to connect their inventory system to a chatbot that actually answers customer questions accurately.

Here's the technical knowledge you actually need:

API connections - This sounds scarier than it is. Most of the time, you're copying and pasting API keys from one service to another. It's like connecting your email to your calendar—follow the instructions, paste the key, test it works. About 3-4 hours to understand the concept, then 20 minutes per new connection.

Data structuring - If you can use Excel formulas and organize a spreadsheet logically, you have 80% of what you need. If you don’t know that, you need to know how to prompt ChatGPT to get the Excel result you need. As I mentioned above, translating natural language to data and formulas is something that modern AI, well…excels at.

Debugging basics - When something breaks (and it will), you need to read error messages and understand what they're telling you. "Authentication failed" means check your API key. "Rate limit exceeded" means slow down your requests. It's pattern recognition more than deep technical knowledge. Here again, giving the proper context to most AI LLMs will provide you with the right answer.

Plain English translation - Perhaps most importantly, you need to explain what's happening in terms your client understands. Not "the webhook payload failed to parse the JSON response" but "the connection between your form and database had a formatting issue—here's how we fix it."

For each core tool in your stack, expect about 20 hours of hands-on learning to reach functional competence. Not 20 weeks. Not a bootcamp. Twenty hours of actually building things and fixing what breaks.

The bar is higher than "I watched some YouTube videos" but much lower than "I can build everything from scratch."

Does an AI Consultant Need to Know How to Code?

No, but let's be precise about what that means.

Traditional coding—writing JavaScript, Python, or SQL from scratch—is not required for most AI consulting work. The tools available in 2025 abstract away the need for traditional programming in 80% of use cases. You can build sophisticated automations, connect multiple systems, and deliver real business value without writing a single line of traditional code.

However, you do need what we might call "technical thinking"—the ability to break down problems systematically, understand how data flows between systems, and debug when things don't work as expected. 

Consider this analogy: You don't need to be a mechanic to be an Uber driver. But you do need to know how to drive, when to get gas, and what to do if the check engine light comes on. Similarly, you don't need to code from scratch, but you need to understand how systems connect and what to do when they don't.

The no-code and low-code tools available today—Gumloop, Zapier, Make.com, Bubble—let you build solutions that would have required a development team just two years ago. These aren't toy tools. They're powering real businesses, handling millions of transactions, and solving complex problems.

That said, basic technical literacy accelerates everything. If you can read simple code, even if you can't write it, you can troubleshoot faster. If you understand basic programming concepts like variables and loops, you'll build better automations. If you're comfortable with spreadsheet formulas, you're already halfway there.

The most successful AI consultants we see aren't necessarily the most technical. They're the ones who combine enough technical capability with deep business understanding. They know when to use a simple Zapier automation versus when to build something custom. They understand which problems need technical solutions and which need process changes.

What Tools Does an AI Consultant Need to Know?

Here are the essential tools organized by what they actually do:

AI Models (Pick 2-3)

• ChatGPT (GPT-5): Your everyday workhorse. Fast, reliable, handles most tasks from proposal writing to meeting summaries. Most client-friendly option.

• Claude: Best for complex analysis and long documents. Excels at legal and compliance work where precision matters.

• Perplexity: Real-time research with citations. Use when clients need competitor intel or industry benchmarks.

Automation Tools (Pick 1 to start)

• For beginners: Gumloop or Zapier - Build working automations in an afternoon, no coding required

• For technical users: n8n or Make.com - More powerful, steeper learning curve, better for complex integrations

Delivering Simple Interfaces (Optional)

• Lovable or Bolt: Build basic dashboards and input forms without code

• Google Docs or Notion: Often sufficient for delivering documentation and processes

Data Storage (Pick 1-2)

• Google Sheets: Handles 80% of needs. Universal, familiar, integrates with everything.

• Airtable: When you need more structure (CRM-style views, complex filtering)

• Notion: Best for documentation and process libraries

• Supabase: Only if building actual applications (you'll know if you need this)

Data Collection

• Apify or Browse AI: Scrape competitor pricing, industry data, LinkedIn leads at scale

Your AI Tool Starter Kit Start with just these:

• Two AI models (GPT-4 and Claude)

• One automation tool (Gumloop if non-technical, n8n if comfortable with complexity)

• Google Sheets

• Well-formatted documents for delivery

Master these before adding anything else. Ship real client work with just these tools. Expand only when clients actually need something specific. For a more comprehensive approach to how to think about the must have AI tools a consultant needs to answer, we’ve written an article dedicated to that.

Which Industries Are Hiring AI Consultants?

The answer here is simply: every industry. From your small corner mom and pop restaurants implementing automatic scheduling and customer support to the largest hospitals implementing custom medical support.

Instead of thinking which industry needs AI it’s better to ask: what unique insights do I have that I can leverage as an AI consultant? This means utilizing your unique professional expertise, connections and know-how to figure out what processes you know very well that need to be improved and someone is willing to pay for.

Size however, does matter.

The Middle Market Sweet Spot (10-500 employees) represents the ideal target. These companies have real problems and budgets but can't afford enterprise consultants. They typically pay $5,000-20,000 for focused solutions.

Here's where domain expertise becomes critical:

Professional Services: If you've worked in law, accounting, or consulting, you understand billable hours and instantly grasp automation ROI. You know that junior associates spend 70% of their time on document review, or that tax prep follows predictable patterns. This insider knowledge lets you identify the exact workflows that need automation—knowledge a pure technologist would miss.

Healthcare: Former healthcare professionals understand HIPAA compliance, clinical workflows, and why doctors will never trust AI for diagnoses but desperately need help with documentation. You know the difference between what's legally possible and what's culturally acceptable in medical settings.

E-commerce/Retail: If you've managed inventory or customer service, you know the real pain points—not what they say in case studies but what actually breaks at 2 AM during Black Friday. You can build solutions that handle edge cases others wouldn't even know exist.

Financial Services: Banking or investment experience means you understand compliance requirements, audit trails, and why a 99% accurate automation isn't good enough when the 1% error could trigger regulatory scrutiny.

The Small Business Market (1-10 employees) needs simple, repeatable solutions. Your advantage here is understanding their actual capacity—not what they aspire to but what they can realistically implement.

The Enterprise Tier (500+ employees) requires deep enterprise experience. If you don't have it, don't start here.

The pattern is consistent: businesses need someone who speaks their language first and understands AI second. They don't want to explain their industry to a technologist. They want someone who already gets it and can show them what's possible.

Your previous career wasn't time wasted before finding AI consulting—it was preparation. Every industry has its unwritten rules, compliance requirements, and political dynamics. Your understanding of these invisible barriers and opportunities is what makes your AI solutions actually work in practice, not just in theory.

How Much Can I Expect to Make as an AI Consultant?

If you're transitioning from a $120,000 salary, that breaks down to roughly $75 per hour after factoring in holidays and non-billable time. As an independent consultant, you need to cover business development, administration, learning, and gaps between projects. Most independents target 2-3x their employed hourly rate, putting you at $150-225 per hour.

But that's your floor for thinking about sustainability, not your pricing strategy.

Project-Based Pricing is where the opportunity lies. Instead of selling hours, you're selling outcomes. A diagnostic audit might run $2,000-5,000. A focused automation solving one specific problem might be $5,000-15,000. A comprehensive workflow transformation could command $20,000-50,000.

The key is understanding value creation. If your automation saves a client $50,000 annually in labor costs, charging $10,000 is reasonable. If your content system lets them avoid hiring a full-time writer at $70,000 per year, a $15,000 setup or renewal fee is a bargain.

The Arbitrage Opportunity exists because most businesses don't understand what it takes to build AI solutions. What looks like complex, high-value work to them—something they'll happily pay premium prices for—might only take you a few focused days to deliver.

This isn't deception. It's leverage. You're not charging for time spent but for knowing which tools to use, how to connect them, and how to avoid the hundred pitfalls they'd encounter trying to do it themselves.

Realistic Income Trajectories vary based on your focus and execution:

Starting out (months 1-6): Expect smaller projects in the $2,000-8,000 range as you build confidence and case studies. If you land 2-3 projects monthly, you're looking at $6,000-24,000 per month. Many consultants keep their day job during this phase.

Building momentum (months 6-12): As you develop templates and processes, projects get larger and faster to deliver. $10,000-20,000 projects become standard. 2-3 projects monthly puts you at $20,000-60,000 per month.

Established practice (year 2+): You have systems, reputation, and referrals. You can charge premium prices for specialized expertise. Monthly revenues of $30,000-100,000 become achievable for focused consultants who position well.

The upper bound depends on your model. Solo consultants focused on high-value enterprise work can bill $500,000-1,000,000 annually. Those building productized services with recurring revenue can scale beyond pure service delivery.

Factors That Affect Your Earning Potential:

Specialization pays more than generalization. The consultant who's known for solving one specific problem for one specific industry commands premium prices.

Speed matters more than perfection. Delivering good solutions quickly beats perfect solutions slowly. Clients pay for momentum.

Confidence correlates with pricing power. Start slightly uncomfortably high, then raise rates as you deliver results and build case studies.

Geography matters less than you think. Remote work is standard in AI consulting. You can serve clients globally regardless of location.

The most important factor? Starting. The gap between thinking about AI consulting and actually doing it is where most potential income disappears. Your first project might be small, but it teaches you more than any course or certification ever could.

The Path Forward Is Clearer Than You Think

After thousands of words answering these questions, here's what matters: the barriers to entry in AI consulting are lower than you imagine but different than you expect.

You don't need to be technical, but you need to think systematically. You don't need to master every tool, but you need fluency with a focused few. You don't need years of experience, but you need to deliver value from day one.

The opportunity exists because businesses are drowning in AI possibilities but starving for practical implementation. They don't need another strategic framework or transformation roadmap. They need someone who can fix their customer service bottleneck, automate their reporting, or help their team actually use AI tools effectively.

Your background—whatever it is—probably prepared you better than you realize. Your industry knowledge, understanding of business operations, and ability to translate between technical and human is more valuable than any certification.

The tools are accessible. The knowledge is learnable. The market is ready.

The only real question is whether you'll take the first step.