What I learned building an AI Automation Agency (and why I think this Business Model is Broken)

Ever since my audience grew to 10k, I get more DMs from two groups: aspiring AI developers asking how to start an agency, and business owners asking if I can help them build an AI solution they have in mind.

They've probably seen YouTube videos or posts like "This AI Agent automates your entire business ops"- complete with colorful diagrams of blocks built in no-code tools like Make or n8n. There's this compelling sense that AI has opened a huge opportunity that can be achieved relatively easily. Business owners want these systems. Developers want to pocket a portion of that value through implementation.

While the claims are generally true, I think the AI automation model as it's currently promoted -like a new SMMA, like an agency, like one-off projects - is fundamentally broken for both agency owners and businesses hiring them. Not because the technology doesn't work, but because the economics, expectations, and operational realities don't align with how these businesses are being built.

I was among the first wave of people providing these services. We took the first hit. We threw ourselves into the messy world of business automation, working on this model in its early stages. We set no-brainer prices and did the work to gain experience and build real automation and business skills. But if you read between the lines of YouTube creators who were also early adopters, you'll notice we all faced very similar problems.

What I'm about to share is a systematic analysis of what I've learned after two years in the trenches, based on real data and real mistakes. This will be useful for aspiring agency owners and businesses thinking about implementing AI - I'll share what I think the future looks like and how both can make the right move.

What you'll learn:

• Why small budgets lead to big headaches for both agencies and clients
• The hidden pitfalls that destroy projects before they start
• How automation can multiply problems instead of fixing them
• The “knowledge leak” no one talks about
• The only three agency models that actually work in the real world
• Where the market is shifting and how to position yourself for success

For context: I worked as a software developer for 10 years (7 of those in an IT agency) - that's how I made my first $100k, moved from an unremarkable town in Russia to Vienna, and got interested in business and AI. This background gives me perspective on how software projects actually work, how teams are structured, and what makes IT projects successful.

The Budget Reality Check

Let me start with numbers from my intake forms over the past 9 months: half of potential clients said their budget was under $2,000.

I don't blame them. First, that's how I initially positioned myself - "automation for small service businesses". Second, that's what they perceive automation costs based on social media posts and tutorial lengths. If an automation can be built in an hour or two, the price shouldn't be high, right?

Here's what neither they nor I initially understood: the build itself isn't the biggest part of the work.

Connecting blocks in no-code tools is easy. The hard part is figuring out which blocks to connect for this specific business, how the system should behave, what existing process we're automating (or if we're creating something entirely new), and how to move from proof-of-concept to production.

It's easy to make AI automation output something. It takes significant skill to make it output something that actually benefits the business.

This budget constraint created immediate problems. I'd try to figure everything out on a discovery call, scope the project, and give a price estimate based on insufficient data. If the client agreed, I'd usually spend more time than expected because of complexities we discovered along the way. More often than not, these projects fell through.

Automation for a fraction of a landing page cost?

Here's a concrete example: I once charged $500 for what seemed like straightforward automation. After a week of development, requirements clarification, hunting for data (the client used tools with no APIs), and overcoming low-code tool limitations, my effective hourly rate was under $10. The system worked perfectly - it's still in use today - but I had fundamentally mispriced the complexity.

Let me put this in perspective:

• Landing pages cost $2K+ with fixed scope and minimal integration. Requirements fit in one questionnaire, solutions are usually templated and repeatable.
• Social media marketing packages cost more and follow standardized processes across Google/Meta. Simple, repeatable tech stack.
• No-code agencies (like Bubble) don't take projects below $10K because (I assume) they've learned it doesn't make economic sense.
• Traditional development agencies charge $50K+ for custom business integration projects. The IT company where I worked in my corporate days had typical project budgets in the millions of dollars.

AI automation falls into a different category entirely. Clients come without tech requirements and often don't know what's feasible. They expect the agency - the experts - to figure that out and give them a quote before committing to the project. But the expert needs their data and deep understanding of their business processes to give that quote.

We've positioned complex business integration as a productized service when it's actually ongoing consulting work.

Solution: Convert custom automation projects into ongoing consulting relationships or introduce a paid discovery phase before implementation and set appropriate budget expectations.

The Process Problem: Automating Chaos creates Faster Chaos

When I started providing automation services, I assumed I'd help businesses do what they already do, but better - with AI, automation, agents. Discovery calls revealed something different: many businesses came because they were struggling with day-to-day operations and thought AI would stop the struggling.

But their real problem wasn't lack of automation - it was lack of processes.

Here's the thing about automation: it amplifies what already exists.

100x a working process = 100x the outcome

100x a broken process = 100x the mess

That's why it's crucial to ensure the process you're automating actually makes business sense. If it doesn't bring business results, if it's not effective - fix the process first, then automate it.

I also had calls where businesses expected me to automate social media channels or blog posts for them, but when I looked at the current state of things - I saw 0 published articles and social media posts with 0 engagement, no content strategy. They thought I'd create social media strategies from scratch with AI, then automate them. But even with AI, implementation is one role. Strategy consulting is another entirely.

I see it can be solved from both sides:

• Businesses can first figure out the strategies and processes internally and then outsource the development
• Or automation agencies can take on the strategy part and become more valuable, but it requires clear specialization, not generic "AI Agents for your business" type of agency.

Why One Person can't run this Business (Team Structure Reality)

Back in my corporate days, even our smallest IT projects required:

• A senior developer to architect the solution
• A junior developer to help with implementation
• A project manager to coordinate with the client
• A tech-savvy client contact to translate business needs
• Business analysts when requirements got complex

As projects grew, we added QA engineers, DevOps, database administrators. Bigger organizations have change management roles to help with IT product adoption, additional roles for contracts, invoicing, and last but not least, sales.

Obviously, small automation projects don't need all those roles, but once I started offering AI services, I realized there's a reason each role exists. If they don't exist on paper, the responsibilities still exist - absorbed by 1-2 people running the agency.

Suddenly the founder's role becomes not just bringing in leads, closing deals, or building the system, but doing business analysis, drawing diagrams, providing documentation, managing client expectations, testing solutions, and so on. That's overwhelming for anyone, especially beginners entering this field. This also ties back to small budget projects that many start with that don't allow hiring for additional roles.

Solution: Successful projects must have bigger budgets and at least 2 people should be involved in the implementation. One can still be on the client side, but then it turns into more like a partnership than an agency model - I have seen successful examples of those myself.

The Uncertainty Problem that Kills Predictability

In my traditional IT corporate role, we estimated tasks, planned development sprints, planned features to add. More often than not, we underestimated the work. This is typical across industries - from construction to tech. You can read about spectacular failures like Sydney's Opera House taking 10 years longer and costing $102 million instead of initial $7 million planned (AUS prices) - that's 14x more than the initial budget!

Why does this happen? Dependencies - on workers, suppliers, existing systems, legal frameworks, stakeholders. AI projects sit on top of this uncertainty because AI is non-deterministic and technology changes rapidly.

Think building a house: you estimate 2 months, then discover weak soil requiring different foundations. Next day the site's locked and you have to wait for the owner. Your key worker disappears "to find themselves in Bali." Finally, mid-project the client decides they actually need a two-car garage, not one - a small nuance they overlooked in the initial plan.

Similar things happen in AI projects:

• Integration dependencies: AI systems should interact with existing CRMs, email providers, data providers, business accounts. These existing systems are not standardized and can be more complex than anticipated, or have nuances you didn't know about upfront.
• Data preparation: Clients underestimate time needed to clean their data and the importance of that data for a fully functioning AI system.
• AI unpredictability: AI behavior depends on the prompt. Unlike regular code, which always gives you the same answer for the same input, AI can respond differently - even when you ask the same thing. It's a bit like your partner's mood: you can try to guide it, but you never know exactly what will work. Sometimes you find a prompt that does the job in an hour. Other times, you spend days tweaking it, still not sure if it will behave the way you want.
• Scope evolution: "Simple" business requirements you started with almost always have edge cases and unforeseen nuances, just like any language that has general rules and a long list of exceptions from those rules that you learn afterwards.

This creates massive issues with estimation and project planning. If you estimated 3 days, those might spread across 2 weeks due to slow responses, data preparation, and feedback loops.

Solution: I've seen agencies include contract clauses making clients responsible for timely replies and data provision. This solves part of the issues. Another part is solved by converting projects into ongoing consulting relationships rather than project-based pricing.

The Knowledge Leak Problem Nobody Discusses

Here's a subtle but deadly issue: when I complete complex automation projects, I walk away with months of accumulated knowledge about the client's systems, processes, and business logic.

The client has invested time, effort, and money explaining their processes to me. But if they need another consultant later, that entire learning process starts from zero - for them and the new consultant. It's like re-onboarding a new employee every time you need work done.

This creates massive inefficiency. The client loses institutional knowledge about their own systems. The next consultant rebuilds understanding that already existed. Everyone pays twice for the same learning curve.

Compare that to switching marketing or web dev agencies - the tech stack is simple, the handoff is clean, and no critical business knowledge walks out the door.

Solution: Switching from "done-for-you" agency model to "done-with-you" team training so that clients can become self-sufficient and thrive without relying on additional help long-term.

This same solution also solves the next problem:

Maintenance Reality

While automation systems can theoretically work indefinitely once built, here's what I've found in practice:

• Underlying systems change and can break automations. Recently, an API I used silently introduced a new status that broke an automation that had worked perfectly for months. I got no notification about expected changes - it just stopped working one day. Things like that happen more often than you'd expect.
• Business processes evolve. Companies introduce new products, retire old ones, change branding, pricing, etc. Someone needs to reflect those changes inside automations.
• AI models and capabilities advance. Old language models become deprecated. When you switch to new models, behavior may change, requiring additional testing and verification. Small tasks, but they need to be done regularly.

The Market is Already Shifting

I noticed something interesting this year. After struggling with one-off projects, during discovery calls I started asking if clients would be interested in training after I built their system. Most said yes. Some asked about that even before me.

I've even started getting requests for pure training from companies wanting to learn automation at the leadership level.

This shift makes perfect sense. Unlike marketing or ads, AI automation touches every business function - operations, customer service, sales, finance, HR. It's not a specialized skill living in one department. It's more like basic computer literacy or spreadsheets - a basic skill that leadership needs to actually understand.

When I look back - my most successful projects were with tech-savvy founders. Usually, they'd already tried to automate something themselves or had an internal IT team - but not enough time or the right guidance. They got it: building automation skills inside the company is a much smarter long-term move than constantly hiring agencies who have to start from scratch every time.

What Actually Works: Three Sustainable Models

After trying every wrong approach and different pricing models, here's what I'm seeing succeed:

Model 1: Internal Capability Building (For Businesses)

The future belongs to companies with automation expertise in-house. Internal teams:

• Understand business processes deeply and know the nuances that external agencies overlook
• Can maintain and evolve systems long-term once business processes change or AI capabilities increase
• Connect automation directly to business outcomes and can prioritize projects accordingly
• Retain institutional knowledge and IP, allowing them to speed up the process with each new project
• Are the cheaper option long-term

If you hire external consultants, hire them to train your team so you can maintain systems internally, or hire them as extended team members on an ongoing basis - not project contractors.

Model 2: "Done With You" Education (For Consultants)

This is where I see the biggest opportunity - it makes the most sense for me and is the path I'm shifting toward.

There's significant demand for education, guidance, and hand-holding from business owners who want to understand these technologies themselves, and from employees wanting to upskill.

The "done with you" model lets you work with more clients simultaneously because you don't need to understand every tiny detail of each business, learn every system independently, maintain systems long-term, or keep all this complexity in your head. You provide expertise and guidance and teach the skill that'll be in demand for the foreseeable future.

Model 3: Deep Specialization or Higher-Leverage Clients (For Agencies)

I'm not saying there's no way for AI agencies to exist - they can work under specific conditions:

Deep Specialization: One industry, one automation type, one toolset. Build it successfully for 3 clients, then confidently sell outcomes to others. Specialists become experts, can charge premium prices, avoid long feasibility phases, and rely on proven solutions.

Higher-Leverage Clients: Focus on companies with budgets that fit the project complexity (at least $10K–20K) and that can truly benefit from automation at scale. Take the lesson from Bubble developers who've already figured out this pricing, build a team, and understand that automation is ongoing business integration, not one-time delivery.

My Evolution

All those hard-won lessons made me understand that while the AI agency model may be easy to start, it's one of the hardest business models to operate. It's definitely not for technical people alone, not for small teams, and not for tiny budgets.

I'm shifting my approach, and instead of building and maintaining automation systems for dozens of clients from different industries, I'm focusing on teaching businesses to build internal automation capability. If your company has been thinking about it, I'd be happy to hear your thoughts.

This model is more scalable for me and delivers more lasting value to clients. They retain system knowledge, keep the IP they create, can adjust as business evolves, and avoid the knowledge leak problem with external consultants.

I still take implementation projects, but only as "done with you" partnerships where client teams learn alongside me and take ownership from day one.

In my view, the future belongs to those who understand both the business side of things and have the technical side to support it long-term. That's a much better outcome for everyone involved.