🚀 TL;DR
- Most consultants misuse AI as a faster Google—optimizing small tasks instead of redesigning how their business works.
- The real advantage comes from building AI-powered systems trained on your data, workflows, and decision logic—not standalone tools.
- Clients now expect faster, cheaper, AI-augmented outcomes, making system-level leverage mandatory, not optional.
- The next decade belongs to AI specialists who codify context, proprietary data, and workflows into infrastructure.
- Start with one system that would 10× your business if it ran without you, then compound from there.
I’ve watched consultants spend the last two years treating AI tools like an overpriced version of Google.
Summarize this call. Draft this proposal. Analyze this data. They’re throwing $500 worth of effort at $50,000 problems and wondering why nothing changes.
The consulting industry has access to more artificial intelligence, more generative AI platforms, and more large language models than ever before. And yet most solo consultants and boutique firm founders I talk to feel more overwhelmed, not less.
It’s a systems problem.
The consultants who are actually pulling away right now aren't asking AI to complete tasks.
They're integrating AI into systems—trained on their business, their clients, their workflows. They've stopped swinging the hammer and started designing the toolbox.
Here’s how I realistically think the use of AI in consulting or solopreneur practices will turn out:
The current state of AI in consulting
The rise of AI copilots and internal assistants
Consulting firms of every size now deploy chatbots, AI assistants, and GPT-based copilots. They summarize research, draft slides, and run data analysis.
The Big 4 have invested billions. Solopreneur boutique firms are catching up fast.
From the outside, it looks like progress. But the reality is different.
Automation of junior roles and service delivery
Machine learning and natural language processing have reshaped the consulting operating model. Tasks that once required a team of analysts—data collection, predictive analytics, initial client research—now happen in minutes.
The workforce planning conversation has shifted with fewer junior hires and more AI-augmented delivery.
Consulting services that once took weeks now promise real-time insights.
Clients now expect AI-augmented outcomes
Your clients have noticed. They've seen what generative AI can do. So they're asking why your proposals still take two weeks or why your data science deliverables cost what they did in 2019.
Client satisfaction increasingly depends on speed, precision, and cost-efficiency that only AI-enhanced workflows can provide. The expectation isn't going away.
But the real problem is: Tools solve tasks—systems solve problems
Here's where most consultants get stuck.
Summarizing a call is a task. Building an AI-powered lead qualification engine trained on your specific client engagement patterns is a system. One saves you 20 minutes. The other changes how your business operates.
I’ve recently had solopreneurs show me beautifully engineered AI setups. Gorgeous work, and it is truly smart. But it was built to optimize a part of their business that didn’t matter.
They’d lovingly built a Ferrari engine to power a golf cart.
People don’t struggle with AI because they’re not smart enough. They struggle because they’re not focused enough.
They chase novelty. They build clever prompt stacks. They obsess over which AI assistant is better. They optimize for $500 problems with $50/month AI subscriptions—when they want to run a $500,000 business.
The one system that would erase the problem entirely? That’s the one they never build.
2026 is the specialist era of AI
Generalized AI use is a trap
Roughly 15% of the working world uses AI now. But almost nobody uses it in a way that builds a 2030 business.
Most consultants use AI to get quick answers or fix tasks. Write a thing. Summarize a thing. Marketing ideas for a thing. It's not wrong—it's just not enough.
The gap between general users (the ones treating AI like a search box) and specialist builders? That's the entire story of the next decade.
Specialists don't use AI—they build with it
The consultants pulling away aren't asking AI to solve tasks. They're integrating AI into systems trained on their business, their clients, and their workflows.
Here are a few ways I’ve used AI:
- When I want to write an app script, I turn to Claude Code—not a general-purpose assistant.
- When I finish a sales call, Fathom processes the transcript using established sales methodologies right in its interface.
- I'm a limited partner investor in Magic Post, which uses millions of posts to train specialized LinkedIn content generation.
None of that is prompt magic. It's specialization.
Systems that compound are the new moat
I was listening to Y Combinator investors recently, including some of the top AI investors in the world. And they said something that made me pull my car over.
Prompts aren't the crown jewels. The data is. The real-world context. The workflow knowledge.
They described sitting next to a tractor sales manager in Nebraska, watching exactly how they work, what they care about, and how they got promoted. Then, translating that into evaluation data that makes sense only for that role.
That obsessive customer understanding, codified into systems that software can consume—that's the founder skill of the next decade. Context, data, specialization. That's the competitive moat.
Here’s what that means for you as a solopreneur:
I've built 17 systems using this framework. One of them—my Offers GPT, built on my own data—has generated six figures this year. That's not a tool. That's infrastructure.
What a system-led AI solopreneur business looks like
Step 1: Codify your expertise
You've spent years developing frameworks, decision logic, and delivery methods that get results. Right now, most of that lives in your head.
That's a problem.
Start documenting how you actually work—not the polished version for clients, but the real patterns. How do you evaluate whether a prospect is a fit? What questions do you ask in discovery? How do you structure a deliverable?
Turn tacit knowledge into explicit logic. That's what AI can actually use.
Step 2: Build with your data
Generic prompts don't scale. Your client notes, sales call transcripts, project retrospectives, and engagement patterns—that's the raw material for specialized AI systems.
Most consultants treat this data as administrative overhead. The smart ones recognize it as their most defensible asset. Your business processes contain insights no general-purpose AI model can replicate.
The consultants winning right now are better at feeding AI the context it needs to be genuinely helpful.
Step 3: Automate workflows, not just tasks
Single-use tools give you incremental gains. Systems give you leverage.
Think about your client journey end-to-end:
- Lead qualification
- Onboarding
- Project management
- Delivery
- Follow-up
Where do you repeat the same decisions? Where do you rebuild from scratch every time?
Those are your system opportunities. Not “use AI to write emails faster” but “build an AI-powered onboarding engine that handles the first three client touchpoints without you.”
One requires your time every time, the other compounds.
Step 4: Start with the one system that would 10x everything
I ask every consultant I work with the same question: What’s the one system in your business that, if it ran without you, would multiply everything else?
Not 27 systems. One.
It could be lead qualification, so you stop spending hours on prospects who were never going to buy.
It could be proposal generation, so you reclaim the 15 hours a month you spend on documents.
It could be client delivery so that you can serve more people without proportionally more effort.
Find that one system. Build it first. Then layer others over time. Focus beats cleverness every single time.
Don’t let AI become the strategy but turn it into your leverage
Most consultants are solving $500 problems with $50/month of AI subscriptions when they want to run $500,000 businesses.
The difference between those who scale and those who stay stuck isn’t access to better AI tools. Everyone has access to the same large language models, the same AI platforms, the same generative AI capabilities.
The difference is architecture.
Tools do tasks. Systems scale businesses.
And the consultants who understand this aren't chasing the next AI solution or optimizing their prompt stacks. They're building specialized infrastructure trained on their expertise, their data, and their client patterns.
That’s what separates an overpriced version of Google from a genuine competitive moat.
The specialist era of AI isn’t about using more of it. It’s about building smarter. One system at a time.
