AI Consulting: Lessons from a Year of Thinking, Talking, and Building

Over the first year of my MBA, I found myself as the de-facto AI expert in many circles. Whether it was classmates curious about what LLMs could actually do, startup founders looking for an AI strategy, or friends navigating their AI career paths, I was regularly being pulled into conversations around how to apply these powerful technologies in real life.

These weren’t just surface-level chats. I was consulting on startup strategy, designing proof-of-concept tools, and acting as a thought partner for dozens of people trying to figure out the “so what” of AI.

Eventually, the idea began to form:

What if I turned this into something more formal — a consulting agency focused on real-world AI solutions?

The Spark: A Conversation That Motivated This Post

A recent deep conversation with my classmate Blue Bookhard brought this idea back to the surface. Blue was curious about the space and wanted to understand how an AI consulting agency could be built — how you get clients, what the margins are, what value you really offer.

As we spoke, I realized: I’ve been sitting on a year’s worth of insights that might help others navigating the same questions.

The Market Is There — But Fragmented

One of the most surprising insights from this year is that the demand for practical AI solutions is very real, especially in the SME (small/medium enterprise) segment. These companies:

• Don’t have internal AI talent
• Often have clear operational pain points
• Want simple, working solutions — not long innovation sprints

But here’s the challenge: while there are many developers and small teams who can build these solutions, they rarely find each other.

This is a classic search friction problem.

At one point, I was approached by an AI consulting firm looking to expand into the UK. They offered me 30% of lifetime sales revenue just for bringing a potential client to the table — I didn’t even have to close the deal. That’s how hard it is to find clients in this space, even when the solutions themselves aren’t that complex or expensive to build.

Why Trust Unlocks Value

Clients don’t just want tech — they want certainty.

Before they'll give you access to their data, processes, or decision-makers, they want to know:

• That you understand their domain
• That you’ve done this before
• That they’ll get a clear ROI
• That you won’t misuse their trust, time, money, or data

But here’s the paradox:

• ROI is often hard to predict up front
• If you give a range (e.g. 10–40%), they’ll anchor on the lowest number

This creates a real obstacle — not because the technology doesn’t work, but because clients don’t feel confident enough to take the leap. The result? Many walk away before you even have a chance to prove your value.

So while your solution might only take $1,000 of effort to build, it can take $10,000 worth of relationship-building, case studies, and confidence to close the sale, if it is even possible.

That’s why trust isn’t just a bonus — it’s the currency that makes AI consulting work.

How Do We Overcome the Chicken-and-Egg Problem?

This brings us to a common early-stage dilemma: clients want proof of value before they commit, but you need clients in order to build that proof. It’s a classic chicken-and-egg problem — and it stops many technical founders and freelancers in their tracks.

So how do you get started when you don’t yet have a track record or portfolio?

There are two key components to breaking this cycle:

1. Build Networks and Demonstrate Value

Start small and close to home:

• Work with your immediate network — friends, former classmates, or former colleagues — to solve real problems.
• Run small pilot projects in low-risk environments to build proof points and confidence.
• Create solutions for people you know are actively seeking help, and then productize or refine them for others.
• Let everyone in your network know you’re looking to help solve AI-related problems and welcome any introductions.
• The best new clients often come via warm referrals. If you want to scale quickly, consider offering incentives for referrals or introductions.

Trust is built over time, but your early wins — even if small — are essential to showing credibility and building momentum. This is your chance to build a story that others can believe in.

2. Invest in Framing and Communication

This is where strategy, storytelling, financial modeling, and domain-specific framing become critical.

I won’t go into the details of these here — I’m still learning and refining them myself — but it’s important to understand that these aren’t optional “nice-to-haves.”

They are essential to unlocking the full value of any technical solution. Without them, it’s difficult for clients to understand why they should invest time, money, or trust in what you’re offering — even if the underlying technology is sound.

Where the Real Value Lies

Through these conversations and small projects, I realized something important:

Tech is the delivery mechanism. But the value is created by people.

The success of any consulting engagement depends on:

• Deep discovery and business understanding
• ROI analysis that speaks the client’s language
• Trust-building through credibility and consistency
• Sales that remove friction and communicate real outcomes

These things can’t be outsourced to a Python script. They need people who know how to listen, learn, and build relationships — as much as they know how to fine-tune a model.

Why I Chose Not to Scale It Full-Time

After a year of exploring this space — advising startups, designing systems, and talking to firms — I eventually decided not to pursue an AI consulting firm full-time.

Why?

Because I realized that I wasn’t personally interested in doing the work needed to scale this into a sustainable business.

The cold outreach, the long sales cycles, the market validation loops — all of those things are essential if you want to build a serious agency. But I wasn’t motivated by that. I didn’t want to spend my time running a sales engine or building a consulting org.

Instead, I found fulfillment in something simpler:

Helping people who reach out to me directly, solving real problems in real conversations.

I still enjoy consulting — but now I do it selectively, when it aligns with my interests, time, and curiosity. I’ve also learned to appreciate just how difficult and valuable it is to turn this work into a business.

Just because I didn’t want to do it doesn’t mean I don’t respect it. On the contrary — I have a deep appreciation for those who are building agencies and firms in this space the right way.

Final Thoughts

For those who are thinking of building in this space, here are a few lessons from my journey:

• Start with real customer problems, not hypothetical ones.
• Be prepared to spend more time on trust and storytelling than on code.
• Validate demand before you build a product or scale.
• Don’t underestimate how much work it takes to sell, even when the tech is great.
• Most of the value is created around the tech, not inside it.

There’s a real opportunity here for those who are willing to do the work — whether it’s one project at a time, or at the scale of a growing firm.

I may not be building a full-scale consulting business right now, but I’m always happy to chat, collaborate, and share what I’ve learned with anyone walking the same path.