Before You Invest in an ML Project - Case Study

Wed, 23 Jul 2025 12:35:00 +0000
Machine Learning, Workshop, ML project, ML project management

More and more companies are beginning to consider machine learning – whether to increase process automation, improve user experience, or keep up with the competition. An idea appears – sometimes inspired by a conference, a conversation with the tech team, or a signal from the market that "others already have something with AI, and we don’t yet."

At that point, there’s often a temptation to jump straight into execution: hire a team, pick a tool, start training something – just to "see how it goes." From my perspective, as someone who has been working on machine learning projects for years, that’s a straight path to frustration and a burned budget.

Machine learning is not a plug-and-play technology. Its effectiveness doesn’t come down to the model alone but to the entire environment: the data, processes, integrations, and, above all, the correct problem definition. That’s why a machine learning workshop is not a formality but a critical project phase. As a proof, take a look at our success story from one of our projects, which started as an ML workshop.

The workshop is the moment when expectations meet reality:

We confront the idea with the actual historical data the company has (or doesn’t have)

We verify whether the problem the company wants to solve makes sense from an ML perspective

We define how to measure success, both technically and from a business point of view

We present alternative ways of reaching the project goal and estimated budget; sometimes simpler, cheaper, or more scalable

We define scope, timeline, and costs - so you can evaluate ROI and verify if the project fits your budget

This blog post explains how we ran a tailored machine learning workshop for a client building a data extraction platform for accounting professionals.

In the first part, you'll learn about the client's business context: who the client is, what problem they wanted to solve, and why they reached out to us. Next, I describe how we structured and prepared the workshop: from designing the template to drafting a realistic project timeline.

We then show how the workshop unfolded in practice: what data we reviewed, which technical and business constraints surfaced, and what insights we gathered about the users and platform. Finally, we explain how this input shaped the final proposal, including cost estimates, success metrics, and a deployment-ready roadmap.

If you're wondering what kind of value a well-run ML workshop can bring, and how it directly translates into smarter investment decisions, this article gives you a complete, real-world example.

It is a long read, so grab a coffee and set aside about 15 minutes. It’s a small investment but definitely worth your time.

Business context: client, need, technology

The client

We were approached by a company developing a platform for accounting professionals. Its core value lies in automating the process of extracting data from invoices and feeding it into ERP systems or finance management platforms.

How did they find us?

The initial contact came through a team...