AI Strategy

AI implementation: in-house, software house, or off-the-shelf

The decision is made: the company is implementing AI. Right behind it comes a second, harder question — how to implement AI in a way that doesn't end as an abandoned project. In practice, there are three paths: do it in-house, hire a software house, or buy something off the shelf.

Jarosław Jaśkowiak
Jarosław JaśkowiakJuly 19, 2026 · 3 min read

In-house

The "we'll do it ourselves" path is tempting — it looks like the cheapest option and it gives you full control. It works if someone in the company understands both the process and the tools — someone able to build the solution and with the time to finish it and keep it running. In most mid-sized manufacturing companies, that person either doesn't exist, or is the most overloaded person in the organization.

The most common failure scenario looks like this: an enthusiast from the IT department or a talented engineer starts building something after hours, a promising prototype appears, and then the project dies. There wasn't enough time, day-to-day duties buried the priority, or the author left and no one knows how to maintain it, because the knowledge lived in one head and was never written down. It's not a question of talent — it's that an implementation treated as a side project gets only leftover attention.

Software house

Hiring a specialized firm solves the problem of time and technical competence. You get a team that will build the solution professionally and on schedule. It works well for projects with a clearly defined, stable scope — when you know upfront what needs to be built.

The weakness shows up in two places. The first is distance from the process: a software house knows the technology, but not your company, so the first weeks go into explaining what work in the department actually looks like — and it's easy to end up with a solution that's technically correct but misses how things really run. The second is dependency after go-live: when the application is built outside the company, every change and every extension goes back to the vendor, and the company is left with a tool it can't touch itself. The better the implementation, the stronger this dependency becomes.

Off-the-shelf software

Buying a ready-made system — with an AI module, prediction, an assistant — looks like the fastest path. Sometimes it's the right call: if a mature product exists that covers your process without stretching it, buying can be more sensible than building anything at all.

The problem is that a mid-sized manufacturing company rarely fits a ready-made product. Processes tend to be specific, data is scattered across spreadsheets and emails, and an off-the-shelf system requires the company to adapt to its logic — not the other way around. That ends with using a fraction of the features while paying for the full subscription, or with costly customization that's closer to a software project than to a shelf purchase. I wrote about why smaller, tailored solutions beat a large system in the guide on microapps.

Comparison matrix of three AI implementation paths: in-house, software house, off-the-shelf — time to launch, process fit, dependency after go-live and main risk.
Three paths compared: time, fit, dependency and risk

What this choice misses

These three paths share a common blind spot: they treat an AI implementation as a one-off act of building a tool. The application gets built — and that's where the thinking stops. But whether an implementation survives depends on something else: whether someone stays in the company who understands how it works and can keep developing it.

That's where the fourth path I run comes in: building the solution together with the client's team, on site, transferring competence along the way. The application gets built fast, the way it would at a software house, but it doesn't leave the company together with the vendor — the knowledge of who maintains it and how stays behind. This combines the speed of an outside engagement with the independence of a solution built in-house. The first implementation stays within the narrow scope of a single department and a cycle of a few weeks, so it's quickly visible whether it hits the real problem.

How to choose

Instead of asking "which path is best", it's worth asking a few questions about your own company. Do we have someone with the time and competence to build and maintain the solution — if so, the in-house path becomes realistic. Is the process we want to support stable and typical — if so, a ready-made product or a software house make sense. Do we care about staying self-sufficient after the implementation — if so, what matters is how much knowledge stays in the company, regardless of who builds it.

The worst choice is no choice at all. An implementation that drifts between paths, because no one decided who's responsible for it and what should remain once it's done, ends up worse than any of the three paths followed through consistently.

What's next

If you're facing this choice, start by identifying where the implementation should deliver results and what the process it's meant to support looks like — because that decides which path fits, not the other way around. I describe the full path, from diagnosis through the first implementation to scaling it up, on the offer page.

See what the implementation path looks likeBook a 30-minute consultation


Jarosław Jaśkowiak

About the author

Jarosław Jaśkowiak

Over 20 years in B2B and technology. I lead Applied AI implementations in mid-size manufacturing companies — from identifying where AI delivers the fastest return, to a working tool in a single department. I write about what actually happens on the delivery side, without the hype.

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