One question keeps coming up in my work: build or buy? For the past 20 years, the answer in most cases was standard. The platforms were set, the licenses were running. Then AI hit, turned the market upside down, and gave the question a completely new meaning. Build-or-buy today isn't about what companies can afford anymore. It's about where they want to differentiate from the competition.
I've spent a significant part of my career on the standard software side: scaling platforms, closing deals, guiding companies through implementations. What I learned is that most build-or-buy decisions were rarely strategic. They were economic. Sure, feature scope and security played a role too. But on feature scope specifically, companies were paying 100% of the license cost for a utilization rate that rarely topped 30%.
And it makes sense; why would you invest tens of thousands in custom software that does the same thing as an off-the-shelf solution? Building custom software costs money, or at least, that's how it looked before AI: six to twelve months of runtime, teams deep in development, project risk. Standard software was the answer because, for most companies, the alternative simply wasn't economically viable, not because standard was always the better solution.
The result is familiar to many:
The numbers behind this are unambiguous:
When the answer is "buy," companies aren't buying software. They're buying access to a platform and paying for what the majority of users only use a fraction of. The business gets built around a product designed for thousands of use cases, not for one specific company.
So the problem was never the vendor. It was that companies had no economically realistic alternative for their differentiating processes. There's a reason more companies didn't recognize this sooner: the economics simply didn't allow for it.
The decision to go with standard software was, until now, rational, not ideal.
Gartner has long distinguished between "systems of record" (payroll, HR, accounting) and "systems of differentiation". Regarding the former: buy. No sensible CEO built their own payroll system 20 years ago, and none does today.
But for the latter, the systems that set companies apart from their competitors, custom software has always been where the truly successful companies built their edge: industry-specific workflows, customer-centric platforms, operational processes.
Mimacom has been building exactly in this space for over 25 years. The clients who came to us even before AI didn't choose custom software because they had money to spare. They chose it because their competitive advantage was baked into their processes: new business models, excellent customer interactions, additional revenue streams. No standard software can capture that without compromise.
That's the actual shift, and the debate regularly frames it wrong. AI doesn't necessarily make software smarter. AI makes custom software dramatically cheaper and faster to build. That's a fundamental difference. And it opens up new possibilities: affordable customizations, easy-to-implement scaling, and much more.
| Area | Before | Today |
|---|---|---|
| Project timeline | 6-12 months | Weeks |
| Development cost | ~€100,000 | ~€50,000-60,000 |
| Time-to-first-value | Quarters | Weeks |
With AI, that barrier has structurally disappeared for most companies.
An important caveat is that these gains don't happen automatically. Teams that simply bolt AI onto existing processes see only marginal improvements. Teams that consistently redesign their entire development process capture the full effect. The technology has changed. The discipline of software engineering has not.
When I show the numbers on AI productivity, the reaction is rarely skepticism. Usually it's agreement, followed by a moment of silence. The next question, the harder one, is: where exactly does our competitive advantage lie, and does our current software help or hold that back?
One of our machinery manufacturing clients runs most of its operational processes on proven standard platforms, and at the same time built a custom compliance solution with Mimacom that brings together proprietary supply chain, material, and regulatory data that no standard vendor could ever map. The results are remarkable: 80% of data reconciliation is automated, product series analyses can be carried out in 2.5 hours instead of days, and compliance becomes a strategic advantage instead of a cost factor.
Here's another example. A regulated financial institution didn't want a single piece of data, a single query, or a single internal document leaving for external cloud infrastructure, often the case with standard software vendors. Ten years of institutional knowledge sat unused in PDFs as a result.
Within two months, instead of the originally estimated eight, Mimacom and the client built a fully on-premise AI assistant that makes internal reports queryable in three languages. Answer accuracy rose from 48% to 97%. And with it came no vendor lock-in, zero data exposure, and full control in the hands of the internal team.
Deloitte has reported that only 34% of companies are genuinely redesigning their business model with AI. The rest are optimizing what already exists. The competitive gap between these two groups will become very visible over the next three years.
I regularly talk with CIOs, product leaders, and executives who consider the build-or-buy decision settled. My take: it's worth questioning again.
The companies that asked these questions early are in a different position today, not because they invested more – but because they invested in the right places. That's the difference between software as a cost factor and software as a competitive advantage.