AI is making custom software development profitable again. But when does it make more sense to build versus buy?

Technical contribution

June 30, 2026

Key Points at a Glance

Off-the-shelf software was often the sensible choice, not the best one. Cost, time, and capacity have long prevented custom software development.
AI is fundamentally changing these three factors. In-house development is becoming economically competitive again—even for small and medium-sized businesses, not just for large corporations.
Not every piece of software has to be developed in-house, but some do. Differentiation occurs when standard solutions are not enough.
The key question in the make-or-buy decision has shifted: it is no longer “Can we build this ourselves?” but rather “Where can we gain a real competitive advantage by doing so?”

Why Companies Have Relyed on "Buy" So Far

Many companies today use off-the-shelf software that isn't really a good fit for them. The decision to use it was made at a time when custom software development simply wasn't a viable option. Too slow, too expensive, too risky.

AI is changing precisely this situation. Development is becoming faster, more cost-effective, and scalable for smaller teams. What was previously considered too resource-intensive is becoming increasingly feasible—even for small and medium-sized businesses.

But economic feasibility alone is not a reason to choose Make. The real question is a strategic one: Which systems map core processes that no vendor is familiar with, and that therefore must be developed in-house?

The Make-or-Buy Decision: What Was Reasonable Wasn't Necessarily Right

For over two decades, one clear preference dominated: buy whatever you can. Off-the-shelf software, SaaS platforms, best-of-breed tools. Custom software for businesses was considered the exception because it was resource-intensive, difficult to scale, and often riskier than the benefits justified.

Under these circumstances, “Buy” was the sensible response, but not a strategic one. Many companies have adapted to standard processes that didn’t really fit their business.

In-house development is becoming profitable again

AI is changing three key factors that previously made Make unrealistic:

Costs: Development Is Becoming More Affordable
Speed: What used to take months is now accomplished in weeks
Scalability: Even small teams can deliver—it's not just big corporations

A GitHub study on developer productivity shows:

55% faster code generation
87% less mental effort required for routine tasks

The extent to which this potential can be realized in practice depends on the organization's level of maturity, its architecture, its data strategy, and the experience of its developers.

It’s not just the technology that’s changing; the role of developers themselves is shifting as well. Today, we talk about “forward-deployed engineers” or “embedded engineers”: software developers who are embedded directly within the business, working in a distributed manner—right where value is created. This proximity to business processes is one of the prerequisites for ensuring that custom software development creates a real competitive advantage—and doesn’t just function.

What used to take several developers months to build can now be implemented in a matter of weeks. This means that the economic rationale behind the “buy” preference no longer holds true—not for all systems, but for more than many small and medium-sized businesses currently anticipate.

Advantages of Custom Software: When Off-the-Shelf Solutions Aren't Enough

However, economic feasibility alone does not justify Make. Another criterion is decisive: Which systems map out the business processes that set a company apart from the competition and for which there is no standard solution?

Off-the-shelf software requires standard processes. Those who adapt to these processes gain efficiency but relinquish strategic control. The advantages of custom software become apparent when the software maps a company’s unique business processes—processes not included in any off-the-shelf product. This is the main difference between custom software and off-the-shelf software.

That is why Make is not a general preference. It is a deliberate choice for areas where custom software makes a difference and dependence on a single vendor becomes too costly.

When Do In-House Development Projects Fail? Not Because of the Code

Anyone seriously considering custom software development today will quickly run into the real hurdle. It rarely lies in the technology, but rather in the foundation.

Make fails when the foundation is missing:

Engineering Platform
Clear Architecture
A Robust Data Strategy
Governance

If any of these elements is missing, it creates a level of complexity that outweighs the benefits of developing custom software. In this environment, AI does not accelerate value creation—it accelerates chaos.

Anyone who takes “Make” seriously as a strategic option must therefore first answer another question: Is the foundation solid? Is the organization already capable of conducting AI-supported software development, or is it still operating on a “Prompt & Pray” basis? The platform is a prerequisite for the “Make or Buy” decision, not its consequence.

(If you're wondering what this foundation actually looks like, we've covered that in this post.)

Make-or-Buy Strategy: The Question Has Shifted

For a long time, the classic “make-or-buy” debate centered on feasibility: Can we build this ourselves? Do we have the capacity? Is it economically viable?

These questions aren't wrong. But they are no longer the most important ones.

With AI, the make-or-buy strategy is shifting from feasibility to strategy: it’s no longer “Can we develop this ourselves?” but rather “Where does our own software determine our competitive advantage?” Which systems contribute to our own value creation, and which are infrastructure that others are better suited to operate?

For example, a company with specific requirements for a CRM system might choose the “do-it-yourself” approach—implementing a solution on its own using Agentic Engineering—rather than heavily customizing a SaaS solution. In other cases, a standardized SaaS solution is exactly the right choice. Of course, even good SaaS providers are improving thanks to AI transformation. Nevertheless, the criteria that previously guided decision-making are shifting and must be reevaluated.

Make-or-buy is therefore no longer an IT issue, but a strategic one. Those who consciously address this question today invest strategically and relinquish control only where it makes sense to do so.

Custom Software Development for Small and Medium-Sized Businesses: How Dataciders Can Help

Reevaluating the make-or-buy decision is a strategic process, not a tool upgrade. As a data and AI consulting firm, we help companies tackle their AI transformation, assess their maturity level and potential, identify the right systems, and set up customized software development in a structured way—from empowering their own developers to scalable, agent-based implementation.

For more insights, visit our page on AI in software development —or contact us directly.

FAQ: Custom Software Development and Make-or-Buy

Is Custom Software Development Worth It for Small and Medium-Sized Businesses?

It’s worth it in situations where software supports unique processes and no standard solution is viable. AI makes this possible even for budgets that wouldn’t have been sufficient in the past.

When is off-the-shelf software the better choice?

With largely standardized processes and reliable vendor roadmaps, “Buy” remains a valid option for infrastructure and non-mission-critical areas.

What role does AI play in the make-or-buy decision?

AI significantly reduces the effort, time, and complexity of in-house development by speeding up code generation and reducing the amount of manual work required for routine tasks. As a result, “Make” becomes economically viable in more areas than before.

What is the most common obstacle to in-house development?

Rarely is it the code. More often than not, it’s an unclear foundation consisting of the platform, data strategy, and governance.

How do you begin a thorough make-or-buy analysis?

By honestly assessing your own level of maturity and having a clear understanding of the value you create. Only then should you make decisions regarding architecture and vendors.

About the author

As a partner at Dataciders, Christopher Klewes helps companies structure their software development and data platforms in a way that enables AI to scale effectively. His focus is on integrating architecture, platforms, and engineering processes to establish AI not merely as a tool, but as a controllable driver of the business model.