Make or buy with AI: when in-house development really pays off - and when it doesn't

Digital transformation with AI is no longer an option, but a necessity for companies that want to remain competitive. However, the decision to use artificial intelligence is just the beginning of the strategic course. One of the most fundamental questions that managers from SMEs to large corporations throughout the DACH region have to ask themselves is: do we develop our own AI solution (make) or do we buy a ready-made solution (buy)?

This decision is far more than a technical detail. It is a crucial pillar of your AI strategy and has far-reaching consequences for your finances, your resources and ultimately your market position. A wrong decision can lead to millions lost, time wasted and a loss of competitive advantage. The right decision, on the other hand, can catapult your company into a new era of efficiency and innovation.

In this article, we look at the critical factors you need to consider when making the make-or-buy decision for artificial intelligence in your company need to consider. We offer you a sound framework for finding the right option for you and show you how to set the course for a successful AI future.

1 The strategic dilemma: Why the make-or-buy question is so crucial

The introduction of AI solutions for companies is comparable to building a new plant or opening up a new market. It is an investment in the future. The appeal of developing a customized solution that is perfectly tailored to your own needs is great. It promises maximum control and a unique competitive advantage.

At the same time, the market is full of powerful, ready-to-use AI platforms and applications. These promise a fast return on investment (ROI), lower risks and access to cutting-edge technologies without the need to build your own team of data scientists.

The truth is: there is no one-size-fits-all answer. The optimal decision depends on your specific context, your goals and, above all, your level of AI maturity. A company that is just starting to work with ChatGPT in the company has completely different requirements than an industrial company that has been using predictive maintenance in its production for years.

2. the "make" approach: when in-house development of AI becomes a success factor

Developing your own AI solution is an ambitious and resource-intensive undertaking. However, there are strategic scenarios in which this path not only makes sense, but is absolutely necessary in order to stand out from the competition.

Main reasons for in-house development ("Make"):

• Unique competitive advantage (USP): If the planned AI application is a central component of your business model and creates a direct, unique customer benefit that no one can copy. Example: A financial services provider develops a proprietary risk assessment algorithm based on decades of internal data.
• Highly specific requirements and data: Standard solutions reach their limits if your processes or data structures are extremely company-specific. If you have unique, valuable data sets that are at the heart of your success, you should retain control over them.
• Maximum control and flexibility: You need full control over the architecture, further development, data security and integration into your existing IT landscape. In-house development allows you to shape the solution exactly according to your wishes and adapt it at any time.
• Building internal expertise: You see AI as a strategic core competence for the future and want to build up internal knowledge and talent in a targeted manner. This is a long-term investment in the future viability of your company.

The flip side of the coin: the "make" approach requires significant upfront investment, specialized experts (data scientists, ML engineers), a robust data infrastructure and staying power. Project durations are often long and the risks of failure should not be underestimated.

3. the "buy" approach: why ready-made AI solutions are often the smarter choice

For the vast majority of companies, especially in the SME sector, purchasing an established AI solution is the more pragmatic and economical option. The market today offers an impressive variety of applications - from AI process optimization to AI in knowledge management.

Main reasons for purchasing a ready-made solution ("Buy"):

• Fast implementation and time-to-market: Instead of investing months or years in development, you can often implement a ready-made solution within a few weeks or months and achieve initial success immediately. Speed is a decisive advantage in the age of digital transformation with AI.
• Solutions for standard problems: Many business challenges are not unique. Highly developed, proven solutions already exist for use cases such as the automation of customer service, the optimization of logistics orpredictive maintenance.
• Lower costs and risks: The initial investment is significantly lower than with in-house development. The costs can be planned (e.g. through license fees) and the risk of an expensive failure is minimized as the solution is already in use with other customers.
• Focus on the core business: You can concentrate your valuable internal resources on what makes your company special instead of building a software development company in-house.
• Access to state-of-the-art technology: Providers of AI solutions invest heavily in research and development. They continuously benefit from the latest technological advances without having to bear the costs themselves.

Of course, "buy" also means a certain dependency on the provider and possibly less flexibility in terms of customization. Careful provider selection is the key to success here.

4. the hybrid middle way: combining the best of both worlds

The reality is often not black and white. A hybrid approach can be the optimal strategy for many companies. An existing AI platform or infrastructure (e.g. from providers such as Microsoft Azure, Google Cloud AI or specialized platforms) is purchased as a basis and specific, business-critical modules are developed in-house based on this.

This approach combines the stability and scalability of a proven platform with the ability to train your own differentiating AI models with internal data. This is often an excellent way to achieve quick wins and build strategic know-how at the same time.

5. your roadmap to the right decision: a structured approach

So how do you find the right path for your company? An informed decision requires an honest analysis and a clear strategy. As experts in AI consulting, we at CorpIn support companies in precisely this process. Our experience shows that the following steps are crucial:

Step 1: Determine your AI maturity level

Before you think about "make or buy", you need to know where you stand. Do you already have a clean database? Are there AI skills in the team? What is the attitude of the management level?

• Our tip: Use our CorpIn Hexagon platformto empirically assess your AI maturity level. The self-assessment is based on our scientifically developed CorpIn Hexagon model and gives you a clear assessment of six key dimensions of AI readiness in just a few minutes.

Step 2: Develop a clear AI strategy

An AI solution is not an end in itself. It must contribute to your overarching corporate goals. Define which specific problems you want to solve and which opportunities you want to seize. Whether AI in production, marketing or finance - the goals must be clear.

Step 3: Identify and evaluate specific use cases

Analyze your value chain and identify processes with the greatest potential for AI optimization. Evaluate each use case in terms of its strategic value, complexity and potential ROI.

Step 4: Carry out a holistic cost-benefit analysis

Don't just compare the pure development vs. license costs. Consider the total cost of ownership (TCO), including maintenance, training, data management and internal resource commitment.

Step 5: Consult external expertise

The make-or-buy decision is complex. An external, neutral partner can help you ask the right questions, avoid pitfalls and make an objective decision. In our AI workshops for companies, we not only impart knowledge, but also work with you to develop the foundations for your strategy.

6 Conclusion: Not a "one-size-fits-all", but a strategic necessity

The decision to "make or buy" AI systems is one of the most important strategic decisions for companies in the DACH region.

• "Make" is the path for pioneers with unique data and processes who define AI as an absolute core competence and are prepared to make a long-term, resource-intensive investment in an uncopyable competitive advantage.
• "Buy" is the smart, pragmatic choice for most companies looking for proven solutions to standard problems in order to quickly increase efficiency, reduce costs and focus on their core business.
• "Hybrid" offers an intelligent middle way to benefit from the stability of ready-made platforms and at the same time build up your own differentiating capabilities.

The key to success lies in honest self-assessment and a clear, data-driven strategy. Initiatives such as our Swiss AI Report 2025 show that successful companies start their AI journey with solid planning.

Your next step towards a data-driven future

The world of artificial intelligence is developing rapidly. Don't hesitate to set the course for your future. Whether you are at the beginning of your journey or already evaluating specific projects, having the right partner at your side can make the difference between success and failure.

Are you unsure which path is the right one for your company?

Let's talk about it. We offer you independent AI advice tailored to your specific needs.

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