AI: Build it or buy it – 6 reasons for each approach

Jan 26, 2023

What does "build or buy" mean?

“Build or buy” decisions deal with the question of whether certain services or products should be procured from external suppliers or produced and handled by the company itself. The criteria of costs, quality, time, resources and risks play a role here. Often, a predefined corporate process takes place for this purpose in order to guarantee an objective decision.

The question of whether your business needs artificial intelligence (AI) is not even up for debate these days – because the answer was very clearly “yes” in almost every industry around the world.

According to forecasts, by 2025, revenue from enterprise applications using artificial intelligence may be as high as $31.24 billion. AI opportunities are being exploited in a wide variety of areas: to improve the customer experience, to generate new revenue, and to reduce costs. So the question that arises is not about the “if” of implementing AI software, but the “how.”

To this end, we want to shed light on the various aspects of a “make or buy” decision with regard to AI software.

The question of whether your business needs artificial intelligence (AI) is not even up for debate these days - because the answer was very clearly "yes" in almost every industry around the world.

Why not just buy existing products?

In many areas of IT and software development, the trends are very clear. There is a product for almost everything and many start-ups have specialized in building scalable and cross-company products, for example, to optimize processes. Software products in general are also very well suited for this, the code can be duplicated and reused very well.

So why not buy AI products as well?

AI needs data to learn. Of course, internal company data helps the most. So there would have to be products that learn on individual company data. These products exist, but only for very specific application areas that pretty much every company has. These include pricing and forecasting, for example. With the help of Salesforce Einstein and many other AI products, these use cases can be covered very well via a product, because these products can be integrated into a CRM system, which many companies use and many companies want to use AI precisely in this area.

6 reasons for standard products of AI software

  • IT infrastructure for implementation already in place
  • Low risk
  • Interfaces to other systems already available
  • Exchange of experience between users possible
  • Quick to implement
  • Likely relatively cheap


In many other use cases and if you want to make major changes to your current business model or even start a new, innovative company, there is no question that the AI software required for this should be produced in-house. This guarantees one as a company the intellectual property of the development and therefore means a great competitive advantage over other market participants. You can develop exactly the features that meet your own ideas and needs. It also offers more flexibility and creativity in development. This means that changes and new functions can be implemented independently as required. If the product succeeds as expected, one saves ongoing licensing costs and maintenance fees in the long run, which are often incurred with purchased AI software.

On the other hand, the risks are of course higher and one must not forget that there is a possibility of the project failing. Developing your own software takes a lot of time and, above all, qualified employees who are not available for other projects during this time – keyword opportunity costs. So there is a risk that the core business will suffer. In addition, training in the use of new software must be carried out by the company’s own employees, which represents an additional time factor. Furthermore, high fluctuations of employees are common, especially in the IT area. If one of the developing or already trained employees leaves the company, additional costs are incurred for training new specialists.

Next consider your context: Got a running Active Directory? Or a set of engineers that already has certifications in cloud X? Got applications that lend themselves to one of the providers? What can help in the transition? In general, there are many questions here and no very obvious answers. Feel free to send me a message as well, I’m always up for talking about this subject.

6 reasons to develop your own AI software

  • All rights to the product
  • 100% fit to your domain and data and thus likely higher quality of results
  • High degree of flexibility
  • Independence from vendors
  • Attractiveness for hiring tech roles
  • Company perception as a thought, technology and innovation leader


If you develop AI software yourself, there is a large cost block at the beginning as well as no income. After completion of the product, however, it is not the case that you no longer have any costs. Permanent optimizations, training of the employees and maintenance cause continuous costs. With the introduction of the product, the revenue of the company is now slowly increased. This can develop rapidly if the performance is as desired. However, there is a risk of not achieving the desired goals.

In contrast, purchased AI software offers a great advantage: due to the possibility of being able to test the performance in advance, there is no risk. The goals you set for yourself can be achieved – right from the moment the software is deployed.

Conclusion: Build or buy AI software

In summary, it can be said that when making decisions regarding AI software, companies should first consider where exactly they want to use it. If the area of application relates to the core business of a company, then the corresponding product should be developed in-house. In doing so, one has more flexibility and later also all intellectual property rights.

However, if you want to use AI software to improve certain use cases like pricing or forecasting, third-party AI software might be suitable. This saves resources in the form of time and costs and reduces the risk by testing the performance.

Robert Kaletsch

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