When organisations begin working with AI, a central question quickly arises: should we buy an off‑the‑shelf solution, or should we build our own? There is no single correct answer, as the decision depends on the organisation’s needs, context, and ambitions. In practice, most solutions sit somewhere between “buy” and “build.”
Building an AI solution in‑house offers a high degree of flexibility and control. It makes it possible to tailor the solution to specific business needs and integrate it closely with existing systems. However, this approach also requires significant resources—both in terms of specialised skills in data science, machine learning, and software development, as well as ongoing maintenance and operations.
Buying a ready‑made solution—typically delivered as a SaaS platform—allows organisations to get up and running much faster. The provider is responsible for infrastructure, updates, and scaling, which significantly reduces internal complexity. The downside is less control over functionality, data, and the future development of the solution, and costs may increase over time—especially with usage‑based pricing models.
Before making a decision, it is crucial to understand the specific problem you are trying to solve. Not all challenges require AI, and it is important to assess whether AI actually creates value in the given context. At the same time, organisations should evaluate their own capabilities, existing technology stack and partnerships, as well as the total cost—both in the short and long term.
Data is a central factor in any AI solution. If an organisation works with sensitive or regulated data, this can significantly influence the choice between buy and build. Regulations such as GDPR and the EU AI Act impose requirements on how data may be used and stored, which can limit the use of certain external solutions—especially if data is processed outside the EU.
Operational requirements and scalability must also be considered. How many users should the solution support? How critical are uptime and performance? Does the solution need to operate in real time? These factors have a major impact on both architectural decisions and cost.
In many cases, the best approach is a hybrid model that combines purchased components with custom‑built elements. This allows organisations to strike a balance between speed, flexibility, and control.
The conclusion is that the choice between buy and build should be based on a holistic assessment of business needs, technology, and data. By taking a structured approach to problem understanding, data readiness, capabilities, and implementation strategy, organisations can make better decisions and ensure maximum value from their AI initiatives.
