The right AI strategy: buy, integrate or build it yourself?

Home / The right AI strategy: buy, integrate or build it yourself? Many companies now realise that they need to get to grips with artificial intelligence. But as soon as it becomes concrete, a central question arises: should we buy AI, integrate it into existing systems or develop it ourselves? The answer depends on several factors: the company's own goals, the existing IT landscape, the available expertise and, of course, the budget. While some companies achieve quick results with ready-made AI tools, others need customised solutions that are deeply integrated into their processes. This article shows the three most important strategies for the use of AI in companies and helps you make the right decision. Briefly explained: Which AI strategy suits which company? Many decision-makers are looking for precisely this answer. So here's a direct summary: Situation Suitable strategy Fast entry into AI Buy AI solution Make existing systems more intelligent Integrate AI Strategic or highly customised applications Develop AI yourself In practice, many companies combine several of these approaches. Why companies use AI at all Artificial intelligence is rarely introduced because it is „modern“. In reality, it is almost always about very specific improvements in everyday working life. Many processes in companies are still heavily manual. Employees search for information, analyse documents, answer recurring questions or transfer data between different systems. This is precisely where AI can make a huge difference. Typical areas of application are, for example: Automatic analysis and processing of documents Support in customer service Intelligent search in company knowledge Automation of routine processes Evaluation of large amounts of data Generative AI in particular has opened up new possibilities in recent years. Modern language models can summarise content, generate texts or serve as intelligent assistants in everyday working life. However, the key point here is that added value is only created when AI is meaningfully integrated into existing processes. Option 1: Buy AI (SaaS solutions) The quickest way to utilise AI is to use ready-made tools. Many providers make their solutions available as a cloud service so that companies do not have to set up their own infrastructure. These solutions can often be introduced within a few days or weeks and are particularly suitable for getting started with AI. Typical examples include chatbots, generative text tools and document analysis systems. Advantages very quick introduction low initial investment no in-house infrastructure required continuous further development by the provider Disadvantages limited customisation options dependence on the provider integration into existing systems often limited possible data protection or compliance issues Ready-made AI solutions are therefore particularly suitable if companies want to gain initial experience with AI quickly or if there is a clearly defined use case. Option 2: Integrate AI into existing systems Many companies decide to integrate AI directly into their existing applications. Instead of using an additional tool, the technology is used where it is actually needed in day-to-day work. A typical example is an ERP system that automatically analyses documents or merges information from different sources. Internal knowledge assistants or AI-based search functions are also often part of this approach. The major advantage of this strategy is that AI becomes part of existing processes. Advantages better integration into existing systems use of existing company data higher acceptance in day-to-day work individual customisation possible Disadvantages higher implementation costs technical expertise required longer project duration Technically, this integration strategy is often based on platforms such as Azure OpenAI or comparable AI services. They enable companies to specifically integrate generative AI into existing applications without having to redevelop entire systems. On this basis, internal knowledge assistants, automated document analyses or intelligent search functions can be implemented, for example. Among other things, the collana Group develops customised solutions based on generative AI in Azure that can be specifically integrated into existing company systems. More on generative AI for companies Option 3: Develop AI yourself In some cases, neither standard solutions nor integrations are sufficient. This is particularly the case for companies where AI is to become a central component of the business model or where there are very specific requirements. In these cases, custom models are developed or existing systems are heavily customised. Advantages maximum flexibility complete control over data and models high adaptability strategic competitive advantage possible Disadvantages high development costs need for specialised AI teams longer time-to-market ongoing maintenance and operation necessary Especially in the case of sensitive company data or strict compliance requirements, it can make sense to operate AI entirely within the company's own infrastructure. In such cases, local LLMs are often used - i.e. large language models that are operated locally and do not send any data to external providers. The collana Group also develops corresponding solutions for companies that want to operate AI entirely within their own IT infrastructure. Such local LLM solutions make it possible to utilise AI functions while retaining full control over sensitive data. More on local LLM solutions Decision-making factors for the right AI strategy The decision between buying, integrating or developing in-house usually depends on four key factors. Costs Ready-made solutions are often cheap to start with, but can become expensive if used intensively. In-house developments require a higher initial investment. Expertise Building your own AI systems requires data scientists, ML engineers and software developers. Without this expertise, an integration or purchasing strategy usually makes more sense. Time-to-market If a solution needs to be available quickly, SaaS products are often the fastest way to go. Flexibility The more individual the requirements are, the more worthwhile in-house development or customised integration is. The best place to start: a clear AI roadmap Many companies do not start their AI journey directly with technology, but with a structured analysis of their processes. This involves identifying possible AI use cases, evaluating the potential benefits and checking the technical feasibility. A structured approach helps to avoid typical bad investments and quickly identify the projects that promise the greatest added value. One example of this is the collana Group's AI discovery workshop, in which companies work with AI experts to develop specific use cases and define a realistic implementation strategy for the use of AI. More about the AI discovery workshop Conclusion There is no one right AI strategy for all companies. Instead, there are three basic options available: Buy AI for quick results Integrate AI for intelligent business processes Develop AI yourself for maximum control and individuality Many organisations combine these approaches and develop their own AI landscape step by step. The decisive factor here is not the technology itself, but the question of which problems in the company really need to be solved. Practical example → AI voice assistant from collana AI-supported voice assistants