Domain-specific AI: why customisation is the future

Tunde Abagun, Sales Lead, West, East, Central Africa, Nutanix.

---

Artificial intelligence (AI) has developed rapidly in recent years. Generative AI (GenAI) is rapidly gaining ground and is now widely used, in applications and from automated customer service to advanced data analysis. 

However, in practice it turns out that a one-size-fits-all model is often not sufficient. Organisations encounter limitations when AI solutions are not tailored to their specific sector or field. 

Gartner therefore predicts that by 2027 more than 50% of AI models will be sector-specific. This customisation will lead to more accurate and relevant results, because the models are trained on datasets that specifically match the issues and dynamics of a particular industry.

Many companies are currently experimenting with general AI models, but in practice they often encounter various challenges. For example, an AI model that is not specifically trained on medical data may struggle to correctly analyse X-ray images. 

In the financial sector, a general model cannot detect fraud, simply because it does not recognise all the complex patterns that are important in this industry.

In addition, training AI models on industry-specific data often requires a different approach. Collecting and processing qualitative and representative datasets is a skill in itself. 

Without well-structured data, an AI model remains limited in its capabilities, which can lead to inefficient use of resources and wrong decisions. 

As a result, more and more organisations are opting for domain-specific AI solutions that better meet their needs and add direct value to their business operations.

The benefits of domain-specific AI are visible in almost every sector. Some examples:

Healthcare: AI is playing an increasingly important role in medical image recognition, such as analysing MRI scans and X-rays. Custom models can detect subtle abnormalities that are difficult for human doctors to recognise. This increases the accuracy of diagnoses and can save lives.

Research and education: Universities and research centres use AI for complex data analyses. Depending on the field, models can, for example, analyse genetic datasets, simulate climate change or study linguistic patterns. Generic models often lack the necessary depth and precision to provide useful insights.

Financial sector: Banks and insurers rely on AI for fraud detection and risk analysis. Algorithms that are specifically trained on transaction data can recognise suspicious patterns that might otherwise go unnoticed. This contributes to a safer financial ecosystem.
Manufacturing: In the manufacturing industry, AI is used for quality control and predictive maintenance. Domain-specific models can detect anomalies in production lines or predict when machines need maintenance, increasing efficiency and minimising downtime.

While the benefits of domain-specific AI are evident, implementing it also presents challenges. Organisations looking to deploy customised AI models must consider several key factors:

• Data quality and availability: The success of AI depends on the quality of the data on which the model is trained. Domain-specific AI requires reliable, well-structured, and representative datasets. This requires a thorough approach to data collection, cleaning, and labelling.
• Infrastructure requirements: AI models require significant computing power and storage capacity. Companies must have scalable hardware and storage solutions to train and deploy models efficiently.
• Expertise: Developing and training domain-specific models requires specialised knowledge. Data scientists and AI experts play a crucial role in this, but these professionals are in short supply. Investing in the right talent and partnerships is therefore essential.

A robust IT infrastructure is essential for the successful implementation of AI solutions, especially when it comes to complex domain-specific applications. A flexible and scalable infrastructure ensures that AI models can be trained, tested and rolled out efficiently without companies having to make large investments in new technologies every time. This lowers the threshold for getting started with AI and accelerates its adoption within organisations.

With a future-proof infrastructure, companies can quickly adapt and optimise AI models based on changing needs. This is especially important for domain-specific AI, where models must be continuously refined and trained on new datasets to remain relevant and effective.

Domain-specific AI is no longer a niche solution, but a necessary step for companies that want to realise the full potential of artificial intelligence. Organisations that focus on customisation benefit from better performance, more efficient use of resources and faster innovation.

The key to success lies in a strategic AI approach, in which the right balance is found between data, infrastructure and expertise. By investing in a solid foundation, companies can use AI smartly and purposefully, gaining a competitive advantage in a world increasingly driven by automation and intelligent technologies.