Artificial intelligence 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. Organizations 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 customization 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.
Why generic AI falls short
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 analyze X-ray images. In the financial sector, a
general model cannot detect fraud, simply because it does not recognize 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 organizations
are opting for domain-specific AI solutions that better meet their needs and add direct value
to their business operations.
Sectors where custom AI is essential
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 analyzing MRI scans and X-rays. Custom models can detect subtle
abnormalities that are difficult for human doctors to recognize. This increases the
accuracy of diagnoses and can save lives.
● Research and education: Universities and research centers use AI for complex data
analyses. Depending on the field, models can, for example, analyze 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 recognize 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
minimizing downtime.
Challenges in implementing domain-specific AI
While the benefits of domain-specific AI are evident, implementing it also presents
challenges. Organizations looking to deploy customized 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 labeling.
• Data security and sovereignty: Many organizations, especially in regulated sectors
such as healthcare, finance, government, and energy, must ensure that sensitive
data remains protected and compliant. Intellectual property, patient records, financial
transactions, and proprietary research cannot simply be exposed to public cloud
training environments or shared external datasets. Maintaining full control over where
data resides and how it is processed is crucial to preserving confidentiality,
compliance, and competitive advantage.
• Infrastructure requirements: AI workloads can grow quickly and unpredictably,
especially as models evolve, are retrained, or require different types of processing.
This makes it important for companies to have an infrastructure that can scale
seamlessly, unify Computation and Storage, and support both development and
production environments without introducing operational complexity. When the
infrastructure is fragmented or built from disconnected systems, performance
bottlenecks, higher costs, and delays in value delivery and usecases can arise.
• Expertise: Developing and training domain-specific models requires specialized
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.
The role of a strong infrastructure
A robust and flexible IT infrastructure platform is essential for the successful implementation
of AI solutions, especially in complex domain-specific applications. An environment that
brings compute, storage, and data processing closer together helps AI models to be trained
and deployed more efficiently, while reducing unnecessary data movement and operational
overhead.
A scalable and easily managed platform ensures that organizations can start small and
expand as AI initiatives grow, without needing to constantly re-architect or replace underlying
systems. This allows teams to experiment, refine, and operationalize models faster,
supporting continuous improvement and adaptation to new datasets and business needs.
In addition, a strong infrastructure plays a crucial role in maintaining data security and
sovereignty. For organizations working with confidential, regulated, or proprietary data,
keeping information within controlled environments is essential. A cohesive platform that
ensures secure data processing, access governance, and compliance controls allows
companies to leverage AI without exposing sensitive datasets to external or unmanaged
environments. This enables innovation while preserving privacy, trust, and regulatory
alignment.
With a future-proof infrastructure, companies can respond quickly to changing requirements
while maintaining performance, reliability, and cost efficiency. This forms the foundation for
domain-specific AI to deliver sustained value in day-to-day operations
Customized AI as a strategic advantage
Domain-specific AI is no longer a niche solution, but a necessary step for companies that
want to realize the full potential of artificial intelligence. Organizations that focus on
customization 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
