Artificial intelligence is no longer just about adopting the latest model—it’s about adopting the right one. As organizations move from experimentation to real-world implementation, generic AI is proving insufficient for complex, industry-specific challenges. Domain-specific AI, built on tailored data and infrastructure, is emerging as the key to unlocking true business value.
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.
“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”
Ahmed Rashad, Sr. AI Specialist, Middle East & Africa at Nutanix
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.
Bio of Author
Ahmed Rashad serves as Senior AI Specialist for the Middle East and Africa at Nutanix, where he leads efforts to design and deploy advanced AI solutions tailored to regional business needs. With deep expertise in both generative AI and domain-specific modeling, Ahmed helps organizations across sectors—including healthcare, finance, manufacturing, and research—translate complex data into actionable insights. His work emphasizes the importance of robust, scalable infrastructure, data quality, and regulatory compliance, ensuring that AI initiatives are both efficient and secure. Through a strategic blend of technology leadership and regional insight, he enables companies to harness AI responsibly and effectively to drive innovation, growth, and competitive advantage.
