Antonio Valderrabanos-PhD in NLP, Founder and CEO at Bitext.
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Businesses face a common challenge in trying to maximize the potential of artificial intelligence (AI): General AI models, while powerful, often do not adapt to the specific needs of each sector. This can result in reduced efficacy and relevance in business applications. The solution lies in verticalizing these models, adapting them to directly address the unique challenges and particulars of each industry.
The importance of this adaptation cannot be overstated. Companies need AI models that not only understand language and data in a broad context, but also grasp the nuances and specificities of the sector they operate in. The lack of precision in general models can lead to misinterpretations and ineffective decisions, which is particularly critical in sectors where accuracy is key, such as healthcare, finance and legal.
To address this issue, companies should consider implementing verticalized AI models. These models are trained not only with large amounts of general data, but also with sector-specific data, allowing them to provide more precise and contextually relevant responses and solutions.
For example, an AI model in the financial sector needs to understand not just general language, but also specific terminology and regulations governing the field. Similarly, in the healthcare sector, the AI’s ability to comprehend specific medical terms and clinical context is crucial for providing accurate diagnoses and recommendations.
Due to the scarcity of large amounts of domain-specific data, one increasingly popular trend is the use of synthetic data. These data allow modifying the model's behavior and adapting it precisely to the rules each company requires, thus avoiding model "hallucinations" and eliminating conflicts with legal or compliance departments.
Synthetic data, generated specifically for training AI models, ensures that models are not only accurate, but also compliant with the internal regulations and policies of each company. This is especially important in regulated sectors, where strict compliance is essential.
According to Bloomberg, companies like Microsoft, Google, and Meta are betting on synthetic data to train AI models more effectively. Synthetic data can replicate real-world scenarios without compromising privacy, making it an essential tool for future AI development. This approach not only enhances the training process, but it also helps overcome the limitations of acquiring and using real data.
Additionally, implementing verticalized AI models can significantly improve operational efficiency. By reducing the time and resources needed to adapt general models to specific needs, companies can accelerate their digital transformation and reap benefits more quickly. This adaptation can also facilitate greater personalization and enhance the customer experience, as the solutions offered are more relevant and tailored to individual needs.
Companies can effectively carry out this verticalization through:
- High-Quality, Sector-Specific Data: Without relevant and well-labeled data, AI models cannot learn and adapt appropriately.
- Synthetic Data: This data allows for modifying the model's behavior and adapting it to specific company rules, avoiding legal and compliance conflicts.
- Collaboration With AI Experts: It is essential to work with professionals who understand both the technology and the sector context, ensuring the developed models are robust and effective.
- Continuous Evaluation And Improvement: Maintain an ongoing cycle of the evaluation and adjustment of AI models to adapt to sector changes and emerging needs.
Verticalizing AI is not just a trend; it's a necessity for companies aiming to stay competitive in an increasingly digitized and data-driven market. Those who invest in adapting their AI models to their specific needs not only enhance their efficiency and accuracy, but they can also gain a significant competitive edge. Ultimately, it’s about using technology to solve real problems effectively and efficiently.
One of the applications where verticalization of GenAI can be particularly effective is in the evolution of user onboarding or AML/KYC processes. As highlighted by Deloitte, the potential for GenAI to revolutionize these areas is immense, making them more efficient and tailored to specific regulatory requirements. For more insights, you can read Deloitte's article on this topic.
In summary, the key to maximizing the potential of AI for businesses lies in verticalizing AI models. Adapting these models to the specific needs of each sector allows for greater precision, relevance and efficiency in their applications. Companies can leverage synthetic data to tackle this challenge and transform AI into a powerful, sector-specific tool for their operations. This strategy not only optimizes AI performance, but it also enables companies to make the most of their technology investments, achieving greater accuracy and efficiency in their operations.
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