How contextualization unlocks the potential of generative AI

As generative artificial intelligence (GenAI) develops at a rapid pace, contextualization is critical for businesses looking to fully leverage its true value

In just a short time , generative artificial intelligence has moved from science fiction to reality, causing companies to catch up with its rapid development and identify its practical applications. According to a recent survey, 70% of organizations are exploring ways to leverage this technology, and nearly half (45%) have increased investments in artificial intelligence (AI) due to the popularity of generative AI.

In this trend, an important question is: How can companies ensure a strong return on investment and fully utilize the capabilities of generative artificial intelligence? The answer lies in contextualization. Enterprises must partner with a solution provider with broad domain expertise to effectively implement the technology

Industry expertise is critical

For generative AI solutions to work, Organizations must seamlessly integrate this into domain and industry knowledge, process expertise, niche technologies, industry-specific applications, cutting-edge data analytics, artificial intelligence capabilities, and innovative business models. This requires integrating large language models (LLM) with industry-specific artificial intelligence/machine learning (ML) models and deploying them within specific departments of the enterprise. Taking the aviation industry as an example, customers often ask Modify or cancel your reservation and request a refund. Handling these inquiries requires fast, accurate responses, a process that can be tedious and error-prone, potentially impacting the customer experience. In this case, a generative AI partner with expertise in the aviation industry can seamlessly embed its model into the airline’s systems, ensuring a faster, more accurate and cost-effective process that enhances the customer experience

In another example, an insurance company is faced with a large number of car accident claims. Contextualized generative AI cognitive data extraction platforms can effectively integrate various elements of unstructured information from insurance companies (such as call center records and bot conversations) to effectively process these claims. Additionally, contextualized generative AI can identify subrogation opportunities and calculate associated amounts. It turns out that training language models in an industry-specific context is more effective than relying on generic datasets.

Industry contextualization and domain expertise can significantly improve the LL.M.’s capabilities in content generation, content extraction, summarization, translation and knowledge management.

The Significance of a Collaborative Approach

Unlike the traditional customer model, contextualization requires a partnership between the enterprise and a new generation of AI providers. By working closely together and actively participating in generative AI pilots, proof-of-concepts, and cross-functional R&D projects, they can unlock the full potential of generative AI.

As the technology evolves, enterprises and their business process management partners must work together to upskill their teams in the use of generative AI, create new roles and opportunities, and enhance operational capabilities and responsibilities .

Ethical Deployment: Ensuring Responsible Generative AI Adoption

Given the concerns expressed about AI by governments, academicians, and industry experts, generative AI providers and their partners must Committed to responsible development and deployment. This includes meticulous governance structures, data privacy and regulatory compliance security measures, and tools to reduce bias.

To fully realize the huge potential of generative AI, enterprises and their BPaaS providers should build ecosystems that enable data scientists and AI experts to work closely with domain experts to apply generative AI to various industries. Enable everyone to benefit from generative AI technologies through proven, ethical and innovative approaches

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