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Contextualization: The Key To Unlocking Generative AI's Potential
Contextualization: The Key To Unlocking Generative AI's Potential

October 24, 2023

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As generative AI (GenAI) evolves at lightning speed, contextualization is essential for businesses wanting to leverage its true value.

In the blink of an eye, GenAI has transitioned from science fiction to reality, leaving businesses racing to keep up with its rapid evolution and discern its practical applications. A recent survey by Gartner revealed that 70% of organizations are exploring ways to harness the power of this technology, with nearly half (45%) increasing their artificial intelligence (AI) investments due to the buzz surrounding GenAI.

Amid this frenzy, the vital question is: How can companies ensure substantial returns on their investments and fully tap into the capabilities of GenAI? The answer lies in contextualization. Businesses must partner with solution providers with extensive domain expertise to effectively implement this technology.

The Paramount Importance Of Industry Expertise

For GenAI solutions to be effective, organizations must seamlessly integrate them into domain and industry knowledge, process expertise, niche technologies, industry-specific applications, cutting-edge data analytics, AI capabilities and innovative commercial models. This requires integrating large language models (LLM) with industry-specific AI/machine learning (ML) models and deploying them within the company’s specific sector.

Consider the airline industry, where customers frequently seek to modify or cancel their bookings and request refunds. Managing these queries demands swift and accurate responses, which can be a tedious and error-prone process, potentially leading to sub-optimal customer experiences. Here, a GenAI partner with domain expertise in aviation can seamlessly plug its models into the airline’s systems, ensuring faster, more accurate and cost-effective processes and enhanced customer experience.

In another example, insurance companies face a multitude of motor accident claims. Contextualized GenAI cognitive data extraction platforms can effectively integrate various elements of unstructured information from insurers, such as call center transcripts and bot conversations, to efficiently handle these claims. Furthermore, contextualized GenAI can identify subrogation opportunities and calculate relevant amounts. Training language models in an industry-specific context can prove more effective than relying on generic datasets.

Sector contextualization and domain expertise can significantly improve features like content generation, content extraction, summarization, translation and knowledge management for LLMs.

The Significance Of A Collaborative Approach

Moving away from the traditional seller-client model, contextualization calls for a partnership approach between businesses and gen AI providers. By closely collaborating—actively engaging in GenAI pilots, proofs-of-concept and cross-functional R&D projects—they can unlock the full potential of GenAI.

As the technology evolves, businesses and their business process management partners must work together to upskill their teams on GenAI usage, create new roles and opportunities and augment capabilities and responsibilities for operations.

Ethical Deployment: Ensuring Responsible Generative AI Adoption

Considering the concerns surrounding AI expressed by governments, academicians and industry experts, GenAI providers and their partners must commit to responsible development and deployment. This includes meticulous governance structures, data privacy and security measures for regulatory compliance and tools to mitigate biases.

To capitalize on the immense potential of gen AI, companies and their BPaaS providers should foster ecosystems where data scientists and AI specialists work hand-in-hand with domain experts to contextualize gen AI across various industries. A mature, ethical and innovative approach will make GenAI accessible to all.

Authored by Sanjay Jain - Chief Business Transformation Officer of WNS, a NYSE-listed leading Business Process Management (BPM) company.

 


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