Our client embarked on a bold innovation path by establishing a Generative AI Lab created with the assistance of advisory services from HCLTech. They wanted to explore intricate use cases within the firm, bridging the gap between ideas and evidence-based solutions. This initiative aimed to enhance research efficiency and drug discovery while adhering to Responsible AI guidelines, ensuring a cognitively equipped infrastructure.
The Challenge
Set up a Generative AI Lab for next-generation analytics and AI solutions
The client wanted to set up a Generative AI Lab to ideate, innovate, co-create and deliver next-generation analytics and AI solutions that would provide a competitive advantage by making significant advancements in research efficiency, drug discovery, user experiences, etc.
The Objective
Maximizing efficiency with the implementation of GenAI solutions
- Reduce manual effort
- Increase accuracy and reliability
- Reduce cost of ownership
The Solution
Through rigorous assessment and experimentation, these use cases were poised to unlock newfound efficiencies and insights
HCLTech stepped up to foster awareness about the potential and practical aspects of Generative AI among business stakeholders.
Establishing the GenAI Lab entailed:
- Crafting a comprehension of Generative AI, discerning hype from tangible opportunities, along with navigating security challenges
- Designing the Generative AI Lab framework to develop low-risk, demonstrable proofs of concept
- Instituting Generative AI guidelines and mature governance protocols for responsible deployment
- Applying HCLTech's Prioritization Framework for discerning and implementing use cases aligned with responsible AI practices
Highlighted potential use cases included:
- Summarizing regulatory documents and elucidating variations with previous versions
- Generating automated test scripts using user narratives
- Training conversational agents on HR policies for streamlined employee engagements
The Impact
The Generative AI Lab yielded exceptional results
- Time efficiency: Automated predictions of audit discrepancies slashed manual review efforts, boasting over 95% accuracy and heightened reliability
- Cost reduction: By significantly reducing development time, the lab achieved a 60-75% decrease in effort, in addition to minimal code alterations required for new document types or business rules
- Accessibility: Summaries garnered a 50% uplift in readability scores, offering user-friendly language that condensed complexities into digestible formats
