Gen AI’s emergence marks a significant milestone in technology, enabling machine capabilities to emulate human intelligence and creativity. Its transformative potential and close relationship with Large Language Models (LLMs) are encouraging individuals and enterprises to reimagine decision-making processes and content creation strategies. Gen AI also offers promising opportunities for enterprises seeking to expand their operations beyond traditional AI and Robotic Process Automation (RPA). Hence, technology providers are introducing an array of related tools and LLMs. However, amid this excitement, enterprises may rush to adopt gen AI without carefully considering its benefits and the necessary investments. Previous technology waves indicate that such initiatives often conclude unfavorably, failing to deliver tangible business value.

To navigate this landscape effectively, enterprises should compare the advantages of gen AI against alternative solutions, carefully select LLMs, optimize them for maximum business impact, and choose appropriate deployment models. By doing so, they can unlock the full potential of gen AI while mitigating risks and maximizing returns. Through strategic planning and thoughtful execution, enterprises can seize new opportunities and drive innovation in this era of gen AI.

In this report, we offer practical advice to businesses, highlighting key areas where gen AI can outperform traditional AI and RPA. The report also advises on selecting the appropriate LLMs tailored to specific requirements and provides strategies for enhancing LLM performance to achieve tangible business benefits. Additionally, it discusses considerations for determining the optimal deployment model for LLMs, aiding enterprises in maximizing the value derived from their gen AI initiatives.

Scope

All industries and geographies

Contents

In this viewpoint, we examine:

• Gen AI’s significant benefits over AI and RPA
• Key factors to consider when selecting an LLM
• Strategies for optimizing and fine-tuning LLMs to realize business value
• Considerations for choosing the most suitable deployment model for LLMs

Membership(s)

Engineering Research and Development

Software Product Engineering Services

Sourcing and Vendor Management