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HCLSoftware: Fueling the Digital+ Economy

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In this era of hyper-digitalization, Generative AI holds tremendous promise for transforming business operations at scale. However, this powerful technology does not come without challenges. In this article, we will explore the limitations of generative AI for enterprise usage and why infusing it in a conversational AI platform can address the majority of these issues. We will also briefly delve into how HCLSoftware’s GenAI-fueled employee experience platform - HCL BigFix AEX can benefit businesses to unlock exponential value.

Limitations with Generally Available Generative AI for Enterprise Usage

Generative AI models often struggle to understand the nuanced context of enterprise-specific interactions. While they can generate coherent text, the depth of understanding required for complex business scenarios is frequently lacking. These models are trained on massive datasets that cover a wide range of general knowledge. This is the reason why they may lack in-depth information on niche topics required for accurate and valuable insights in highly specialized fields such as pharmaceuticals, finance, or engineering.

There is another key issue related to biasness. AI models are only as unbiased as the data they are trained on. Generative AI can inadvertently propagate biases present in the training data, leading to outputs that reflect incorrect assumptions. For some enterprises, such output can be problematic as it can affect decision-making processes and perpetuate unfair practices. On top of that, GenAI chatbots lack robust mechanisms for truth-checking and moderation. 

Then, there is a risk of "hallucinations," generating responses that are plausible-sounding but entirely fabricated and unrelated to the input. This phenomenon can be particularly detrimental in enterprise settings where accuracy and relevance are critical. The iterative process of refining prompts and responses to achieve useful results can also delay decision-making.

Finally, integration with existing enterprise systems and workflows is another major limitation. Limited integration capabilities can impede their utility and the value they add to business processes. 

How Conversational AI, Infused with Generative AI, Can Solve Enterprise Issues

Combining an enterprise-grade cognitive virtual agent with generative AI can produce more human-like responses, enhancing user interactions. Conversational AI's ability to understand and manage dialogues helps contextualize generative AI outputs, making them more insightful and improving decision-making and user satisfaction.

Additionally, conversational AI can be programmed to enforce enterprise policies and guidelines, ensuring that the generative AI outputs align with organizational standards. This moderation capability helps maintain consistency and compliance across interactions.

By training AI models on both enterprise-specific and public data, organizations can achieve a balance between general knowledge and specialized information. This hybrid approach improves the relevance and applicability of AI-generated content in specific business contexts. Conversational AI systems can also be designed for ease of maintenance and scalability. They can dynamically adapt to new information and evolving business needs without requiring extensive manual updates, reducing the operational burden on IT teams.

Enterprises can orchestrate multiple large language models to leverage their combined strengths. This orchestration allows for more sophisticated and comprehensive responses, addressing a wider range of scenarios and queries. Conversational AI can guide generative AI to focus on specific tasks, improving the precision and relevance of outputs. This task-oriented approach ensures that AI-generated responses are aligned with the immediate needs and goals of the enterprise.

Conversational AI can facilitate richer integrations with existing enterprise systems. This seamless integration enhances the utility of generative AI, allowing it to interact with various tools, databases, and workflows more effectively.

Generative AI Use Cases Across Industries

Generative AI can have a far-reaching impact on three broad areas for enterprises – Search, Conversational Interaction with Apps, and Content Generation. 

With Search, end users can enjoy faster and more accurate access to the organization's knowledge base. Large volumes of complex data can be processed to extract relevant insights and present them in an easily understandable format. 

Secondly, enhancing software applications with conversational interactions can significantly improve user experience and developer productivity. Natural conversations make applications more intuitive and engaging, leading to higher user satisfaction and efficiency.

Lastly, GenAI excels in content creation, enabling the rapid generation of code, text and images. This capability accelerates business processes, boosts employee productivity and streamlines workflows, allowing teams to focus on higher-value tasks.

Enterprises can ensure employee satisfaction using GenAI to enable effective and faster service, personalized experiences and captivating content. These improvements build stronger relationships and increase retention rates.

How Can HCLSoftware Help?

Generative AI holds immense potential for transforming enterprise operations, but it is essential to address its limitations to realize its benefits fully. By integrating Generative AI with our employee experience platform – BigFix AEX, HCLSoftware enables enterprises to overcome significant challenges by deploying powerful use cases for end-users. These use cases can range from simple conversational AI, to addressing more complex requirements like agent assist and self-heal for endpoint remediation, user experience monitoring and live agent chat management. A secure application of generative AI can drive innovation, improve productivity and deliver significant business value, positioning enterprises for success in the digital+ age.

To register for a 30-day free trial of BigFix AEX, click here.

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  |  November 11, 2024
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