Unlocking Proprietary Wisdom: Generative AI's Role in Knowledge Management

Generative AI knowledge management systems have the potential to revolutionise the way companies handle their proprietary knowledge and information. Many companies have already experimented with large language or image models like ChatGPT and have been amazed at their ability to express complex ideas with clarity.

However, these systems are primarily trained on internet-based information, making it difficult for them to respond to proprietary content or knowledge-related queries.

Leveraging a company's own knowledge is crucial for its competitiveness and innovation in today's dynamic environment. Knowledge within organizations is generated and captured from various sources like individual minds, processes, policies, reports, discussions, and meetings.

As this diverse knowledge is often challenging to organize and deploy effectively when needed, Generative AI can empower organizations to gain valuable insights, enhance information retrieval, and foster knowledge collaboration among employees

The Challenges

Bias and Fairness

Generative AI models learn from the data they are trained on, and if an organization's internal data has inherent biases, the AI model may amplify and perpetuate those biases. This can lead to biased outcomes and discriminatory decision-making, which can have severe consequences, including legal challenges and public backlash.

Resource and Expertise Constraints

Implementing generative AI solutions requires significant computational resources and specialized expertise. Developing and maintaining AI models necessitates powerful hardware, extensive training time, and skilled data scientists and machine learning engineers. Smaller organizations or those without in-house AI expertise may face challenges in deploying and managing these types of generative AI systems.

Ethical Considerations

The use of generative AI with internal data raises ethical questions regarding the potential misuse or unintended consequences of AI-generated content. Organizations need to define ethical guidelines for AI usage, ensuring that the generated content aligns with their values and principles and protect their corporate reputation.

Approaches

Three primary approaches exist for incorporating proprietary content into a generative model:

• Prompt-Tuning an Existing LLM: The most common approach involves customizing the content of an LLM through prompts. By using prompts containing domain-specific knowledge, the model can answer questions related to that knowledge. This approach is computationally efficient, relatively cost effective and does not need a vast amount of data to produce results.

• Fine-Tuning an Existing LLM: This approach involves adjusting parameters of an existing model to add domain-specific content.

• Training an LLM from Scratch: Creating and training a domain-specific model from scratch requires massive amounts of high-quality data and substantial computing power, making it uncommon among most companies.

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Other Considerations

Implementing generative AI-based knowledge management systems also involves content curation and governance, quality assurance and evaluation, and addressing legal and governance issues related to intellectual property, data privacy, and bias.

Shaping user behaviour is crucial, and companies should develop a culture of transparency and accountability to make these systems successful.

While building and using generative AI systems trained on a company's own knowledge content poses challenges, the long-term benefits of easily accessing important knowledge for employees and customers are worth the effort.

The development of a planned strategic approach for the development, deployment and sustainable use of a Generative AI knowledge management system cannot be underestimated.