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Conversational search, or ‘ask a question’, transforms search into a more natural and interactive experience thanks to AI. Instead of relying on keywords, users can ask questions in everyday language and receive tailored responses. AI interprets the subtleties of language, providing personalized answers that target more specific results based on context and intent. This is very powerful across resources for members of organizations requiring specific subject matter help.

As AI technology evolves, it will increasingly recognize not just the words we use, but the meaning and context behind them. This could turn conversational search into a powerful tool, capable of offering deeper insights and a focused understanding of the organizational resources and knowledge - going beyond surface-level information.

However, since conversational AI is still relatively new, many people struggle to understand how it works or what data it collects. This lack of transparency can breed mistrust. On the flip side, some worry that relying too much on AI may lead users to accept its answers uncritically, opening the door to potential manipulation. That's why many AI tools include warnings, reminding users to treat the chatbot’s guidance cautiously. We've seen how early automation systems, when misused or over-relied upon, resulted in adverse consequences.

Interestingly, research from the Harvard Business Review* revealed that when interacting with AI, 94% of respondents ended up asking questions they wouldn't have otherwise considered. AI's data-driven insights helped research teams ask more creative, less-obvious questions—sparking fresh ideas about future trends lurking in unexpected places.

When you know AI can sift through far more data and make connections faster than you ever could, it encourages bolder inquiries—questions that might seem impossible for humans to answer on their own or ones that challenge entrenched cognitive biases.

But an AI algorithm is only as reliable as the dataset used to train it. Organizations are considering which data sets their AI systems rely on, and any biases in those data sets can influence the responses users receive. For example, a Chatbot over your office document files could be dangerous as often folders hold draft documents, reviewer’s comments, third party licensed materials and non-public confidential information. That is why human oversight remains crucial in evaluating the quality of AI-generated answers. The human feedback loop.

As AI becomes increasingly embedded in our daily lives, ensuring it promotes fairness, transparency, and ethics is essential. Despite AI's promise, we must address these concerns to create tools that are trustworthy, equitable, and accessible for everyone.

A consideration before rushing into finding an AI Virtual Assistant for all your enterprise knowledge.

We are championing GenBI outputs from everyday business engagement or research documents and data as a simple start for most membership led organizations. Generative Business Intelligence (GenBI) is the interconnection of Generative AI with specific business data. It uses the power of organisational documents and data with research concepts to create new insights and analytics. It basically makes the output of GenAI useful. Now ‘Ask a Question’ supports engagement. Librios sits in this space.