As analytics teams strive to foster data-driven decision making across every layer of the enterprise, improving the accessibility of the data is paramount. LLMs have sparked a revolution in conversational analytics using AI agents to refine the user interface and integrate analytics seamlessly into daily workflows.

Yet, challenges remain in ensuring these data queries return accurate, unbiased insights.

Level up your practical analytics workflows by:

• Learning how to build AI agents and generative tools based on your data lake to allow stakeholders to query data directly without needing deep knowledge of data structuring or dashboards, accelerating decision making and analytics adoption with field users
• Evaluating how generative AI can create new outputs beyond text-based answers as part of workflows
• Understanding which parts of your firm are best suited to the implementation of these tools to reduce the risk of incorrect decisions
• Creating backstops to ensure LLM-driven insights are reliable and actionable within these defined workflows
• Adopting strategies for making conversational analytics as effective as possible, including prompt engineering and contextual queries for better results to educate end users on how specificity and context directly influence the value of AI outputs

CASE STUDY