To wrap up our blog posts on GPT and LLMs, let’s dive into how we can successfully leverage LLMs.
The fact that Large Language Model capabilities fall short of the need for effective data analysis at scale does not mean that this technology cannot be leveraged in a corporate BI ecosystem. Instead, it means that thoughtful application of this solution is required to leverage its strengths while mitigating weaknesses properly.
Develop a broad strategy with specific achievable first steps
When considering applying LLMs to BI, it is essential to consider more than just data. While the overall long-term strategy may be to develop one-stop-shop AI assistance that will help users with all their data needs across the entire enterprise, this broad, long-term strategy will require that LLM technology significantly improves beyond the current state of the art. To succeed, any broad strategy must be paired with short-term achievable goals that immediately generate value for the organization. The following process can be used to identify such actionable goals:
- Identify the most common inquiries that users make of the BI team.
- Segment the common inquiries list into items that users could address via self-service if they are connected with the appropriate resource.
- Ensure that the initially deployed AI Bot effectively delivers users to these resources.
Some examples of solutions that could be developed based on the above process are Bot Enablement for the following types of requests:
- Identifying the appropriate report to use to answer a common business question.
- Request additional licenses for a BI Tool.
- Obtain usage information for existing license consumption for a team.
- Open a Help Desk request to gain access to a report.
- Request training in the use of a BI Reporting Tool
- Obtain documentation on reports or internal processes
Webinar: Avoiding the Pitfalls When Using LLMs for BI
Watch this webinar featuring our CEO, Marius Moscovici, and VP of Sales & Marketing, Mike Smitheman, to learn more about this exciting topic we just covered in the last few blog posts.
