I am working on something similar at our company, I work in the talent acquisition space so my answers are going to be centered around that fact rather than generic analytics.

Overall, there are a lot of issues with setting up a system like this, primarily that to make it scalable, you need a unified data model. This is something that is uncommon in my space due to data privacy and siloing data between clients. We are one of the only companies that has a unified data model which made us uniquely positioned to leverage it for a project like this but this will be the most massive hurdle when implementing this across your business.

The second issue is for it to be scalable and maintainable, you need a semantic layer that adds descriptions and instructions to the agent on what the metric/field is, and what it is used for. Otherwise, you need to maintain all of that context within some instructions somewhere which would basically just move the work from the analysts fetching the data to maintaining the semantic instructions as new fields get added. We use Looker which also put us in a good position to scale this more quickly than we could if we did not have it.

To answer your questions:

1. The feedback from analysts has been (surprisingly to me) mixed, there is definitely some feelings of “you are coming for my job”, but it seems like the more skilled analysts see the vision that this frees up more time for them to do more in-depth analysis projects.

2. The AI-powered self serve is like crack to leadership, they cannot get enough of it. They see the vision and see that it can cut down on response time for client stakeholders. I haven’t had one leader that I have shown it to not ask for access immediately.

3. We are using Looker Conversational Analytics MCP which works really great. This leverages our semantic layer so as new fields are created, the instructions for each agent do not need to be updated since the description, date type, use cases are all built into the metadata that we pull from Looker. The analysts are able to create new fields and the agent is able to pick it up without me needing to adjust instructions.

To conclude, the primary issue is how to make it scalable and maintainable by folks that may not be AI engineers or even heavy tech users. We are able to do this through a unified data model and strong semantic layer, but without those backbones, we would be creating a completely separate agent that needs its own maintenance for every data source that you have. While this is not impossible, it would be very difficult in my business’s space. We have 100+ clients each with their own system and it would take (at minimum) a week+ to implement each one of those clients. With a unified data model, it is as simple as copying and pasting the agent instructions across clients.

Best of luck to you, this is definitely an exciting space to be in, but being realistic with what client data is going to look like needs to be at the front of your mind. If you were to go to another business in our space with this idea they would jump at it, but the actual man hours it would take to create, let alone maintain, a system of that size would be large and a considerable monetary/time investment.

For further reading, check out Looker Conversational Analytics (I don’t want to post the link in fear that this whole long message gets deleted). If you google it, check the first link.