# Minutes of Meeting - Enterprise Brain Workshop
**Date:** February 12, 2026  
**Source transcript:** [Feb 12, 2026.txt](Feb%2012,%202026.txt)

**Context:** The workshop narrowed Enterprise Brain into a landing experience, an AI-first behavior model, and the role-specific journey the team wants to build before settling the architecture.

## Act I - Context before blank screens (~00:00 - ~00:12)

Rakkesh opened by assuming the system already knows the user's role and department on first login. That changes the entire shape of the landing page: it should feel more like situational awareness or a daily briefing than an empty entry point.

The core idea was to reduce the burden of asking users to explain themselves. If the platform already knows enough context, it should surface the most relevant state immediately and let the user decide whether to go deeper.

Yeshwanth kept pushing toward the same underlying question: what should the system do before the user types a prompt? That question mattered because it separates a reactive product from one that can proactively lead the user into the right context.

## Act II - AI first vs user first (~00:12 - ~00:28)

The team spent the middle stretch separating AI-first, user-first, and hybrid behaviors. Rakkesh wanted the system to surface issues proactively, but not to the point of hiding the user's ability to ask follow-up questions or drill down when needed. Enterprise Brain should not begin as a pure search box; it should surface clusters, alerts, and contextual state, and then let the user query deeper from there.

> *"I don't want to ask user for any information."*
> — Rakkesh Yenugudhati _(~00:24:56)_

The important implication was that the landing page itself is part of the intelligence. It should not just wait for an input field to be used; it should make a judgment about what is worth showing first based on role, recent activity, and the user's likely next question.

## Act III - Patterns for the landing state (~00:28 - ~00:48)

The discussion then moved into the pattern library. The team kept asking how the system should represent different levels of context, how it should surface clusters, and how it should shift from one representation to another without forcing the user to restart the interaction.

> *"I want an executive system which tells me everything right."*
> — Rakkesh Yenugudhati _(~00:42:38)_

This was where the group started treating the landing page as an active decision layer. The landing state should not be a blank canvas; it should be a set of defaults, prompts, and contextual cues that can be adjusted as the system learns what role and what state the user is in.

## Act IV - Give me one role journey (~00:48 - ~01:08)

The turning point came when Rakkesh asked for something concrete rather than abstract architecture talk.

> *"Give me a user experience for one role for enterprise brain that connects Salesforce."*
> — Rakkesh Yenugudhati _(~01:02:58)_

That request was the real output of the workshop. He wanted a repeated-user journey that combined Salesforce and email context so the team could see how onboarding, proactive context, and drill-down behavior would actually work. The architecture conversation only becomes useful once the role journey exists, because then the team can reason about what the system must know, what it must predict, and what it should show on day one.

## Act V - Architecture follows the journey (~01:08 - ~01:18)

Once the role journey was on the table, the conversation moved back to architecture. The team discussed how the pattern selection, clustering logic, and role-aware defaults should map into something implementable rather than remaining an abstract vision. The point was not to over-design the UI; it was to make the architecture respond to a real user flow.

## Act VI - Closing the loop on product shape (~01:18 - ~01:28)

The final stretch focused on what an AI-first landing page should actually do: show context, cluster data, update dashboards dynamically, and keep user input minimal. The team discussed the possibility that a question can drive a pattern selection, which then generates the right view or narration for that specific role and state.

The end state is not a chat app with a nicer coat of paint. It is a pattern library, a role-aware landing model, and a clear architecture path for moving from abstract ideas to something the team can actually build and demo.

## Todos

<todo>
  Draft a role-specific Enterprise Brain journey for a repeated user that combines Salesforce and email context.<br/>
  <span class="owner">Yeshwanth / design team</span>
  <span class="deadline">Next workshop</span>
</todo>

<todo>
  Define the pattern library for AI-first landing states, cluster views, and drill-down behavior.<br/>
  <span class="owner">Design + AI team</span>
  <span class="deadline">Before architecture review</span>
</todo>

<todo>
  Map which information should appear proactively on the landing page versus what should remain user-invoked.<br/>
  <span class="owner">Product + engineering</span>
  <span class="deadline">Next planning pass</span>
</todo>

<todo>
  Document how alerts, clusters, and dashboard updates should work for role-based users.<br/>
  <span class="owner">AI team</span>
  <span class="deadline">Roadmap review</span>
</todo>

<todo>
  Use the role journey to drive the architecture decision for the first AI-first Enterprise Brain prototype.<br/>
  <span class="owner">Rakkesh / Yeshwanth</span>
  <span class="deadline">Next decision meeting</span>
</todo>