Should Atlan prioritise connector breadth or deeper AI features over the next two quarters?

Introduction

Every data catalogue lives or dies by its connectors. Atlan connects to warehouses and lakehouses (Snowflake, Databricks, BigQuery, Redshift), transformation and orchestration tools (dbt, Airflow, Fivetran), BI tools (Tableau, Power BI, Looker), and many more. Customers routinely ask for new connectors or deeper ones: column-level lineage for a niche BI tool, metadata from a SaaS application, or support for a new open table format. At the same time, the market wants AI: context agents, Atlan AI improvements, AI governance and the MCP server.

The question: for the next two quarters, should Atlan prioritise breadth of connectors or depth of AI features? Both compete for the same engineers and product managers. Getting the balance wrong either loses deals (missing connectors) or loses the narrative (falling behind on AI).

Step 1

Clarifying Questions (3 minutes)

• Context: How often do we lose deals because of missing connectors versus missing AI capabilities?

• Hypothetical answer: Win/loss data shows connector gaps in about 20% of losses; AI capability gaps in about 15%, but rising.

• Business context: What matters most this year: new logos, expansion or retention?

• Hypothetical answer: Expansion and retention, with new logos in enterprise.

• User impact: Which connectors are requested most?

• Hypothetical answer: Deeper lineage for Power BI and SAP, plus several SaaS sources.

• Technical: Can connectors be built by partners or through the app framework?

• Hypothetical answer: Partially; the app framework launched in 2025 with partners, and a connector SDK exists.

• Resource: How many engineering pods are available?

• Hypothetical answer: Six pods to allocate between the two areas for two quarters.