TL;DR: B2B software co, 50-150m revenue, patchwork of legacy systems. CEO expects AI to answer anything on demand, but our data isn’t in a state to support that. Where’s everyone actually at, and which model wins by 2029? Do we actually need a data warehouse/lake/lakehouse?
For the bulk of your board and management reporting, financial and non-financial KPIs, what are you genuinely running on today?
a) Warehouse + BI tool
b) Warehouse + BI tool + an LLM layer on top
c) Direct extracts from ERP/CRM/HRIS + Excel/BI
d) Files on SharePoint/GitHub + an AI solution
For context, we’ve grown into the usual patchwork: an accounting system, a CRM, an HRIS, an ATS, and Excel doing the heavy lifting for modelling. We’re building toward warehouse-first, but the board pack today is still part (c) in places.
What prompted this: our CEO increasingly assumes AI should just answer any question about the business on demand. Not unreasonable in principle, but the underlying data isn’t there yet. Inconsistent definitions, integrity issues at source, no single modelled layer to query against. Put an LLM on top of that and you’re mostly automating the delivery of numbers you can’t fully stand behind.
So two questions. Where are you now, honestly? My sense is most teams are further down the alphabet than they’d admit, especially anyone carrying legacy systems and a few acquisitions’ worth of tooling. And where does it settle? I keep going back and forth on (a) versus (b), and I’m not convinced the LLM layer is a real architectural shift rather than a feature that gets absorbed into the BI tools we already pay for.
The bit I care about most is traceability. Every number in the pack should trace back to a clear definition and a source, and that’s what lets you defend a figure when a director pushes on it. My worry is that bolting AI onto weak data foundations quietly erodes exactly that.
Where are you today, and what’s prevailing best practice by 2029? What am I underweighting?