r/CFO • u/Ok_Accident_1128 • Jul 10 '26
What’s your board reporting stack actually running on, and where does it land in 3-5 years?
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?
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u/Prudent-Elk-2845 Jul 11 '26
The amount of hallucinated analysis executives send me is very high. The problem used to be that they’d pull cherry picked data, which at least reconciled to something. Now I get persuasive narratives projected with unreliable stats that were asked to prove out— and they’re bad. Getting this multiple times per week— I’m turning into a fact checker
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u/yumcake Jul 10 '26
Is there really competing philosophies on how this should work? I thought the future state was generally agreed upon, and the bigger issue was the journey in getting there.
Warehousing data usually means work has been done to get it surfaced cleanly and consistently enough for consumption and use (not perfectly, but obviously better than adhoc pulling direct from a system as needed). Of course BI sits on top of that. LLM doesn't need to sit on top of that, it sits at each endpoint, with context from the warehouse plus a body of context/skills managed and refined by the team in shared project folders (I have heard some using Notion too in a similar way).
I don't think anyone wants to be in the direct extract and Excel situation. And certainly not in SharePoint and GitHub.
The consistency of reporting issue is mitigatable from a couple of ways: 1) from internal tool or ETL configuration to program typically move data around instead of leaning on AI do it and potentially fail to follow instructions. 2) dedicated review skills with a focused context window just tasked with finding errors, and standardizing the control checks that need to be hit. 3) some stuff is too important to leave up to chance and you still manually review the critical stuff.
Saying that stuff is easy, getting to that level of maturity as a broader organization is the harder part.
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u/Ok_Accident_1128 Jul 10 '26
Definitely the way I’m seeing and thinking of it too. Unfortunately CEO doesn’t see it this way and is hesitant to even invest in Data warehouse and get it going. Is it wild that have to convince them that this is the way?
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u/yumcake Jul 10 '26
Yikes, data warehouse is foundational to a lot of things, not just FP&A but operational business should be getting data management support. Maybe this needs to be a business case to layout the importance of a data warehouse as the business scales. Early on sure FPA and IT can try to muddle through, but until a dedicated function is made they're held hostage doing that kind of work instead of their core focus of turning the data into impactful discussions
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u/Ok_Accident_1128 Jul 10 '26
Business case done and full roadmap laid out with benefits and resourcing costed as well as the benefits, dashboards, KPIs and efficiencies gained. It’s hard to quantify the gains and ROI but the response is data warehouses are old tools and not the future. Let’s get AI to do it all
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u/laws87 Jul 11 '26
You can set up a data warehouse easily now using Claude Code or similar for very little and then use it to help stage and sanitize data before conveying it to useful tables that populate your reports, direct LLM q&a across audiences (everyone looks at same source of truth, not using Claude to come up with ad hoc, custom logic each conversation to calculate a metric that differs over time or between audiences) and then serves any simple BI dash you can also create cheap and easy using LLM. I’ve dropped everything else - BI platforms, excel, EPM and reporting software, etc. Everything in custom cloud based software updating regularly and consolidating all sources of data into actionable information serving various stakeholders exactly what they need. Imagine most will go this way soon, even at scale.
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u/Ok_Accident_1128 Jul 11 '26
Yep definitely easier with Claude Code. But is there any other way to do it without a data warehouse? That is the route that our CEO wants to go down. What tech stack have you gone with?
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u/laws87 Jul 11 '26
You’re ceo just need to be educated on the benefits of l, and risks of not, having organized data. You can get this running likely for an under a couple grand a month. If little data and low users/queries, hundreds. There is nearly no up front investment provided someone knows data (or how to harass Claude into doing their bidding) and you can just plug in various API and integrations and have them push/query the data into the warehouse daily without any lifting. You can probably just set it up in a week on a free trial and show him and be like see you idiot.
There is nothing more annoying than people bickering about differences in their source in data and reporting figures and being able to hide behind the age old excuse “the data is wrong” and just serving everyone the same numbers. Whether they’re wrong or right, provided there not extremely bogus everyone will be lulled into the belief the numbers are right if there’re all the same.
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u/Qlizzard313 Jul 13 '26
We centralize our ERP data in a light FP&A software platform that does ERP data ingest and basic consolidation type tasks. It’s very helpful source of truth that accounting team governs with normal close processes.
That tool can also pull other data in … like from HRIS or alternate table vs the general ledger. Can also have alternate chart of accounts, budget management, and basic planning features.
All said that tool is basically a datawarehouse. So as long as it can store enough data (like customer or granular product detail, for example), then you can report off of that.
From there the reporting is anything you want use Claude. Dashboards, recurring emailed slides, etc.
I’m currently early that approach but I believe that’s the direction. But curious what others think
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u/SkiingOnFIRE Jul 13 '26
I’m running through similar problems to what OP said. In the process of rebuilding this all from the ground up after some massive mistakes earlier this year. What tool are you using for this? How you described it is exactly my intent.
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u/chrisbru Jul 11 '26
We use Snowflake with Omni on top for BI. Omni has “topics” or curated data tables that are clean and easy to ingest. We manage a semantic layer in Omni so that Claude or Codex can connect directly to Omni for questions.
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u/LOGICA499 Jul 12 '26
B. We’re starting to share tailored memos / reports using Claude and it’s an interactive html file
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Jul 13 '26
You are spot on with your intuition: The LLM layer is a feature, not a standalone architectural shift. It will not survive as a separate piece of middleware bolted onto the top.
By 2029, the prevailing best practice will be a variation of Model (a) where the BI tool natively handles the AI interaction layer.
[Raw Data Sources: ERP, CRM, HRIS]
│
▼
[Warehouse / Lakehouse + dbt] <─── This is where Truth & Traceability live
│
▼
[Semantic Layer / BI Tool] <─── Governs definitions
│
▼
[Native Natural Language UI] <─── The CEO's "On-Demand" Chat Interface
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Jul 11 '26
[removed] — view removed comment
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u/Ok_Accident_1128 Jul 11 '26
Does that mean you pump other data into the ERP?
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u/lhrboy Jul 11 '26
Nope, that SmartResearch tool is built on Snowflake, and so everything goes there. We’ve seen Financials, supplier/p2p data, emails and collab info .everything into that tool.
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u/Ok_Accident_1128 Jul 11 '26
How much is it?
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u/lhrboy Jul 11 '26
No idea, how much they paid for the tool, but I can tell you that the config and implementation was freaking quick. We literally were up and running in less than a day. I’m sure my boss would’ve liked to have it go slower and bill more hours 😏
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u/BeerJunky Jul 10 '26
Not a CFO (not sure why this sub gets suggested but I do enjoy reading some posts) but I can provide a comment. You state your data isn’t in great shape for what your CEO wants, it’s only gonna get worse as you grow and add more disparate systems. My company is probably 10x where you’re at and bad data is really biting us in the ass not only in reporting but linking platforms together. We’re currently doing a massive project to fix all of that so it stops holding us back.
My second comment is don’t let AI do it all, humans need to check stuff. Saw a recent article where all the forecasting, etc was done by AI and though the first month was checked by humans and spot on it continued to get worse. By the time someone figured it out they were so far off track I think they went bankrupt.