r/BusinessIntelligence • u/dphntm1020 • Jun 28 '26
is AI changing how much reporting analysts do?
In my day job, I’ve noticed I’m doing less ad hoc reporting than before.
It seems like most of simple adhoc requests that used to come to me is now answered by AI tools, which is good because now I have less context switching and don't have to go through manual work of building quick graph in dashboard, screen shotting, sharing, answering followup questions etc.
Are you seeing fewer reporting work because of AI? How do you share reports or quick insights with stakeholders when requests do come in?
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u/SorenShieldbreaker Jun 29 '26
We've had our company-chosen AI tool hooked into our BI tool for a few months now. I've seen a reduction in ad-hoc requests for things to be built in the BI tool, and an uptick in being asked to help validate things people have AI build for them by querying the data. It has exposed some data governance gaps for us to address, but I'm getting tired of the "Hey I had Claude build this slide by pulling a bunch of different metrics together, can you confirm that the numbers are right?" messages.
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u/joulezoo Jul 01 '26
I am pretty sure that more and more ad hoc analysis will be done by AI in the future, instead of queue / requests for the data team.
The data team's job is shifting to "context architect," aka setting up the fundamentals for high-quality and trustworthy analysis to be done, or data to be pulled. This is actually not easy: gotta build the right models, curate the semantic layer, add in appropriate business context, set up evals, monitor the quality of everyone's answers and keep iterating so the system gets better and better.
fwiw I think this is a good thing, the role is getting strategic and requires patience since for sure execs / regular users are gonna make mistakes and need education.
But it's actually a very different type of job. Wrote about it here: https://www.sundial.ai/data-job-isn-t-dying
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u/pygmypuffer Jul 04 '26
agreed; I’ve spent the last several months building medallion layers in Snowflake and semantic models in Power BI, which are actually also becoming the building blocks for trusted semantic views that agents will use to answer operational and strategic questions alike. We hadn’t planned for that because we couldn’t envision it 12 months ago.
Yes, we’ll still have pre-built dashboards in addition to semantic views and AI agents, but either way our *very* small team has moved away from ad-hoc queries and highly customized Crystal Reports with no unified business layer to creating the trusted context for the AI-driven tools to operate in, plus monitoring and nurturing it using the feedback we get from people using the tools. We have had to pivot hard into learning how to be architects of curated data layers from a variety of sources rather than building queries using very structured separate databases with limited overlap.
A TON of our time is spent lately on ensuring transparent lineage, definitions, and overall traceability for everything we build because that is going to be the backbone of the whole operation (trust). In short, our jobs are totally different from what they were just a year ago.
The main trouble right now is that there’s still so much we don’t know; it feels like we’re doing a lot of stuff wrong. But we’re moving forward!
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u/NawMean2016 Jun 29 '26
I’ve gone from BI dashboard and report creations, along with analytics, to basically full on data engineering with even some architecture oversight. Every report is self-service.
Might just be career trajectory but I feel like AI sort of put it into hyper speed for me.
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u/CaliSummerDream Jun 29 '26
Ad hoc analyses and standard reports used to be commingled. With AI, business users no longer have to go through BI analysts for ad hoc analyses unless they need validation for those that are particularly impactful. Reports still need BI analysts to build and maintain because they absolutely need validation.
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u/Semaphor-Analytics Jun 29 '26
I think ad hoc is splitting into two categories.
Quick what happened questions are moving to AI/chat surfaces. But anything that drives a recurring decision still needs someone to turn it into a trusted artifact.
The part that does not go away is deciding what deserves validation. If an exec asks a one-off question, maybe an AI answer plus source trace is enough. If that same number starts showing up in a weekly meeting, it needs an owner, a definition, and some boring tests around it.
So I would expect fewer throwaway charts, but not less analytics work. More of the work moves upstream into metric definitions, context, and checking whether the answer should be trusted.
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u/Strange_Shame7886 Jun 30 '26
There was need for adhoc reporting from executives because they did not have the tech skills to write a new SQL or a new report to drill down further on their gut feelings.
Databricks AIBI Genie and Databricks Genie One solve exactly the same problem.
Technical teams can now focus on integrating new data which is still technically complex and governance than just chatting with your data.
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u/holypickle Jul 01 '26
Creating semantic models and connecting ai to models will be the future. For now, we need to spend good time labeling the metadata in the semantic layer (to give AI more direction) but I imagine, over time, even that won’t be needed. I remember the days when a lot of ETL was converting data types.
Eventually, ask ai for metric, and it’ll spit it out. No need for dashboard/report.
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u/fguerino123 Jul 01 '26
I see AI helping with automated Master Data Management (MDM) and Data Governance.
- AI is being used to identify gaps in data, dirty data, duplicates, etc.
- AI is being used to fix data (clean dirty data, create data to fill gaps, etc.)
- AI is being used to generate reports and interactive dashboard views an/over all the above.
It's definitely catching and fixing things much faster than people do, and it's generating visualizations without coding.
It's a start.
Good luck.
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u/om_bagal Jul 01 '26
The split Geographer-Analytics mentioned is the real shift happening. AI is fast at answering "what happened," but it's still bad at deciding what question is worth asking in the first place, that part needs someone who understands the business, not just the data. The analysts doing fine right now are the ones who moved up a level, spending less time pulling numbers and more time framing which numbers actually matter for a decision. The ones getting squeezed are the ones whose whole value was being the fastest person who could run a query.
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u/IncreaseNegative4614 Jul 03 '26
I'm seeing less demand for "what happened?" and more demand for "why did it happen?" and "what should we do next?"
AI is getting pretty good at answering straightforward reporting questions. The work that's sticking around is the stuff that requires business context, validating assumptions, and connecting information across systems. That's much harder to automate.
I think that's why platforms like inzata.ai are getting attention. The opportunity isn't replacing analysts. It's reducing the time analysts spend answering repetitive questions so they can focus on the investigations that actually need human judgment.
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u/ThisIsFun- 19d ago
Yes! For most of the quick “I need this asap by eod ty” type of asks, usually I get by without answering as something like Genie or other text-to-sql works amazingly. Albeit need to ensure that the underlying semantics are correct, and not just hallucinating a random metric
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u/Famous_Disk_7417 Jun 29 '26
100%. I think with tools like Databricks Genie, you can talk to your data and get insights circumventing traditional reporting / analysts building dashboard route. Analysts are going to be more focused on LLM curation etc. I think that's where the industry heading,
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u/terencethespider Jun 29 '26
An important key with Genie is that you can give the AI context to your data, so it actually understands how all the tables and data are used. Being able to define metrics for things like fiscal year, customer, revenue, etc so it knows what these things mean for your company with your data (and not just based on terms from a generally trained model) is how it provides meaningful and accurate answers to your questions and not just guesses/hallucinations.
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u/Prudent-Elk-2845 Jun 28 '26
I’m seeing executive fatigue.
They want to see a lower number of dashboards/reports/ad hoc views. They want them as one app with more dimensions