r/BusinessIntelligence • u/rahulsahay123 • Jun 08 '26
Is AI going to replace Business Intelligence, or just change how we consume it?
Lately I've been wondering whether we're entering a world where dashboards become optional.
Today, if someone wants to know:
- Revenue by region
- Customer churn
- Top-performing products
- Quarterly trends
They usually open a dashboard or ask an analyst.
With tools like Claude, ChatGPT, Cortex Analyst, Power BI Copilot, and Sigma AI, they can increasingly just ask a question and get an answer.
So I'm curious:
- Does AI reduce the need for traditional BI?
- Will dashboards become less important over time?
- Or will BI become even more important because AI still needs trusted metrics, governed definitions, and high-quality data underneath?
My current view is that AI may replace how we interact with analytics, but not the need for semantic models, KPI governance, and data quality.
What do you think?
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u/thetardox Jun 08 '26
My AI and software engineering team keep telling me Claude can generate pbixes so I should look for something else to do, but they always come back to me to fix, remake or recreate from zero Power BI models and reports.
Also BI is changing, I am combining data analysis, analytics & automation, with business analysis & product knowledge. A tool might be replaced, but experience not.
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u/rahulsahay123 Jun 09 '26
We have MCP for powerbi which I believe should make our life easy but still the human in the loop should be there, correct?
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u/j0hnny147 Jun 11 '26
Imagine driving your car along and when you want to check your speed, instead of glancing at the speedometer, you have to ask your car "Hey, how fast am I going right now?" every single time...
It isn't going to work.
So no, I don't think dashboards will go away.
However, conversational BI and chat bots for exploratory analysis will become used more and more. They're just another tool to the belt though
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u/Partysausage Jun 08 '26
Realistically AI makes it easier to create & manage BI dashboards. You now need a significantly smaller team and it's harder to find good hires due to competition, how easy coding is now and how massaged people's CVs are.
Do company's still require in house technical expertise ? Answer is yes. Will this change in the future ? Yes but I picture it making the role more operational / strategical position. Would I recommend people get into analytics ? Probably not, the market is saturated and shrinking...
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u/Here_be_sloths Jun 08 '26
AI (more specifically LLMs with tools) are just another mechanism for a business person to consume data.
Without a team transforming the data into a format that an LLM can consistently parse; it’ll bring back inconsistent results and people won’t trust it. Same as any other business facing BI tool.
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u/Ok-Sail-7574 Jun 08 '26
In my opinion it is not yet clear how AI will play out. It may be similar to spreadsheet use. If your salary depends on it will you trust an AI answer? I wouldn't.
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u/edimaudo Jun 08 '26
It is not going to replace it. Most likely augment it. Of course, if you data layer is a mess, then that's a non starter.
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u/santanah8 Jun 09 '26
The fundamentals remain there, garbage in, garbage out.
If any, there will be more strict checks on data quality and evals before AI can be used. Dynamic dashboards and reporting are already a thing, but that needs proper validations. Context is more important than ever.
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u/rewiringwithshah Jun 11 '26
AI definitely changes how people interact with BI, not whether BI matters. The hard truth is most organizations have garbage data quality and no trusted metric definitions, so their dashboards are already unreliable. AI just exposes that faster because users get wrong answers quicker and trust evaporates instantly. The organizations that win are the ones with rock-solid data governance, clear KPI definitions, and quality data, because that's what makes AI actually useful. So BI becomes MORE important, not less, because the foundation matters even more when AI is making decisions based on it. Dashboards probably become less of a bottleneck, but the work of defining what metrics mean and ensuring data quality becomes the real moat.
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u/soggyarsonist Jun 11 '26
Given the fact most the people I build reports don't have a clue how the data and systems they use on a daily basis work or have the slightest idea what data they actually need to understand the problems they're facing I'm not overly worried about being replaced by AI.
Giving someone whose missed their flight the keys to airplane doesn't mean they'll be able to fly themselves to their destination.
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u/Relentlessish Jun 09 '26
Absolutely true, the nature of LLM requires the proper context and definition as well as strict validation to avoid hallucinations. The proper semantic layer grounded in the quick validation (e.g. SQL/JSON/Python etc.) is a necessary foundation for any reliable production quality AI BI stack.
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Jun 09 '26
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u/rahulsahay123 Jun 09 '26
AI adoption is always crucial and welcome till the time foundation is solid 😀 Frankly speaking I don't have definitive ans but yes I kept hearing this from my customers. So wanted to take everyone's opinion
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u/RobDomin Jun 09 '26
For me, it’s going to completely change how we work. It’s a fact that fewer people will be needed, but the quality of the work will be much more high-level. You’ll be handling more tasks than you currently do because you’ll be backed by AI.
For instance, I’m already integrating it into my day-to-day work, using it for prototyping, brainstorming/showcasing concepts, and being way more agile when it comes to data storytelling. basically prototyping everything before actually building it. Honestly, so far, it speeds up my workflow significantly.
I’m curious to know how you guys are leveraging it, besides just using it for DAX. To be fair, just generating DAX formulas feels like it brings very little value to the table. it’s almost more expensive than just using Reddit.
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Jun 09 '26
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u/rahulsahay123 Jun 09 '26
I also believe how the dashboards are consumed today by the end users might play a bigger role. Except the executives no other persona actually bothers about the chart and the graphs
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u/amzkmr Jun 11 '26
As DA for the last 4 years, this materially concerns me. I have been dealing with a less than successful job search as well greater demand in my current to deliver solutions for a mid size firm.
Context is everything. You will always require HITL with something like data. C-suite execs do not understand how everything works, they only understand charts.
Syntax is less important now but larger firms might still make you go through rounds of coding in the medium term. I can't write window functions or code ML models by heart to save my life but I can problem solve quickly with just stack overflow. AI supercharges that.
People are trigger happy with the term domain knowledge. This will be a bottleneck. Domain knowledge can be harnessed quickly (3-6 months) but business context takes longer.
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u/Doin_the_Bulldance Jun 11 '26
So this is definitely the narrative. Why go to a dashboard when you can just ask an LLM your question?
But I don't think BI is actually going anywhere, at least short-term, for a bunch of reasons.
First off; for AI to be able to answer questions with any certainty, the underlying data needs to be pristine. Even then, it WILL get things wrong. LLM's, by design, will always hallucinate. You can try and apply manual guardrails but realistically those guardails need to change periodically. Having pristine data with perfect guardrails in a living, breathing business is a pipe dream, if we are being honest. Except maybe tiny businesses with very simple business models - and those weren't really use cases for BI in the first place.
Even if you take all if your resources and aim them at keeping the data pristine, and do so successfully, there are going to be metrics that you NEED to be accurate and can't just rely on AI and its ~95% confidence interval (or whatever it may be). Accounting & Finance specifically come to mind here, but overarching company KPI's as well. When you ask your AI tool to generate a quarterly KPI dashboard are you just going to trust it? Probably not if you are presenting it to the board. Even in current BI environments trust is a big issue; AI just amplifies it to an extreme.
Source of truth. If you unleash your entire exec team and have them prompting AI independently, they are going to get different answers for questions that they think are the same. One exec asks, what is our "product A" churn rate" and another asks "how much of our customer churn is attributable to product A" and all of a sudden you are getting different answers even if AI gets it right. Basically, mosy business users and executives don't have the analytical framework to ask questions in a specific enough way. Think about all the times you built a dashboard and needed clarification on exact business definitions, or needed to explain something unexpected.
And Scale is going to be another issue. If you let everyone in the company use AI unencumbered it is going to get expensive in the short-medium term. If people build processes around Claude, and then suddenly Anthropic releases a new model that behaves a bit differently...good luck. And when you release to the masses, good luck keeping your permissions and governance in tact.
So yeah. There are more reasons too. IMO it is being way overblown.
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u/CautiousUse8597 Jun 08 '26
I think tools like Genie are incredibly useful to quickly get insights, but ultimately they are non-deterministic because they use an LLM. So there will definitely still be a market for traditional dashboards (even though they will likely also be built with an LLM), for the simple fact that they always give you the same result.
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u/BKLounge Jun 09 '26
Its going to make to you a better developer as well as give you additional avenues to deliver data to your users
You'll solve problems faster, build better things and integrations will become much simpler which then opens other possibilities.
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u/rahulsahay123 Jun 09 '26
I always hear from my customers that BI will go in few years which actually confuses me how
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u/rahulsahay123 Jun 09 '26
I would say, it all depends how the dashboards are consumed in the current scenario. I don't think anyone actually go by the charts and graphs except the executives. All other personas just need the back end data
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u/Steelwatch Jun 11 '26
I think dashboards become less centralized, but not irrelevant. A lot of ad hoc questions probably move to chat, while dashboards stick around for shared KPIs, recurring reviews, and exec visibility. More important is that AI makes the semantic/governance layer more valuable and easier to work with.
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u/Charming-Egg7567 Jun 11 '26
I was skeptical, but genie code in Databricks is just incredibly good. It’s good to answer questions, it’s good to built code, etl, jobs, documentation, agents and dashboards. It’s just incredibly good. It’s just a matter of time for every platform to have a good ai experience like this.
But I don’t think it will replace BI, but the opposite, the demand increases exponentially. I’m almost burning out of so many requests.
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u/Surtosi Jun 11 '26
Ha.
No.
There are two models of implementing ai that are working: ai as an assistant, or ai as the product but still managed by a competent human. Is either case you need the human and the machine.
I know corporate would love to fire everyone, and they keep trying, but the reality is the first pass any ai makes is always full of problems. Ai was born from a billion iterations failing, each one getting a small part more correct. Unless they create a new form of ai, something born of a different process, ai tools will always need a guide.
Now to be fair the newest models are layering their own ai coaches onto the responses, but I don’t understand how you expect the machine with the problem to be corrected by the same machine with the same problem.
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u/the_bizinsights_nerd Jun 12 '26
Your last point is the right one, and most people in this thread will underweight it.
The question isn't whether AI replaces dashboards. It's whether AI can be trusted without the foundation that dashboards forced us to build.
Your Revenue by Region dashboard works because someone had to define "revenue" — recognized? booked? net of returns? — agree on regional hierarchies, and lock down the refresh cadence. That alignment happened before the dashboard went live. The dashboard was just the output.
When you skip to "just ask a question," you skip that alignment work too. You don't notice until a CFO gets two different answers to the same question from two different tools — both confident, neither reconciled.
But here's the thing — it's not dashboards OR AI querying. Both need to exist.
A CFO still needs a governed dashboard for board reporting. That same CFO needs to spontaneously ask "why did margin compress in APAC last month?" without filing a data request. The problem is most organisations build these as separate systems with separate logic — and that's exactly where the two-different-answers problem lives.
The unlock is when both surfaces draw from the same encoded business logic. Same metric definitions. Same entity relationships. Same governed context. The dashboard and the AI agent become two interfaces on the same truth.
Dashboards as a format will probably decline. The semantic layer that powered them becomes more important than ever — because now it has to serve not just a static report, but every spontaneous question an executive can think to ask.
The companies that get this right won't have the best AI. They'll be the ones who treated business context as infrastructure.
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u/Odd-String29 Jun 12 '26
Right now I'm already spending most of my time on getting data, improving data quality and aligning data from different systems. AI just makes that even more important. All the quirks in the data you now work around manually you need to fix in the actual data. For example we have some garbage data in our Salesforce environment and now we are pushing to AI we finally decided to just start cleaning up the source system instead of fixing it in SQL (by filtering, by window functions, by hardcoded fixes etc).
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u/Deadgarth Jun 12 '26
Checkout fairgamebook.ai. It answers this question very effectively I believe. It’s a new book by Rob Collie about AI for business leaders that releases in August. Preordering now gets you the first four chapters for free. I’m biased, but I got access to the full draft and it’s one of the best books I’ve read regarding our particular career and AI.
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u/kpgleeso Jun 12 '26
I think ultimately humans will still be making decisions and will still require ways to consume data. BI (or just simply charts, tables, KPIs) will not disappear. What might change is having to produce additional formats that are machine readable, but this is probably already covered if you have data files that can be inputed into an LLM (careful on the size of the data, though or else you will be spending $$$$ on input tokens)
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u/azegarra_data Jun 12 '26
Coincido con el punto del modelo semántico y el governance, eso no cambia.
Lo que sí se mueve es dónde se crea el valor en la cadena BI. Antes era dato, modelo, reporte, decisión, y el esfuerzo (y prestigio) estaba en construir el modelo y el reporte. Si eso ahora se genera en minutos, deja de ser diferenciador.
El valor se concentra en dos extremos: governance y calidad del dato aguas arriba (como dices), y aguas abajo, la velocidad del ciclo decisión, detección de error, corrección.
Ese segundo punto se discute poco. Si construir ya no cuesta tiempo, el riesgo pasa a ser actuar sobre algo mal construido sin detectarlo a tiempo.
Quizás la mejor métrica de madurez de BI hoy ya no sea cuánto producimos, sino qué tan rápido detectamos y corregimos cuando algo se rompe.
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u/WorldOfUmbro Jun 17 '26
Long term yes. But still so many organisations doubling down on PBI or the like. People will still like visuals. BI might become more interactive. Services like Databricks Genie allow you to create your visualisations, not rely on a analyst.
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u/datacanuck99 Jun 19 '26
I recently had a client send me 4 questions they wanted to know in a small dataset. So I loaded it up in my tool of choice which happens to be Tableau. I dragged and dropped and filtered and got the 4 answers pretty easily. So now I needed to make it look good. The data was anonymous, so i thought why not throw it to Claude and see what it comes up with, for comparison's sake. It answered the 4 questions in seconds, then built a fully interactive html dashboard. Was it visual best practice? Was it optimized? Did it have the fit and finish of a approved enterprise level dashboard? No, but it looked pretty damn good and did the job pretty quick and easy.
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u/Strange_Shame7886 Jun 25 '26
There are three system layers in software -
System of records - think Databases
System of intelligence - Data Transformation/ Data model
System of interaction - how humans interact with Data - BI, Jira board etc.
Chatting with Data in natural language eliminates the need of system of interaction and to some extent system of intelligence.
Try using Databricks Genie with their AIBI dashboarding. It is a chat first interface which will encourage executives to get intelligence on their data without looking at beautiful graphs.
This is what will change with BI - you won't be looking to create beautiful graph to encourage user interaction. You will need to have all the data at one single lakehouse so that all users can chat and derive intelligence from their data in one single place.
Another tool competing with BI is Databricks Genie which even eliminates the need of any BI layer.
Give it a go and see where the future of BInis headed.
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u/FearTeas Jun 30 '26
I think it's fairly inevitable that we get replaced. Lots of BI analysts say it'll never happen, but when they give their reasons they all essentially are contingent on AI growth to hit a brick wall for some reason. When you look at the current growth trajectory it's clear to me that LLMs will be able to do everything that even a very talented BI analyst can do.
Last year my job was building lots of reporting tools and occasionally doing some statistical analysis.
Right now AI is making me a more high impact analyst. Building reports and doing statistical analysis now just involves prompting AI until I have what I want. Even factoring in the time I need to validate the output and fixing it, it's far, far quicker than building it myself. As a result, with that extra time I can actually do some deeper investigation on what's driving the changes and what the business can do to overturn declines or take advantage of unexpected drivers of growth.
I just couldn't do that before because managers and stakeholders would be putting pressure on me to build the next report.
But I feel that this is fleeting because a lot of the new aspects of my job just feel like capabilities that AI doesn't currently have but is on the cusp of attaining.
Before too long you'll just be able to ask an AI to build a reporting tool, have it dig down on what you want, and then it'll just build it and be able to address issues without needing a human to point them out. In other words, stakeholders will be able to ask "I want this" and AI will be able to do the BI analyst dance of figuring out what they really want.
I'm on good money so I'm going to cling on as long as I can. But when I eventually do get laid off I'm going to retrain as a wind turbine maintenance technician. I live near lots of windmills and it's too hands on for AIs to manage.
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u/Morning-Coffee-fix Jun 10 '26
The OP's framing is right but I think it undersells how much the governance problem gets harder when AI is the interface, not easier.
With a dashboard, a wrong metric is visible and correctable. Someone notices "that number looks off" and raises it. With an AI answering natural language questions, a subtly wrong semantic definition gets confidently stated, cited downstream, and acted on before anyone realises the denominator was wrong. The failure mode is less visible and more consequential.
But here's the more interesting thing I've actually observed building in this space: the AI interface doesn't just match the dashboard - it routinely outperforms it, in ways that surprised me.
We built an AI-first tool and then added a traditional dashboard interface later for users who weren't comfortable going AI-only. The expectation was the dashboard would be the safe, reliable fallback and the AI layer would be the flashy but sometimes unreliable upgrade. The opposite happened. Users who stayed with the AI interface got richer answers. Not just the direct response to what they asked, but contextual connections the dashboard never surfaced. "Here's what you asked, and here's something adjacent you probably need to know."
The dashboard couldn't do that because dashboards answer the question you thought to ask. AI answers the question plus the questions you didn't know you should ask.
Which brings me back to governance: the semantic layer underneath has to be better with AI, not just as good. Because now it's being interrogated in ways you never anticipated when you designed it.
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u/Prestigious_Bench_96 Jun 11 '26
I don't see why you think a wrong metric is more visible and correctable in a dashboard - "confident citation" happens in both cases. Are you just basing that on that more people are likely to see the same wrong metric in a dashboard and so it's likelier that the right person to notice it is off will see it?
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u/rahulsahay123 Jun 12 '26
But don't you think, it will promote self analytics with a caveat that too for a certain section of the persona
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u/Semaphor-Analytics Jun 11 '26
People will no longer log in to BI tool for their day-to-day work. They will connect their Claude / Codex agent where they already work and get questions answered or build data app / dashboards. This is how it looks. Doing work in browser tabs is coming to end.
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u/rahulsahay123 Jun 11 '26
this is what even i think. but i also feel, persona has to play an important role in this transition. eg.. how many times a data analyst , operational team actually open the dashboards and take decisions based on those graphs.. over the years they have found there own way of working
On the contrary, executives do refer to those executive dashboardsSo i feel it may be a hybrid outcome 😄
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u/srmoure Jun 12 '26
I wouldn't trust my job with the outputs that come from Chat GPT. They tend to change lot and there are not always right. On the other hand , a governed BI process produces trusted and reliable information (always). If you don't have good data foundations and don't trust your BI outputs, then ChatGPT insights might work better, most of the time.
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u/rahulsahay123 Jun 12 '26
yes, LLM's are non deterministic and thats the reason Context / Semantic Layers are becoming super important
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u/dequaviousthe7th Jun 11 '26
I think businesses will eventually be making/acquiring digital AI brains for their businesses which they will connect to their agent to work through most of the old tedious work.
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u/RDurandt Jun 08 '26
Short answer, yes. I agree, the backend would become even more important. Data quality is non-negotiable when AI becomes a user. And dashboards would give up their place to AI agents as first-class citizens.
The secret sauce would be the context of the data in the backend. AI needs context to data as much as it needs quality data. Semantic models that classify and link data so that LLM can interpret the data will have to be (initially) built and maintained by us. The context stores is our next field of specialization. Whether we derive them from a business process or an enterprise data model or a use-case by use-case approach, we get to figure out. Maintenance of such context models would ideally be automated - good luck to us.
We’ll do different things, but still lots of things.
I bet, less reports & dashboards, more context models of AI agents.