BI vendors love to promote their AI features. From auto-generated dashboards to natural language queries, AI is pitched as the magic button for insights. The idea is simple: plug in your data, ask a question, and watch the tool do the rest.
The promise is that anyone, even non-technical users, can explore data and get answers without involving analysts or data scientists. It sounds great. But in practice, this vision of AI-driven business intelligence isn’t ready for prime time.
The big promise of AI-powered BI
Many BI platforms and embedded analytics tools advertise AI as the next evolution of self-service analytics. The pitch is that these tools can automatically surface trends, detect anomalies, and even predict outcomes without manual input.
It’s a tempting story.
Imagine a sales manager typing, “Why did our revenue drop in Q3?” and instantly getting a chart with explanations and suggestions for next moves. AI is supposed to remove friction and bring data insights to everyone, regardless of technical skills or lack thereof.
The goal behind all of this is data democratization: giving more people access to answers without waiting on data teams. But there’s a disconnect between that ideal and what today’s tools can actually do.
Data definitions and modeling
Data, especially in its raw form, is complex and often abstracted. User queries to the AI are, by definition, free-form – i.e. the user might ask one thing in many different ways. This makes it very hard for an AI model to infer the ‘real’ meaning of the request and map the messy data to the user’s request. Ultimately, this presents a challenge when you want to interpret data with accuracy.
For example, if a user asks, ‘what was our CAC (customer acquisition cost) in May last year?’, there could be any number of ways to calculate this.
Customer Acquisition Cost (CAC) measures how much a company spends, on average, to acquire a new customer.
However, how you calculate metric can vary a lot depending on what costs you include (e.g., marketing salaries, software tools, paid ads only, event sponsorships, sales commissions); the time period you consider (e.g., monthly, quarterly, annual costs) how you treat multi-touch attribution (e.g., do you credit one channel, or multiple?), when you recognize the customer (e.g., at signup, first payment, or after onboarding?) etc.
Assuming you have all the data that the AI model needs to answer the question in the database. The question then is, which calculation will the AI select? And, does it have the context to calculate all of them simply from your raw data?
One way to make this more reliable is to use a semantic modeling layer, like Cube, which provides a semantic definition on top of an SQL-based model of your data. Think of this as a context-rich map for your team or your AI model to use when navigating the data.
If you have a robust and exhaustive data model, and you’ve made sure that all of the context needed to answer any question is actually available in the database – you’ll still need to make sure that your team, or your end users, understand how to provide the right context to the AI model so that it can map that request to the context it has available.
AI still needs human help
Most AI features in BI tools aren’t fully autonomous. They’re better described as “augmented analytics.” That means they can assist users but can’t operate independently.
AI is good at spotting correlations or anomalies, but it doesn’t inherently understand the context around the data. A tool might highlight that revenue is up in a region, but it won’t know if that’s due to a seasonal spike, a marketing campaign or a pricing change. A human still needs to interpret what the data actually means.
Even the best AI tools can return results that look insightful but miss the bigger picture. And because the logic behind these insights isn't always clear, users can be left unsure about whether to trust them.
In short, AI in BI is more of a co-pilot. It can suggest things, surface ideas, and make some tasks faster. But it can't explain the “why” or “what now” without human judgment.
Training the AI
AI models are trained using a feedback loop that tells the model when it gets something right or wrong. An AI model that’s just been introduced to your database and given a prompt will have no training on your database or business context, and this might lead to inaccuracies or inconsistencies in your answers.
Make no mistake, a trained AI model will provide a high degree of accuracy, but you’ll need to invest in training the model. This is typically done with human feedback on the accuracy and relevance of the responses – so be aware that if you’re investing in AI for data exploration, you’ll want to invest some time in this before setting it live for your team or your customers.
The hidden cost: data prep and modeling
Behind every AI-powered BI feature is a lot of tedious, manual work that users don’t see. These tools don’t just “understand” your business out of the box. Data engineers and analysts do the heavy lifting to make things work.
Before AI can find insights, your data has to be cleaned, joined, labeled, and structured. That means standardizing formats, resolving duplicates, handling missing values, and more. This takes time and usually involves writing code or setting up complex workflows.
Then comes data modeling. BI tools such as Embeddable need to know how different tables and fields relate to each other. Someone has to define what terms like “top customer” or “Q3 revenue” actually mean. Without this, the AI won't know where to look or how to answer even basic questions.
Even the most advanced BI features rely on this foundational work. Without it, AI outputs can be inaccurate, incomplete, or misleading.
Why data democratization is still a myth
One of the boldest promises of AI in BI is that it can help “democratize” data. In theory, this means everyone in an organization can find and use data without needing a technical background.
But in practice, most companies haven’t reached that point. If the underlying data isn’t clean or structured, AI features won’t deliver useful results. And most business users still need help interpreting what the tools show them.
It’s also common for different teams to have different definitions for the same metric. If marketing and finance are looking at two different numbers for “revenue,” no AI is going to fix that disconnect.
Non-technical users also face challenges with trust and understanding. If the system says there’s an anomaly, many users won’t know what to do next or whether to believe it. This leads people back to analysts or IT teams, which defeats the purpose of “self-service.”
True data democratization requires not just better tools but also better training, clearer definitions, and a strong data culture. AI can help lower some barriers, but it can’t remove all of them on its own.
The reality check: AI is still a co-pilot
That doesn’t mean AI in BI is useless, far from it. These features can save time, reduce repetitive tasks, and help analysts explore data more quickly. But they work best when paired with human expertise.
AI might tell you that a metric is trending up, but only a person can decide whether that matters. It might suggest a forecast, but a human needs to sense-check it and explain what to do next.
Organizations that get the most from AI-powered BI treat these tools as support systems, not replacements. They build strong data foundations, invest in data literacy, and use AI to make analysts more productive, not obsolete.
For now, the smartest strategy is to see AI as an assistant. It can help you ask better questions and explore your data more efficiently. But the hard thinking, the business context, and the big decisions? Those are still up to you.
Final thoughts
The vision of AI-powered business intelligence is exciting. The idea of asking natural language questions and getting reliable answers is appealing to anyone who works with data.
But we’re not there yet. Most AI features in BI tools today depend on extensive behind-the-scenes work. The tools can suggest and surface ideas, but they can’t reason or explain. And they certainly can’t replace data professionals.
Until AI can understand and prepare data on its own (which is still far off), companies need to keep investing in data teams, modeling, and training. The promise of embedded analytics tools is real, but realizing their full potential still takes effort.
In the end, AI in BI is evolving. It's useful, and it's improving. But it’s not a magic wand. It’s more like a flashlight. It helps you see, but you still need to decide where to go.
