Long the preserve of experts, Business Intelligence no longer waits to be questioned: it anticipates, alerts and recommends. With AI, dashboards become true decision-making co-pilots: they detect weak signals, generate insights and respond in natural language to business managers. The result: BI is no longer consulted, it speaks!
Imagine for a moment the opposite of a traditional dashboard: a business manager opens his BI tool at 8am, and instead of seeing frozen graphs, the software speaks to him directly in natural language. It detects a correlation between an increase in customer returns and a coming heatwave, and suggests preventive action. Sounds like science fiction? But that's precisely what artificial intelligence is bringing to Business Intelligence (BI).
We grew up with dashboards designed like rear-view mirrors: they told the story of the past, or at best the present. AI transforms them into augmented cockpits, capable of reading the road, anticipating bends and even suggesting shortcuts.
No more frozen dashboards. Thanks to AI, Today, these dashboards are becoming real decision-making co-pilots: they anticipate, suggest, summarise and, increasingly, act with the help of the company's management.’Agentic AI. AI can be used to ask questions in natural language, generate visuals, explain variations, detect anomalies and trigger certain workflows.
But this revolution does not eliminate the need for seasoned analysts. On the contrary, it is changing their profession and requiring them to develop new reflexes.
Welcome to the augmented cockpit
Let's take the image of the logistics manager. Until now, his morning ritual was limited to looking at the gauges: breakage rate, delivery times, cost per parcel, and so on. But this morning, a notification pops up: «Based on an analysis of time series and local weather forecasts, the risk of a breakage at the Lyon depot is estimated at 87 % within 48 hours. Would you like to simulate a rerouting?»
The dashboard no longer simply displays information: it engages in dialogue, anticipates and makes suggestions.
This mutation has a name: augmented analytics. Behind this term lies a cocktail of machine learning, natural language processing (NLP) and predictive analysis that makes the data talk, detects weak signals and proposes «what if? scenarios to help you decide.
The static dashboard is not dead, but it is no longer sufficient. It is still useful for monitoring recurring indicators. It becomes insufficient when you need to explore, explain or act quickly..
The most striking symbol of this evolution is undoubtedly the arrival of conversational search. Amazon is a good example of this evolution. In May 2026, the American giant announced Dataset Q&A in its BI Quick Suite tool. The user asks a question in everyday language, such as «What were our best quarters for product X», and the tool transforms it into an SQL query that executes the requested query. No need to write a single line of code!
The uses of AI in BI: how AI is reinventing the daily lives of analysts
AI doesn't just add a coat of varnish to existing tools. It is profoundly changing the way data is queried, understood and exploited. Here's what AI can now do, operationally, in the main BI platforms.
Enhanced BI is based on 4 building blocks:
- Conversational search which transforms a business question into a query or display
- Proactive surveillance identifies anomalies, trend breaks and weak signals
- Generation of visuals and reports which produces a first version of the analysis from a prompt
- Agentic workflows which propose or trigger an action, often with human validation
Conversational search and generation of visuals
No more long minutes configuring a graph or writing an SQL query. The analyst formulates his question in French or English, and the AI generates the appropriate visualisation while explaining its reasoning. This is what Looker (Google) offers with Gemini: the user asks «What was our turnover in Q1 2026 by region? and immediately gets a table, a graph, and even the option of refining the query in natural language.
Another example: in 2025, Tableau integrated a Tableau Agent (formerly Einstein Copilot) capable of suggesting relevant questions as soon as a source is connected. The aim is to break what the designers call «the hesitation in front of the empty workspace», the blank page syndrome that paralyses even experienced data analysts.
Automatic fault detection and proactive monitoring
AI never sleeps. In the background, it scans time series, learns normal patterns and alerts you as soon as a significant deviation appears. Qlik, a pioneer with its Insight Advisor from 2019, is now offering Qlik Answers, an agentic version capable of monitoring thousands of indicators simultaneously and signalling deviations before they degenerate into a crisis.
Generation of visuals and reports
An enhanced dashboard no longer simply says «-5 %». It now tells a story: «The drop in sales on 14 October is due to a breakdown in the payment tunnel for iOS users. Excluding this effect, the trend would have been +0.3 %.» This enhanced intelligibility, present in both Power BI (Explain the decrease mode) and QuickSight (display of generated SQL and step-by-step reasoning), makes the data comprehensible to everyone, from the data scientist to the marketing director.
Automated actions (agentic BI)
The most radical evolution is undoubtedly agentic BI: AI no longer simply advises, it can trigger actions. In April 2026, at Google Cloud Next ‘26, Google announced the availability of Dashboard Agents in Looker. Their promise? «They don't just provide static answers, but trigger business actions», such as automatically relaunching a supplier in the event of an expected delay, or adjusting an advertising budget if a peak in demand is detected. The assistant becomes a player.
The uses of AI in the main BI tools
Here is a summary of the main functional building blocks that AI is bringing to BI platforms today.
| AI function | Power BI (Microsoft) | Tableau (Salesforce) | Qlik | Looker (Google) | Amazon QuickSight |
|---|---|---|---|---|---|
| Natural language search | Copilot standalone (integrated conversation) | Tableau Agent / Einstein Copilot | Qlik Answers / Insight Advisor | Gemini Conversational Analytics | Amazon Q in QuickSight + Dataset Q&A |
| Automatic generation of dashboards / visuals | Copilot generates report pages | Tableau Agent creates sheets from questions | Insight Advisor suggests analyses | LookML wizard generates code | Generate Analysis (full dashboards on prompt) |
| Detecting and explaining anomalies | «Explain the increase / decrease» | Einstein Explain Data (integrated) | Qlik Agentic AI (since 2025) | Dashboard Agents (April 2026) | Q&A dataset with explicit SQL |
| Predictive analysis / forecasting | Power BI native forecast (built in) | Native forecasts (visuals) | Qlik Predict (ex AutoML, 2025) | Gemini predict (via Code Interpreter) | Integrated Forecast + ML Insights |
| Generative AI for development (code, DAX, Python) | Suggested DAX measures / Power Query | Not relevant (no code) | - | LookML Wizard + Python Code Interpreter | Generation of calculations / SQL via Amazon Q |
| Agentic workflow (automatic actions) | Coming soon via Fabric | Not documented | Agentic AI framework | Dashboard & Agentic Workflows (beta 2026) | - |
Limits and safeguards: why the human factor remains central
While these technologies are promising, they are not without risks. In 2026, the debate has evolved from «how to do more AI» to «how to do trusted AI».
Boundary nᵒ 1: built-in biases and data quality.
A model trained on biased historical data will amplify them. IBM pointed out in early 2026 that around 42 % of companies believe they do not have access to proprietary data of sufficient quality for AI. Poor data governance makes AI counterproductive.
Limit no. 2: black box effect
Complex models (neural networks, boosting) produce results without any clear justification. Some tools try to remedy this: Amazon QuickSight systematically displays the SQL generated for each query, and Looker uses its LookML semantic layer to reduce natural language query errors by two-thirds, according to Google.
Limit no. 3: overconfidence and loss of critical thinking skills
Faced with an AI that appears to be authoritative, business teams can delegate their judgement to it. The experts recommend a golden rule: the AI proposes, the analyst disposes and validates, by checking the variables used, the temporal scope and causality.
What skills do you need to pilot your co-pilot?
In the face of this overhaul of BI, the job of data analyst is changing. Three skills are becoming critical.
Critical thinking
Understanding the limits of AI, knowing how to ask for justifications, detecting overlearning or statistical hallucination. As Antoine Krajnc, founder of the Jedha School, points out, in 2026, «it's no longer enough to produce analyses; you also need to know how to fetch data from its source, industrialise data flows and challenge the output of models».
Data governance
AI is only as good as the data it is given. The analyst therefore becomes the guarantor of freshness, semantics and access rules. ”.
Hybrid storytelling
Knowing how to transform an AI recommendation («probability of stock shortage at 87 %») into understandable and actionable business language, by integrating its uncertainties.
So the analyst does not disappear; he delegates repetitive work to the AI and refocuses his activity on strategy, the quality of models and confidence in decisions.
AI does not replace the BI expert: it transforms his work. But if this revolution is to benefit everyone, one golden rule must prevail: stay in control. Understand the models, audit the data, and dare to say no to a flawed recommendation. A co-pilot, however brilliant, is no substitute for the captain.
