AI in BI in action – use cases across industries
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This is the second article in our AI & BI series. In the first piece, we explored how business intelligence has evolved - from static dashboards to dynamic, AI-powered insights - and how artificial intelligence is now transforming the very core of data-driven decision-making. In this article, we shift focus to real-world use cases: how AI in BI is driving impact across industries, the types of processes being enhanced, and where the value is most visible. In our next piece, we’ll explore the challenges that come with this transformation.
1. How AI enhances decision-making across business processes
Artificial intelligence is fundamentally reshaping the way organizations make decisions. BI used to be primarily descriptive – summarizing what happened and why. But with AI, we’ve entered a new phase that emphasizes forecasting, scenario planning, and intelligent recommendations. The shift is not just technological – it’s behavioral. Decision-makers now expect insights in real time and with context.
AI in BI enables capabilities like forecasting revenue, demand, and operational risks with far greater accuracy. Anomaly detection models run in the background, surfacing irregularities instantly rather than relying on manual reviews. Prescriptive analytics pushes this further by offering potential actions, not just insights. Scenario simulation, once reserved for advanced financial models, is now possible inside BI platforms – enabling leaders to ask “what if?” and get credible answers on the spot.
In my work, I’ve seen these kinds of capabilities adopted across industries. In the pharmaceutical space, sales forecasting is being updated daily and surfaced in BI dashboards to help field teams adjust their focus. In automotive, churn modeling has been integrated into reporting to identify potential customer losses early. In retail and e-commerce, customer segmentation and product recommendation models are powering smarter merchandising decisions, often embedded directly within BI tools.
The real breakthrough is when AI and BI begin to loop together. BI platforms visualize the outputs of AI models in ways decision-makers can trust and act on. At the same time, user interactions within BI tools can help train and refine the AI. This creates a feedback loop where data, insight, and action become one continuous cycle.
2. Where to seek value - AI & BI across business functions
While the potential for AI in BI spans the entire enterprise, we’ve explicitly seen strong value delivered in areas like finance, sales and marketing, human resources, and ESG. These domains benefit not only from high data availability but also from urgent decision cycles, making AI integration highly impactful.
In finance, AI has long been used for forecasting and risk assessment. With BI integration, these models become more accessible, enabling real-time updates on cash flow projections or cost variance analyses. The addition of large language models (LLMs) introduces a new level of accessibility – finance leaders can now interact with data through Ask Your Data interfaces, asking questions like, Why is Q4 revenue trending below forecast? and getting contextual explanations directly within their BI tools.
In sales and marketing, AI models power customer segmentation, lead scoring, and churn prediction. We’ve seen how these insights, once locked in backend systems, are now visualized within BI dashboards – bringing predictive analytics closer to the people who need it most. With LLMs, sales managers can query data like, Which regions saw the biggest shift in customer behavior last month? without relying on new dashboard builds or IT requests.
HR teams are increasingly using BI to understand employee engagement, track performance, and anticipate attrition. AI models enhance this by spotting early warning signs and surfacing themes from employee feedback. With LLMs, leaders can ask questions like, What’s driving attrition in our engineering teams? and receive grounded answers in natural language – turning BI into a more intuitive tool for people management.
In ESG and compliance, predictive models support emissions forecasting, energy usage tracking, and incident detection. BI platforms have long helped visualize these trends, but with the addition of conversational AI, the experience becomes more interactive. Now, users can ask, How is this KPI calculated? or What could improve our ESG score? – helping organizations not just report, but act.
These use cases reflect a broader shift: BI is no longer just for analysts. Thanks to AI – especially generative AI – business users across departments can now explore insights, ask complex questions, and make data-informed decisions without needing to interpret raw datasets or learn technical languages.
3. Best practices for AI-driven BI implementation
Making AI in BI work at scale requires more than tools – it requires intentional design, governance, and cross-functional alignment. One of the biggest differentiators we’ve observed is data readiness. Without high-quality, well-governed data, even the most powerful AI models struggle to deliver meaningful results. This includes ensuring reliable data pipelines, clear business definitions, and secure access structures.
Designing with generative AI in mind also matters. LLMs don’t just need data – they need context. Building semantic layers, tagging metadata, and maintaining aligned KPI definitions ensures that when someone asks a question through natural language, the response is relevant and trustworthy. The better structured the data model, the more useful the AI interface becomes.
It’s equally important to make these tools approachable. Business users shouldn’t feel like they need a data science degree to interact with AI-driven BI. Interfaces like Ask Your Data help bridge this gap, but adoption also depends on training, documentation, and clear examples of what the tools can do. We’ve found that involving both data and business teams in the design process leads to more relevant and widely adopted solutions.
The final piece is integration. AI and BI should meet users where decisions are already being made. Rather than launching new platforms or standalone dashboards, the most successful implementations embed AI-generated insights directly into existing workflows. This allows business users to act quickly and confidently on what they see – transforming BI from a reporting layer into a decision-making engine.
If you’re considering a journey toward AI-driven BI, there’s no one-size-fits-all path. But building strong data foundations, focusing on usability, and thinking beyond dashboards are all critical starting points.
“AI and BI have been leveraged for years in many industries and business functions. What’s new is the additional intelligence that LLMs offer - making data not only easier to access, but easier to understand and act on. Still, success starts with the right data foundations.”
- Key takeaways
AI in BI enhances decision-making through forecasting, simulation, and prescriptive insights.
Finance, marketing, HR, and ESG are all benefiting from conversational and predictive analytics.
Ask Your Data enables intuitive, LLM-powered access to business insights – expanding BI’s reach.
Successful implementations depend on quality data, contextual modeling, and thoughtful integration into existing workflows.
AI in BI is no longer optional—it’s becoming a core business capability. If you’re exploring how to make your analytics more intelligent, interactive, and business-ready, now is the time to take action.
Sources:
Wiktor Zdzienicki
Senior Portfolio Lead D&AI
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