September 8th
February 22nd
As AI-driven systems gain momentum, many business leaders wonder if the promise of streamlined data analysis, faster insights, and more democratic decision-making truly outweighs the uncertainties. Are these tools secure? Will analysts need new skills? And if so, what’s the cost of getting started?
These questions, along with others about the practical impact of AI on Business Intelligence (BI), took center stage at Zebra AI’s November event, The New Era of Business Intelligence: AI-Powered Business Decision-Making. The panel included:
They discussed where AI stands in BI today, what’s actually achievable, and how organizations — big or small — can get started.
For decades, BI has been held up as the key to data-driven decisions. Yet traditional BI setups remain complex, costly, and time-consuming. Gathering data, performing ETL (Extract, Transform, Load) processes, modeling, designing dashboards, and adding narrative context often require extensive technical expertise. Smaller organizations, which may not have dedicated teams and big budgets, find these hurdles even more daunting.
AI could significantly shorten this workflow. It promises to automate data preparation, accelerate modeling, and even suggest visualizations and narratives. Rather than spending months setting up a BI stack, companies might rapidly produce actionable insights. The ultimate goal: to truly democratize data analysis and make “data-driven decisions” more than a buzzword.
Nicolas Boucher provided tangible examples of AI’s current impact, especially in Finance:
AI can handle repetitive tasks — like generating text or email drafts — and speed up data analysis in familiar tools like Excel. With AI’s help, finance professionals can work faster and focus on more strategic activities.
Systems can now read and validate receipts or invoices in seconds, cutting down manual effort and reducing errors.
Even general-purpose AI tools like ChatGPT can help produce quick forecasts or highlight trends. While these insights might not replace expert analysts, they provide a starting point that’s faster than doing everything by hand.
During the webinar, today’s AI tools were categorized into three main groups:
Companies like OpenAI aim for broad, human-level intelligence. Their tools are powerful generalists — not deeply specialized for BI, but useful for tasks like coding assistance, summarization, and basic analysis.
Giants like Microsoft and SAP are integrating AI into existing platforms, enhancing products such as Power BI or Excel. These embedded features — like Microsoft’s Copilot — provide smoother workflows without forcing users to learn entirely new tools.
Startups and new AI-driven platforms, like Zebra AI, built with automation in mind from day one. They prioritize ease of use, quick setup, and specialized data storytelling. Although these tools can be powerful, they must prove reliability, scalability, and return on investment.
Nicolas also highlighted how approaches differ by organization size:
As Nicolas pointed out, smaller businesses should start with accessible tools (ChatGPT, Copilot), digitize key processes, and leverage simple automations (like Python scripts) to consolidate and analyze data.
Invest in specialized AI tools for specific finance and BI tasks, incorporate machine learning models, use OCR/NLP for unstructured data, and consider fine-tuning large language models with proprietary data. AI chatbots can also replace traditional helpdesks for quicker support.
Nicolas, Benjamin, and Andrej also showcased how ChatGPT, Copilot, and Zebra AI work on a specific dataset. Here’s what they found:
Uploading data and requesting visualizations can produce useful outputs, though mostly at a beginner level. It’s good for quick, rough insights but lacks sophistication without detailed prompts or analyst guidance.
Embedded directly in Excel, Copilot keeps data secure and makes analysis convenient. However, the output can feel limited. It’s helpful for basic charting and summary insights, but doesn’t always deliver advanced analytics or polished visuals.
Zebra AI offers near-instant dashboards — under 15 seconds for a fully interactive visualization. Our tool focuses on business scenarios like sales and finance reporting, letting users filter, drill down, and customize dashboards easily.
While Zebra AI is still evolving (visualizations are currently limited, custom calculations must be pre-encoded, and large datasets pose challenges), it does deliver actionable insights very quickly, useful even for users with deep technical skills.
Data security and trust are top concerns in any organization. During our event, Benjamin (Zebra AI’s AI Team Lead) addressed these issues and how we tackle them at Zebra AI:
Zebra AI does not retain data beyond the session. It encrypts data in transit and at rest and sends only minimal slices of data to external AI services.
Zebra AI uses specialized agents and controlled function calls to reduce “hallucinations” (false AI outputs). It aims for transparency and reproducibility, clearly showing how results were generated and where data came from.
Future iterations will offer private deployments and respect existing role-level security settings. The idea is to let companies reap AI’s benefits without compromising on privacy or compliance.
Looking ahead, AI is on track to become as commonplace in BI as spreadsheets are today. Our panelists highlighted three trends shaping the future:
Specialized AI solutions will handle niche tasks, while large ERP and BI systems embed AI directly into their platforms. Users might soon interact with data conversationally instead of navigating complex dashboards.
Rather than relying on predefined rules, AI agents will understand intent, provide context-driven insights, and reduce the need for constant human intervention. This frees professionals to focus on strategic thinking rather than data wrangling.
Future BI will blend diverse data sources, organizational knowledge, and user preferences to produce tailored analyses. Decision-makers will get insights that align with their strategic goals, delivered in real-time, without manual setup.
As AI matures, the best outcomes will come from human-AI collaboration. Professionals will leverage AI to handle grunt work — creating dashboards, summarizing trends, and highlighting anomalies — while they interpret findings, guide strategy, and bring the human perspective.
For organizations ready to move beyond traditional BI roadblocks, today’s AI tools offer a glimpse of a more agile, accessible data future. The key is to balance optimism with caution, choose the right tools for your needs, and remain open to the evolving landscape. If done thoughtfully, AI’s role in BI won’t just be about saving time — it will be about unlocking smarter, more informed decision-making at every level.
