To illustrate the concepts of moving from traditional BI reporting to the world of AI-assisted analytics, let’s imagine you are a conductor, and your organisation’s BI tools are your orchestra. Every day, you step onto the podium, ready to conduct your data orchestra. You skilfully transform raw numbers into harmonious insights, guiding your organisation’s decisions. But what if your instruments, your favourite BI tools like Qlik, Power BI, and Tableau, had even more powerful yet often overlooked talents and capabilities to amplify your performance? Let’s look at how we can unlock some of these overlooked talents and build a Sibelius symphony of AI-driven data insights.
AI is already in your hands
The AI revolution isn’t on its way; you’re already living in it. Beneath the familiar dashboards and reports of your business intelligence tools, a sophisticated layer of artificial intelligence has been quietly evolving, ready to assist. Over the years, we’ve had such features as Qlik’s Insight Advisor, providing visualisations and analysis of data from free-form text questions, together with drag-and-drop chart creation based on the data you want to analyse, making self-service easy for end users. Power BI similarly has had its own free text tool in Power BI Q&A, a powerful tool for quick insights and chart generation. Together with Q&A, there is the hidden and often overlooked Quick Insights feature that automatically scans datasets for interesting patterns such as trends, outliers, and correlations.
These have been small, overlooked features of BI tools for years, but what you might not know is that a new wave of powerful generative AI-driven capabilities has been growing in the very data tools you already have at your disposal in your organisations. Here is what’s new:
Qlik Answers, now a standard part of the Qlik Cloud offering, brings powerful AI capabilities for unstructured data directly to your fingertips. This secure, generative AI service creates a comprehensive knowledge base from diverse sources like PDFs, Word documents, PowerPoint presentations, and HTML. It intelligently combines this unstructured data with your existing Qlik reporting data, enabling you to ask natural language questions and receive comprehensive and context-rich answers that truly unlock deeper insights from all your structured and unstructured data.
Power BI recently introduced Copilot into its offering. Copilot takes user assistance to a new level by integrating generative AI. It allows you to create entire report pages, summarise key insights, and even suggest questions to ask of your data, all through natural language prompts. Instead of meticulously dragging and dropping elements, you can simply tell Copilot what kind of report or analysis you need, and it intelligently drafts visuals and narratives, making the process of data exploration and report creation significantly faster and more intuitive.
Tableau has also been enhancing its AI capabilities, designed to make data exploration more intuitive. Features like Ask Data empower users to simply type questions in natural language and have Tableau instantly generate relevant visualisations, while Explain Data provides AI-driven explanations for unexpected values, helping you quickly uncover the ‘why’ behind outliers or trends. Furthermore, Tableau’s robust forecasting capabilities allow for predictive analysis directly within your dashboards, enabling you to anticipate future outcomes without needing advanced data science expertise.
Practical ways to start
You don’t have to use every AI feature at once. A good starting point is to try one or two of the tools you already use daily. So, how do you take the conductor’s stand and truly leverage the power of these AI assistants? It’s often simpler than you think, starting with exploring features you might already have enabled. For instance, in Qlik Sense, don’t just build your next chart manually; let Insight Advisor guide you by suggesting relevant visualisations based on your data, or pose a natural language question to Qlik Answers to uncover sentiment from customer feedback.
In Power BI, don’t just build your next report page manually; use Copilot to generate visuals and layouts from a simple natural language prompt, or ask it to summarise key insights from an existing dashboard. You can also pose questions through the Q&A tool and get instant chart-based answers. You could use Smart Narratives for automated data summaries or let Quick Insights surface trends and outliers in your data.
What to consider
These AI features sound amazing, maybe even a little magical and possibly unbelievable. Some of you right now are probably saying: “I’ve tried these and it didn’t work”. Just like an orchestra needs good quality instruments and a solid stage to play on, your AI-powered BI needs a solid foundation of data before it can really shine.
This is where clean, context‑rich data sources come into play. To really take advantage of these AI capabilities, you need to give them something solid to work with. As the old saying goes, “garbage in, garbage out”. AI will happily amplify whatever you feed it, good or bad. That means things like the following are important to get right:
- Set clear, consistent definitions for customers, products, locations, etc.
- Create sensible data models that reflect how your business actually works.
- Provide enough history and granularity for patterns to be meaningful.
- And an increasingly important need, good metadata: table and field names, descriptions, and business terms that a human and an AI assistant can understand.
You don’t need perfect data, but you do need data that’s structured, joined and labelled in a way that makes sense.
Don’t worry though, help is at hand. Modern data platforms like Snowflake and Databricks have come to the rescue. On Databricks, Genie sits on top of Unity Catalog and can support users with generating table and column metadata, descriptions and synonyms to understand your data and answer natural language questions against it. Snowflake, in turn, provides a central place to store and govern your data, along with features like tags, descriptions and policies that help you standardise structures and semantics across sources.
Investing a bit of time in curating those table and field descriptions pays off twice: it helps humans read your models, and it helps AI tools understand what kind of data they’re looking at, so their answers are more accurate and relevant. This kind of consistent, well‑described data is exactly what AI features in Qlik, Power BI and Tableau thrive on.
These data lake and data warehouse platforms won’t magically fix everything for you, but they can support your journey to build cohesion and context into your structured data sources, the foundations that AI-powered BI needs. In most organisations, this means some deliberate work on modelling or remodelling core data structures, and cleaning and documenting key datasets so that both humans and AI tools can understand them. Often the most effective approach is a joint effort: your domain knowledge combined with some outside help when you need it…and we’re always happy to roll up our sleeves and help with that kind of challenge.
Why AI won’t replace you
Once you’ve got that data foundation in place, the interesting bit isn’t just that the AI features start working better, it’s what that means for your own role. When the basics are under control, AI starts to look more like a very capable assistant sitting beside you.
It’s natural to feel a touch of apprehension when discussing AI’s growing role, perhaps wondering if it signals a diminished need for human analysts.
Let’s be clear: AI doesn’t understand your organisation’s unique strategy, its political landscape, or the nuances of human behaviour. That is your irreplaceable role.
AI excels at processing vast datasets and identifying patterns with incredible speed, but it cannot interpret the ‘why’ or formulate the ‘what next’ with the contextual depth that only you possess.
Leveraging AI’s ability to do the ‘boring everyday stuff’, lets you spend more time discovering hidden insights in your data, generating new value and allows you to spend more time doing the ‘fun data stuff’ that many of us got into analytics for in the first place.
Embracing these AI features isn’t just about learning what button to press and where to type questions; it’s about fostering a cultural mindset that champions curiosity and augmented intelligence. Encourage your colleagues to experiment with these built-in AI helpers, turning routine data exploration into a collaborative discovery process. The more you interact with these intelligent assistants, the more they adapt to your way of working, constantly refining their suggestions to be more relevant and impactful.
Become the orchestrator
You already possess the powerful instruments, and now you know that AI isn’t a distant, futuristic threat, but an integrated, eager assistant in your daily symphony of data. It won’t play the music for you, but it will help you compose richer, faster, and more insightful pieces. So, take up your baton, explore the AI capabilities within your familiar BI tools, and let your data sing with a clarity and depth you might never have imagined.
Start your journey today to become an orchestrator of your own data symphony, and if you’d like a partner on that journey, teams like ours are always happy to help you figure out what “AI in your BI” might look like in your organisation.