Boost your Business Intelligence Using AI

While BI, Business Intelligence, makes data analysis and decision-making in your organization easier, adding AI to your BI initiatives is the real game changer. 

‍According to research by PwC, AI could contribute to up $15 trillion to the global economy already in 2030 and in surveys by Finance Online a staggering 86% of business executives claimed that AI was going to be a mainstream technology in the year to come. And that is already last year’s news.

One of the clearest use cases for AI is aggregation and analysis of large data sets, which is also at the core of the Business Intelligence discipline.

Many BI teams are starting to learn just how beneficial AI can be in terms of identifying patterns and trends and then enabling efficient decision-making down the chain. As Business Intelligence (BI) can be the difference between you and your most fierce competitor, understanding what AI can add to your BI initiative in terms of clarification, analytics and forecasting is vital. 

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BI then and now

Business Intelligence refers to analyzing performance via data-driven insight. Artificial Intelligence is the art of mimicking human intelligence within computer systems. The similarities and touchpoints are obvious, and AI already has a wide range of use-cases in business and more are added every day. Thousands of companies are using Google’s Vision API to extract and classify text in images, startups like PostClick use AI to optimize marketing campaigns, and OpenAI’s Dall-E initiative can already create astounding images from just plain text (behold the raccoon astronaut if you haven’t already met).

BI as a separate business function has been around for a couple of decades, but modern BI relies heavily on data capturing, automated processing and smart analytical tools. Since data warehousing solutions and big data methodologies became mainstream, BI as we know it today has exploded. 

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The data is in the lake – now what?

However, the actual “intelligence” in BI has not been particularly evolved before recent times, and the key part of the intelligence has actually come from human intelligence in the form of smart BI analysts. And even when the information has been accessible, knowing what to do with it has been a challenge, since it’s easy to drown in your data lakes as they increasingly tend to become “data oceans”.

From the organization’s perspective, BI reports in the form of colorful pie charts and dashboards tend to lack the necessary proximity to the day-to-day operations of the individual employee.

With AI, that is about to change, since various AI tools are exceedingly well-suited for capturing, aggregating, structuring and analyzing large volumes of data.

Basically, with the use of AI, Business Intelligence can finally transcend from “descriptive”  to “prescriptive” analytics, promising a new era of smarter and more adaptive BI tools. While classical BI rely on a handful of in-house data science experts to analyze all the information – which in most companies is a tangible bottleneck – AI offers the promise to make Business Intelligence more readily available, and perhaps also more transparent since the employees out in the organization can get closer to the data and the insights in real time and provide rapid feedback-loops.  

Using an AI solution, you can structure, segment and cleanse the data automatically, making it easily grasped and actionable. In addition, AI can also do the actual decision-making for you, or at least provide you with everything necessary to push the button.

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Multiple Scopes with Proven Effect

The right AI solution can complement and work wonders together with your BI tools. At Violet AI, we have identified four specific areas where you can apply AI/ML and swiftly start seeing the differences:

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1. Advanced Data Science

Data extraction is fundamental in Business Intelligence. AI will enrichen BI by providing more advanced data from free text or speech than what is possible using previous solutions. Recent advances in NLP (Natural Language Processing) and Machine Learning are allowing for entirely new BI features, like automatically extracting positive and negative comments (“feelings”) in, e.g. social media streams or in large volumes of customer support tickets.

Keyword extraction and topic analysis from different platforms can be identified to see changes in topic volume and conversation over time. To name a few examples. This has certainly been possible before but enormously time-consuming. With the help of AI this type of analysis is now possible at speed and scale.

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2. Forecasting

Can AI work as a crystal ball for your business? Sort of. At the very least, it can give you a more advanced prediction of things yet to come. Forecasts built on Machine Learning together with statistical models can be both more versatile and more accurate than simpler statistical models. They take several variables into account simultaneously and predict how these will affect each other in the future. AI solutions can also use external sources of different types and shapes, like weather data or text segments from chats, to further enhance their predictions. Both Meta and Google have presented new innovative algorithms within the past 12 months (Prophet and Temporal Transfusion Transformers, respectively)‍

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3. Prescriptive Analytics

One of the more novel fields in next-generation BI is the concept of “prescriptive analytics”. While AI algorithms can analyze data and make forecasts through predictive analytics, they can also suggest actions and activities based on insights and simulations of possible scenarios through prescriptive analytics. 

As predictive and prescriptive analytics go together like pieces of a puzzle, decision-makers no longer have to rely on biased intuition when considering what actions to take to achieve organizational goals.‍

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4. Hidden Truths

Identifying anomalies is one of the key features of Business Intelligence. An AI/ML algorithm can find patterns and see things that even the most trained human eye would miss. You can use it to see how different elements – like bad weather during the holidays or noise disturbance from nearby traffic – affect your business, or what geographical locations perform the best. 

Or why not use it to cluster your customers depending on certain preferences that are normally easily overlooked or hard to recognize, like “loyal customers who prefer to shop late on Tuesday afternoons, when it rains”? Try it - AI is simply gold when it comes to reading between the lines.

Finally a prediction to all BI professionals: Some type of AI will become your new hardworking and versatile colleague within the next 24 months. 
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Would you like to discuss how AI can support your BI team or learn more about AI driven forecasting? Please contact us for case studies or to book a meeting.