How to use Generative AI in your Business Intelligence: 3 examples

June 21, 2023
4 min

Did you know that the last campaign by Coca Cola was created 100% by generative artificial intelligence tools?

Copywriting, music, and advertising have assisted a massive adoption of AI in the last six months (according to McKinsey it has the potential to automate work activities that absorb 60 to 70% of employees’ time today).

What about data & analytics and business intelligence?

In this article, you can get your head around the use of generative AI in business intelligence, through 3 different examples.

  1. Pattern identification
  2. Personalized Data Visualization
  3. Risks and Opportunities management

This is a kind of ‘invisible AI’, compared to popular tools like Dall-e, Midjourney or Jasper, but it is so powerful that it can enhance your data experience, help you make faster decisions, and save you lots of time, money and effort.

AI adoption and Data & Analytics

Much before the chatbot advisor era, businesses were looking at artificial intelligence (AI) and machine learning to help them make sense of their data.

Why? Because AI provides predictive analysis capabilities to anticipate customer needs, market trends better, anticipate problems and embrace new opportunities.

In short, it helps the business. A lot.

Machine learning and deep learning are able to identify patterns over a large amount of data and provide insights that show the reasons why something happens (diagnostic analytics), what’s going to happen in the future (predictive analytics) and what it would be better to do (prescriptive analytics).

Example | It’s like having a map of what’s going to happen and be ready to take action well in advance. If you know that it’s going to rain, you take the umbrella with you before leaving home, don't you?

Traditional Business Intelligence is data-centric

Despite the benefit of AI and the massive investments in business intelligence tools  - according to Gartner - only 30% of organizations are leveraging data & analytics effectively.

Why? There is a gap between the way business intelligence tools were created, and the way real people (managers, business executives, accounts etc…) can interact with them.

Traditional BI tools were designed to provide solutions for technical people, and not with people in mind.

Everyday life experience vs Data & Analytics experience

This led to: 

  1. Data complexity and low adoption
  2. Data accessibility issues
  3. Time and money loss 
  4. Employee engagement, satisfaction, and performance drop

The future of Business intelligence is human-centric

So, if artificial intelligence can help businesses make sense of their data, generative AI can help make data human, shifting BI tools towards a more human-centered approach.

How? Think of Chat GPT, but for numbers. 

Generative AI in business intelligence is able to read data and create tailored insights, predictions, and visualizations, making BI platforms more accessible, intuitive and easy to use. 

Here are a few examples.

Identify patterns and trends in data 

You can use generative AI algorithms to analyze large amounts of historical data, and identify patterns (clustering) and trends to make predictions about future events (predictive analytics)

This can help you better anticipate market changes and make more informed decisions about how to respond to those changes. 

Example | Google AI recently presented a case study in which they showed that an advanced retina scan used to diagnose diabetic retinopathy (a leading cause of blindness) turned out to be able to detect things that humans did not know to look for: that same eye scan can predict the 5 year risk to develop cardiovascular events. This is a clear example of AI and machine learning systems offering new insights, helping doctors predict medical events and creating the basis for non-invasive solutions to detect potential health risks. 

Risks and opportunity management

You can use generative AI to simulate different scenarios and identify potential risks before they happen, and to proactively make decisions to implement mitigation strategies, or adjust investment portfolios.

In the same way you can identify new opportunities to leverage for your business; maybe there is a potential for growth and expansion, such as new markets, products or services, data can tell you that.

Example |  Generative AI can replicate real financial transactions to provide more complete and realistic datasets for training fraud detection models and improve their capacity to discern fraudulent trends.

Personalized data visualization 

Generative AI can help with data visualization, by creating unique ways to get the insights. This is because the AI model can generate new images and designs based on the data, rather than simply plotting the data on a chart.

100% tailored to the specific needs of the user, these data visualizations are also interactive to explore data in real time and overall, facilitate the interpretation of data.

Example | By analyzing medical images and crafting detailed 3D models of a patient's anatomy, generative AI enables surgeons to plan procedures with incredible accuracy. This is a clear example of how generative AI allows for a personalized approach to medical surgery that minimizes risk and enhances patient outcomes.

Generative AI in Business Intelligence 

As we learned at the Gartner D&A Summit in London, generative AI impact on data & analytics and business intelligence is huge, and it has all it takes to benefit: business people by making data easier to access, understand and use; data people from getting the rewards of their hard work, increasing data adoption and improving decision making; and organizations that can see the return of investment in data and business intelligence resources, mitigate risks and capitalize on new opportunities. 

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