Leveraging intelligence has been the main way companies have secured a competitive advantage for as long as anyone reading this can remember. But more powerful computing capabilities and more sophisticated business intelligence platforms have changed that. Now, everyone has data and everyone has the computing power to mine it.
Today, competitive advantage comes from the speed at which companies can learn and adapt to changing market conditions. This means that companies not only need to mine their data, but they need to collect and analyze it in near-real time, make consequent adjustments to business plans, then act on them fast. And not only do they need to learn and adapt fast, but they need to get out ahead of the changes through predictive and prescriptive analytics.
The obvious conclusion is that enterprises can’t be competitive in the Age of AI without merging AI and automation with business intelligence. The blurring of the lines between artificial intelligence and business intelligence (BI) has evolved into what’s now called generative BI (GenBI) — a term that highlights the combination of AI’s predictive and generative capabilities with the robust analytics of BI systems.
GenBI isn’t just an evolution of business intelligence – it represents a tectonic shift that changes everything about the way data is analyzed and used, for everyone (not just the tech-savvy), forever.
In this article, I show how organizations can unlock unprecedented value from their data by embracing this trend.
The evolution of generative BI: a brief history
The convergence of AI and BI is not an overnight revolution; it has been building momentum for over a decade. The integration of R Studio and Python into BI platforms in the mid-2010s marked the beginning of this transformation. In 2014, Tableau introduced R script integration, allowing users to access statistical computing power directly within dashboards.
A year later, Microsoft Power BI followed suit by supporting R scripts for creating custom visuals and performing advanced analytics. As the decade progressed, both platforms embraced Python, expanding their capabilities with machine learning, natural language processing (NLP), and automated insights.
These early steps laid the groundwork for more sophisticated AI-driven analytics. From predictive models that forecast customer churn or sales trends to advanced fraud detection systems, AI-powered BI tools allowed businesses to enhance decision-making and optimize operations.
Use cases of generative BI
GenBI is a term now gaining traction for describing the fusion of AI and BI. Unlike traditional BI systems that primarily focus on data reporting and visualization, generative BI leverages AI to go beyond descriptive analytics and into predictive, prescriptive, and even generative insights.
Predictive and prescriptive analytics
One of the most impactful areas is in predictive and prescriptive analytics. For instance, companies can now use AI-powered BI tools to better understand customer behavior and segment their audiences with greater precision. By identifying patterns that indicate customer churn, businesses can proactively add value at pivotal moments to keep customers loyal.
Similarly, generative AI enhances sales forecasting by analyzing both structured and unstructured data—ranging from social media trends to historical purchase patterns. This results in more accurate sales predictions and reveals opportunities for cross-selling and upselling. With this intelligence, businesses can optimize their inventory management, staffing, and marketing strategies.
Customer sentiment analysis
When it comes to understanding customers more deeply, customer sentiment analysis is another powerful use case. Leveraging the natural language processing (NLP) capabilities of generative AI, BI platforms can sift through vast amounts of unstructured data—such as customer feedback, reviews, and social media interactions—to gauge sentiment. This gives companies the ability to hyperpersonalize their interactions with customers and strengthen their relationships with them.
Operational efficiency
Generative AI is also improving operational efficiency across industries. In manufacturing, for example, AI-driven BI systems can predict equipment failures before they happen, allowing companies to optimize their maintenance schedules and reduce costly downtime. This predictive capability extends to supply chain management, where AI helps forecast demand and optimize inventory levels.
Related reading: See how iTD partnered with a compliance operations organization to achieve operational excellence.
This efficiency boost can be applied to human resources, as well. HR departments now have the ability to analyze employee performance data more effectively, spotting trends and identifying areas for improvement. By using these insights, organizations can better manage their workforce, boost productivity, and enhance employee engagement.
Industry-specific solutions
Needless to say, it didn’t take long for industry verticals to see how GenBI could help them overcome tough challenges and stay competitive. Beyond the retail, manufacturing, and supply chain logistics use cases already mentioned, healthcare, financial services, and energy are other noteworthy examples.
In healthcare, generative BI can analyze huge datasets to predict individual health risks and prescribe personalized treatment protocols. On a societal level, predictive models can identify potential disease outbreaks, enabling healthcare providers to respond more effectively.
In financial services, generative BI allows platforms to analyze transaction patterns in real time, enabling faster detection of unusual activity and the prevention of fraud.
In the energy and utilities sector, GenBI is helping energy providers optimize grid management by predicting demand fluctuations to reduce strain on the grid and prevent outages. It also helps minimize service disruptions through predictive infrastructure maintenance.
The impact of large language models and generative AI
The recent advancements in large language models and generative AI have further accelerated the evolution of GenBI. Technologies like OpenAI's GPT models, Google's BERT, and Microsoft's Copilot (built on OpenAI’s GPT technology) are bringing new capabilities to BI platforms.
Data augmentation is a good example of this. AI can create synthetic datasets, so companies can test and develop AI models even when real-world data is scarce or incomplete.
What’s more, generative AI’s scenario analysis capabilities allow businesses to create different “what-if” scenarios by simulating potential business outcomes based on varying conditions (e.g., changes in fuel costs or exchange rates).
This AI technology can also automate and schedule the creation of interactive visual reports, so enterprise leaders can have relevant information at their fingertips without having to ask for them.
Related reading: See how iTD partnered with Microsoft to leverage AI and Power BI to generate interactive dashboards and customizable reports.
AI assistants like Microsoft Copilot have brought real-time assistance into the BI landscape. It allows users to query databases in plain language, for example, “What were the top-performing products last month?” They can also ask Copilot to answer visually: “Show me a bar chart of monthly revenue growth for the past two years.”
One of the most impressive abilities of generative AI assistants is to identify and even fix inconsistencies in data, such as missing values, duplicates, or errors. It can also automate processes like data normalization and categorization. Before AI, this type of data cleansing was extremely (and for some companies, prohibitively) resource-intensive.
Related reading: Read about how iTalent Digital’s award-winning intelligent data forensics solution identified hundreds of millions of dollars of leaked revenue.
Another powerful feature of AI assistants is their ability to enhance collaboration. By integrating directly into workflow tools like Microsoft Teams, they help teams make data-driven decisions together.
Leaders in the convergence of AI and BI
When it comes to the convergence of AI and BI, both ThoughtSpot and Microsoft are at the forefront. ThoughtSpot is one of the most mature generative BI platforms. Its intuitive interface and advanced AI capabilities make it a powerful tool for organizations seeking to democratize data access and empower non-technical users with data-driven insights.
Microsoft, with its Power BI platform and its heavy investments in OpenAI, has also made significant strides in integrating AI features, including natural language querying, real-time data processing, and automated insights. Both platforms exemplify how generative BI makes intelligence accessible to more people and empowers businesses to better deliver on their brand promise.
Harnessing the power of GenBI with iTalent Digital
As AI and BI continue to converge, enterprise leaders have an unprecedented opportunity to harness the power of their data. With real-time insights and predictive analytics, the integration of AI into BI platforms is enabling businesses to learn and adapt faster.
At iTD, we specialize in helping organizations leverage the latest AI and BI technologies. With our expertise in platforms like Microsoft Power BI and ThoughtSpot, we guide our clients through this evolving landscape, ensuring they stay ahead of the curve and unlock the full potential of their data.
To learn more about how iTalent Digital can help your organization navigate the future of AI and BI, visit https://www.italentdigital.com/ai-consulting or contact me at itbi@italentdigital.com.
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