GenAI can help businesses everywhere
make smarter decisions.
Popular generative AI (GenAI) tools like ChatGPT have achieved tremendous adoption. Under the hood, these tools are trained to help computers infer, understand, and communicate information much like how the human brain gains, stores, applies, and shares knowledge.
GenAI tools are fueled by massive amounts of data that help them understand desired results and create incredible, intuitive content designed to fulfill a user’s prompted specifications. Since business intelligence (BI) has long been the analytics practice that helps organizations make sense of their data, significant advancements in GenAI will also prove powerfully disruptive to the BI space.
Breakthroughs in natural language technology, cloud computing, and data science make this all possible. But many people still want to know—how will the convergence of GenAI and BI impact their profession?
What does generative AI mean for business intelligence?
When BI is done best, it transforms an organization's ability to process and gain meaning from all sources of business information and fosters better decision-making.
To achieve impact, BI must be based on a sophisticated data fabric that weaves together raw data, data definitions, business logic, security privileges, and governance rules. This data model allows users to find, explore, and interact with trusted facts and insights about their business. Here are 3 examples of how GenAI can improve the current state of business.
Enhanced data storytelling
With GenAI, BI solutions can explain the context behind a dashboard or its underlying data without it being pre-defined or hard-coded. Users can simply ask a question and get a precise answer and a contextual explanation through a seamless, chat-like experience.
Efficient content creation
Leading BI tools offer great flexibility in what users can build, usually only limited by the developer’s allotted time for the task. With GenAI, simple prompts can create sophisticated dashboards in seconds, leaving more time for enhancing analyses and user experiences.
Automatic advanced analytics
BI has long empowered data scientists to conduct deep statistical analyses and visualize results, but it can be complicated and require IT. GenAI can make data and tool integrations more seamless, simply passing the correct information across underlying tools.
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What challenges exist with generative AI?
GenAI presents opportunities to enhance not just BI, but all aspects of how businesses operate. Process automation in IT, messaging creation for sales, and content generation for creative disciplines are just some of the many ways organizations are experimenting with GenAI today.
But, as with any emerging technology, GenAI still has drawbacks that are important to understand. GenAI is probabilistic, meaning each response is based on probability instead of certainty. The exact same questions can often yield different results, making users distrustful of the information at hand. The extensive model training process powering it can also be expensive and time-consuming—major drawbacks when you’re trying to impactfully implement organizational GenAI.