The Role of Generative AI in Data Analysis and Decision-Making

Few technologies have impacted businesses across industries the way generative artificial intelligence (AI) has. Still, its potential for automating creative roles often takes much of the spotlight when it may be most useful as a data analysis and decision-making tool.

How Generative AI Improves Data-Driven Decision-Making

Organizations today can only remain competitive if they drive strategic decisions through data analytics. However, manual approaches are slow and prone to error. Generative AI can improve the practice in several ways.

Predicting and Simulating the Future

Many companies already use predictive analytics to predict future market trends and respond accordingly. While predictive AI and generative AI are technically distinct technologies, genAI can push predictive analytics to new heights.

Most notably, it can create new datasets that behave like real-world information when analyzed — a resource known as “synthetic data.” Despite not being real, these datasets show no difference in accuracy in predictive models. Predictive AI works best when it has more information to analyze — consequently, genAI drives more reliable predictions by generating synthetic data to complement existing real-world info.

Generative AI also enables scenario modeling. Users can ask it about hypothetical situations the business may encounter, and the model can use predictive analytics to simulate what may happen. Leadership can use genAI this way to determine optimal strategies or assess risks without real-world trial and error.

Streamlining Data Analysis

In addition to providing deeper insight in data-driven decision-making, generative AI can accelerate the process. Algorithms excel at analyzing vast amounts of information in minimal time. It’s how AI-driven sales teams produce proposals in 27% less time while achieving 17% higher conversion rates.

Generative AI has an advantage over other intelligent models because of its natural language capabilities. Analysts can ask it to perform certain research or request specific insights the same way they would when talking to another employee. Using conversational language instead of complex coding requirements makes it easier for even less technically experienced teams to capitalize on AI data analytics.

Simplifying Complex Concepts

Another unique use case for generative AI in data analysis is visualization and storytelling. Analytics — however accurate — are only useful when decision-makers can understand them. Putting insights into an easy-to-understand context and format is key.

A staggering 93% of IT leaders say data storytelling drives revenue increases. However, 82% of organizations rely on dashboards to illustrate analytics, which over half say omit context and require too much interpretation. Generative AI can bridge the gap by explaining insights in natural language, even tailoring presentations to unique needs.

When something is unclear, leaders can ask the models to explain the data differently, and it will. This technology can adapt insights into a virtually limitless range of visualizations, reading levels, organizational styles and combinations thereof. Thus, in-depth analytics become easier for all stakeholders to understand, leading to better decision-making.

Best Practices for Implementing Generative AI

In light of such potential, it should be no surprise how generative AI could drive $7 trillion to the global GDP in the coming years. However, it’s important to note that optimal results are not always easy to achieve. GenAI is only a tool, and like any other tool, it requires proper usage.

Three of the five most common reasons why 80% of AI projects fail have to do with misuse. Enterprises misunderstand which problems the technology should solve, focus too much on AI over relevant use cases and apply it where it’s minimally effective. Stakeholders can address all these issues by adopting a needs-based AI strategy.

First, identify where current data analytics and decision-making workflows fall short of goals or expectations. Then, recognize which tasks generative AI is best suited for — generally, these include data-heavy and repetitive technical tasks, not nuanced or strategic ones. See it as a way to gain and explain information and not a solution to act on these insights. Teams should apply genAI to areas of crossover between the two categories.

Data restraints also deserve consideration. All AI works best with large amounts of relevant, reliable data. Two key strategies can help here — cleansing real-world information before feeding it to generative models and supplementing it with synthetic datasets.

Similarly, businesses must scrub all AI databases of sensitive details that may worsen the impact of a breach. Employ strict access restrictions and security monitoring software. Always let human experts have the final say in any decisions, including whether an AI-driven insight deserves additional research to confirm before acting on it.

GenAI Is a Powerful Tool When Used Appropriately

Generative AI is not an all-in-one solution to optimize strategic decision-making. However, it can greatly improve the accuracy, detail, speed and comprehension of data analytics when leadership applies it properly. Learning what genAI is best at and what it can’t do is key to capitalizing on its potential.