The Role of GenAI in Analytics

As generative AI (GenAI) is rapidly becoming a part of our day-to-day lives, businesses across the globe are starting to implement the technology into their day-to-day operations. Despite this, many business leaders still hold reservations surrounding the technology.

In today’s economic climate, businesses are increasingly focused on ROI and doing more with less. Executives want immediate results from their AI investments. For example, a recent Forrester study found that 49% of U.S. GenAI decision-makers expect ROI within one to three years. Pressure to deliver returns quickly means that executives need to understand how GenAI can contribute to their business's bottom line, even if those returns take time.

Technologies that don’t deliver value are, understandably, seen as poor investments, but businesses may not be realizing the full potential of their investments because they’re not applying it to the right use cases or failing to factor in people and process change. A recent report from ThoughtSpot and MIT highlights how GenAI, when applied to data analytics, can deliver substantial financial gains by democratizing data. The right tool, applied to the right problem is where businesses can realize results. By using GenAI in analytics, companies can create more intuitive experiences like dashboards that simplify data analysis, making it more accessible to a broader range of users. This accelerates data-informed decisions by business users, reduces the workload for data analysts, and frees up valuable time for data scientists to work on higher value applications.

Early Adopters See Real Results

Companies that have adopted GenAI for data analytics are already seeing tangible benefits, particularly in sales. By integrating AI products into their operations, early adopters have been the first to identify and address integration challenges, enabling them to reap the benefits and capture maximum value. One of the key drivers behind this success is GenAI’s ability to streamline sales processes.

Traditionally, businesses rely on specialized analysts for different functions— such as business, finance, and sales analysts—which can slow down decision-making. However, the real advantage comes from a federated approach that enables teams to retain and leverage their domain knowledge while tapping into shared technical resources. This is where GenAI comes in. By lowering technical barriers to entry, GenAI enables each team to focus on their specific area of expertise. This collaborative, distributed model accelerates the flow of insights, empowering businesses to make faster, more informed decisions to gain a competitive edge in fast-moving markets.

GenAI in Action

Organizations like Ecolab and Verizon demonstrate how early adoption can drive significant ROI. Ecolab, a global sustainability provider, has trained GenAI on clients’ operational and financial data to quickly identify the most effective solutions, saving time and resources. Verizon, on the other hand, has established a "center of excellence" to monitor the ROI of its GenAI applications, ensuring that they stay on top of the technology’s financial impact. These companies aren’t just using GenAI to improve efficiencies—they’re also building the infrastructure to track its value and ensure they’re staying ahead of the curve.

By using GenAI for data analytics, companies can ask questions of their business data in natural language, making the process more intuitive. This simplifies data analysis for both customers and internal teams, providing actionable insights more quickly and driving improvements in business performance. The immediate benefits are clear: faster decision-making, reduced operational overhead, and more precise targeting of key business goals.

These early success stories highlight the power of GenAI in analytics. Businesses that hesitate to adopt the technology risk falling behind competitors who are already reaping its benefits. While the technology offers clear advantages, it’s crucial that organizations take a strategic, thoughtful approach to implementing GenAI. A rushed or poorly planned adoption could result in wasted resources and missed opportunities.

Successful Implementation

To ensure successful integration of GenAI for analytics, businesses should focus on the following areas:

• Building the Right Skills: Invest in the technical skills required to fully leverage GenAI’s capabilities in analytics. This includes training teams to understand how to use AI tools effectively and interpret their insights.

• Fostering Collaboration: Encourage cross-team collaboration between data experts and business leaders to ensure insights are actionable and data is easily accessible. Creating a unified vision for how GenAI fits into the business will help drive adoption and engagement.

• Ensuring Human Oversight: Human oversight remains essential to AI success. Companies should prioritize responsible AI practices to build trust, ensure the accuracy of insights, and avoid over-reliance on automated systems. Ethical AI practices also ensure that decision-making remains transparent and accountable.

The time to act is now. The window of opportunity for businesses to adopt GenAI for data analytics is closing fast. By focusing on skill-building, collaboration, and human oversight, companies can maximize the ROI of their GenAI investments and ensure they remain competitive in the data-driven future. The businesses that act now will not only enhance their decision-making processes but will also position themselves as innovators in an increasingly AI-driven world.