It is challenging to overestimate the importance of generative AI as a transformative force for technology and society, comparable to the emergence of the public Internet. This is reflected in market trends and the expectations of senior management and their boards. If your CEO hasn’t made developing and implementing a generative AI strategy a top priority, you probably don’t work in IT.
Generative AI has impressive capabilities, but also limitations. It delivers extraordinary experiences in forecasting and intuitive interaction, transforming the way we apply data. Using machine learning (ML) and natural language processing (NLP), AI can reveal future trends and find convoluted, subtle and powerful patterns that would be missed by time-honored analytics. However, while some forms of AI can be explained, the most popular AI/ML platforms operate like a “black box” in which inputs generate outputs and no one knows how the results were achieved. This fundamental lack of transparency raises questions about the integrity and reliability of the predictions.
Additionally, Gen AI has a well-known problem hallucination, which creates significant problems for decision-makers who want to apply this technology to make better data-driven decisions. After all, it’s more natural and competent to ask a generative AI bot, “Give me sales forecasts for the Midwest region for the next quarter,” than to search through dashboards. But simplicity and speed are worthless if executives fabricate imaginary numbers.
One way to meet this challenge is to apply generative artificial intelligence in combination with business intelligence (BI), which is a proven, reliable and widely trusted technology. This allows decision makers to benefit from the versatility and natural language interaction of generative AI while being confident that they are achieving reliable and consistent results.
Enterprise BI is the basis for data-driven decision-making in the business world. Collects, processes and explains vast amounts of structured data to discover trends in past and present performance. However, widespread adoption of BI systems, especially among frontline workers, is challenging due to the inherent complexity of BI and dependence on dashboards. Users may have difficulty understanding and using the insights they offer, and a significant amount of training is required to develop dashboards that incorporate the various metrics and visualizations needed across the enterprise.
By combining AI’s ability to predict and interpret with BI’s ability to analyze and validate, a powerful partnership is created that streamlines the data analysis process. Generative AI enables users to obtain structured BI insights in real time using natural language through a bot. With this integration, all users can rely on the data and all staff can make astute and informed decisions every day.
Combining artificial intelligence and BI requires a pragmatic approach that takes into account the ethical apply of data, transparency of algorithms and building trust among users. It is essential to ensure that this connection complies with ethical standards and regulatory requirements to maintain the integrity of the decision-making process.
Technological advances promise to further enhance the value of artificial intelligence integrated with Enterprise BI, offering deeper insights and more intuitive access to data that anyone can apply. This evolving landscape marks a shift towards a more mainstream, data-driven approach to decision-making that is timely and actionable, and where organizations can realize the full value of their data through the ingenuity of their employees.
By leveraging the strengths of AI+BI, companies can better transform data into a strategic asset that increases agility, efficiency and competitive advantage. As this integration deepens, it paves the way for a recent era of intelligence, defined by the speed of insight and speed of action for anyone and everyone across the organization. Like a symphony where each note creates a greater melody, AI+BI highlights the transformative power of data in shaping the future of business.
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