One can hardly exaggerate the significance of generative AI as a transformative force for technology and society, comparable to the emergence of the public Internet. This is reflected in the market trends and the expectations of senior executives and their boards. If your CEO hasn’t made developing and implementing a generative AI strategy a top priority, you likely don’t work in IT.
Generative AI has impressive capabilities, but also limitations. It delivers remarkable experiences in forecasting and intuitive interaction, transforming how we engage with data. Using Machine Learning (ML) and Natural Language Processing (NLP), AI can reveal future trends and find complex, subtle and powerful patterns that would have been missed by traditional analytics. However, while some forms of AI are explainable, the most common AI / ML platforms work like a “black box,” where inputs produce outputs, and no one knows how the outcomes were reached. This fundamental lack of transparency raises questions about the integrity and reliability of its predictions.
Additionally, Gen AI has the widely known issue of hallucination, which poses significant problems for decision-makers who want to use this technology to make better data-driven decisions. After all, it’s more natural and efficient to ask a generative AI bot, “Give me the Midwest region’s sales projections for the next quarter” than it is to go searching through dashboards. But simplicity and speed are worthless if executives get fabricated, imaginary numbers.
One way to address this challenge is to use generative AI together with enterprise 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 ensuring that they’re getting dependable and consistent results.
Enterprise BI is the foundation of data-based decision making in the business world. It collects, processes, and explains large amounts of structured data to uncover trends of previous and current performance. However, widespread adoption of BI systems, particularly across frontline workers, is challenging due to typical BI complexity and dependency on dashboards. Users may struggle to understand and use the insights they offer, and there’s a significant amount of training required to develop dashboards that address the various metrics and visualizations needed across the enterprise.
By combining AI’s ability to forecast and interpret with BI’s ability to analyze and validate, a powerful partnership is created that enhances the data analysis process. Generative AI allows users to get BI’s organized insights in real time using natural language through a bot. This integration enables all users to rely on data and allows entire workforces to make smart and informed decisions every day.
Combining AI and BI requires a pragmatic approach that incorporates ethical data use, algorithmic transparency, and the creation of trust among users. It is vital to make sure that this combination follows ethical standards and regulatory requirements to preserve the integrity of the decision-making process.
Technological advancements promise to further enhance the value of AI integrated with Enterprise BI, offering deeper insights and more intuitive access to data that anyone can use. This evolving landscape signifies a shift toward a more widespread, data-informed approach to decisions that are timely and actionable, and where organizations can fully leverage the value of their data through the ingenuity of their people.
By capitalizing on the strengths of AI+BI, businesses can further transform data into a strategic asset, driving agility, efficiency, and competitive advantage. As this integration deepens, it paves the way for a new era of intelligence, defined by speed to insight and speed to action for anyone and everyone across the organization. Much like a symphony where each note contributes to a grander melody, AI+BI underscores the transformative power of data in sculpting the future of business.
About the Author
Saurabh Abhyankar, EVP and Chief Product Officer, MicroStrategy. Saurabh has been innovating in the analytics market for 20 years and holds a number of patents in self-service analytics, the semantic graph, and HyperIntelligence. Since 2016, he has held various product leadership positions at MicroStrategy including SVP of Product Management and EVP of Marketing. Prior to joining MicroStrategy, Saurabh was the Chief Product Officer and head of engineering for MRI Software, a leader in technology solutions for real estate. In his nearly 3 years at MRI, the product organization doubled to 1,000 employees while also doubling annual revenue. Mr. Abhyankar received a B.Sc. in Computer Science from the University of British Columbia.
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