News 2024-09-28

Big Models Invade Business Intelligence

In the rapidly evolving landscape of technology, a bold prediction has emerged from the influential CEO of OpenAI, Sam Altman. He recently took to social media to forecast the arrival of artificial superintelligence (ASI) within “a few thousand days.” This timeframe may seem optimistic compared to many experts who previously speculated that superintelligence might materialize within just five years, yet it reflects a growing optimism surrounding the future of artificial intelligence.

Artificial superintelligence represents a level of intelligence far surpassing human capabilities. Altman envisions a future where machines can learn any pattern from data distribution and infer the underlying rules governing data. This notion places "data" at the forefront of humanity’s ambition for ASI. As businesses increasingly recognize the value of data, we find a proliferation of tools and applications designed to harness this potential effectively, one of the most notable being business intelligence (BI) systems.

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According to forecasts from the Qianzhan Industry Research Institute, the commercial intelligence software market in China is set to exceed $3 billion by 2029, with an anticipated compound annual growth rate (CAGR) of approximately 20% over the next five years. This estimate underscores the growing momentum behind BI, even as the industry grapples with a pressing challenge: the limitations of product capabilities. For years, BI has struggled with accessibility issues due to its highly technical nature, making it difficult for all employees within organizations to engage effectively with the tools.

The advent of AI along the path to ASI has opened new avenues for addressing such challenges. The evolution from narrow artificial intelligence (ANI) through general artificial intelligence (AGI) and ultimately towards ASI emphasizes the deepening understanding of data's potential within AI frameworks. This journey is characterized by a progressive move toward scenario-driven value realization across diverse sectors, with intelligent BI emerging as a quintessential representation of this synthesis.

Smart BI, exhibiting characteristics of conversational interfaces, has caught the attention of numerous companies. For instance, Microsoft has integrated advanced capabilities into its flagship BI product, Power BI, improving usability through the incorporation of Copilot features. Similarly, Smartbi introduced Smartbi AIChat Bai Ze, an AI agent designed to streamline data analysis with a chat-based interaction style, enabling users to extract insights with ease—much like ChatGPT. This integration highlights a transformative shift in how data is analyzed and utilized within businesses.

The impact of large models on the capabilities of BI tools is presenting significant breakthroughs. From the vantage point of product innovation, the integration of large models with BI systems showcases notable advancements in several key areas. For starters, the elimination of barriers concerning usage has come to the forefront. Smart BI interfaces allow business users to engage with data without requiring the intermediary steps often necessary with traditional BI tools, such as relying on data analysts or IT professionals to interpret and convey results. Simple natural language commands can now generate desired visualizations, such as bar graphs, without needing to drag components or construct dashboards.

Moreover, the representation of data has become increasingly diverse. Intelligent BI solutions can automatically produce a variety of chart types and reports, enhancing users' comprehension of data. For instance, users may directly invoke pre-configured chart elements like bar or pie charts through natural language commands, adjusting elements such as titles and legends seamlessly. Consequently, users can explore and innovate new display methods, enhancing business support and data analytics.

Crucially, the customer value derived from data analyses has seen marked improvements. While the ease of interaction and diverse output formats serve as an attractive exterior, the core value lies in the accuracy of the data being analyzed. Market leaders such as Microsoft and Smartbi are breaking new ground by expanding the analytical capabilities beyond mere dashboards and traditional reports. They now integrate advanced functions such as time calculations, attribution analysis, and predictive analytics directly into their BI systems.

For instance, by querying Smartbi Bai Ze, users can investigate anomalies in contract amounts for any given month and receive potential reasons for these discrepancies. A securities firm, during a business analysis meeting, could leverage this functionality to delve deeper into unexpected metrics displayed on their dashboard, facilitating informed decision-making.

The evolution of Smart BI from basic descriptive analytics to a more comprehensive diagnostic and prescriptive analytics approach illustrates a significant shift in how BI can contribute to organizational strategy. Intelligent BI is now empowering business personnel and management alike by providing actionable intelligence rather than simply presenting data.

However, behind the façade of enhanced product capabilities lies the complex interplay of AI and BI. While the infusion of AI into BI is driving growth and creating new opportunities, it also raises barriers to entry. Not every organization can easily navigate the AI+BI landscape. This challenge is prompting intensified competition within the BI sector, which is already characterized by fierce rivalry.

Remarkably, the competitive dynamics between domestic and international firms have begun to shift. Established BI providers in the West are gradually facing challenges from emerging domestic players in China. The “2023 China Business Intelligence and Analytics Software Market Tracking Report” indicates a notable presence of local firms, with the top ten BI market players now including domestic brands such as FanRuan and Smartbi, alongside industry giants like Microsoft and SAP. This signals a transition from a market dominated by foreign entities to one characterized by a blended competitive landscape.

Simultaneously, Smartbi has demonstrated remarkable growth, achieving an astounding 45.7% increase in revenue, outpacing its competition by a factor of twelve. This achievement underscores the latent potential of intelligent BI, firmly positioning Smartbi at the forefront of the industry.

As the industry continues to evolve, it becomes increasingly clear that succeeding in the AI+BI domain relies on a balanced integration of AI capabilities, BI foundations, and practical application within industry contexts. First and foremost, AI leverage must stand on a solid foundation of long-term intelligence integration.

The successful deployment of BI tools like Microsoft's Copilot in Power BI, which enables users to generate the desired data models effortlessly, showcases the significance of significant investments in AI capabilities over extended periods. Companies like Smartbi, which embraced AI long ago, have laid the groundwork for integrating AI with data analysis, culminating in the 2023 launch of Bai Ze, an advanced dialogue analysis model.

This evolution reflects the notion that the use of large models in BI is merely a step towards greater integration. An oversimplified approach of merely adding AI components to existing BI products may falter without the requisite foundation. The underlying technological sophistication evident in Smartbi's capabilities facilitates nuanced analyses beyond basic operations like data queries.

Furthermore, the shared emphasis on cultivating BI strengths remains paramount. The successful enhancement of product capabilities through AI+BI hinges on resolving long-standing issues within BI systems. Ultimately, solidifying essential data architectures allows organizations to maximize their analytical capabilities.

When examining the impressive growth of Smartbi within the banking sector, facilitated by its experienced workforce and deep sector understanding, it becomes evident how crucial established industry knowledge is to leveraging AI+BI effectively. As the market matures, companies that can bridge the gap between technology and industry-specific challenges will secure a competitive advantage.

Looking ahead, it’s clear that AI, BI, and pragmatic applications must coexist symbiotically to implement a powerful intelligent BI framework. The trend is unmistakable, as businesses have increasingly begun to realize the full extent of data’s value. In this context, intelligent BI does not merely assist users in extracting insights from data; it transforms how enterprises operate and deliver services. Ultimately, as the realms of AI and BI continue to merge, organizations will discover new avenues for efficiency, innovation, and sustained competitive advantage.

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