At a Glance

• Gen AI can strengthen business intelligence by automating and enhancing data analysis.

• AI could help engineers respond faster to market trends, customer behaviors, or operational challenges.

• Solid data management strategies involve ensuring data appropriateness, integrity, and privacy.

Artificial intelligence (AI) has evolved from a buzzword into a transformative force, driving innovation and reshaping how enterprises operate. From anomaly detection in system performance to accelerating incident response and improving software quality, AI is fundamentally redesigning how engineering teams build, deploy, and manage systems.

One of the biggest benefits of AI is its ability to unlock the full potential of data, offering new ways for engineers to analyze logs and large-scale datasets and to act upon hidden insights. AI-driven platforms can analyze vast amounts of data at scale, uncover hidden patterns, and predict trends, enabling enterprises to proactively address issues, optimize processes, and identify new opportunities.

According to the 2025 Reveal Top Software Development Challenges survey, 72% of tech leaders expect their focus on business intelligence (BI) to grow. With a combination of BI and AI, engineering teams can significantly enhance operational efficiency, debugging, and reliability.

In order to get the most out of their BI and make more informed, data-driven decisions, engineers must gain a nuanced understanding of how to use AI in analyzing their data. This article explores how AI can be integrated into BI workflows, the key considerations for its successful implementation, and how organizations can maximize AI’s potential to drive informed decision-making.

The role of generative AI in business intelligence

Gen AI is revolutionizing business intelligence by automating and enhancing the data analysis process. In traditional BI workflows, analysts sift through massive datasets to identify trends and correlations, often requiring significant time, effort, and technical expertise. Gen AI, however, accelerates and simplifies this process by using machine learning algorithms to identify hidden patterns and relationships within data, thereby providing valuable insights much more quickly and efficiently.

For example, Gen AI can analyze logs from distributed systems and create a summary of what happened before an incident, highlighting patterns and contributing patterns. AI can also review data and highlight bottlenecks in workflows. In addition, AI can evaluate cloud usage to identify underutilized resources and performance regressions.

Where these analyses formerly required teams of DevOps engineers or site reliability engineers (SREs) with specialized skill sets, AI-powered embedded analytics platforms now allow engineers to query system states and trends using natural language. This accessibility empowers developers and engineers to make data-backed decisions earlier in the development lifecycle.

What makes Gen AI particularly powerful is its ability to streamline the insight-generation process. Traditional BI tools often require specialized knowledge to interpret data and generate reports. In contrast, AI-powered embedded analytics solutions make it easier for all users—regardless of their technical expertise—to access and understand data. By responding to natural language queries, AI-powered tools can instantly deliver visualizations and insights, making it possible for teams to make faster, more informed decisions.

The role of AI in streamlining decision-making

AI's ability to quickly and efficiently analyze data has the potential to significantly improve decision-making processes within organizations. By uncovering insights in real time, AI enables faster responses to market trends, customer behaviors, and operational challenges. As a result, engineering leaders can make data-driven decisions with greater confidence, improving outcomes across the board.

The benefits of AI-powered decision-making extend beyond speed of analysis. According to the Slingshot Digital Work Trends survey, AI is improving productivity—79% of workers reported saving one to two hours a day due to AI-powered tools. With AI handling repetitive tasks like data analysis and reporting, employees can focus on higher-level strategic initiatives.

Engineering teams make critical decisions every day. With the help of AI, engineers can:

• Predict software failures before they occur.

• Recommend performance tuning based on real-world traffic patterns.

• Automate test coverage analysis, highlighting risky areas.

• Detect configuration drift or security anomalies in infrastructure.

Key considerations when using AI for business intelligence

Despite the vast potential of AI, several key challenges must be addressed to fully leverage its power for business intelligence. One major hurdle for many enterprises is the lack of adequate training and understanding of AI. According to the Slingshot Digital Work Trends survey, only 23% of employees feel fully trained to use AI effectively. This knowledge gap can hinder the successful adoption of AI tools, particularly when it comes to generating valuable business insights.

As Zdravko Kolev, product development manager at Infragistics, points out, the effectiveness of AI depends on how well users frame their queries. Experienced developers tend to use AI more effectively by providing clear, specific questions that yield better insights. Less-experienced junior developers, on the other hand, may struggle with vague queries, resulting in less useful or overly general responses. This highlights the importance of training employees to interact with AI tools intelligently and purposefully.

For AI to generate reliable and meaningful insights, data must be organized, centralized, and accessible. Without proper data governance, businesses risk generating unreliable or biased conclusions that could lead to poor decision-making.

A well-structured data management strategy is essential to ensure that the right data is available for analysis and that it is structured in a way that AI models can effectively process. Enterprises should invest in scalable data platforms that can integrate data from diverse sources and maintain the integrity of that data.

Another critical consideration is the security and privacy of data. As AI technologies become more advanced, so do concerns around data breaches and the ethical use of AI. Organizations must implement strong security measures, ensure transparency in AI algorithms, and protect sensitive customer and business data. Addressing these issues will help build trust in AI systems and ensure that they are used responsibly and ethically.

Overcoming challenges to unlock AI’s full potential

For engineering leaders to successfully unlock the full potential of AI in business intelligence, they must take several key steps:

1. Prioritize employee training. Organizations must invest in ongoing AI education and training programs to ensure employees understand how to use AI tools effectively. This includes developing skills in data interpretation and how to craft specific queries for better AI insights.

2. Implement robust data management systems. Businesses must invest in robust data management systems to ensure that data is properly structured, centralized, and accessible. AI can only generate meaningful insights if it has access to clean, well-organized data, and companies must ensure that their data governance practices support AI adoption.

3. Address security and privacy. To ensure trust in AI systems, companies must address data security and privacy concerns. As AI models become more integral to business intelligence, organizations must take proactive measures to safeguard data, ensuring compliance with regulations and protecting user privacy. This includes implementing strict data protection measures, conducting regular security audits, and establishing ethical guidelines for AI usage.

Conclusion

Generative AI has the potential to transform business intelligence and decision-making by providing faster, more accurate data analysis—uncovering hidden insights and automating routine tasks. However, to fully realize AI’s potential, engineering leaders must address key challenges related to employee training, data management, privacy, and security. By taking a strategic approach to AI adoption, engineers can unlock the power of their data, improve productivity, and stay competitive in an increasingly data-driven world.