The Benefits of Generative AI in Business Intelligence
Artificial Intelligence (AI) and Business Intelligence (BI) are two essential technologies that are currently transforming the way businesses operate in today’s competitive market. When they merge as one, they offer a potent solution for transforming raw data into actionable insights, elevating decision-making for companies worldwide.
According to McKinsey, AI could handle routine tasks currently occupying 60-70% of employees' time, giving them time back to focus on more strategic activities. AI benefits are endless, but when it comes to numbers and analytics, they become crucial for companies looking to remain competitive and efficient in the long run.
The use of generative AI in BI simplifies complex data, making insights accessible to everyone, and not just data analysis specialists.
This article unveils how generative AI in BI can benefit your company and how you can integrate AI in your BI strategy to enhance your business’ overall performance.
What are AI and BI?
Artificial Intelligence (AI) is technology that uses computers and machines to simulate human learning, understanding, and problem solving. Generative AI is the use of AI to generate high-quality content based on the data AI models were trained on. For example, when AI is used to automate the creation of marketing content, it delivers personalized emails or social media posts, tailored to individual customer preferences.
Business Intelligence (BI) refers to the tools and processes used to collect, analyze, and report on business data, ultimately empowering businesses to make better data-driven decisions. For example, BI tools can help a retail company track sales trends in real-time, allowing them to optimize inventory management and adjust pricing strategies based on consumer behavior patterns.
AI in BI refers to the use of AI-powered tools and machines to interpret complex data and return insights that anyone in a company can leverage.
Generative AI in Data & Analytics
Traditional BI tools were originally designed for tech experts, which didn’t make them accessible to non-technical users. This wasted time and resources, and hindered performance improvement and business growth. Ultimately, they left companies dissatisfied, as they didn’t see a return on their investment, and users were frustrated because the tools proved too complicated to use.
AI changed everything.
Unlike traditional BI, AI for BI streamlines data processing, making it faster and easier to detect trends and patterns for anyone in a company, no matter their technical expertise.
AI-powered technology makes it possible to get deeper insights and more precise predictions, enabling businesses to stay one step ahead of the competition.
Not only can AI translate data into actionable insights, it can also understand past events, predict future trends, and provide recommendations.
In other words, this technology provides a strategic roadmap for better decision-making for anyone and at any time. By turning raw data into actionable insights, companies can rely on generative AI to enhance their efficiency, avoid risks, and increase their profit margins, while employees can act on the insights delivered to them and make better informed decisions.
3 Ways to Integrate Generative AI in BI
1. To identify data patterns and trends
AI algorithms can analyze past data to find patterns and trends to predict future events. For example, by processing a company’s data, an AI-powered tool can identify which business areas might see a performance increase, or which ones might be at risk of a downturn.
Additionally, by providing market insights in natural language, generative AI can guide businesses to proactively adapt their strategies and make smarter investment decisions.
For example, if an AI-powered tool detects a recurring sales peak pattern every two years, it can suggest the timing of the next peak, and the company can therefore plan a strategy accordingly.
2. To detect risks and opportunities
Beyond identifying trends, generative AI can simulate different market scenarios and identify potential risks before they happen. For example, generative AI can create realistic simulations of financial transactions to train fraud detection models, and improve their capacity to detect fraudulent trends.
Similarly, AI can enhance a company’s business performance by bringing scenarios to life. Companies can study those simulated scenarios and respond accordingly, to either seize opportunities or reduce risks.
For example, if a bank uses generative AI to simulate the impact of potential interest rate spikes on its loan portfolio, it will identify the consequences on the most vulnerable customers. Following this simulation, the bank could proactively step forward with a strategy aimed at mitigating potential losses.
By studying scenarios and adapting their strategy accordingly, companies can stay ahead of the competition and avoid potential pitfalls.
3. To personalize data visualization
Generative AI can create unique personalized visualizations of insights tailored to a company’s specific needs. AI models can generate new graphics based on data, leading to a seamless and intuitive data interpretation experience.
For example, when a company asks a generative AI tool to break down the sales performance by region, they can receive the result in the form of a pie chart or diagram.
In other words, generative AI’s customized visualization of a company’s data makes it easier for users to get a clear picture of what it communicates, enabling them to reach conclusions faster.
Generative AI in BI: The Benefits
The benefits of AI in BI can vary depending on the specific goals or roles within a company. For example, sales managers might need insights to unlock their priorities for the next quarter. Marketing teams might use it to identify customer behavior or the best times to launch their campaigns and maximize engagement. However, the end goal for all is to get generative AI to enable data-driven decisions.
For business stakeholders
AI makes data easier to access, faster to understand, and smarter to use. AI helps non-technical users make informed, data-driven decisions. In other words, whether a business manager can dissect data or not, generative AI can provide straightforward answers in natural language, cut through the complexity and allow them to focus on implementing data-driven strategies and plans in real time
For data specialists
AI can give data specialists time back to concentrate on more meaningful tasks and strategic interpretation of data. If AI is used to clean and transform raw data – automating routine tasks and delivering reliable results – data analysts and specialists can use their time to work more closely with other departments. They can leverage the automated legwork provided by AI as support for more valuable contributions to the overall business.
For enterprise companies
AI in BI can help companies see the return on investment of data and BI resources. AI can deliver detailed reports to evaluate a company’s performance and present a full picture of its past, present, and future. Through generative AI in BI, companies can spot their greatest strengths and opportunities, optimize budget allocation, and adjust their strategy. In short, they can improve their overall effectiveness and maximize their profits.
How to implement AI in BI
Establishing a strategy is vital to integrate AI in your BI, to maximize the benefits of the merging of these new technologies. Here are a few key points to take into consideration as you shift towards AI-powered BI:
Assess business objectives
Before integrating AI in BI, it is important to conduct a thorough assessment of the business’ objectives, to identify the areas where AI could bring the most value. Is the company looking to automate BI processes? Optimize operations? Improve prediction accuracy? Define what your goals and needs are to determine which solution is right for your business.
Prepare your data
Interacting with an AI-powered tool is always more efficient with a thorough and detailed prompt. In the case of an AI-powered BI tool, preparing and collecting a comprehensive set of data is vital to get AI-driven insights. Identify the best data sources, clean and structure the information inside, to ensure that the AI tool will only handle accurate, quality datasets. Investing time and resources in the preparation phase of your AI integration is mandatory for your company to get reliable and accurate insights.
Select the right tool
Finding the right tool to fit your business is paramount. Each AI-powered BI solution on the market comes with its own set of functionalities. Businesses should select tools that are compatible with their objectives and needs, whether they include predictive analytics, conversational language, or automated data extraction.
If your company needs a Decision Intelligence AI-powered tool to make data-driven decisions, Crystal simplifies and translates complex data into actionable insights.
By connecting data sources to Crystal, your business can get insights based on your data, as well as answers to specific, detailed questions, in natural language, just like speaking to another team member.
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The benefits of generative AI applied to BI are vast and transformative. Integrating AI as part of your business strategy is no longer just a “nice-to-have” – it’s a must-have.
Companies have to act now to harness these advantages, streamline operations, and empower their workforce. By embracing generative AI, businesses can let their employees work on more strategic or creative tasks, with the support of tools that empower them to make better informed decisions and take action.
Thomas Edison said, “There’s a way to do it better – find it.” The future belongs to those who are ready to adapt and innovate. Will you seize this opportunity?
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Frequently Asked Questions
What are the differences between BI and AI-powered BI?
Traditional BI focuses more on descriptive analytics, using structured, historical data to explore past and current performances. However, AI’s machine learning algorithms can process both structured and unstructured data and forecast future trends. By automating complex processes, it can adapt to changing data patterns and support proactive decision-making.
What are the risks of AI in BI?
To work as accurately as possible, AI models require constant maintenance and supervision. A model is only as good as the data they’re trained on. As such, companies using AI in BI should commit to preparing, monitoring, and updating their data in order to fully rely on the quality of the insights – like certified knowledge – provided by their AI-powered tool.
Can I integrate my BI systems with AI?
Absolutely! In fact, a seamless integration of BI systems and data infrastructure in AI tools is key to ensure maximum efficiency and quality results. Get your tech experts involved to configure the connections with as little disruption of ongoing operations as possible!
