Nearly eight out of ten corporate strategists believe AI is critical to their organisation’s success over the next few years, according to a recent Gartner study. Adding AI to business intelligence creates fast, data-backed insights that can be used to maintain a competitive advantage. As generative AI evolves, business can uncover fresh ways to refine strategy, cut costs, and serve customers more effectively.
Below are some essentials of using AI in business intelligence work. We will explore how generative AI speeds up decision-making, highlight real-world examples from global brands, and show ways to overcome any hurdles .
Understanding AI and Generative AI in Business Intelligence
What Makes AI and BI a Powerful Team
Business intelligence (BI) refers to the set of tools and practices that gather, organise, and analyse information. By applying machine learning and other AI-driven techniques, businesses can go from sifting through static reports to generating meaningful insights on the fly. Deloitte’s research found that 59%of executives view AI as the catalyst that helps them glean more actionable insights from the metrics they track.
AI in Business Intelligence can:
- Automate tedious data-processing tasks
- Spot anomalies or trends before they become big problems
- Predict future outcomes using advanced modelling
- Personalise experiences for each customer
- Present data in an easily digestible format
Enter Generative AI
Generative AI is a specialised subset of AI that learns from massive data sets and then creates brand-new content such as text, images, or even code. Instead of just identifying patterns, generative AI can generate new possibilities, from automated reports to predictive suggestions. It goes beyond finding correlations, aiming to guess what might be the next best solution to a business challenge.
In BI, generative AI can:
- Summarise complex datasets in plain language
- Generate “what-if” scenarios to test potential strategies
- Craft personalised recommendations for users based on historical and real-time data
- Proactively suggest ways to boost efficiency or lower costs
The global AI market is set to grow from €576.84 billion in 2024 to an astounding €2.55 trillion by 2032 (University of San Diego).
Key Characteristics of Generative AI for BI
Faster Data Analysis
Gone are the days of spending hours combing through spreadsheets. Generative AI quickly reads and interprets vast datasets in seconds. With AI-powered tools sifting through real-time data, teams can spend that reclaimed time on strategy, creativity, and innovation. For example, Netflix relies on advanced analytics to recommend content to viewers, and over 80%of streams on the platform stem from these data-driven suggestions (NetSuite).
Smarter Decision-Making
AI in business intelligence is not just about speed,it is about depth. By running simulations and scenario analyses, generative AI can weigh probabilities, highlight risks, and propose the best route forward. According to a Deloitte survey, 59%of executives say AI-driven insights shape more confident decisions. This means making data informed decisions when forecasting sales or anticipating inventory demands.
Personalisation and Customer Experience
Preparing a one-size-fits-all strategy rarely works in today’s customer-centric world. AI lets businesses customise marketing campaigns based on individual preferences. It can also power interactive experiences such as Sephora’s Virtual Artist tool harnesses AI to recommend makeup shades tailored to a user’s skin tone (HBS Online). Businesses can employ AI chatbots or AI personal assistant solutions to give customers quick, intuitive responses around the clock.
Mitigating Risks
In an environment where data breaches and fraud are on the rise, AI systems add another layer of security. AI-enabled tools monitor a constant stream of transactions, flag unusual patterns, and respond in real time. Deloitte research suggests that advanced analytics can spot anomalies earlier, push rapid fixes, and provide robust defence. These capabilities can also extend to areas like AI in accounting or AI in supply chain to detect potential issues before they escalate.
Practical Applications Across Industries
Healthcare
Generative AI can match patient data against known medical research, offering practitioners personalised treatment options. IBM’s Watson provides an example of this in oncology, guiding doctors with evidence-based recommendations (HBS Online). The result is more targeted care and a reduction in potential human errors.
Retail
From predicting foot traffic to deciding which items to keep in stock, AI can interpret real-time sales data and point to upcoming trends. Walmart taps into BI data to correlate online behaviour with in-store purchases, learning what days people are more likely to buy eyeglasses or stock up on groceries (NetSuite). Other opportunities for AI include inventory planning or marketing, or even using AI marketing tools to launch well-timed promotions.
Financial Services
Firms are tapping into AI in business analytics to forecast market movements, detect fraudulent activities, and offer tailored investment strategies. American Express utilises analytics to spot which customers might close accounts, enabling the company to proactively retain them (NetSuite). Similarly, generative AI can comb through historical patterns for deeper insight into risk profiles and product recommendations.
Marketing and Advertising
Personalised messaging is key, and that’s where AI tools excel. Platforms for AI in advertising assess user interactions, identify preferences, and tailor campaigns so effectively that it can even produce higher conversion rates. Tools for AI content marketing can even generate customised email sequences, landing pages, or social content for each user segment.
Logistics and Operations
AI can be vital for route optimisation, warehouse management, and demand forecasting. Shell uses AI-driven predictive analytics to maximise drilling site selection, while leading shipping companies employ artificial intelligence in logistics to guarantee timely deliveries at the lowest cost.
Generative AI can augment an operation’s agility by generating what-if scenarios: “What if consumer demand shifts faster than expected?” or “What happens if a supply chain partner drops annual capacity by 15%?” The AI can simulate multiple scenarios to help users see a clearer path to efficient solutions.
Challenges and Considerations
Data Privacy and Security
With AI-driven BI, businesses frequently process sensitive data about customers, employees, and their own operations. While these tools offer real-time security checks, they also collect large volumes of personal data. That level of exposure raises privacy concerns and calls for robust solutions, especially in fields like healthcare or finance, where compliance demands are strict. Consider adopting privacy-by-design measures such as regular data audits, encryption, and strict access controls.
Data Quality
Quality-in, quality-out is a mantra. AI algorithms learn from the information they are provided, so flawed or irrelevant data leads to unreliable insights. One way to potentially guard against such mishaps by establishing formal data governance protocols such as continuous monitoring, cleansing, and validation.
Integration Issues
Many businesses already use multiple productivity tools or digital marketing tools. Implementing AI typically means fitting these new capabilities into existing infrastructure. Minimising friction requires building a flexible data architecture from the outset, or using a vendor who ensures compatibility with existing software.
Transparency
Companies may abandon AI if there is no clear explanation behind the decisions. To inspire confidence, businesses may need interpretable models and built-in reports that clarify how the tool arrived at each conclusion. This approach is often called “explainable AI,” ensuring there’s a transparent reason behind recommendations or forecasts.
Staff Training and Adoption
Robust onboarding, ongoing training, and open troubleshooting channels can boost confidence and accelerate adoption. For instance, businesses might hold interactive workshops for direct, hands-on practice with the new system.
Setting Up for Success With Generative AI
1. Define Clear Goals
Starting with a clear goal is an important step to successful generative AI implementation. Without a clear goal, it’s difficult for businesses to determine if they’re getting the full ROI that they expected.
2. Gather Clean, Relevant Datasets
AI models need solid data to learn from. Before using a generative AI platform, ensure duplicate records, missing values, and data format inconsistencies are resolved. Investing in data governance not only raises the quality of current analytics but also builds a strong foundation for future expansions into areas such as AI forecasting tools or more advanced AI business solutions.
3. Choose Tools That Fit Your Ecosystem
From major cloud providers to smaller, specialised platforms, there are no shortage of AI options. Look for solutions that integrate to existing tech stacks.
4. Automate Gradually
A common misstep is trying to automate every piece of the intelligence process at once. Instead, businesses should automate manual tasks gradually. This will help with change management and ensure smooth operations.
5. Monitor, Measure, and Iterate
Keep track of key performance indicators: data accuracy, speed of analysis, and user satisfaction. If employees feel the AI results are too complicated, consider adding a more user-friendly interface or retraining the model. By adopting a continuous improvement culture, businesses can help their AI-driven BI remain relevant long term.
Real-World Examples of AI-Driven BI
American Express: Proactive Fraud Detection
American Express scans transaction data to flag suspicious activity and adapt its fraud-defence strategy in real time (NetSuite). By combining AI-driven alerts with BI dashboards, they can target at-risk accounts more accurately. The result is improved fraud prevention and a better sense of security for cardholders.
Netflix: Boosting Viewer Engagement
Netflix uses data to create and refine its original content, deploying AI to recommend shows or movies for each subscriber. With more than 80%of streams driven by AI, Netflix’s success in personalisation is a textbook example of how advanced analytics and user data can fuel sustained subscriber engagement (NetSuite).
Shell: Smarter Site Selection
Shell employs AI’s predictive analytics to locate optimal drilling points, resulting in higher yields and fewer wasted resources (HBS Online). Tapping into geophysical data, plus existing knowledge from past drilling attempts, Shell refines its site-selection process continuously, improving operational efficiency and boosting profits.
Putting It All Together
Using AI in business intelligence can open a door to rapid, data-driven insights that were previously buried under endless spreadsheets. This shift means:
- Greater agility: Real-time forecasting for demand spikes or market volatility.
- Leaner operations: Freed-up staff time for high-priority tasks.
- Deeper focus on customers: Personalised service with less guesswork.
Modern tools simplify the process. Businesses can start small, perhaps by adopting AI tools for small businesses or layering AI functionality onto existing BI dashboards. Even a large enterprise with distributed systems can scale up from pilot projects to fully integrated solutions.
Quick Recap and Encouraging Next Steps
- Understand the core roles of AI and generative AI in BI, including spotting trends, automating tasks, and forming confident strategies.
- Plan carefully. Clean data sets, define objectives, and pick tools that integrate smoothly with existing systems.
- Stay mindful of data privacy, data quality, and transparency. Build a data culture in which people trust the insights AI provides.
- Implement step by step. Start with a small pilot project before expanding into more ambitious initiatives.
- Keep measuring and refining to keep it relevant and unbiased.
To explore further, you can also think about generative AI for business or push into specific areas like AI in market research.
