Artificial intelligence (AI) is becoming a key element in business strategies, supporting companies to make better decisions through advanced data analysis. In recent years, the development of AI has reached an impressive pace, enabling increasingly complex and precise analysis of information in real time. In this article, we will outline how artificial intelligence is impacting business analytics, what tools are currently available and how companies can use them to gain a competitive advantage.
The development of artificial intelligence over recent years
Over the past few years, the development of artificial intelligence has accelerated significantly. Algorithms that learn from massive data sets are becoming increasingly sophisticated, and computational capabilities are increasing yearly.
A breakthrough in the development of AI was the advent of so-called deep learning, which allows the building of multi-layered neural networks capable of recognising patterns in a way that resembles the human brain.
Technological advances are making it possible to analyse vast amounts of data in real time, which is invaluable for companies operating in a global market. Today, AI is used in data analysis and process automation, trend forecasting, and personalising the customer experience.
Application of artificial intelligence in business analytics
Artificial intelligence is used extensively across various industries, and its analytical capabilities allow companies to outperform and adapt to changing market conditions.
AI-assisted data analysis in e-commerce
Examples of AI applications in analytics include e-commerce basket analysis, which can predict what products customers want to buy. By analysing consumer behaviour, AI can suggest complementary products and adjust offers in real-time. Dynamic pricing is also popular in the e-commerce industry, where AI algorithms analyse supply and demand, competition, and seasonality to adjust prices automatically and maximise profits.
AI-assisted data analysis in finance
In the financial sector, artificial intelligence is used for credit risk analysis. By analysing hundreds of thousands of transactions, AI can better assess customers’ creditworthiness and predict the risk of default. Banks and financial institutions also use AI in fraud analysis (fraud detection), identifying and blocking unusual transactions in real-time, significantly improving security.
AI-assisted data analysis in manufacturing
Meanwhile, AI supports supply chain optimisation and inventory management in the manufacturing industry. Thanks to advanced machine learning algorithms, manufacturing companies can better predict raw material requirements, optimise production and minimise downtime. Real-time data analysis allows production schedules to adapt to changing orders, reducing operating costs and increasing production efficiency.
AI-assisted data analysis in healthcare
In the health sector, AI is supporting the analysis of medical data. AI analyses huge sets of patient data, helping doctors make more accurate diagnoses and propose personalised therapies. Thanks to machine learning, it is possible to predict the development of diseases, allowing for early medical interventions and better hospital management, which can optimise the number of beds, staff, or drug stocks.
AI-assisted data analysis in marketing
AI is also finding applications in the marketing industry. Artificial intelligence algorithms analyse social media data, customer interactions and purchase history to deliver personalised marketing content.
Thanks to AI, it is possible to create real-time advertising campaigns that dynamically adjust the content of ads according to user behaviour. For example, an ad targeting a specific user can change depending on what that user has recently browsed or bought.
AI-assisted data analysis in logistics
In the logistics industry, AI helps optimise routes and manage fleets of vehicles. Algorithms analyse traffic, weather conditions and other factors that can affect the efficiency of deliveries, thus minimising delays and optimising transport costs. In warehouses, meanwhile, AI supports inventory management and process automation, helping to improve operational efficiency.
AI is adapting to changing market conditions in these industries, making analytics more dynamic and adaptive. Companies that implement AI in their processes can respond faster to changing customer needs, optimise their operations and make better business decisions based on data.
Benefits for companies using AI in data analytics
Firms that implement AI into their analytics processes gain numerous benefits. Firstly, AI saves time and resources. Traditional data analysis, especially in large companies, can be time-consuming and costly, but AI algorithms perform the same tasks faster and more accurately. Secondly, AI increases precision in forecasting and analysis.
Algorithms can, for example, better assess financial risks or predict changes in consumer behaviour. Thirdly, artificial intelligence supports process automation, which enables companies to operate more efficiently, especially in areas such as logistics, inventory management or marketing personalisation.
AI tools available to companies
Today, companies have access to various artificial intelligence-supported tools. One of the most popular tools is business intelligence (BI) systems, such as Power BI or Microsoft Fabric, which collect and analyse data, transforming it into helpful company reports and forecasts. More and more BI platforms are being enhanced with AI features, such as automatic report generation or predictive trend analysis.
Natural Language Processing (NLP) analysis tools are also being used to enable companies to better understand customer feedback by analysing data from social media, product reviews or customer service conversations. Customer Relationship Management (CRM) systems are also an important tool, which, thanks to AI, can better personalise offers to customers, anticipate their needs and increase sales efficiency.
Challenges of implementing AI in business analytics
Despite the numerous benefits, implementing AI in business analytics has some challenges. One of these is proper data preparation. AI requires large, well-organised and accurate datasets to operate effectively.
Another challenge is the lack of suitably qualified staff who can effectively use advanced AI tools. Companies need to invest in their employees’ training and development to exploit artificial intelligence’s full potential. Another aspect is the ethical issues related to the automation of AI-based decisions, especially in areas such as data privacy or liability for wrong decisions by algorithms.
The future of AI in business analytics
The future of artificial intelligence in business analytics promises to be very promising. The increasing data availability, the development of cloud technologies and increasingly sophisticated algorithms will drive further progress. In the coming years, we can expect to see even more advanced predictive tools that will analyse data and suggest specific actions based on the results of the analyses. Companies that invest in AI will be able to manage their resources better and react faster to changing market conditions, giving them a long-term competitive advantage. Artificial intelligence is not just the future but already the present in business analytics. It is worth understanding and using its potential to build your company’s competitive advantage.
