AI, ML, and BI: Unravelling the Intricate Threads of Data-Driven Technologies


The digital age, characterised by an ever-increasing data deluge, continues to challenge traditional information processing methodologies and paradigms. This era is fuelled by the abundance of relatively cheap computing power and sophisticated computational disciplines such as Artificial Intelligence (AI), Machine Learning (ML), and Business Intelligence (BI) which have been growing faster than ever as pivotal enablers of data-driven decision-making. The concepts of AI or ML are not new – they originate from the middle of the previous century.

Over the years, they experienced waves of excitement followed by despair, the latter being partially caused by the limitations in the available computing capacity. Today they are certainly on the rise, and many products or services are claiming they are powered by AI or ML. Not all of these claims are justified and the terms are often misused. It is particularly easy as none of them has a clear, concise and commonly accepted definition. Understanding the distinctions between these three realms is vital for any data-driven audience aiming to exploit the potential of these technologies optimally and not get fooled by vendors.

"Artificial Intelligence - developing intelligent systems", "Data Engineering - building scalable data infrastructure" and "Data Science - driving business and users decisions" intersects on "data"

The prime objective of Artificial Intelligence (AI) is to create machines capable of performing certain tasks, which are perceived to require intelligence to be completed, as good or better than a human would do. It involves creating systems capable of understanding, learning, reasoning, problem-solving, and potentially exhibiting perceptual capabilities. AI is fundamentally about mimicking the cognitive functions that humans associate with their minds, such as „learning“ and „problem-solving“. AI operates on the philosophy of enabling machines to think and make decisions independently, essentially making them “intelligent”. Speech is certainly the prominent activity which has been perceived as reserved for humans.

Hence, from the very beginning, much of the effort was put into creating a machine capable of communicating in words in the same way as humans do. This also explains the recent hype on generative or conversational AI. Not every machine or computer program capable of executing a certain task or class of tasks more efficiently than an average human should be labelled “AI” though. In many cases, the performance gain is achieved thanks to the fact computers can do some simple operations much faster than a human can do. They can evaluate or analyse the multitude of potential solutions in a fraction of a second. If enough computing power is given then even very simple or primitive, “brute-force” algorithms will perform better than a human.

Under the broad umbrella of AI, Machine Learning (ML) is a subset of methods that focuses on developing algorithms and statistical models that machine uses to extract knowledge from the data, observations or feedback. Instead of coding software routines with specific instructions to accomplish a particular task, in ML the instructions are given on how to learn or develop a strategy from the available information. The more data it processes, the better it becomes, hence embodying the principle of „learning from experience“.

Similarly to AI, there are no clear boundaries between what is ML and what is not. It is more of a collection of very different classes of algorithms. Some of them are statistical methods in slightly different packaging (e.g. all the regression-based algorithms), some of them take heavily from probability theory, while others are nearly fully deterministic (some clustering or classification algorithms). There is also a wide range of algorithms inspired by biological systems, e.g. particle swarm optimisation or ant colony optimisation. Another notable class of biologically inspired algorithms are artificial neural networks, including the very popular recently “deep learning”. These different classes of algorithms do not have much in common, yet all of them are typically classified as ML methods.

ML applications range from email filtering and fraud detection through optimisation to predictive analytics and image recognition. It must be noted that ML is not a synonym for a sophisticated, cutting-edge method. Many of the solutions labelled as “ML” in fact uses very simple algorithms, in some cases the “learning” part may even be reduced to a set of simple rule base.

While AI and ML have primarily been associated with enabling machines to mimic or enhance human intelligence and capabilities and found applications in many fields, Business Intelligence (BI) is an application domain targeting business-specific challenges. BI refers to the strategies and technologies used by enterprises for data analysis of business information. It provides a historical, current, and predictive view of business operations by transforming raw data into meaningful and valuable information for business analysis and decision-making purposes.

BI uses a variety of tools, applications, and methods that enable organisations to collect data from internal systems and external sources, prepare it for analysis, run queries against the data, and create reports, dashboards, and data visualisations. The end goal is to provide business managers and corporate executives with actionable insights that facilitate taking informed strategic and tactical business decisions.

AI, ML, and BI are not mutually exclusive; instead, they often function in a synergetic manner. AI takes from ML to the extent that ML is practically a subset within a broader AI domain. On the other hand, BI leverages AI and ML methods to enhance data analysis, provide deeper insights, and enable predictive capabilities.

To illustrate, consider a retail firm seeking to optimise its supply chain. A BI system could provide insights into past sales trends and the effectiveness of previous supply chain configurations. Within BI as an application field, ML and AI methods and algorithms can be applied. For example, an ML algorithm could forecast future sales based on patterns learned from historical data. Next, a model-based optimisation system could be used to suggest decisions about optimising the supply chain.

AI, ML, and BI are distinct yet interwoven domains that are integral to modern business processes. Understanding the differences between AI, ML, and BI helps to appreciate their unique value propositions and synergistic potential. Integrating these technologies can help businesses harness data more effectively, driving innovation and gaining a competitive edge. For businesses to remain competitive, it’s crucial not only to understand the difference between AI, ML, and BI but also how these technologies can be integrated and leveraged to their full potential.

The domain of AI and ML is quite complex and a variety of methods and algorithms exist, ranging from very simple to incredibly sophisticated. Moreover, taking advantage of the popularity of these methods, many products purporting to use them are emerging. Sometimes these claims are true, sometimes they are not much more than a marketing trick. This is why, on the way to adopt AI or ML it is good to work with a trusted consultancy, which can greatly expedite the introduction and effective utilisation of these methods. While AI tools and ML methods can help enormously when applied wisely it worth remembering that none of them is a silver bullet and not all the problems require AI. The independent consultants can verify the vendor’s claims and help select the tools and technologies most suitable for the problem and in consequence let strengthen an organisation’s competitive edge while avoiding costly mistakes.