Artificial intelligence (AI) is everywhere. AI algorithms power fraud detection systems, automates customer conversations, analyses medical scans, generates content, and supports decisions at every level of an organisation. Despite this growing presence, the term “AI” is often used without much precision. It serves as a label for a wide range of technologies that operate in fundamentally different ways.

The starting point is not the technology itself. It is the business challenge. Once the problem is clearly defined, along with its context and constraints, it becomes possible to assess which type of AI offers the best solution. A well defined business challenge can help choose the correct AI toolings.

This article provides a structured overview of today’s AI landscape. It explains the major types of AI solutions in practical terms, showing what they are, how they work, and where they fit. The goal is not to hype any particular method, but to help organisations make informed choices. Finally, you will find a practical framework for selecting the right approach based on your business’s specific needs.

Some problems do not require learning or prediction. They simply require a logical structure. Rule-based systems are the most straightforward form of artificial intelligence. They operate through a predefined set of logical instructions. If the condition(s) is met, one or more specific action follows. The system is not required to adapt itself or improve over time. It behaves consistently, based entirely on the rules written by humans.

These systems are ideal when business processes are stable and clearly defined. They are often used in eligibility checks, validation tools, compliance workflows or basic automation. Since there is no training involved, they do not require data science expertise or historical trends.

In contrast to machine learning or Generative AI, rule-based systems do not rely on pattern recognition or large datasets. They are easy to audit and explain, which makes them useful in sectors where traceability and consistency are paramount. However, their biggest strength is also their biggest limitation. They cannot handle ambiguity, complexity or variation well. When things change or when data becomes unpredictable, rule-based systems break down quickly.

When the decision logic is complex to write manually but patterns exist in historical data, machine learning becomes a more suitable option. In a typical machine learning workflow, a business appropriate model is trained using a dataset that includes both input variables and known outcomes. The system then learns to associate certain input combinations with specific results and then applies that understanding to make predictions on new data that it has not seen before.

This approach is widely used in applications such as churn prediction, demand forecasting, customer segmentation or fraud detection. The models work best when the data is structured and clean, meaning it is presented in well-organised tables with consistent fields. The algorithms rely on mathematical relationships and statistical reasoning rather than human-defined rules.

Traditional machine learning, sometimes referred to as classical AI, differs fundamentally from newer forms like Generative AI. It does not create content or simulate conversation but instead focuses on extracting predictive value from structured input data. Unlike deep learning (see next section), traditional machine learning typically produces models that are more interpretable. Decision trees, linear regressions and support vector machines, for instance, offer insight into how the model reaches its conclusions. One can follow the path of decisions to see how each input affects the output. This balance between accuracy and explainability makes traditional machine learning a reliable option for many business cases, especially where regulation or stakeholder trust requires transparency.

Some tasks are simply too complex for traditional models. When dealing with unstructured data such as images, video, sound or natural language, traditional models hit their limitations. In such scenarios deep learning becomes essential. Deep learning relies on neural networks with many interconnected layers. Each layer extracts increasingly abstract features from the data. These deep neutral networks are capable of recognizing nonlinear patterns that humans would struggle to describe explicitly.

Deep learning has enabled major advances in areas such as speech recognition, medical image analysis, real-time translation and autonomous vehicles. It is also the foundation for many Generative AI systems, including large language models and image synthesis tools. Unlike traditional machine learning, deep learning does not require manual feature selection, as the model discovers the relevant patterns on its own during training. However, this also makes deep learning models harder to interpret. They behave like black boxes, producing accurate results without offering much insight into the chain of reasoning behind them.

Training these models demands large volumes of labelled data and significant computational power. The development process is more complex and more resource-intensive than traditional methods. Yet the performance gains can be substantial when the right data and infrastructure are available.

Foundation models are large-scale deep learning systems trained on vast and diverse datasets, both structured and unstructured, across many domains. Unlike task-specific models, foundation models are designed to perform a broad range of functions. They are not built for a single goal such as predicting sales or segmenting customers. Instead, they are built to understand language, generate content, answer questions and complete tasks in a variety of contexts and processes.

These models include well-known large language models (LLMs) like GPT or PaLM, and are often grouped under the label of Generative AI. They are trained once and then reused across many use cases. This makes them extremely efficient for organizations that want to explore different types of AI capabilities without building custom models from scratch. The general knowledge encoded in a foundation model allows it to understand prompts, summarize documents, generate responses and even reason over open-ended questions.

However, foundation models are not perfect. As they are trained on publicly available data, they can reflect biases when answering on certain contexts, produce inaccurate statements or fail to grasp the nuances of specialized domains. They are broad in scope but shallow in domain expertise. Their outputs are based on statistical likelihood of words following the previous words and not on verified facts. For critical business tasks, that distinction is crucial.

To make foundation models more useful in specific environments, organizations can fine-tune them. Fine-tuning involves retraining a general model on smaller, more targeted datasets that reflect a particular industry, tone or process. This enables the model to use its broad general knowledge more effectively within a defined business context. In machine learning terms, this process is known as domain adaptation.

Foundation models serve as base models, which can then be adapted to support specialized tasks without having to rebuild everything from scratch. For example, a healthcare provider might fine-tune a model using anonymised patient records to improve its ability to summarize medical reports. A legal team might refine legal terminologies to reduce ambiguity during a contract review. A retailer could adjust tone and vocabulary to match brand guidelines in their automated communications.

Fine-tuning offers a practical middle ground. It avoids the cost and complexity of building a model from zero, while still offering more precision and relevance than an off-the-shelf tool. However, the process requires expertise in prompt design, data preparation and human evaluation. In some cases, the customised version might make more mistakes or lose the broad knowledge that made the original model useful. So even though fine-tuning is meant to improve performance, doing it poorly can lead to worse outcome, and the general version of the AI might actually work better.

Some organizations have access to high-quality proprietary data that gives them a competitive advantage. In those cases, it may be more useful to build a custom model from scratch — a route often referred to as developing a proprietary AI model. This involves collecting relevant data, defining a clear objective, selecting appropriate algorithms, training the model and integrating it into production systems.

Custom models provide full control over inputs, outputs and behaviour. They are especially useful in cases where off-the-shelf tools or general-purpose models fail to capture business-specific nuances. They also offer greater flexibility in adapting to changing requirements or integrating with existing systems and workflows of the organization.

However, custom development is not a light decision. It demands significant time, talent and financial investment. Success depends on both data maturity and operational readiness. While building proprietary AI can offer long-term differentiation, it only makes sense when the business case clearly outweighs the cost of adapting an existing solution. When done well, it results in models that are deeply aligned with organizational goals and difficult for competitors to replicate in short term.

In some domains, being right is not enough. Organizations must also be able to explain how and why an AI system reached its conclusion. This is where explainable AI, often referred to as XAI, comes in. It refers to the use of models and techniques that prioritise interpretability alongside performance.

This can be achieved by choosing inherently simple models such as decision trees, or by applying post hoc explanation tools that provide insights into more complex models. In both cases, the goal is to provide decision-makers with understandable justifications that go beyond model outputs.

Explainability is essential in various sectors, particularly those that are regulated or high stakes such as healthcare, finance, insurance and government. It also plays a key role in building trust among users and stakeholders. In many situations, an understandable model that performs slightly less well is preferred over a high-performing black box.

On top of all these, we need to be compliant of the AI act that is being enforced in phases starting August 2024. The AI Act, introduced by the European Union, is the first major legal framework designed to govern the use of artificial intelligence.

It takes a risk-based approach, meaning that the level of oversight depends on how an AI system is used and the potential impact it may have. Companies will face strict requirements related to transparency, accuracy, and human oversight. Systems deemed unacceptable, like those that manipulate behaviour or use social scoring, will be banned entirely. For companies developing or deploying AI, the Act brings new responsibilities, including documentation, testing, and ongoing monitoring. For users and the public, it offers greater protection, aiming to ensure that AI serves people in a fair and trustworthy way.

By now, the key takeaway should be clear. Artificial intelligence is not a single system, and choosing the right approach depends entirely on what you are trying to achieve. Every AI model comes with trade-offs. Simpler AI models such as rule-based systems or traditional AI techniques offer clarity and control. More complex solutions like deep learning or Generative AI provide power and flexibility, but often at the cost of transparency. General-purpose foundation models are quick to deploy and highly versatile, while custom models offer a near perfect fit with business-specific requirements but require significant investment for development.

To help navigate these choices, LACO developed a practical decision tree that maps the reasoning behind each AI option. It connects your use case characteristics with the most suitable technology, whether that is a fine-tuned large language model, a classic supervised learning algorithm, or a rule-based automation. The tool is designed to support teams in making grounded, informed decisions based on data availability, business goals and regulatory expectations.

You can download the full decision tree here and use it as part of your internal planning or evaluation process.

Next to people, LACO is also focused on the fight for a better planet. Using cutting-edge data science technologies, we worked out a sustainable business approach for the aviation industry to combat food waste and the wasted fuel to transport 6.1 million tonnes of unnecessary food. Check it out!