Exploring the foundations of Artificial Intelligence: A guide for modern IT leaders

At WAES, we bring the knowledge and practical experience to guide businesses through these shifts, ensuring that AI works in alignment with organizational goals. This article introduces five foundational AI topics - Data Science, Generative AI, Large Language Models (LLMs), Deep Learning, and MLOps - to demonstrate how these technologies can help shape effective and future-ready IT strategies.

1. Data Science: Turning data into actionable insights

Data science is the bedrock of AI, transforming raw data into actionable insights. For companies in sectors like energy, finance, and retail, leveraging data science means gaining a competitive edge by making data-driven decisions that optimize operations and reduce costs. For instance, data science can play a pivotal role in the energy transition by analyzing historical and real-time energy consumption data, which helps predict and balance energy demands. For businesses aiming to meet sustainability targets, data science enables them to model their energy use and identify potential efficiency improvements.

In practical terms, data science empowers organizations like Shell or ASML to spot trends and uncover insights that support better planning and operational strategies—essential steps for companies navigating the complex demands of today’s markets.

2. Generative AI: Automating creativity and problem solving

Generative AI is reshaping creative processes and automating complex problem-solving tasks. From content creation to workflow automation, generative AI can enhance productivity across departments. An impactful application lies in renewable energy, where generative AI can model complex grid designs or generate optimized layouts for solar panels, helping engineers and project managers accelerate planning and reduce energy waste. This capability is particularly beneficial in industries with a high focus on the energy transition, enabling faster innovation cycles and informed decision-making.

In Western Europe’s energy-conscious market, generative AI provides tech leads and hiring managers the tools to empower their teams, allowing them to innovate faster and adapt more readily to new sustainability standards.

3. Large Language Models (LLMs): Enhancing customer experience and knowledge management

Large Language Models (LLMs), such as Meta’s LLaMA and Google’s PaLM, provide an advanced way of interacting with data by interpreting and generating human-like text. These models are valuable for companies needing fast and accurate responses to customer queries or streamlined access to internal knowledge. For example, companies like Ahold can use LLMs to power customer service chatbots, automating responses about sustainable products or energy-efficient practices.

LLMs also improve efficiency in onboarding new employees or keeping team members informed about best practices—critical for large IT teams that manage constantly evolving data and workflows.

4. Deep Learning: Unlocking patterns for predictive insights

Deep learning is a subset of machine learning that focuses on analyzing large datasets to recognize patterns and make predictions. In industries like finance and energy, deep learning enables predictive analytics to improve decision-making processes. This capability is valuable for companies such as Rabobank or ASML, where data-driven insights ensure that resources are used effectively, reducing both operational costs and environmental impact.

Deep learning can be applied across various scenarios, from fraud detection in banking to predictive maintenance in manufacturing, making it a versatile tool that modern IT leaders can rely on to future-proof their operations.

5. MLOps: Ensuring scalable and reliable AI operations

MLOps, or Machine Learning Operations, is the practice of managing and scaling AI models in production, ensuring they remain reliable and accurate. This is essential for organizations that rely on AI-driven solutions for critical operations. MLOps provides a structured approach to monitor and update AI models as new data becomes available, keeping models relevant over time.

For example, energy companies adopting MLOps can continuously monitor the performance of AI systems that predict energy usage or manage renewable resources. This helps maintain optimal energy distribution, minimizing waste and supporting sustainability goals. MLOps is particularly valuable in sectors like finance or retail, where the adaptability of AI models directly impacts customer experience and business performance.

Driving business transformation with AI at WAES

At WAES, we believe that AI is not just a tool for automation but a strategic asset that can help businesses address critical challenges and achieve ambitious goals. As IT departments and hiring managers seek ways to future-proof their organizations, understanding these foundational AI concepts can provide valuable insights into how technology can support broader business initiatives, such as the energy transition.