Over the last few years, there has been a widely predominant topic in the business world: Artificial Intelligence (AI). You may have read many headlines and articles talking about how “AI is on the rise” or that “AI supports innovation”. And, although to many it may sound like a very fresh and recent discovery (specially with the recent boom of ChatGPT), AI (and Machine Learning) have been around for decades.
AI has become a transformative force in various sectors of society. It is transforming everyday life by impacting both personal and business activities, from minor tasks to significant changes. In the present, AI is being integrated into 42% of enterprise-scale businesses and it is expected to have an impact on every job that exists. It is driving healthcare advancements, new scientific discoveries and business proliferation through its advanced functions, such as fraud detection and decision-making in self-driving cars. Additionally, AI capabilities are enhancing various business functions like IT, sales, and customer service, leading to faster decision-making and improved customer interactions. But let me ask you this, if you were assigned to start integrating AI inside your business tomorrow, would you be prepared to harness the full potential of AI?
In this publication, we will demystify AI and explore its transformative impact on the business landscape. We will start by explaining the beginnings of AI and examining how AI is fundamentally reshaping the way companies operate and compete in today's market. Then, we will break down core AI to cultivate a better understanding of the topic. Through real-world examples across various sectors, we'll showcase how AI is driving sustainability initiatives, revolutionizing ERP systems, and accelerating digital transformation. Finally, we'll conclude with practical, actionable steps to help you begin or advance your organization's AI initiatives, also addressing the crucial aspect of responsible AI adoption.
Although the idea of artificial humans had already been a recurring troubling thought to many brilliant minds for hundreds of years, Artificial Intelligence has its origins as a scientific research field in the middle of the twentieth century. Alan Turing’s research on machine intelligence carved the path to the founding of this academic discipline. The first “intelligent machines” displayed symbolic reasoning, a technique which in the following years was proved to be unsuited for solving more intricate tasks. Due to a mix of different problems related to the evolution of AI research, the rate of new ground-breaking discoveries stifled and this field went into the so-called AI Winter.
But, with the years, the conditions for optimal AI research started to be met. We created faster and more efficient computers, started collecting huge amounts of data, and Neural Network approaches (which had already been proposed in the 1980s by a physicist that recently was awarded a Nobel prize) began to produce great results. In the 2010s, deep learning brought machine learning techniques a step further and the cherry on top was brought by Google researchers, which presented the transformer architecture in 2017, the same architecture that Large Language Models are built on until this day. As you can see, there has been a long journey for AI. It has evolved from ancient dreams to a present reality, nevertheless there is still a long way to go.
Even though AI has been a very relevant topic in the last 4-5 years, some companies have already been implementing AI as far back as the 1980s. Amazon, for example, started by using artificial intelligence for personalized article recommendations. Then, in 2014, they unveiled Alexa, which uses Natural Language Processing and Machine Learning algorithms to be able to carry out users’ requests. AI has already been revolutionizing the business sector for a long time, but it was only recently that it became a more accessible asset. AI is starting to become a commodity and not an expensive tool that just big companies can afford to utilize. AI as a Service (AIaaS) is already becoming mainstream. With recent advancements, small and middle size companies have obtained the opportunity to start implementing this technology into their business strategy and innovating like never before.
AI has been proven an exceptional tool to help make business processes thrive in an extensive number of ways. For example, the financing sector has been using AI to reduce fraud. Marketing teams in a lot of firms have been using AI to understand consumer behaviour which enabled businesses to anticipate future needs and tailor marketing strategies. The retail industry uses AI-powered tools to analyze purchase data and to make better recommendations to customers. From healthcare and transportation to manufacturing and consumer goods, AI is transforming industries by enhancing efficiency, streamlining processes, and extracting valuable insights from massive datasets. AI tools can optimize inventory levels and improve supply chain management, showcasing its significance in strategic planning and decision-making for businesses. The era of AI is just starting, and we can expect to experience an even greater shift in the business landscape as more businesses of all sizes implement and innovate using AI-powered tools.
Currently, “Artificial Intelligence” stands as an umbrella term for all the disciplines that, through different methods, are trying to get computers to perform very complex tasks and to display an intelligence comparable to that of humans. Some technologies included in the broad term of AI are:
Machine Learning could be considered as the cornerstone of AI. ML is applied in many fields such as NLP, computer vision, speech recognition, and many others. ML algorithms are used for predictive analysis. Given an input they can predict what the output should look like. For example, let’s say you have gathered a considerable amount of traveling data that contains all the flights that operated in the last year, the weather conditions of the route and if those flights were delayed or not. By training an ML algorithm on that dataset, you could then predict if a flight would be delayed or not just by giving the information of the flight and the present weather conditions.
This example included a data set that was well labeled, ergo, that contained a level of human involvement. But what if we don’t have the time or resources to label a data set, or we just can’t make sense of it? Here is where Deep Learning comes in. Deep Learning is a sub-field of machine learning. It can ingest big amounts of unlabeled data and understand the characteristics that set different categories apart. For example, in the automotive industry, deep learning algorithms can be used for passenger safety, such as lane line detection. By feeding the model large quantities of images that contain lane lines in different scenarios it can help detect when a car is off course.
Deep Learning algorithms are composed of multilayered Neural Networks (NNs). A neural network model has a structure similar to that of the human brain and tries to mimic the way humans think and learn. NNs have proved to be very powerful for multiple use cases, one of them being Natural Language Processing (NLP). NLP uses NNs to understand the structure and meaning of text and to generate human-like text and speech. NLP has been the driver of Generative AI, enabling ChatGPT to generate creative texts or helping Alexa understand your voiced needs.
Recently, AI has been making headlines helping businesses improve their business strategies and operational efficiency and drive financial success through business analytics. We have already discussed how AI found its entryway in the business landscape as early as the 1980s, and, although it had a slow start, it has steadily gained increasing recognition over the years. But we still have not studied how companies are currently leveraging AI and, consequently, improving their operations. Thanks to the versatility of Artificial Intelligence, AI models can be used for a plethora of purposes. In this section we will present AI applications in the fields of Digitalization, ERP and Sustainability.
PepsiCo is a prime example of how AI is enhancing digitalisation in the manufacturing sector. By combining advanced sensors with AI-driven analytics, PepsiCo listens to its shop floor operations in real-time, gaining valuable insights to optimize production. Yes, you have read it well, it listens! The AI-powered sensors record vibration, temperature, and magnetic emissions from the shop floor machines. Any changes in these factors are consequently analyzed, enabling the company to detect anomalies well ahead of time, predict potential failures, and conduct preventive care and repairs before disruptions occur. This proactive approach minimizes downtime and ensures a more smooth production flow, ultimately reducing operational costs.
The integration of AI in PepsiCo's manufacturing processes showcases how digitalisation efforts are transforming traditional workflows. By leveraging AI for predictive maintenance and real-time data analysis, companies can not only optimize equipment performance but also make data-driven decisions to improve overall productivity.
Nestlé is harnessing the power of AI to help achieve its sustainability goals. Nestlé’s AI initiative is focused on two main goals: reduce the company’s carbon footprint and promote resource efficiency. To be able to fulfill these goals, the company is using ECCO’s Whitebox, a machine-learning based system that captures Scope 1 carbon dioxide emissions and recycles wastewater. Using this technology, Nestlé has been able to start converting CO2 emissions into environmentally friendly products, and optimizing its wastewater recycling processes, ensuring that water used in manufacturing is efficiently treated and reused. By employing AI, Nestlé is not only addressing environmental concerns but also driving innovation in sustainable production practices.
This case highlights how AI can play a pivotal role in transforming sustainability efforts across industries. As companies like Nestlé embrace AI-driven sustainability, they set an example of how digitalisation can lead to more eco-friendly and efficient business models, benefiting both the environment and the company itself.
Zara, known for its fast fashion business model, has taken its digital transformation further by integrating an AI-powered ERP solution to streamline its operations. By implementing Odoo's AI-enhanced ERP system, Zara is able to efficiently manage everything from inventory and supply chain to customer relations and financial planning. This AI-driven system allows Zara to make real-time decisions based on data analysis, ensuring that inventory levels align with demand and that production and distribution processes remain agile. This digitalisation effort is a key factor in maintaining Zara's competitive edge in the fast-paced fashion industry.
The introduction of AI-powered ERP systems like Zara’s demonstrates how artificial intelligence can elevate traditional business functions. With AI optimizing workflows and providing predictive insights, companies can better anticipate market changes, reduce inefficiencies, and enhance customer satisfaction.
At Dreher Consulting, we bring over 30 years of expertise in ERP implementation, digitalization strategies, and sustainability solutions, leveraging cutting-edge AI technologies to only deliver the highest of standards when it comes to our clients. We always strive to innovate and collaborate with our partners to find a solution that adapts to your business needs. If you'd like to explore how we can transform your business, get in touch with our CEO:
As you can see, AI doesn’t have any limitations when it comes to business branch, use case, size of company, etc. In the current business landscape, AI use cases can mainly be distinguished in two categories: helping with workflow efficiency and the automation of routine tasks, and driving innovation. AI can be a very useful tool when wanting to automate routine tasks inside your workflow, do predictive maintenance, personalize customer interactions or carry out real-time data analysis.
To do that, a robust data management strategy is crucial for effective AI systems, as the quality of AI's output heavily depends on the quality and organization of the underlying data. Nevertheless, AI also enables businesses to explore new revenue streams through personalized customer experiences, data-driven insights, predictive analytics, and the development of innovative products or services. This dual role of AI—as an operational enhancer and an innovation driver—is reshaping industries, allowing companies to stay competitive in an increasingly digital world.
Including AI into your business strategy is a whole team effort. It is not just an idea that can be executed from one day to another. The organization needs to have a clear vision on how it wants to use AI or how it expects AI to help.
It is very important to educate yourself on AI, and that also includes learning about how to develop it and use it responsibly.
When developing an AI model, it is important to:
