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Generative AI

Updated: Feb 3



Generative AI is a broad sub-set of Deep Learning (Marr, 2023) that has shown the most potential in re-shaping industries. Unlike ‘traditional’ Ai that is used to analyse data sets and make predictions or find solutions, Generative AI is trained on massive sets of data to learn underlying patterns, analyses those data sets, and generated new content based on its pre-trained knowledge (Marr, 2023). This new content includes text, audio, code, images, simulations, and videos.


Recent advancements in Generative AI from OpenAI led to the development of the GPT, Generative Pre-trained Transformer. GPT models, particularly the Transformer architecture that they use, represent a significant breakthrough in AI research (AWS, 2023).


ChatGPT and DALL-E, have drastically changed our approach to content creation. The value of these models lies in the speed and scale at which they operate.


For the most part, AI has played a periphery role in our lives, assisting us with tasks we may not have even been aware utilised machine learning. However, with the recent advancements made in Generative AI by the likes of OpenAI, the general public has become far privier to the role that AI is, can and will play in our day-to-day lives. The adoption of AI has more than doubled since 2017 and the proportion of organisations4 using AI is between 50 and 60%(McKinsey & Company, 2022).


Armed with new capabilities and prospects of previously ‘fictional’ advancements in technology, industries are racing to integrate the latest AI systems into their business, and with increased social awareness of the capabilities of AI, customers have raised expectations for service delivery as a result.


In practical terms, AI technologies are being increasingly utilised across various industries and applications, including:


  • Natural language processing and chatbots for customer service and virtual assistants.

  • Image and speech recognition for medical diagnosis, autonomous vehicles, and security surveillance.

  • Predictive analytics for personalised recommendations, fraud detection, and financial forecasting.

  • Robotics, automation and virtual reality for manufacturing, logistics, and household tasks.

As AI continues its rapid advancement, its profound impact on society, the economy, and daily life is anticipated to increase significantly, reshaping our methods of work, communication, and interaction with technology.


Similarly, Enterprise Resource Planning (ERP) systems have experienced exponential growth, a topic explored in the previous section, driven by advancements in technology and computational power, alongside societal demands for quicker, more optimised, and accessible work management solutions. Consequently, the evolution of ERP systems has naturally gravitated towards integrating AI over the past 15 years.


The debate is no longer about whether to incorporate AI into ERP solutions, as this has already been realised and implemented by numerous leading software companies. Instead, the focus shifts to how ERP systems can fully harness AI’s potential to not only benefit enterprises but also serve the wider public, both now and in the near future.

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