By Tony Monisse
As more and more commercial Artificial intelligence (AI) applications become available, we consider how AI can significantly improve many business processes by automating repetitive tasks, analysing large amounts of data, and making better decisions.
One of the main ways that AI can improve processes is through automation. For example, AI-powered robots can be used in manufacturing to complete tasks such as welding, painting, and assembly. By automating these tasks, companies can reduce labour costs, increase efficiency, and decrease the risk of human error. Similarly, AI-powered chat bots can be used to handle customer service inquiries, providing quick and personalised customer service, improving the customer experience and reducing response times. This would also free up human employees to focus on more complex or higher-value activities.
AI can improve processes by analysing large amounts of data and predicting future outcomes, allowing businesses to make data-driven decisions. For example, an AI system could be trained to analyse sales data to identify trends, predict future sales, and recommend improving sales strategies. AI can also be used in healthcare to analyse patient data and assist doctors in making diagnoses and treatment plans. In finance, AI-based systems can help detect fraud and make trades in milliseconds.
Another benefit of AI is that it can make better decisions in many circumstances than humans. AI-based systems can make better decisions than humans by leveraging data-driven models, eliminating biases, analysing vast amounts of data quickly, and making decisions based on consistent rules and algorithms. This can lead to improved accuracy and consistency in decision-making compared to humans, who may be influenced by emotions, biases, and limited data processing capabilities.
AI-based systems can be trained to learn from past decisions, so they can learn to make better decisions over time. This is known as Machine Learning which is one of the broader aspects of AI and it allows the system to be trained with more data to improve its predictions and decision-making.
However, it’s important to note that AI is not a magic solution for all business problems. And for each process, it should be evaluated in terms of how AI can help and the potential limitations. Limitations of AI in business processes include:
- Lack of common sense: AI systems cannot understand context and emotions, making it difficult to respond to situations that deviate from their training data.
- Limited understanding of causality: AI systems can identify patterns and make predictions based on data, but they lack an understanding of why those patterns exist, which can limit their ability to make causal inferences.
- Data dependence: AI systems rely on large amounts of high-quality data to make accurate predictions and decisions, and their performance can suffer if the data is limited, biased, or inaccurate.
- Explainability: AI systems can be opaque, making it difficult for humans to understand why they made certain decisions, which can limit their transparency and trustworthiness.
Additionally, implementing AI systems requires a significant investment in time and resources, and companies may need to hire specialised personnel to develop and maintain the systems.
AI has the potential to greatly improve many business processes by automating repetitive tasks, analysing large amounts of data, and making better decisions. However, companies should carefully evaluate the potential benefits and limitations of AI before implementing it and make sure that the processes are well understood by the team.
