It is undeniable that the use of Artificial Intelligence (AI) and machine learning are now one of the most popular topics when it comes to the future of business. AI has been revolutionizing how organizations operate. Therefore, it is vital to kickstart this exciting integration by learning how to make AI work for your business, as this will construct a performance system that ensures your organization evolves efficiently.
In most cases, AI acts as a form of Business Process Automation (BPA) as its solutions typically include repetitive, time-consuming assignments and generating methods to accomplish them efficiently.
Although BPA often deals with highly repetitive and predictable tasks, evolutions in computing technologies have significantly widened the range of automatable subjects, such as automation of texts and images. Still, framing AI solutions this way can help to configure the integration approach for your business in a more familiar manner.
Nowadays, technology has advanced to allow us to build AI systems that, for example, impressively identify objects in images, or perceive the meaning and emotion of texts and voices.
However, the ability of AI systems is nothing transcendent, rather it is all about their journey of being trained to recognize data patterns and then automatically processing other data much more quickly and on a much larger scale. AI-powered automation is often distinguished from general BPA by the fact that AI is trained, rather than built.
Effective AI integration requires sufficient training and realistic expectations. Machine learning simply indicates the process whereby machines learn - AI systems’ performance depends heavily on the quality and quantity of data they have been trained with.
It is common when discussing automation or AI, to relate these concepts to incredibly fast and accurate processes. However, it is important to understand that tasks such as annotating texts and images are complicated for machines and AI systems – they also need time to learn through training data. In other words, your business can benefit from AI from the start, but the best results certainly take time.
Instead of expecting fast and efficient automation straight away, it is vital to integrate AI to your business process step by step. Executing this incremental approach will also help you to decide which tasks should be automated and which should be given to humans.
It is important to note that to evaluate AI systems’ performance, we should use different strategies from those that we often have for human workforce’s performance. This is due to several key reasons:
Firstly, humans and machines operate differently, especially since AI systems can function at a much larger scale than humans do, so there must be differences in evaluation approaches.
Secondly, again because humans and machines work differently, the types of error made also vary. It is interesting that machines would often create errors that humans would not and vice versa. With this in mind, too much concern for minor errors made by AI can cause you unnecessary distractions. Instead, it is recommended to consider evaluating your system at a greater scale by selecting between two techniques: cross-validation and creation of a specific, large test set of products to assess the AI model’s predictions.
In order to keep up with the constant evolution of everything from your business growth to the training data your business is using, and to sustain the accuracy of your AI models, there is an important need to execute ongoing assessment and maintenance.
The first and most critical ongoing maintenance task is to keep your training data up to date. New data guarantees that new industrial trends are accurately and timely addressed and accessible to the system. It is also essential to ensure the consistency level of the labels used for your data and to update or remove old data in a timely manner to prevent confusion for your system.
Human-in-the-loop (HITL) learning can be seen as one of the key steps in the whole AI integration process. With HITL learning, the AI model can request human input on specific items which it is not sure of, meaning human workforce is used very efficiently here and the system receives the exact data it needs.
To wrap up, AI has been opening up an array of technologies that helps businesses make quicker and smarter decisions with the use of data collection and processing, hence stimulating efficiency and profitability. It can take time for businesses to familiarize themselves with the concept and application of AI, but it is worth-while. Now is the time to invest in AI.