Artificial intelligence (AI) and human intelligence have always been inherently intertwined. AI was born from studies into the inner workings of the human brain. Although it previously sought to explain how the brain works, it is now tasked with augmenting and enhancing human capabilities. Tasks that require repetitive, mundane input, or those that pull from almost incomprehensively vast data sets, are exactly the kind AI was introduced to solve. Embracing AI for such tasks stops employees from wasting the energy and resources necessary to imitate a computer and instead lets them tackle problems that need a touch of humanity, such as empathy-based skills like the creative design or critical thinking.
There is a false dichotomy that there is a human versus machine battle at play. The reality of the relationship is much more symbiotic, AI acts as a means to further facilitate human intelligence rather than replace it. The best analogy for this is to think of the way that we use a calculator. The device is directed by the user to solve specific problems, but the calculation of these problems is not the end goal of the process – it takes a human to interpret the results of the calculation and put the information to use.
By augmenting human intelligence in this manner, tasks that were once considered too time-consuming and labor-intensive can be stripped back. This results in minimal human input, producing results at a level of intricacy well beyond what an individual or team could hope to accomplish. It is this combination of human and artificial intelligence which is truly formidable. The applications of this approach are almost endless, but to truly unlock their potential, organizations must remove the complexity of utilizing AI and accessing data-driven insights.
The goal of any business is to assign and manage resources that provide the desired outcome, whether from a product or through a service for its customers. However, the global landscape for these products and services is far more complicated than ever before. Rapid digital transformation has raised the bar for both business and consumer expectations, creating an environment where speed, accuracy, reliability and 24/7 availability are no longer best-in-class features; they’re expected as standard. Thus, highlighting the need for digital Automation to keep up in this demanding business environment.
It’s important to contextualize the application of AI for different industries, as there are boundless combinations of industries and implementations. Electronics, for example, use Robotic Process Automation (RPA) in the manufacturing process of electrical devices. T Computer Vision (CV) is also used in the QA process and in fraud detection, identifying nascent issues long before they become problematic.
In these examples, the requirements as dictated by the market are beyond human capabilities. This doesn’t mean that humans are excluded from the process, it’s for the human to understand the context around the problem and design the wider system that meets the intended outcome.
