Original article was published on Artificial Intelligence on Medium
The power of Intelligent Automation & AI
The rise of Intelligent Automation propelled by Artificial Intelligence
AI is still a thing for the future. As in right now, people often do not want a machine to make decisions on complex matters that can have real consequences in our work and life. Moreover, who’s to blame in the case the machine fails in making a decision? An example was when a woman was killed by an uber car in autonomous mode back in 2018. We don’t have AI capable of solving complex problems, but we currently have machine learning models that use statistical models that can find patterns in different systems, and help us implement small decisions.
Anything repetitive and boring can be replaced by software that will solve the problem faster and more accurately. This is what we call intelligent automation. AI won’t displace humans in most jobs but will aid them in making those jobs more efficient, effective and reliable in the form of intelligent automation.
The development of IA today
There are three classic examples of how Intelligent Automation is being deployed. First of all, Coronavirus is accelerating the pace of decision-making in companies that have deployed RPA solutions but have human labor still involved with them. RPA/Automation is only being used in less complicated functions and is lacking expertise in understanding how to deploy it in across multiple functions.
Several large corporations have come across an RPA solution and are deciding to deploy it into the market via a platform. An example of it is IBM, which is offering an automation platform for designing, building, and running intelligent automation services, applications, and digital workers on any cloud, using low-code tools wherever possible. Finally, in the last case, we have a solution in which a large scale organizational transformation is deployed using AI with orchestration and changing the operating model.
The need of Intelligent Automation
The reason that we use automation and AI at every step of the process is to find solutions and products that adapt specifically to every individual need. In the new days, we are labeling pipelines to support machine learning. We are going to see a future in which we touch all points of the supply chain in very fundamental ways.
If we take a specific truck, we will see that there are trillion build combinations of that truck. It will take six months to program the whole line, but we can combine machinery and humans to speed up the process. Humans do things that machines can’t do, and machines can do things that humans can’t do. We have optionality and configurability to offer different features for different customers. It’s not only the programmers who can tweak the program, but the line workers may also change it. There’s a combination of human activity and machine activity. The companies that are having the most success are the ones that are employing collaborative intelligence.
There are two prominent families. The first one is people that are needed to help machines. Let’s take, for example, a chatbot for an airline; it needs to represent the brand and values of the company. People assist by helping AI and machines promote the values and brand of the airline. Then there’s AI that helps humans enhance their tasks. An example is the wingman chatbot that helps a salesman look for information and gives options to them.
The bulk of the population spends 30% of their time getting data for AI algorithms. In the context of the manufacturing floor, the goal of extracting data is for tremendous improvements in quality and traceability. Machines have improved this process tremendously, and now they don’t have to spend 30% of their time extracting information as the machine helps with that. Between 14 and 15% is data that can be eliminated, 4% completely transformed, and slightly tweaked.
Nothing has moved as fast in the organization as AI; no trend has grown as quickly as AI. AI is remarkably diversified, and to get it right, we have to take into account MILDS 5 behaviors. The first one is Mindset, which is about reimagination of work, thinking about the work differently. Some examples are digital twins, virtual agents, or AI-enabled R&D. Then there’s Imagination, the fact that experimentation is critical. Then Leadership, which is about doing it right with recognition, the bias, and into account failures and be transparent. The fourth factor that we need to take into account is Data, the number one thing that slows down companies, influences measurement, and determines the proper set up for the supply chain to capture the right data. Last we got Skills, which is about investment in talent to do things in AI, hire competent engineers, and deploy training.
Intelligent automation is paving the way into a future were production design and processes will look much different, potentially rendered more efficient, faster and precise. Companies need to get prepared in order to assess the impact of new technologies and, and they will need to redesign or adapt their processes, operations, and business strategies to embrace and optimize for intelligent automation.