Advantages and Disadvantages of Artificial Intelligence in Companies
Artificial Intelligence (AI) is one of the fastest growing technologies today. According to recent data released by the consulting firm Gartner , organizations that have implemented AI grew from 4 to 14% between 2018 and 2019.
In fact, the same consultancy includes Artificial Intelligence in its technological trends for the year 2020. Specifically, AI focused on improving IT security.
AI is a key technology in Industry 4.0 for all the advantages it brings to companies and all those who want to start a digital transformation process should adopt it in their processes.
What is Artificial Intelligence?
The concept of Artificial Intelligence comes from afar. In fact, John McCarthy created the term Artificial Intelligence in 1950 and Alan Turing already began to talk about this reality that same year in an article entitled “Computing Machinery and Intelligence”.
Since then this discipline of computing has evolved a lot.
For Massachusetts Institute of Technology professor Patrick H. Winston, AIs are “constraint-enabled algorithms, exposed by representations that support model-driven loops that link thought, perception, and action.”
Other authors such as the CEO of DataRobot Jeremy Achin, define Artificial Intelligence as a computational system that is used for machines to perform work that requires human intelligence.
For the person in charge of Tech Target’s technological encyclopedia, Margaret Rose, it is a system that simulates different human processes such as learning, reasoning and self-correction.
As we can see, the three definitions of AI refer to machines or computer systems that think. They emit reasoning emulating human intelligence to perform tasks that only people can perform.
However, other sources go further and define AI as a computer system that is used to solve complex problems that exceed the capacity of the human brain.
In this sense, AI takes advantage of the power of machines to solve complex problems that the human mind cannot reach.
The president of the Future Life Institute, Max Tegmark, shoots in this direction and states that “since everything we like about our civilization is a product of our intelligence, amplifying our human intelligence with artificial intelligence has the potential to help civilization flourish like never before.”
Regarding this question, Google Deep Mind and the University of Oxford carried out an investigation whose conclusions indicate that the AI is capable of deciphering damaged and illegible Ancient Greek texts. While the error rate of historians and epigraphers is 57.3%, that of the algorithm responsible for this feat is 30.1%.
These examples show us how AI goes beyond the human ability to solve complex problems. But how does AI work?
How does AI work?
AI works through algorithms that act from programming rules and their Machine Learning (ML) subset and the different ML techniques such as Deep Learning (DL).
Machine Learning (ML)
It is a branch of Artificial Intelligence and one of the most common that is responsible for developing techniques so that the algorithms that have been developed learn and improve over time. It involves a lot of code and complex mathematical formulas to allow machines to find the solution to a given problem.
This aspect of AI is one of the most developed for commercial or business purposes today, since it is used to process large amounts of data quickly and deposit it in a way that is understandable to humans.
A clear example of this is the data that is extracted from production plants in which the connected elements feed a constant flow of data on the state of the machines, production, functionality, temperature, etc. to a central core. This enormous amount of data derived from the production process must be analyzed to achieve continuous improvement and appropriate decision making, however the volume of this data means that the human being must spend a large amount of time (days) in the analysis and traceability.
This is when Machine Learning comes into play, allowing data to be analyzed as it is incorporated into the production process and identifying patterns or anomalies in operation more quickly and accurately. In this way, notices or alerts can be launched for decision making.
However, ML is a relatively broad category. The development of these artificial intelligence nodes has given rise to what is already known as Deep Learning (DL).
Deep Learning (DL)
It is an even more specific version of Machine Learning (ML) that refers to a set of algorithms (or neural networks) that are intended for machine learning and engage in non-linear reasoning.
In this technique, the algorithms are grouped into artificial neural networks that pretend to act like the human neural networks present in the brain. It is a technique that allows learning in a deep way without a specific code for it.
Deep Learning is essential to perform much more advanced functions allowing the analysis of a wide range of factors at the same time. For example, Deep Learing is used to contextualize the information received by the sensors used in autonomous cars: the distance of objects, the speed at which they move, predictions based on the movement they are making, etc. They use this information to decide how and when to change lanes, among other things.
We are still in a phase where the DL is still in a very early development phase of its full potential. We see it being used more and more in business by turning data into much more detailed and scalable sets.
Artificial intelligence (AI) in the business environment
AI is already used today in numerous business and production applications, including automation, language processing, and production data analytics. This allows companies to be optimizing both their manufacturing processes, operations and improving their internal efficiency at a general level.
AI works through different computer programming rules that allow a machine to behave like a human and solve problems.
The interest of companies in implementing AI techniques in their processes lies in the advantages it brings them.
Advantages of Artificial Intelligence (AI)
Different voices from the technology sector defend the benefits of Artificial Intelligence (AI) .
The Product Manager of Infinia ML, Andy Chan, in a TED Talks with more than 40,000 views on Youtube breaks down the different advantages that AI brings to work.
Kai-Fu Lee, founder of the venture capital fund Sinovation Ventures and a leading figure in the technological field, also breaks down the main benefits of AI in a TED Talks video with more than 600,000 views.
Taking these two experts into account, these would be the main advantages of AI applied to a business sector:
- Automate processes
Artificial intelligence allows robots to carry out repetitive, routine and process optimization tasks automatically and without human intervention.
- Empower creative tasks
AI frees people from routine and repetitive tasks and allows them to spend more time performing creative functions.
- Provides precision
The application of AI is capable of providing greater precision than the human being, for example in industrial environments, machines can make decisions that before, without AI, were made manually or monitored.
- Reduces human error
AI reduces failures caused by human limitations. In some production lines, AI is used to detect, through infrared sensors, small cracks or defects in parts that are undetectable by the human eye.
- Reduces the time spent on data analysis
Allows the analysis and exploitation of data derived from production to be carried out in real time.
- Predictive maintenance
Allows maintenance of industrial equipment based on their operating times and conditions, allowing their performance and life cycle to be increased.
- Improved decision-making at both the production and business levels
By having more information in a structured manner, it allows each of those responsible to make decisions more quickly and efficiently.
- Control and optimization of production processes and production lines
Through AI, more efficient processes are achieved, free of errors, obtaining greater control over the company’s production lines.
- Increased productivity and quality in production
AI not only increases productivity at the machinery level, but also increases the productivity of workers and the quality of the work they do. Being able to enjoy more information allows them to have a more focused vision of their work and make better decisions.
Artificial Intelligence (AI) Disadvantages, Risks and Barriers
Some voices believe that Artificial Intelligence (AI) has risks. Especially if the potential of AI is explored and is not limited only to reproducing human tasks. Authors such as Stephen Hawking or Bill Gates and different researchers have expressed their concern about AI.
With regard to access barriers, these would be some of the most common that can occur in the business environment:
Data is often siled across businesses or is inconsistent and of low quality, presenting a significant challenge for businesses looking to create value from AI at scale. In order to overcome this barrier, it will be vitally important to map out a clear strategy from the beginning to be able to extract AI data in an organized and consistent way.
Lack of qualified professionals
Another obstacle that usually occurs at the business level for the adoption of AI is the lack of profiles with skills and experience in this type of implementation. It is crucial in these cases to have professionals who have already worked on projects of the same magnitude.
The cost and implementation time of AI projects
The cost of implementation, both in terms of terms and economics, is a very important factor when choosing to execute this type of project. Companies that lack internal skills or are not familiar with AI systems should consider outsourcing both implementation and maintenance in order to obtain successful results in their project.
In short, AI has become a very important resource for companies as it allows them to be much more competitive and obtain greater profits, especially in manufacturing and production environments.
It is for all this that this type of professional profiles are increasingly in demand in the industrial sector, making it essential to have groups of experts in the field that allow the development of efficient digital transformation strategies.