Artificial intelligence in business applications
Although the term artificial intelligence (AI) appeared in 1956, much has changed since then. Currently, it encompasses a set of technologies capable of helping companies meet their objectives, such as, for example, optimizing their supply chains or improving their customer service. Given the importance of the subject, in this article we will talk extensively about this concept and the benefits it brings to any company that wants to evolve constantly and favorably.
What is artificial intelligence and why do we need it?
In summary, we will say that AI allows machines to solve problems efficiently, through automatic learning or optimal natural language processing. We cannot talk about a single technology because the concept of artificial intelligence encompasses many of them. At the end of the day, it is about computer software being able to understand and act with a level of intelligence similar to that of people.
Thus we find, among many others, concepts such as machine learning or deep learning. These specifically use mathematical algorithms and even neural networks similar to those of the human brain so that machines can learn from the input of data.
When asking why we need AI, the correct answer might be that systems based on this type of intelligence help us reduce human labor time and effort. In addition, they increase the speed to process data accurately. This at the business level is really beneficial to be able to focus attention on the objectives of the company and that the machines are the ones that automate and centralize a series of certain tasks. Also to facilitate us to make complex decisions and manage them properly.
The power of artificial intelligence in business decision-making
We all know how important data is in the present for companies to function in a balanced way and always with guidelines to follow (Data Driven Organizations). However, if this data is not analyzed and used properly, it will be worthless and will be the same as if it had never been generated.
AI is capable of processing untold amounts of data in a very short space of time. If, in addition, it is combined with robotic process automation (RPA) technology, it will facilitate decision-making processes and companies will obtain concise, orderly and updated data.
Having an intelligent business is a treasure and, therefore, many companies are already implementing AI systems in their organizations. In fact, in our entity, specialized in designing solutions for companies, that is one of our professional services precisely with more demand.
A service highly demanded by our clients is AI Business Data Discovery that allows with a quick consultancy to discover / identify potential projects where to apply AI that provide more benefit to the company, once identified we can also help in the development of them.
This way you can, among many other examples, study customer behavior, make efficient segmentations, know which products or services are the most demanded or those that bring more problems, etc. In short, AI allows to optimize decision-making with confidence, resulting in an improvement of negotiation processes.
We talked about machine learning and deep learning before, but we also want to add powerful big data and IoT. These concepts are what professionals use to meet the needs of companies. This is because everyone uses this data that we have been talking about in this section, enhancing them and taking advantage of them 100%, as happens, for example, in the MotoGP teams.
Undoubtedly, AI will continue to evolve and, possibly, in a while the human factor will not have the last word. However, we must not forget that, today, decisions in companies are made by humans, although technology is a great help. Therefore, we want to make a turning point to reflect on the need for AI to analyze data and companies can get the most out of them.
How can a company use artificial intelligence?
It is crucial to know in depth this set of technologies to know how to implement them in companies and what benefits it brings. Thanks to our extensive experience in the sector, below we will present some examples of uses at a general level and, subsequently, delving a little deeper, depending on the business sectors.
However, first we want to highlight the data from some studies that cite that companies, highlighting those in the retail sector, that have opted for AI, have increased their revenues and, on the contrary, reduced their costs.
General uses of artificial intelligence for any sector
Among the most prominent are:
- Increase sales opportunities and, therefore, productivity.
- Generate decision-making processes with greater agility and precision.
- Creation of new products and specialization of existing ones.
- Save time and reduce costs.
- Eliminate financial, marketing or sales errors, resulting in more satisfied customers.
- Optimize the management of company resources.
Prominent uses of AI in companies
Artificial intelligence can be used in a multitude of processes within companies. Some of the most common uses are the following:
Chatbots
, perfect for serving customers in order to solve doubts or problems 24 hours a day.- Virtual assistants to organize meetings and tasks, as well as track employee activities.
- When there is a low level of productivity, AI is able to detect where the problem lies.
- In logistics, it optimizes route planning and customer service, providing greater efficiency in economic results.
- In industrial factories, it accelerates routine processes, such as creating parts or food packaging.
- Predict customer behaviors and apply marketing tasks to potential customers to offer what they need at all times.
- Detection of patterns, defects in image processing.
In conclusion, betting on artificial intelligence in any company will make it a great rival for competing companies. Putting yourself in the hands of specialized companies, such as ours, will multiply their value and obtain greater profitability. In short, it will be much easier to meet growth objectives.
AI Business Data Discovery, Big Data, Deep Learning, Iot, machine learning
Go back