In a simple way, the answer to the title question could be: Artificial Intelligence dramatically accelerates data analysis, benefiting both an organization and its customers. That is because organizations receive data insights and with them in hand, they can generate recommended solutions that are better suited to their customers’ individual needs, with an incredibly fast and efficient response time.


Machine learning (ML) models can be generated when a large data set is applied to certain algorithms. The models are able to learn from the different patterns obtained from the available data. As more data feeds into the model, prediction and accuracy are improved. Thousands of different types of radiology reports are used to train the system on machine learning models that identify malignant and benign tumors, for example. A pattern that can be used by any industry, since it can be customized based on the needs of each project. The data needed to do this predictive analysis comes in different forms – raw data, unstructured data, etc.

On a larger scale, resources related to artificial intelligence work in the enterprise cloud computing environment to make organizations more efficient, strategic and insight-driven. Cloud computing is capable of offering companies greater flexibility, agility and cost savings when hosting data and applications. Therefore, artificial intelligence resources, in the new “era” of digital transformation that we live nowadays, form layers with cloud computing and help companies manage their data, look for patterns and insights in information, provide customer experiences and optimize workflows.

Machine bias

Artificial intelligence is not as error prone as humans – its great advantage is that it makes decisions based only on available data. It has no opinions or emotions. However, AI can reflect the people’s beliefs who feed the data into the system. A very dangerous dilemma, as we can assume, and which has a name: machine bias. Techopedia defines the term as follows:

“Machine bias is the effect of erroneous assumptions in machine learning processes. Bias reflects problems related to the gathering or use of data, either because of human intervention or as a result of a lack of cognitive assessment of data.”


One way to avoid machine bias is to make use of a representative dataset. Feeding the algorithms with representative datas is one of the most important aspects when it comes to avoiding errors in machine learning. Grouping all the different types of data groups into a data set is challenging,  because it will be necessary to segment the data to make sure it has been properly grouped.

Choosing the right model is important. Each artificial intelligence algorithm is unique and there is no specific model to be used to avoid a bias. However, there are frameworks that can be used to measure bias in several stages of application development.

Monitoring and reviewing tests in the real world, from real world data sets, is also critical. That’s because having successful results in controlled test environments can create a false belief that the algorithm is failsafe. Therefore, it is important to find out how much bias exists in an algorithm and, when discovering unexpected biases, make sure that they are resolved to the point that they no longer exist.

Cases involving Artificial Intelligence

It is no longer news that major brands have used shopping and social activity data to predict the type of product consumers are likely to buy. Insights allow companies to deliver personalized content and messages to their customers. With artificial intelligence, this entire process ends up being fully automated, from the identification and combination of decision-making patterns for creating personas. The “trick” is that targeting content based on machine learning is more efficient for each persona: studies show, for example, that segmenting and personalizing customer communications using AI increases click-through rates by an average of 14%.

In highly competitive fields, such as Human Resources, in the arduous search for small talent groups among a huge volume of data, AI is able to reduce the time to fill open positions, analyzing professional profiles at a faster pace than any human would do. When PepsiCo needed to fill 250 jobs in just two months, they used Robot Vera to conduct the first stage interviews. Vera robot was able to interview 1,500 candidates in just 9 hours, something that a human team would take 9 weeks to do.

According to a recent McKinsey study, companies using AI will increase cash flow in volume by more than $13 trillion by 2030. It is already clear that AI will add value to customer service, help generate new revenue and to reduce costs. Other applications of technology to boost business appear to be endless, limited only by human imagination.

Artificial Intelligence as a service (AIaaS)

AIaaS is short for “Artificial Intelligence as a Service” and refers to companies that provide AI solutions ready to use in cloud application development. The artificial intelligence used as a service allows individuals and companies to experience AI for a variety of purposes, without major initial investment and with lower risks. Experimentation may include, for example, sampling multiple public cloud platforms to test different machine learning algorithms.

AIaaS vendors offer a variety of machine and AI learning models. These variations may be more or less suited to the needs of artificial intelligence in business for an organization since each organization needs to assess resources and prices to see what works best in each case. Cloud AI service providers can offer the specialized hardware needed for some AI tasks, such as GPU-based processing for intensive workloads.

Purchasing the hardware and software needed to launch an AI cloud on-premise is expensive. Along with personnel and maintenance costs, as well as hardware changes for different tasks, investment in AIaaS is still prohibitive for many organizations.

Cloud AI offerings, including Amazon Machine Learning, Microsoft Cognitive Services, and Google Cloud Machine Learning, can help organizations in any way possible with the transformation of their data. Having the opportunity to try algorithms and services from different providers allows companies to find out what works and allows sizing before committing to a more serious contract. When something needs to be sized according to specific requirements, the resources of these major providers are there to back up the sizing with sufficient computing capacity.

AIaaS makes artificial intelligence technology accessible to everyone. Through APIs and intuitive low-code tools, users can take advantage of the AI power without writing a single line of code.

In addition, instead of months, AIaaS solutions can be configured and available ready to use in just a few weeks.

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