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Why the industry is investing in Artificial Intelligence

Artificial Intelligence: According to Google research, respondents revealed the main reasons to invest in artificial intelligence in the industry. These are:

Increase Efficiency 

The predictive model provided by AI reduces delays caused by problems previously not detected in time. This reduces production line downtime.

Support Business Continuity 

The pandemic forced the industrial sector to adopt disruptive technologies to keep up with changes in society’s consumption model. In addition to disrupting the supply chain, compliance with security protocols brought the need for broader use of technology.

Help Employees In General Activities 

Using the AI ​​vision, production line workers invest less time in repetitive product inspections and focus their efforts on more complex tasks, such as identifying the root cause of the problem raised by automated data analysis.

However, in this process, several challenges are inherent to AI projects to be overcome. Below, we list some of the main ones.

Challenges In Using AI In The Industry

As in any business, applying new technology brings benefits. However, we cannot ignore the possible risks and challenges accompanying the process. What are the challenges in the use of artificial intelligence in the industry? 

Lack Of Talent With Developed Digital Skills

It is not enough to have several technological solutions if they are not understood and applied correctly. The market still lacks qualified professionals in this regard.

In a Talent Neuron survey, 53% of respondents said the inability to identify needed skills was the biggest impediment to workforce transformation.

However, this problem can be solved by hiring partner companies that outsource IT professionals, such as Super. They make the entire process: the recruitment and selection of professionals suited to the need, the hiring and onboarding of the employee in your company. 

Lack Of IT Infrastructure

To succeed in AI projects, it is essential to have a robust IT infrastructure, given the volume of data generated. Preparing your company to operate in cloud computing or to enable the promising edge computing will be a fundamental need for the success of more robust AI initiatives. 

Another point related to this is connectivity. Many companies in the industrial sector stay away, which can affect the quality of your connection.


One of the biggest challenges CIOs face is the cost of AI projects, often with no guarantee of success. Indeed, the price can be high, especially in a failed project. 

The investment ranges from specialized teams and equipment that support the processing of suitable software to the development, proof of concept, and scale of the AI ​​project. 

Having a comprehensive digital transformation strategy and starting small are ways to keep costs from spiraling out of control.

Lack Of Guarantee Of Success

Getting the pilot project off the ground and putting it into practice is one of the main difficulties – and few companies succeed. According to a study by Accenture, only 20% reach maturity in AI projects. The data is corroborated by research carried out by Gartner, which shows that only 21% of industries have active AI initiatives in production.

However, as difficult as they are, the same studies show that organizations implementing a digital culture aligned with business strategies with C-level support have doubled their results. But executive and stakeholder support is often another hurdle.

Lack Of Stakeholder Support

The use of artificial intelligence, as well as other technological innovations, generates a common fear. Will machines replace human work? The fear of the unknown, called by Gartner, creates discomfort, so much so that 42% of respondents to their survey do not fully understand the benefits of AI.

Here comes the need for an engaged and recognized leader to sponsor initiatives that disseminate a digital culture aligned with the company’s strategies. At the same time, employees feel comfortable facing the risks of their decision-making.

Data Quality

Big data is not synonymous with data quality. Even decisions based on wrong information can – and certainly will – cause damage.

Common problems faced by companies in this regard are: 

  • Several sources for the same information;
  • Non-integrated systems;
  • The large volume of data;
  • Data not collected.

Data Security And Privacy

As technology advances rapidly to deliver benefits, data security and privacy threats keep pace. To face problems of this nature, it is essential to be aware of security updates for AI software and regulatory standards to avoid lawsuits.

Also Read: How Does Artificial Intelligence Transform Logistics?

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