Some Constraints in the Adoption of Artificial Intelligence in Companies

Currently, more and more companies are implementing AI (Artificial Intelligence) or planning to implement it. Defined as technology that can mimic intelligent human behavior, AI has the potential to transform a business as a whole, and increase its competitive advantage over its competitors. The business functions that benefit the most from AI are manufacturing and risk management.

AI adoption in companies does not always run smoothly due to various obstacles. Some of the difficulties that may be found are, first: inability to formulate business problems to be solved. Second, the initial investment is too expensive and risky. Third, the absence of expertise and adequate human resources.

Business Problem Formulation

Although many companies are interested in adopting AI, many are hindered because they cannot explain the business problems that must be solved. As a result, they cannot determine the part of the company that should receive the AI ​​investment. Organizations must have a strategy in place before adopting AI, with clear and measurable goals and desired benefits.

Initial Investment

Like many other technologies, Artificial Intelligence will require an expensive investment. This is of course risky if the company itself is still in the exploration stage and cannot prioritize which business problems to solve, or how AI can help gain a competitive advantage.

Interesting adoption scenario is many companies derive cognitive technology capabilities from the enterprise software it uses. This can help companies to get an idea of ​​where AI is best suited to apply. But of course this is not enough if a company wants to develop its own unique AI solution for its organization.

Cloud-managed networks can help companies that want to build AI solutions that are faster and more affordable. With cloud services, companies can access compute resources and data storage effectively without spending too much capital.


Skills and human resources are the most frequently cited problems. Lack of the qualified resources and professionals needed to implement organizational and infrastructure changes can hinder planning.

One of the reasons for this scarcity is that for quite some time AI has only been the subject of academic research and has not been used in industry. Only recently has AI expertise been in demand and is being sought after by various companies. Educational institutions, especially universities, have begun to adapt. But it will still take time for educational institutions to produce more skilled and knowledgeable workforce in the field of artificial intelligence.

Several cloud network management providers are starting to provide supporting infrastructure that can help companies to start developing or adopting this technology. With the GPU as a Service service from Teldat, a business can create applications that utilize Artificial Intelligence without having to have infrastructure and personnel for its management and maintenance. Finally, companies can focus more on creating applications that solve the business problems they face.