How to implement artificial intelligence (AI) in companies?


How does Variatys help companies work on the subject of artificial intelligence (AI)?

For many companies, artificial intelligence (AI) is still a distant subject and reserved for tech giants i.e. Alphabet (Google), Meta (Facebook), Amazon, Microsoft, Tencent and Alibaba. AI is a new technology and investing in it is complex in terms of time and cash. Nevertheless, the technology is advancing rapidly and improving day by day, which makes it possible to affirm that the ROI that this technology can bring is considerable (maximizing revenue, reducing costs, improving experience and reducing risks).

In order to take advantage of artificial intelligence and the potential for return on investment, it is important to rethink the way humans and machines interact in your business. Focusing on applications that will change the way your employees work and the way your customers interact with your business is key to thinking about this topic. Embarking on AI therefore means deploying it in all key functions and operations to facilitate new processes and data-driven decision-making.

In terms of use cases, AI can be applied to all areas i.e. banking, insurance, health, government, etc. and all functions. Here are some examples:

HR function:

  • NASA is now using artificial intelligence for its human resources department. By implementing AI for the HR function, NASA managed to ensure that 86% of HR operations are carried out without human intervention.

Insurance Domain:

  • Deloitte’s audit and assurance teams created their proprietary AI platform called Omnia to improve service quality around the world. Learn more here

Industrial field:

  • Seagate Technology has decided to use AI for its production line. During the entire silicon wafer manufacturing cycle, multiple microscopic images are extracted from various tools. Using the data provided by these images, the Seagate factory has created an automatic system that allows machines to directly find and classify wafer defects. The accuracy of visual examinations has thus increased from 50% a few years ago to more than 90% today. Learn more here

Any company wishing to work on artificial intelligence must integrate the key points below into its transformation:

  • Artificial intelligence being complex to set up (time, cash, data, human resources and talents), it is important not to develop a single algorithm model per process but to find a unified approach that can be reproduced in all the organization.
  • The foundation of AI is to have the right data sources in a flexible and modular IT architecture. Having data is a good start, but having more accurate data is better.

Variatys is currently working with a banking establishment in Geneva on the subject of artificial intelligence in order to improve the efficiency of the bank’s internal processes. The analysis of this use case made it possible to note that data quality problems existed and to work on the quality of the data to be used before moving forward on the redesign of processes thanks to AI.

  • The implementation of AI must be accompanied by a fundamental change in corporate culture. Leaders must create a corporate culture that emphasizes data-driven decisions and actions. This new culture must make employees enthusiastic about using it to improve processes and customer service in the company. Without such a culture, it will be impossible to recruit expert talent in AI and machine learning and to fully exploit the technology put in place.
  • This new corporate culture involves “educating” employees about this new technology, how it works and the opportunities it represents. Companies embarking on AI need extensive training in artificial intelligence, data science and machine learning.

Variatys supports companies in their transition to artificial intelligence and is convinced that this technology, if applied strategically and at scale, will be a key element in the success and competitiveness of companies in the near future.

AI enables informed decision-making based on an incredibly vast amount of data, and business leaders who decide to deploy it now and at scale will dominate their industry in the coming decades.

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