Why Business needs an AI Center of Excellence and how to create it

Artificial intelligence has the power to make a difference in a business. The main thing is that it can optimize all operations throughout the organization. Moreover, it is a valuable tool for achieving business goals that’s why companies spend a lot of financial resources on it.

Many experts believe that AI will generate new centers of excellence or competency centers and job roles within them. Deloitte’s survey of U.S. executives from the big companies applying AI showed that 37% of them have already such centers. For example, Procter & Gamble, Deutsche Bank, Pfizer, J.P. Morgan Chase, Anthem, and Farmers Insurance are such firms that have established them. You will find these companies pursue the most innovative solutions as well.

DeFacto Global CEO Bob Bedard said that they want to set the first Artificial Intelligence Solutions Center of Excellence in New York. Their goal is promoting an ecosystem of AI development and research. This software company believes that the center will form the background for AI-related things and will boost the development of an AI-technology hub. It is important for various industries, especially life sciences, video gaming, nanotechnology.

The AI Center of Excellence is also made to gather the best AI talents. Besides, the center is working on building strong partnerships with leading Universities involving Rensselaer Polytechnic Institute and Columbia University.

Below are some of the tasks an AI Team Needs to execute in order to be successful.

#1 Gain a vision

Company’s executives know that everything starts from the vision. That’s why it is valuable to discuss everything at first. It’s better to gain help from the AI experts to dive deeper into the data how AI can enable innovative business models.

#2 Recognize business-driven use-cases

Keep in mind that programmers need to have a prioritized list of apps or use cases within the organization. They have to balance valuable issues with what is achievable. Organizations can create some of such use cases as prototypes. Moreover, your developers must have a “pipeline” that is permanently monitored by the AI center and by the company’s leaders.

#3 Identify the needed level of ambition

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Take into account that AI usually maintains some specific assignments, not the whole business processes or jobs. That’s why it is better to take less ambitious projects. The best idea is creating a “road map” with a series of small projects in each area of the business.

#4 Develop a target data architecture

After you’ve captured all the data, it is important to manage unstructured, external and structured information. Hadoop is one of the most popular data management platform nowadays. You may choose it or self-maintained open source solutions, cloud variations, and versus licensed solutions. Most organizations will take advantage of applying various analytics tools for fast modeling. Today, companies switch their attention from the packaged tools that are BI-oriented (for example, old versions of SPSS or SAS).

#5 Handle external innovation

Bear in mind that an AI center has the power to boost relationships with vendors, AI startups, universities, and other sources of innovation and expertise. The organization may create a whole AI ecosystem.

#6 Create and support a network of AI champions

An AI center requires a network of tech experts. Deloitte’s survey emphasized that 45% of companies had assigned senior executives throughout the organization as AI champions. Given the templates of programming (with ready scripts in languages like Python), the focus for the technical in-house team needs to be on mathematical and statistical modeling.

#7 Talent Acquisition

One of the most important factors in successful creating an AI center is sourcing, attracting, hiring and building talents. Experienced data scientists and AI engineers are challenging to find and hire. Most companies look for a few people with the ability to create and integrate AI algorithms. But many of these assignments may be carried out by MBA-level professionals like analysts. They have made themselves well-grounded with AI capabilities and may apply automated machine learning instruments. You may also speed up the hiring process by reaching out vendors or consultants to work on early stages of the project. After that, you can mix their efforts with internal employees.

Some organizations, involving Cisco Systems, cooperated with universities to create data science training programs for their in-house employees that developed a lot of certified professionals. Besides, companies like DataRobot and Reply collaborate with universities like MIT that offer short courses to provide fast switching on AI-related skills.

Now, you are well equipped with the knowledge about what it takes to set an AI center and why your company needs it. Software development options will help you to gain needed approach and implement a good strategy.

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