How to create a successful AI team

AI is an essential tool for companies today, but businesses no longer need huge team to utilize it


AI forms the frontier of information technology in the twenty-first century, but the fact remains that not everyone has access to the same tools. Huge companies like Apple and Google have hundreds of people on their respective payrolls to innovate, experiment and discover new ways of utilizing AI in everyday life. Sadly, the average business doesn't have access to nearly the same level of human resources. 

However, thanks to how technology develops, an SME may not need that many people. Having that many people might even be a drawback to companies like Apple and Google. Harvard Business Review has noted that new technology is usually not implemented to realize its fullest potential after it's been released onto the market. For a smaller company, the ability to use this technology successfully rests solely on the shoulders of their AI team.

The elements of a good AI team

Forbes notes that AI is a heavily disruptive technology and because of that it commands special attention. In a business especially, having AI and combining it with machine learning (ML) could have far-reaching and massive changes to how business is done. Within any AI team, three fundamental roles exist that reflect the people that are necessary to adapt AI to a business role. The data engineer will teach the AI about ML and set things in place so that over time the AI learns and becomes progressively more adept at doing certain tasks. The data scientist will take into account the different stimuli that exist in the business' day-to-day operations and use them to develop algorithms that will allow for predictions about the company's immediate future. Finally, the software developer will take what the previous two do and incorporate them into applications that are easy to use yet give the same insights, demystifying the reams of data that his or her teammates try to make sense out of. Together this team is a formidable force - one that could bring about change for a company as soon as they start developing.

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Adapting to evolving AI within the industry

No information technology remains stagnant and AI is no exception to this rule. However, business has been moving away from the reinvention of the wheel and into the adaptation of AI systems so that their non-IT staff can utilize it. According to Gartner, the onus is on companies to encourage data literacy within their organizations with the aim of turning the jargon of technology into something of a second language. While in the infancy of AI, having a team of data scientists and analysts was a necessity in order to make sense out of the numbers, now thanks to software engineers and their use of visualization coupled with user interface design, even someone who is only lightly trained in IT but who understands business metrics can benefit from AI and ML. Lowering the bar to entry for this technology makes it more useful to a business since people who are not traditionally IT-based or data-based can offer suggestions that are out-of-the-box.

Having a limited data processing team

We've gone past the idea of trying to innovate within the confines of our own industries - the tech giants are far better equipped to attempt to do something like that. No, in SMEs, our aim should be to develop a competent team based around those three trained specialists (the data scientist, the software developer and the data engineer) and task them with making the system more understandable to the rest of the staff. 

Building an AI team might be costly, but overall the benefit to the company is more than realized after employees start using the technology in their everyday decision-making processes. By getting on the AI and ML bandwagon early, a company sets itself up as a pioneer of a new technology and potentially a new emerging leader within the industry.

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