BUSINESS STRATEGY
professional development ), it should be able to maintain its everyday AI culture .
THE AI PROFESSIONAL IS NOT A SINGLE CATEGORY WITH A SINGLE APPROACH TO ATTRACTION AND RETENTION .
The AI professional is not a single category with a single approach to attraction and retention . Different roles call for different considerations throughout the employee lifecycle . When hiring an analyst , for example , their skillset would be so broad as to necessitate a thorough assessment of their understanding of the business and its specific needs . When hiring a data scientist , their predilection towards examination , curiosity and problem solving presents a challenge in presenting enough challenges to attract and retain them . Leaders , architects and engineers have their own unique recruitment and engagement hurdles too . The road to everyday AI is a twisty one . Many bends are talent acquisition issues , so let ’ s examine the trail as a series of dos and don ’ ts .
Do pursue balance
Data scientists are gems of talent but without enough data architects to ensure the right database architecture , the organisation will not be able to efficiently deploy , enhance and scale Machine Learning models . A lack of business impact can lead to low morale and resultant attrition in talent . Likewise , if data leaders are in short supply , things like communication , strategy and prioritisation can fall by the wayside . This can lead to silos and missed opportunities for model reuse . Get the balance right , however , and you can build a slick innovation factory staffed by productive and satisfied employees . abilities and backgrounds . And appoint strong leaders who can get the most out of these disparate profiles to infuse the entire organisation with an AI culture that can deliver unbiased , responsible , transparent systems . This is the kind of organisation to which young AI talent will flock .
Don ’ t fly blindfolded
Before recruiting , you should know what your expectations of the new hire will be . What projects will await them ? From where will the data come ? What business goals do those projects address – entire operational chunks like the supply chain or a production line , or smaller tasks like new reporting or self-service features ? The answers to these questions will help you with the job description and the questions you ask in interviews .
Don ’ t chase unicorns
The truth is it takes a village to form an AI team . Trying to locate a one-person band with every required skill is unrealistic ( unless you are Google , Facebook or Microsoft ) and unnecessary . It is also counterproductive because if you were able to find such a person , they would quickly become irreplaceable , which would represent a threat to the AI function . Instead , concentrate on what skills you need in order to fulfil your specific business goals . If you can define a business problem that you wish to solve , present it during interviews and ask
Do hire a diverse workforce
Discussions about AI in the region often swerve towards responsible AI . It has long been argued that the best guarantor of ethical systems is a diverse team . The everyday AI enterprise understands that a diverse workforce , split among teams that do not collaborate , is a waste . Diversity can only add value when as many viewpoints as possible are allowed to coexist in each project . This extends to collaboration between skill levels . Restricting data and AI operations to only the most specialised roles will prevent scalability , sustainability and democratisation . So be sure to mix different
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