One of the big risks when introducing AI technology is that the enterprise will not have the right people to ensure the digital transformation process is implemented smoothly and the applications are used safely and effectively.
“As with any emerging technology the talent pool needs time to upskill and mature to meet the demands – we invest and mentor early talent and university graduates through our university partnerships, providing the right culture and fostered environment to help them grow, develop and flourish,” says Eleanor Harry, CEO of HACE, which uses data to combat child labour. “ We strongly advocate for diversity in data and technology and are conscious of the challenge of promoting diversity in the development of AI.”
“It goes beyond gender and ethnicity, it’s about representation of many viewpoints, opinions and roles, it’s about data scientists, clients and the communities who may be impacted by the AI systems all being equally gripped with the input and development process as much as the output,” Harry adds.
Nairah Thaha, immersive technology engineer, Monstarlab, says the company takes a strategic approach to finding the right AI talent: “First, we clearly define the specific AI-related skills that are needed, such as machine learning and data engineering. We actively work with data science communities and universities to find potential candidates that match our requirements.”
“Our acquisitions of digital transformation companies around the world, such as Genieology in Dubai, allows us to gain insight on the local and regional landscape and tap into the right talent that can implement the right solutions for our clients,” Thaha continues. “Their experience and teams of design experts, data scientists, researchers and strategists have played a crucial role in the successful rollout of AI projects for our clients in the region.”
Dom Couldwell, head of field engineering EMEA at DataStax, says that because AI is a new area, “the number of people with all the skills needed to roll out AI today may seem fairly small.”
“We’re looking to democratise that knowledge by making the building blocks of generative AI programmes as easy to use and as familiar as possible to developers,” Couldwell says. “Yes, you may need specialised skills to do the hard core model training, but creating vectors, storing features and predictions should be available to all developers.”
“You can support your team to skill up and innovate by trialling out services and getting started – this is very much an opportunity to learn by looking at some pilot projects and how to implement them quickly,” Couldwell continues. “For example, we have made a free service available that covers up to 80GB in data storage and 20 million read/write operations, which should help developers get started quickly around how to use data for their generative AI projects.”
Courtesy Georgia Lewis
Image credit: nappy/pexels
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