The AI ​​talent factory – what it is and how to build it


If there’s one thing all regional technologists agree on, it’s the presence of a shortage of high-level talent. Digital skills in general are rare, but the ratio of available positions to qualified candidates for expert positions is particularly high. Across the region, governments, businesses, and nonprofits need artificial intelligence (AI) specialists and data scientists. The future depends on using AI to solve problems that humans alone cannot solve.

Goal 5 of the UAE National Strategy for Artificial Intelligence 2031 focuses on attracting and nurturing AI talent. The country’s AI ministry intends to deliver AI training courses to government employees and the public, as well as upskill students by selecting one-third of the country’s STEM graduates each year – approximately 2,000 people – for special training.

The UAE has long been a proponent of leveraging AI to do just about anything that will improve society and governance. His AI department was the world’s first, and the government’s focus on using smart technologies for everything from smart grids to public health will require a talent base in the years to come.

A way forward
Acquiring the skills to implement every AI project currently underway means hiring the services of a third-party software vendor or a consulting firm, but either rarely improves the pool of talent. talents of a long-term company. Once these options are ruled out, hiring companies need to hire the best data scientists available, but the talent acquired may not be of the desired quality.

Today, especially given the support provided by the government for professional development, businesses in the UAE are best served by developing in-house skills and letting them explore AI alongside adding value to the business. ‘company. So-called “academic companies” like this include some international giants such as PepsiCo, Goldman Sachs, JPMorgan Chase, General Electric, Amazon and Netflix. The talent factories built by these organizations have three things in common. First, they acquired an easy-to-use AI technology platform and made it available to everyone. Second, they provided self-service development programs suitable for all levels. And third, they’ve built a platform that can identify emerging talent.

Successful implementations have three layers in common.

1. Factory model
The tools should integrate seamlessly into the technology stack and span the entire lifecycle of AI products, from data collection and cleansing to model development and governance. The talent factory should ensure that teams mix and learn from each other, and that the platform caters to all skill types. This is important because, typically, model factories serve a workforce of 5% data scientists, 10% software engineers, 20% data engineers, and 65% other analysts.

In addition to making tools accessible to all skill levels and easy to pick up by those who don’t practice AI full-time, organizations need to ensure that AI solutions can demonstrate productivity gains. compared to previous technology. Some models of factory productivity gains reach 300%, with the right technology.

2. Adoption program
An AI program can quickly lead to a negative ROI if the purchased tools are not widely used. AI talent factories should launch adoption programs to encourage use of the tools and create an AI culture. These programs should include development workshops, AI maturity assessments, hub-and-spoke organizational design, business value assessments, onboarding, training, hackathons, badges, laptop stickers, and a peer learning community.

3. Development program
Only when a large enough sample of employees are using AI tools can upskilling begin. Business analysts can train to become data engineers. Data engineers can move into data science. And data scientists can elevate their delivery to video, image, audio, natural language, and deep learning solutions.

But as this upskilling accelerates, governance should not be forgotten. IT managers need to be able to monitor and manage data access, compute costs, projects, model releases, teams, and analysts. Not only will this help control costs, but it will allow stakeholders to single out analytics followers for further development.

Resource allocation
Much of the disappointment in implementing AI comes from the assumption that once a homogenized data lake and modeling factory is built, that value should flow out naturally. But AI culture isn’t just about technology. In fact, analytics budgets should arguably be split evenly between technology and adoption and refinement. This is the very nature of the AI ​​talent factory – recognizing that technology does not work on its own. It should be used often and thoroughly; and those who do must be skilled enough to add value in different ways while continuously supplementing their knowledge.

Remember that it can be counterproductive to scour regional job markets for a fully trained and relatively expensive data scientist who may or may not possess the ability to add value. Ten years ago, that’s exactly what employers did blindly: they recruited AI specialists and taught them business and industry knowledge. The results were varied. But now that AI best practices are better understood, the most effective route to ROI is to create an AI talent factory. The business and domain talent is already there, so why not use it? And with the right tool purchase, adoption, and refinement strategy, businesses can dramatically increase productivity and become an AI academy. After that, the sky is the limit.

Sid Bhatia is the Regional Vice President – Middle East and Turkey at Dataiku

Read: Majority of UAE workers believe AI and data science will impact their jobs in five years


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