By: Ilana Jucha
Blog 1 minute read

How to find the right talent to implement AI

November 22, 2021

Key data has shown that the demand for AI talent remains strong despite the economic crunch caused by the COVID-19 pandemic. 

Nonetheless, finding the right talent to implement AI is still a massive challenge for AI adoption in today’s market. McKinsey’s survey on the adoption of artificial intelligence reports that 42% of executives believe that finding talent with appropriate skill sets for AI is a significant barrier to AI adoption. 

Job trends show a strong global demand for AI professionals. At the same time, reports have indicated that there are only around 36,524-300,00 self-reported AI practitioners currently in the job market globally. This is a relatively tiny pool of talent to meet the growing needs of technological business trends. 

AI is changing the know-how that is and will be demanded in the workforce. Today, statistics, modeling, and technological skills are highly sought after. However, people who work with AI need to have more than just technical skills. In reality, in the marketplace, this is not a significant shortage of people with quantitative skills. However, there is a  significant shortage of people who have the right quantitative skills combined with methodological skills and the right thinking processes needed to develop AI systems. 

So while companies are working hard to build up their data, unfortunately, they are finding that they can’t find anyone to make sense of that data. It is evident there is a significant skills gap in the job market. 

AI is not a single technology – it is many different technologies. When sourcing AI talent it is important to consider taking a multi-pronged approach. 

Building your AI talent pipeline

So how do organizations find the right people to manage their data and create AI value: 

Outsource: 

If you can’t find the right resources internally, look outside your four walls and employ a data science professional services company or an external AI solution to drive your digital transformation.  These companies can provide highly trained data scientists and developers, along with the infrastructure to manage vast amounts of data. By partnering with these agencies, organizations can help address talent shortages as well as infrastructure issues. In the long term, this could be a more cost-effective solution compared to building internal AI teams. 

Building capabilities in-house: 

Many organizations prefer to source AI professionals externally rather than investing in training for internal resources. However, early AI adopters believe that internal development is key for building strong and robust AI systems. Two-thirds of early AI adopters are training their developers to create new AI solutions. Moreover, these innovators are also facilitating training for general employees to help them use AI in their everyday roles. Many larger enterprises are investing heavily in digital literacy for their employees. For example, tech giant Amazon has dedicated $700 million to upskilling its US employees with tech skills. Major organizations are seeing significant value in building their AI capabilities in-house to foster their competitive advantage. 

Diversify: 

When developing an internal or external team, ensure that you put together a multidisciplinary team that balances different backgrounds –  this is key for reducing bias in modeling. Studies have shown that AI systems significantly perpetuate gender and racial biases that can affect modelling. The need for diverse teams is critical to avoid any bias when building machine intelligence.

The data science ecosystem: 

Remember that the Data Scientist is only one part of the AI-driven solution. Data science requires the support of an ecosystem to thrive. One other challenge business leaders face is understanding what types of roles they exactly need to hire. Organizations need to make sure they are not just focusing on hiring a brilliant researcher or data scientist but also understand that multifaceted skills are needed to implement AI.

Specialist AI practitioners include: 

  • AI researchers to develop statistics models
  • Data scientists to derive insights 
  • Software developers to program AI
  • Project managers to follow the project
  • Business leaders that translate business needs into solutions 
  • SMEs that bring their expertise into AI systems 

Final thoughts 

AI adoption is not slowing down. Despite the challenges the recent COVID-19 pandemic has brought, AI adoption is still popular across many industries, including insurance. 

However, today AI talent is not exactly in abundance – the industry will take some time to catch up. Therefore, companies that want to adopt AI today need to look at strategic ways to incorporate AI talent into their businesses. 

Understanding the different ways they can tap into AI intelligence is key for building your AI machine. 

To learn more about adopting AI into your organization, get in touch with Stat-Market today.