As organizations continue to advance their artificial intelligence maturity, AI talent is an oft-cited concern and possible barrier to achieving AI success.
Yet, the AI talent challenge has evolved in recent years. When enterprise interest in AI first began to rise, finding and attracting AI talent was the most prominent problem for businesses. As AI has become more mainstream, the development and management of AI talent are a bigger concern. In other words, organizations today usually have less trouble finding qualified AI professionals -- but they do struggle to retain AI talent. Poor talent retention hinders AI success as organizations struggle to build sustainable initiatives in the face of heavy turnover.
Here is what data and analytics leaders looking to expand their AI teams should know about how to recruit, hire, organize, train and retain AI talent, in order to ensure long-term AI success.
The State of the AI Talent Market
Whereas AI skills were scarce five to 10 years ago, there are many more professionals in the field today. There are still enterprises facing a talent shortfall, but that gap is narrowing due to the expansion of AI and data science programs at universities as well as upskilling among professionals.
AI does have some idiosyncratic pitfalls that organizations may fall into when seeking out talent. The most common pitfalls include:
- Poor hiring strategies. This includes hiring AI experts before the organization is ready, not hiring the right support personnel or looking for “unicorns.”
- Inflated expectations. Many organizations think AI deployments will be moonshots or gamechangers, when in reality, AI teams will likely be working on straightforward value projects.
- Mismanagement. Organizations must strike a balance between giving AI teams projects that they’re excited about, while also ensuring that deliverables are useful to the organization’s business objectives.
- Inefficient organizational structures. If data science teams are siloed and don’t communicate effectively with each other, they may be duplicating efforts and are not benefitting from each other’s work.
- Lack of an outsourcing strategy. Some organizations may rely too heavily on outsourced talent when they’re better off being patient and developing AI talent in-house. Others may be able to accelerate their AI initiatives if they relied more on outsourcing.
Data and analytics leaders should first assess whether their hiring and management processes are vulnerable to any of these pitfalls, before moving on to the recruiting stage. It’s important to have a strategy in place about what the AI team should look like within the organization.
Recruit AI Talent
Once the organization has fine-tuned their AI talent strategy, data and analytics leaders can dive into the recruitment process, which brings a whole new set of challenges.
AI professionals tend to be concentrated geographically in tech hubs and populous areas. While this may change as remote work becomes more prevalent post-pandemic, organizations located in large cities will likely have an easier time finding talent. AI talent is currently most prevalent in certain industries, including high tech, banking and financial services, manufacturing, healthcare, and retail, so organizations in these sectors will likely see more applicants for open roles.
When evaluating candidates for AI roles, be wary of padded resumes. As AI is still an emerging field, there are many so-called “experts” who overstate their skills, experience, credentials, education and more. It’s also important to look beyond a candidate’s technical expertise and assess their soft skills, such as business acumen, communication skills and leadership abilities. Keep in mind that elite AI talent is getting recruited all the time, and it can be a challenge to build sustainable AI initiatives when key team members leave. Ensure candidates’ previous job tenure is compatible with your organization’s strategy and rely on the interview to make sure they’re a cultural fit.
Finally, it’s important to note that salary expectations are high in the AI field. True experts are worth paying for, and given the high competition for AI talent, it may be necessary to break typical salary banding structures for these hires. If you feel like you’re getting a “deal” on an expert, it’s likely that somebody else will offer them the right amount of money fairly soon (or their expertise isn’t what you were expecting).
Retain AI Talent and Keep Teams Happy
The harsh reality is that there will always be AI talent turnover. Even citizen data scientists and other professionals that you upskill may take those skills elsewhere. While there are ways to increase retention, it’s important to be realistic and incorporate a certain amount of talent turnover into your AI strategy.
The No. 1 reason that AI talent leaves is money. The best way to protect your organization from poachers is to ensure that you’re offering competitive salaries. Beyond financials, the other top reason that AI talent leaves is boredom and frustration. Either experts are unhappy with the projects that they’re working on or unhappy with the technology that they’re using. Ensure that you’re providing the technology that AI teams need to be successful, and frequently consult with them to ensure that they’re able to use the skills and techniques that interest them most.
Hiring a supporting cast of experts is also important. Data engineers, business analysts or application developers may be easier to hire and can take over some of the less specialized work from data scientists. This ensures your scarce experts can stay focused on high-value AI projects.
Another way to protect against AI talent poaching is to develop a pipeline of in-house talent. This will ensure that a portion of your AI team is comprised of people that you know are a cultural fit and who have shown commitment to the organization. Look to upskill a broad range of quantitative professionals as citizen data scientists, priming the path for them to eventually move into more technical AI roles.
Finally, don’t forget to upskill your experts. As the enterprise matures its use of AI, more senior data scientists will be needed, who are able to go beyond working with models and start leading others and telling the story of how AI can benefit the organization. Identify existing AI talent that may eventually move into more senior roles and focus on improving their communication and management skills. Ensure that there is a clear career ladder and that experts are aware of skill development and growth opportunities.
Recruiting and hiring the right AI talent is essential for deploying AI, but training and retaining that talent is what will ensure AI projects generate true business value. Empower your organization to succeed with AI by taking a thoughtful approach to building out -- and keeping -- your team of AI experts.
Peter Krensky is a Research Director at Gartner, Inc. specializing in data science and machine learning. Peter and other Gartner analysts will further discuss the latest trends in data, analytics and artificial intelligence at the Gartner Data & Analytics Summit, taking place virtually this week in the Americas.