Navigating the AI Hiring Boom in 2023
Demand Outstrips Supply
According to a recent report by global job search platform Adzuna, there were 7.6 million open jobs in the U.S. in June, out of which 169,045 required AI skills. We're seeing a significant shift towards in the recruitment landscape for organisations to meet demand and combat competitive advantage pressures. Businesses have to balance investment and consider where the best ROI is when it comes to implementation (see cover photo for disaster case).
What Roles are in the AI Boom?
As reported by Coursera
AI Engineer: use AI and machine learning techniques to develop applications and systems that help organisations become more efficient.
Machine Learning Engineer: research, build, and design the AI responsible for machine learning. They maintain and improve existing AI systems, run experiments and tests, perform statistical analyses and develop machine learning systems.
Data Engineer: build systems that collect, manage, and convert raw data into usable information. They make data accessible so that organisations can use it to evaluate and optimize their performance.
Software Engineer: create software for computers and applications. They use programming languages, platforms, and architectures to develop anything from a computer game to network control systems. They may also test, improve, and maintain software built by other engineers.
Data Scientist: determine what questions an organisation or team should be asking, and help them figure out how to answer those questions using data. They often develop predictive models used to theorise and forecast patterns and outcomes.
AI Skills Beyond Tech Companies
The AI hiring boom is not just a phenomenon restricted to tech companies. Non-tech firms, such as accounting consultancies, health services, and financial institutions, are integrating AI into their workflows. As AI continues to permeate various sectors, the demand for AI-competent professionals is only expected to grow.
Attracting and Retaining AI Talent: Strategies for Companies
On the other side of the equation, companies seeking to attract AI talent must consider several factors. Firstly, competitive salaries are crucial in a market where demand outstrips supply. However, remuneration alone is not enough. Candidates also value opportunities for continuous learning and development, meaningful work, and a company culture that supports innovation. Personally, I've seen countless job positions discounted because they aren't using modern technologies and professionals don't want to upskill in old tech.
Outsourcing AI Capabilities vs. Hiring In-House
Insourcing versus Outsourcing
As companies grapple with the increasing need for AI skills and capabilities, a decision a decision needs to be made on whether to build in-house AI teams or outsource AI functions to specialised vendors.
In-house Hiring: Developing AI capabilities in-house has several advantages. It enables companies to have full control over their AI projects, ensuring that these initiatives align closely with their business goals and values. In-house teams also allow for more seamless communication and coordination, as well as the ability to build proprietary knowledge and intellectual property. However, building an in-house AI team can be costly and time-consuming, particularly given the current shortage of AI talent. It also requires a significant commitment to continuous learning and development, as AI is a rapidly evolving field.
Outsourcing: On the other hand, outsourcing to AI vendors can provide companies with immediate access to top AI talent and cutting-edge technology. It can be a cost-effective solution, particularly for smaller companies or those just beginning to explore AI. Outsourcing can also provide a degree of flexibility, allowing companies to scale their AI initiatives up or down based on their needs. However, outsourcing also has its drawbacks, including potential issues with data security and privacy, less control over AI projects, and potential challenges with vendor management. Not to mention cost.
In many cases, the best approach may be a hybrid one that combines in-house talent with external expertise. Companies might choose to develop core AI competencies in-house, while outsourcing more specialised or ancillary tasks. This blended approach can allow companies to leverage the best of both worlds, balancing control and cost-effectiveness.
Conclusion: The Future of AI Hiring
The AI hiring boom presents significant opportunities and challenges. For prospective job seekers, it represents a chance to enter a high-growth, high-paying sector. For businesses, it signifies a need to adapt their recruitment and retention strategies in the face of rapidly evolving skill requirements. The successful navigation of this changing landscape will shape the future of work in the years to come.
Building an AI team or looking to join one? I'm Douglas, former data analyst and Founder of DR Analytics Recruitment - we work with data professionals to help businesses build data teams through hiring the right people. Reach out of learn more via the below mediums:
