The Great Resignation may be a myth or reality, depending upon your viewpoint. But one thing is clear: The labor shortage is real.
But it is likely not due to a decline in the participation rate. According to the Federal Reserve Bank of St. Louis, the post-pandemic labor force participation rate has not declined dramatically. In fact, the core ages of the workforce, 25-54, saw a dip at the onset of the pandemic that was mild compared to other age groups – especially workers 55+.
Rather than a Great Resignation, this would suggest a Great Reallocation of the workforce. As a global search consultant, we are seeing this precipitous shift in positions, with great demand for skills in artificial intelligence and machine learning (AI/ML).
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With that in mind, here are seven artificial intelligence (AI)-related roles to consider prioritizing right now as the workforce reallocates talent to new jobs that drive economic value for leading companies:
1. Head of Robotic Process Automation (RPA) – Director, VP, SVP
Description: This role is responsible for improving business process automation technology based on metaphorical software robots (bots) or on artificial intelligence. They lead teams in areas of process improvement, product design, and business transformation, which may involve tasks such as implementing new data sets, R&D or Revenue dashboards, and automation of NLP processes.
Why it is important: Effective management of RPA improves speed, quality, and productivity by minimizing the need for human assistance in the automation process.
[ Get the cheat sheet: How to explain RPA in plain English. ]
2. Head of Artificial Intelligence & Machine Learning (AI/ML) – VP, SVP, EVP
Description: This leader is responsible for building innovative, world-class products and capabilities by leading a team of data and machine learning engineers in building high-quality products for both internal and external use. They lead and mentor teams implementing cutting-edge Machine Learning/Artificial Intelligence and analytics systems, tools, and services that operate at an unprecedented scale.
Why it is important: Every organization is challenged to remain competitive in the market, increasing revenue and reducing operating costs. AI is the single most powerful tool that organizations are using to make informed decisions, drive new lines of revenue, attract new customers, and optimize the costs of business operations. A technical leader to run this program is imperative to the success of the program.
3. Head of Customer Insights
Description: The customer insights leader strives to improve marketing by gaining a better customer understanding through both data and research. These analytics and insights apply to all marketing channels and are delivered at preferred customer touchpoints.
Why it is important: Consumer insights play a huge role in reducing inefficiency and repetitive tasks. Brand leaders can also use consumer insights to measure key performance metrics for optimizing operational excellence and product innovation. This helps reduce churn, waste, and redundancy.
4. Product Management – Artificial Intelligence/Machine Learning
Description: This role is responsible for defining and owning the data science product roadmap within an organization. He or she prioritizes the research and development of optimization, statistical, and machine learning models, and takes to market new product capabilities to automate customers’ business processes.
Why it is important: A deep focus on the customer experience is critical to assure that organizations find solutions to customer challenges and keep up with market trends.
[ Related read: Artificial Intelligence (AI): 3 strategies for advancing your career. ]
5. Chief Data Officer (CDO)
Description: The chief data officer is responsible for providing world-class information management and data operations. The CDO builds enterprise intelligence and automation capabilities to ensure that pertinent data is available, reliable, consistent, accessible, secure, and timely to support the mission and activities. This includes:
- Defining an ongoing data strategy in partnership with leaders from across the organization
- Implementing an appropriate and well-defined data governance approach
- Creating constructs to support optimal access/retrieval, security, storage/caching, movement/transformation, and classification/encryption
- Management of data across multiple teams
Why it is important: Many organizations are drowning in data, and the ability to “make data speak” with the right information at the right time is critical to organizational success. The CDO is key to building foundational data and analytics competencies and ensuring that data generates these key business benefits:
- Business leaders receive advanced analytics that help decision-making that drives desired business outcomes
- Business leaders are empowered to explain to the board and C-level how data is applied in their organization’s use cases
- Data becomes a powerful tool to guide strengths, weaknesses, opportunities, and threat discussions
6. Data Scientist
Description: Data scientists are responsible for providing data, analysis, statistical modeling, and machine-learning capabilities to inform key business initiatives.
Why it is important: Data science can add value to any business that can use its data well. From statistics and insights across workflows and hiring new candidates to helping senior staff make better-informed decisions, data science is invaluable in today’s business environment.
7. Chief Digital Officer
Description: The chief digital officer is responsible for leading and building key technologies to drive digital transformation across the enterprise. This role serves as a strategic leader for key IT business processes, including the architecture committee, capital planning, and security processes.
Why it is important: Organizations need to drive growth and strategic renewal by transforming traditional analog business into digital. This role focuses on creating new value through the smart use of digital tools, platforms, technologies, services, and processes. In addition, as companies digitize, many have rushed to the cloud, with mixed results. Managing a cloud strategy is critical as improper management can cost enterprises millions.
While these seven AI roles are critical, finding talent to fill them is difficult. AI, machine learning, and data analytics are new fields, and few people have relevant experience.
This leads us back to the fact: We are dealing with a Great Reallocation of the labor force to an AI/Machine learning, data-driven world.