How to Overcome AI Talent Shortages with a T-Shaped Hiring Strategy

Enterprise artificial intelligence (AI) is the most significant technological advancement in the business world since the advent of cloud computing, and it’s now become an indispensable part of the corporate toolbox. But, executives worldwide report that the AI talent gap, or a lack of qualified workers with AI experience, hinders AI initiatives and prevents companies from innovating with this new technology.

Over the past five years, companies have spent over $200 billion on enterprise AI platforms. In addition, 91% of executives have already or are planning to scale their pilot AI programs and launch new AI initiatives in other departments.

However, there’s a major roadblock preventing them from executing this vision: many companies lack the experienced talent they need to build AI tools, deploy AI platforms into IT infrastructure, and train workers how to use AI in their daily workflows.

In this guide to overcome AI talent shortages, executives will learn how to bridge their organization's AI skills gap. By the end, readers will understand how to build an AI-ready enterprise through targeted hiring, employee upskilling, and flexible work arrangements with contractors. 

Why the AI Skills Gap is Growing Every Year

The demand for talent is growing yearly, showing no signs of slowing down. From 2018 to 2023, the St. Louis Fed reported a 257% increase in technical AI job listings. Interestingly, nearly a quarter of all non-technical job listings now ask for AI skills in the description. 

‍

‍

Looking ahead, this crisis shows no signs of letting up. Bain & Company says, “Demand for AI skills has grown 21% annually since 2019 amid an AI talent shortage likely to persist through 2027.” The firm also predicts that half of all AI jobs may remain unfilled by 2027.

Companies are also struggling to respond to the current talent paradox. Despite widespread layoffs in tech and a large talent pool, organizations struggle to find the “right” talent for their needs. Few laid-off workers specialize in AI, meaning companies must pour through hundreds or thousands of resumes to find niche skill sets.

Combining this talent paradox with a “noisy,” and increasingly complex hiring environment makes it even harder to find in-demand talent. Key drivers of the noisy hiring environment are:

• Automated recruitment tools
• AI tools for applicants
• Large resume volumes

‍

In addition, many companies face difficulty in evaluating candidates for AI skills. Many candidates list AI skills on their resumes but lack proven experience and the ability to execute in real-world conditions. There’s also a lack of AI-aware product leadership with a proven track record of successfully implementing AI in enterprise environments. 

‍AI Skills Gap Threatens Enterprise AI Innovation

The persistent shortage of AI skills and qualified candidates is two of the primary reasons why companies are failing to get AI initiatives off the ground—and will continue to struggle in the near future. An estimated 70-80% of all AI projects fail, with surveys pointing to the AI talent shortage as one of the main causes of failure. 

Even when companies close this gap through AI talent acquisition, hiring delays often lead to:

• Missed deadlines
• Inflated costs
• Stifled innovation

‍

These hiring challenges are often secondary to leadership roadblocks. These include unclear executive strategies, internal misalignments, and a lack of readiness to launch and refine AI programs. This represents a much larger problem than a lack of talent. 

Despite this frustrating reality, there is a path forward. Sarah Elk, Americas head of AI, Insights, and Solutions at Bain & Company, says: “Companies navigating this increasingly competitive hiring landscape need to take action now, upskilling existing teams, expanding hiring strategies, and rethinking ways to attract and retain AI talent.” 

‍Define Clear AI Use Cases Before Acquiring Talent

It’s not enough to find new talent with AI skills. Before posting the first job or interviewing the first candidate, companies must define their AI use cases to understand their AI talent acquisition needs.

To accomplish this, first define the problems that leaders hope to solve with AI. This could include:

• Improved efficiency
• Streamlined workflows
• Cost savings
• Reduced headcount
• Increased innovation

Once leaders have identified solvable problems, it's much easier to understand what talent the business needs to hire to accomplish these goals. This approach will save businesses time, money, and frustration over the long term. 

‍Strategic Hiring: Building a T-Shaped AI Workforce

Enterprises can help bridge the AI talent gap by implementing a T-shaped AI talent management strategy. This approach uses a combination of specialists and generalists to drive AI progress and help organizations achieve successful outcomes.

‍

‍

In the T-shaped approach, the vertical line in the “T” represents technical specialists, like data scientists, machine learning engineers, and leading AI researchers. These experts are the heart of a company’s AI team, and they’ll drive technical progress forward and produce innovative solutions to problems.

On the other hand, generalists are represented by the horizontal line in the “T.” Common generalist titles include data engineers, product managers, and individual domain experts. They complement experts and help them drive progress forward, even when they lack in-depth AI skills themselves. 

Generalists can accomplish this by providing broad cross-functional knowledge about other departments, functions, and areas of expertise. They are a crucial bridge between the insular AI team and other departments within the organization, and they help AI experts focus on business outcomes and overall strategic priorities, rather than innovation for the sake of innovation. 

‍How NN Investment Partners Used T-Shaped Hiring to Boost AI Innovation

NN Investment Partners, a Netherlands-based investment firm with offices throughout Europe, Asia, and the Americas, recently used a T-shaped hiring strategy to close the AI skills gap and reshape its investment teams. 

‍

‍

The firm completely reorganized its Innovation Team by including specialists (like data engineers, data scientists, portfolio managers, and analysts) and generalists (with extensive experience in technology and operations) to refine its AI-driven data analytics capabilities. Today, innovation teams consist of experienced professionals from:

• Technical and operations teams
• Innovation platform
• Investment team

As a result of this T-shaped team-building strategy, the company identified “opportunities across the organization where AI and big data technologies can be used to improve current investment processes or develop new methods.” 

Using Fractional and Flexible Models to Source External Experts

While companies should prioritize hiring experienced AI talent, the ultra-competitive hiring market in AI means that it may not be possible. Remember, nearly 50% of all AI job openings will be unfilled by 2027. 

Flexible consulting models may be the key to success for organizations that still struggle to hire the right specialists. Many enterprises are turning to external consultants, fractional workers, and freelancers to access critical expertise while focusing on long-term AI talent acquisition. 

By contracting with these senior AI experts on a part-time or freelance basis, companies gain access to their unique insights and experience. This allows them to find innovative solutions to their AI challenges and supplement in-house teams with invaluable knowledge and problem-solving skills that can make the difference between success and failure. 

Partnering with outside consultants can also bring diverse perspectives and battle-tested approaches from past projects. This is invaluable and can save time and money over the long run. 

In addition, AI-as-a-service platforms are ready-made solutions that help companies bridge the AI skills gap and are perfect for organizations with modest in-house teams. They allow for the rapid implementation of AI solutions without long-term salary commitments or a lengthy hiring process. 

‍Unilever Leans on Freelancers to Supplement In-House AI Teams

Unilever, the multinational consumer goods company, makes extensive use of consultants and freelancers to improve its data analytics capabilities and launch new AI tools. It recently worked with PA Consulting to supplement the in-house development team and develop new AI capabilities.

The company sought to launch a new AI product description tool to reach new consumers and develop personalized language for specific customer segments. Kumar Subramanyan, Director of Digital R&D at Unilever, said “We wanted to use data to look beyond annual product and brand planning cycles to come up with better ideas, more quickly that unlock the consumer delight that we strive for.” 

Unilever partnered with the AI consulting firm to supplement its in-house team with in-demand specialists they couldn’t find through traditional AI talent acquisition. PA Consulting provided AI experts, data scientists, and strategic business guidance throughout the process. PA Consulting said, “This meant co-creating an AI vision and communicating and engaging with Unilever’s people to gain buy-in and commitment.” 

‍Upskilling the Existing Workforce

While external experts and targeted AI talent acquisition are a great short-term solution, over the long term, companies need to focus on upskilling current employees to cultivate in-house AI talent. By upskilling the existing workforce, organizations can build an AI-ready company that thrives in the AI era (and dramatically reduces recruiting and consulting costs over the long term). 

To start upskilling the current workforce, organizations should conduct an AI skills gap analysis to map current competencies against future needs. Once the AI skills gap has been identified, leaders can build a customized learning and development (L&D) program that helps employees acquire the necessary AI skills. 

In addition, employee resource groups, hackathons, and pilot programs are great ways to test these newfound skills in low-risk, structured environments. These collaborative events encourage employee innovation and help upskill AI novices into experienced leaders. 

‍Randstad’s Digital Academy Teaches Employees How to Become AI Experts

Randstad is a leader in business insights and analysis. The firm was one of the first to identify the AI skills gap and its implications for business success. The firm launched the Randstad Digital Academy to overcome the AI talent gap and maintain its competitive advantage.

Sander van ‘t Noordende, CEO of Randstad, says “When it comes to AI, demand continues to grow at an unprecedented rate, and so does the AI equity gap it is creating. Unless we recognize and take active steps to address this, the pool of workers who are prepared for the future of work will be too small - creating even more shortages across industries.” 

The Digital Academy helps employees learn how to use popular AI platforms and develop foundational skills “to deliver deep business insights for smart decision-making, harnessing emerging machine learning and Gen AI capabilities and retaining data integrity throughout the process.”

‍How to Use Data to Demonstrate ROI and Improve Outcomes

Implementing a data analytics strategy to accompany any AI talent acquisition campaigns is also important. By collecting and analyzing data to measure success, leaders can help their organization refine the hiring, onboarding, and training process to reduce costs and increase AI program success.

The first step in this process is establishing a capability assessment, allowing leaders to establish a baseline of AI skills and readiness across the organization. This is a clear starting point for AI talent management and is a yardstick for measuring hiring, consulting arrangements, and upskilling programs over time. 

Leaders should collect and analyze the following key performance indicators before launching an AI talent acquisition program and throughout its entire lifecycle:

• Project Completion Rates
• Time-to-Market
• Return on Investment
• Employee Retention
• Skills Gap Analyses
• Time-to-Fill

‍Conclusion: How to Close the AI Skills Gap with Talent Acquisition

The AI talent shortage may seem like an intractable problem. But organizations that follow this proven strategy for closing the AI skills gap will be able to find the right talent for their needs and outpace the competition in the race for AI adoption. 

Executives play a crucial role in this process. They must lead by example, define and communicate a clear organizational strategy, and align departments towards shared goals based on the company’s overall business goals. Once executives have defined success and motivated employees to pursue shared goals, departmental leaders can improve the odds of success by using a T-shaped approach to hiring new talent.

Finally, it’s important to recognize that the severe AI talent shortage means many positions will go unfilled. Leaders should lean on external consultants, freelancers, and AI-as-a-Service to plug these gaps and ensure teams have the resources to launch and scale AI initiatives successfully. 

‍