A 7-Step Guide for Bridging the AI Skills Gap

Between 2015 and 2023, the number of technical AI job listings increased by 257%. This unprecedented surge in job openings shows that companies are scrambling to bridge the AI skills gap and hire enough engineers to develop and implement AI technologies.

The demand for AI skills isn’t limited to technical workers. AI is a general-purpose technology that’s being integrated into every department and workflow in the modern organization, from HR to accounting (and everywhere in between). That’s why, in 2024, nearly a quarter of all job openings listed AI skills as a requirement—regardless of occupation or technical skill level. 

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But, despite increasing demand for AI skills among all workers, there aren’t enough AI-skilled workers to go around. 68% of executives report that there’s a “moderate-to-extreme” AI skills gap within their organization. 

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This AI talent gap prevents organizations from fully deploying AI solutions, allowing AI-mature organizations to thrive while their competitors fall behind. The latest research backs this up. MIT Sloan discovered that:

‍Companies with advanced artificial intelligence capabilities—those most effectively using AI to improve operations and customer experience, and to support and develop their ecosystems—outperform their industry peers financially.

Executives agree. A recent survey of enterprise leaders found that 65% of executives “report that AI technologies are enabling organizations to move ahead of the competition.”

In this 7-step guide, we provide leaders with a streamlined enterprise training strategy designed to bridge the AI skills gap and help their organization thrive in the AI era. 

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Step 1: Conduct an AI Maturity Assessment & AI Skills Gap Analysis

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The first step to bridging the AI skills gap is to conduct an AI maturity assessment and understand the organization’s current AI status. This includes a two-pronged approach: measuring the organization’s AI maturity and conducting an AI skills gap analysis for employees.

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To start the AI maturity assessment, you’ll want to use a proven methodology that helps you measure your organization’s:

• Data analytics capabilities
• Current employees and skill sets
• Technology and infrastructure
• Overall strategic vision for AI

We recommend using a proven tool like Gigster’s AI Readiness Assessment. In just 10 minutes, you can determine an organization’s current AI readiness level and receive recommendations on how to quickly improve. 

This will help leaders understand where they stand on current AI pilot projects, assess their data infrastructure, and determine whether employees hold favorable attitudes towards AI or need targeted messaging. 

Next, it’s important to conduct an AI skills gap analysis. This helps leaders map existing AI skills against future needs, and visualize the organization’s AI talent gap before developing training programs or hiring new employees. 

The top AI skills gap analysis tools are:

• iMocha -  Uses AI-powered analytics for enterprise skills gap analyses, using both technical and soft skill assessments
• The Predictive Index - Talent optimization platform that uses behavioral and cognitive tests to conduct skills gap analyses
• argyleOS - Uses real-time data and talent analytics for skills gap analyses, helping companies upskill and reskill their workforce
• Muchskills - Visual skills management platform that helps organizations map, assess, and analyze employee skills and technical competencies
• Nestor - Combines cutting-edge AI and data analytics to identify skills gaps and create personalized learning paths

With these tools, leaders can quickly evaluate current skills, pinpoint gaps, and develop a strategic learning and development (L&D) program to bridge the AI skills gap. Leaders can also use these insights to determine the best key performance indicators (KPIs) to measure and estimate the possible cost savings (and efficiency gains) with AI.

DHL Transforms Its Workforce with Skills Assessments

DHL Express revamped its employee training program in the early 2020s. The firm accomplished this by focusing on building an L&D program based on strategic skills gap analyses, which allowed leaders to uncover critical skills gaps and introduce new learning paths to help employees acquire in-demand skills.

As a result, DHL was able to transform its workforce, increasing productivity and employee retention rates across the board. The organization also boosted employee engagement to unprecedented levels: “It went from 56 to 88 [percent over a ten-year period] which in a business like ours, is almost unheard of.” 

‍Step 2: Develop a Comprehensive AI Learning & Development Program

Once leaders have assessed their organization’s AI readiness level and identified critical skills gaps, developing a comprehensive L&D program tailored to their organization’s specific needs is time.

Ideally, this program should be flexible and offer personalized learning paths for employees based on their job function, AI literacy, and potential for efficiency gains. 

For example, an operations associate may take courses that help them use AI for demand forecasting, inventory management, and predictive maintenance. On the other hand, an HR specialist’s learning should focus on talent acquisition, employee development, and annual review optimization. 

No matter which role or department a leader is focused on, they should include key courses on the basic principles of AI, practical applications within the employee’s department, and learning how to collaborate with AI-specific technical teams.

‍Amazon’s AI Ready Program Uses Personalized Learning Paths to Address the AI Talent Gap

In 2023, Amazon created the AI Ready program to help its employees (and outside workers) develop the skills necessary to thrive in the AI era. The program was first launched as a pilot program for Amazon employees only to help address the AI skills gap, helping the tech and retail giant become a global leader in AI adoption.

Participants can choose from various personalized learning paths based on their current job role, existing skills, and career goals. 

Employees can also enroll in the company’s Machine Learning University, which “is taught by more than 400 Amazon Machine Learning scientists who are passionate about furthering skills in the field.” 

‍ Step 3: Create a Personalized and Multi-Modal Training Curriculum

The key to creating a successful L&D program is building personalized learning paths, including multi-modal training, into the program. Multi-modal training involves exposing students to several different learning styles and mediums. These include:

• In-person workshops
• Instructor-led online classes
• Self-paced learning modules
• Real-world projects
• Peer learning groups
• Mentorship programs

In addition, many companies are using innovative learning approaches, like gamification and AI hackathons, to help employees understand how to implement AI solutions in the real world. 

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AI hackathons are a fascinating example. These collaborative events bring employees from different departments together to quickly build prototype AI solutions that address specific challenges. They’re helping companies rapidly solve workflow bottlenecks and allow employees to gain invaluable real-world experience they can bring back to their own departments.

‍Bank of America Transforms Workforce with Personalized Training Modules

While most people know of Bank of America (BOFA) as a retail bank, the company is also becoming a leader in AI training and adoption—helping the financial institution bridge the AI skills gap and compete in the global marketplace. 

BOFA’s The Academy “includes on-demand education that provides self-directed sessions to help teammates advance their skills in areas such as client engagement, problem solving, data management, and more.” This also includes AI and machine learning. 

The L&D program uses a combination of AI, virtual reality, and real-world practice simulations to help employees master new topics. 

As of April 2025, BOFA says that “more than 90% of employees use AI-driven virtual assistant for increased efficiency.” According to Aditya Bhasin, CTIO of BOFA, “AI is having a transformative effect on employee efficiency and operational excellence… [which] enables us to further enhance our capabilities, improve employee productivity and client service, and drive business growth.”

‍Step 4: Create Tangible Goals and Progression Paths

Now that leaders have created the L&D program, getting employees on board is time.One of the most effective ways to do this is by tying employee review scores, pay raises, bonuses, and promotions to program completion. Linking AI skills with career advancement opportunities is a powerful motivator and can quickly increase participation rates. 

In addition, people are naturally receptive to performance-based rewards. Business unit leaders can increase program participants' rates and close the AI skills gap by tying the completion of AI training programs to:

• Paid time off
• Bonuses
• Internal recognition
• Higher pay scales

‍UK Law Firm Makes Waves by Tying Bonuses to AI Usage

The UK-based law firm Shoosmiths recently announced that it’s tying a 1 million pound bonus pool to AI usage among staff members, with the goal of increasing AI usage across the organization and motivating employees to proactively close the AI skills gap. 

Incredibly, the firm did more than tie annual reviews to AI training—it’s also tying bonuses to real-world AI usage throughout the firm. 

In doing so, Shoosmiths “became the first major law firm to link a firmwide bonus to the use of artificial intelligence (AI) by its workforce.” Shoosmiths’ CEO, David Jackson, says” This initiative is about creating a culture where everyone plays a role in embracing change and using technology to raise the bar on client service.” 

‍Step 5: Fill Any Outstanding AI Skills Gaps with Targeted Hiring

Internal training and development are critical to success. But, sometimes they’re not enough to close the AI skills gap. Leaders may need to hire additional AI specialists strategically to become an AI-first organization.

Leaders must use the AI skills gap analysis to identify which departments face the most significant challenges. L&D programs will help upskill some employees, but targeted recruitment may also be necessary to hire in-demand talent with highly sought-after AI expertise.

Collaborate with HR to target these candidates through customized job postings. Still, especially hard-to-fill roles may require headhunting, or manually contacting currently employed workers to lure them away from their company. 

‍ How Google Uses Targeted Headhunting to Bridge the AI Skills Gap

Google has developed a reputation for being a leader in the AI field. However, this reputation didn’t come from employee upskilling and formalized L&D programs alone. The company is also aggressive about poaching top-tier AI talent from competing firms.

The tech giant uses a multi-pronged approach to headhunting, targeting AI specialists by:

• Acquiring AI startups and their employees
• Building dedicated AI talent acquisition teams
• Using external headhunting firms for senior roles
• Recruiting PhD graduates and post-doc researchers from universities

For example, Google purchased Character.AI in 2024 to acquire rights to the GenAI technology and use its team of AI specialists to fill Google’s AI talent gap. Fortune Magazine describes the merger: “It’s the new blueprint for Big Tech companies scrambling for dominance in artificial intelligence: Hire the cofounders of a high-profile AI startup along with some of its staff.”

Step 6: Create an AI-Driven Enterprise Culture

It’s not enough to teach employees how to use AI; leaders must also create an AI-driven culture emphasizing AI adoption and pilot programs. This requires a fundamental transformation of the entire company culture, starting with senior leadership.

Executive buy-in is crucial for bridging the AI talent gap. Leaders need to publicly champion AI as a transformative general-purpose technology that will impact every employee in the organization. They must also thread the line between emphasizing the importance of AI, and assuring employees that it will be used as a supplement to humans (not a replacement). 

Consider using the following content to build employee enthusiasm:

• Internal newsletters
• Win wires
• Employee celebrations
• Video testimonials
• Social media

In addition, employee communities are a great way to foster a culture of collaboration and encourage the use of pilot projects to demonstrate ROI for AI. Encourage “AI evangelists” to share their successes, best practices, and lessons learned in these groups.

Microsoft Drives AI Innovation with CEO Buy-in

Microsoft’s CEO Satya Nadella has helped transform the tech giant by serving as the company’s primary AI evangelist. Nadella has publicly celebrated the transformative potential of AI, saying AI “is as significant as the PCs in the ‘80s, the web in the ‘90s, mobile in the 2000s, the cloud in the 2010s.”

In addition, the company uses internal L&D programs, mentorship opportunities, and employee communities to encourage AI adoption and experimentation. In particular, the company’s innovative Microsoft Garage is “an innovation platform that enables collaboration and experimentation through hackathons, workshops, talks, training sessions, and more.” 

Step 7: Measuring Training ROI and Refine L&D Programs

Once an L&D program is in motion, there’s still work to be done. The world’s best enterprise leaders place a heavy focus on measuring the ROI of their L&D programs and refining the curriculum over time to optimize impact.

There are several KPIs that leaders should monitor from program conception to enterprise-wide adoption and beyond, including:

• Employee productivity
• Profit-per-employee
• Cost savings through automation
• Annual review aggregation
• AI tool usage
• Real-world AI applications

When added together, these KPIs help leaders determine whether L&D programs are having a real-world impact. Are they effectively bridging the AI skills gap and creating real-world value for the organization?

Successful AI adoption leads to increased AI tool usage and, hopefully, the number of real-world AI applications. But, it also increases several other KPIs, including employee productivity, profit-per-employee, and cost savings through automation. 

In addition, annual review aggregation lets leaders know whether front-line managers notice a difference in their employees’ overall performance.

It’s also important to continuously adjust the overall L&D program and individual learning paths based on these outcomes, changing responsibilities, and future AI technologies and tools developments. Create continuous feedback loops to optimize the L&D program and close the AI talent gap over the long term.

AT&T Uses L&D KPIs to Continuously Improve Personalized Learning Paths

AT&T’s upskilling program, aptly called Future Ready, represents a $1 billion investment in upskilling for the digital and AI age. To successfully upskill its employees and bridge the AI talent gap, AT&T uses a combination of employee satisfaction surveys, promotion rates, and business growth KPIs to measure the impact of its L&D programs. 

AT&T has used this valuable data to improve its L&D outcomes and boost employee engagement with the resource. In particular, the firm used these KPIs to deliver “job-specific training, learning resources, and continuous improvements to ensure employees are set up for success. 

Conclusion: Putting it All Together

The AI talent gap continues to grow as organizations worldwide scramble to hire experienced talent with in-demand AI skills. Nearly 1/4 of all American job postings require AI skills (for technical and non-technical roles alike), but most executives say that their employees lack the foundational skills to effectively use AI in the workplace. 

With this 7-step guide to bridging the AI skills gap, we’re helping enterprise leaders bridge the AI skills gap and upskill their employees for the AI era. Leaders who follow this guide will be able to quickly assess their organization’s current level of AI-readiness and quickly find skill gaps among employees.

Armed with this information, executives can quickly build an effective L&D program that teaches all employees the right skills (and AI program knowledge) they need to thrive in an AI-first organization, develop personalized learning paths for each department and function, and refine their training over time to maximize impact.

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