Using AI to Close Skills Gaps and Optimize Workforce Performance – with Leaders from Workera and The Coca-Cola Company

This article is sponsored by Workera and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.

Skill gaps are quietly throttling enterprise performance. Across industries, employees’ actual capabilities often fall short of what’s needed to execute strategic initiatives. The result: slowed innovation, stalled transformation, and wasted investment in technology and growth.

Research from the MIT Center for Information Systems Research (CISR) found, in a 2022 survey, that an average of 38 % of workforces required “fundamental retraining or replacement” within three years to close skills gaps.

CISR further notes that organizations that build future-ready capabilities — including workforce skills alignment — achieved average revenue growth 17 percentage points above their industry peers and net margins 14 percentage points higher, demonstrating the tangible business gains possible when skill gaps are addressed.

Globally, skills mismatches, including overqualification and obsolescence, can significantly reduce labor‑market efficiency and productivity, as highlighted by the ILO (International Labour Organization). According to ILOSTAT research, more than 935 million workers worldwide are mismatched to their jobs, with 258 million over‑educated and 677 million under‑educated for their roles.

These global mismatches translate into costly mis‑hires, stalled initiatives, and wasted L&D spend at the enterprise level, underscoring why executives must treat skills verification as a business priority, not just a workforce issue.

Meanwhile, the National Skills Coalition reports that 92% of jobs in the U.S. require digital skills, yet one‑third of workers lack even foundational digital competencies — a gap that directly constrains enterprise readiness for transformation.

In a recent workforce transformation series on the ‘AI in Business’ podcast, Emerj Editorial Director Matthew DeMello hosted Taylor Sullivan, Ph.D., Head of Product and Assessments at Workera, and Trish Vassar, Ph.D., Vice President of Global Learning & Development at The Coca-Cola Company.

During the series, both guests share how AI-powered, data-driven skills assessment can provide leaders with actionable visibility into workforce capabilities, accelerate skill development, and guide more precise talent decisions that align workforce development with strategic outcomes.

The article highlights the critical insights from both guests, showing how organizations can leverage AI to close skills gaps, map competencies effectively, and identify bottlenecks to maximize workforce performance:

• Measuring workforce skills with precision to close gaps: AI-driven psychometric assessments evaluate employee proficiency at the sub-skill level, enabling targeted learning pathways that accelerate capability building.
• Mapping competencies to align talent strategy with business needs: Skill intelligence platforms analyze team-wide capabilities, guiding evidence-based staffing, promotions, and succession planning.
• Identifying bottlenecks to accelerate workforce impact: Advanced analytics surface skill shortages and internal mobility opportunities, helping leaders deploy talent faster and prepare employees for mission-critical roles.

Listen to the full episodes below:

Episode: How to Leverage AI for Skill Verification – with Taylor Sullivan of Workera

Guest: Taylor Sullivan, Ph.D., Head of Product and Assessments, Workera 

Expertise: Industrial-Organizational Psychology, Product & Assessment Development, Workforce Transformation

Brief Recognition: Taylor Sullivan is a product and workforce strategy leader with over 15 years of experience in industrial-organizational psychology and assessment innovation. Before joining Workera as Head of Product, she held senior roles at Codility and HumRRO, driving AI-powered talent solutions and multi-million-dollar credentialing projects. She holds a Ph.D. in Industrial-Organizational Psychology from The University of Georgia.

Episode: A Practical Path from Roles to Skills for AI-Ready Teams – with Trish Vassar of Coca-Cola

Guest: Trish Vassar, Ph.D., Vice President of Global Learning & Development, the Coca-Cola Company

Expertise: Talent Management, Leadership Development, Organizational Design

Brief Recognition: Trish Vassar is a global talent and learning leader with extensive experience designing and operationalizing integrated Talent Management and leadership development solutions. She currently serves as Vice President, Global Learning and Development at the Coca-Cola Company, and previously held senior roles at Cigna, Express Scripts, and Monsanto. She holds a Ph.D. in Experimental Psychology with an emphasis in Industrial-Organizational Psychology from the University of Memphis.

Measuring Workforce Skills With Precision to Close Gaps

Taylor Sullivan frames the current skills gap by calling out the limitations of traditional talent assessment methods. Many organizations rely on indirect indicators — resumes, LinkedIn profiles, or prior project experience — to infer employee capabilities. These sources often remain incomplete, outdated, or misleading, leaving talent decisions vulnerable to guesswork.

She argues that AI-driven psychometric assessments measure proficiency at the sub-skill level, distinguishing between capabilities like pivot tables, statistical modeling, or machine learning to ensure leaders can differentiate assumed and verified skills.

“With skill verification, leaders gain both trust and speed in their decisions. When you know that an employee’s skills are accurately measured, you can confidently assign them to roles or promotions that matter for business outcomes. It also allows learning programs to be tailored specifically to what each person truly needs, so time and resources aren’t wasted on content they already know, accelerating overall workforce readiness.”

— Taylor Sullivan, Ph.D., Head of Product and Assessments at Workera

Taylor offers a practical AI-enabled skill measurement and personalization framework:

• Rapid Taxonomy Modeling: AI maps required skills for each role across job descriptions, competency models, and industry references.
• Psychometric Assessment Deployment: AI generates assessments validated for reliability, fairness, and contextual relevance.
• Accurate Skill Profiling: Proficiency at the sub-skill level reduces bias and error.
• Personalized Learning Pathways: Verified skill data guides targeted development plans, helping employees close gaps efficiently.

Taylor also champions fairness as a cornerstone of psychometrics; effective assessments must enable employees to demonstrate their skills regardless of demographic background, language proficiency, or prior experience. By controlling for noise and bias in measurement, AI systems ensure skill verification is both reliable and equitable — a critical factor for organizations adopting AI-based assessments.

Integrating skill verification with workflow insights allows organizations to prioritize urgent skill gaps and plan long-term development strategically.

In her own episode, Trish Vassar of the Coca-Cola company adds the perspective of organizational design and personalization, emphasizing breaking roles into component skills rather than relying on aggregated role definitions. “If we continue to select or train people based on high-level role definitions, we’ll never reach the level of personalization that employees expect today.”

Measuring skills at a granular level enables organizations to target learning where it matters most, accelerate employee capability building, and close critical skill gaps efficiently. Such an approach moves organizations from assumptions to verified insights, guiding precise learning investments and improving performance.

Mapping Competencies to Align Talent Strategy With Business Needs

Trish also stresses the link between skills and strategic business outcomes. Role-based definitions often aggregate tasks and capabilities too broadly, obscuring critical gaps and limiting alignment with organizational priorities.

“The most important thing for any company is to start with the business problem you’re trying to solve. Doing skills for its own sake is just a lot of work that may not yield meaningful outcomes. By linking the roles and skills to the results you want — like increasing sales or growing market share — you can target the right skills to the right people and ensure that learning, promotions, and staffing decisions actually drive business value.”

— Trish Vassar, Ph.D., Vice President of Global Learning & Development at the Coca-Cola Company

She emphasizes that organizations must define the logic chain behind skills: the business outcome, the tasks required to achieve it, and the specific skills that enable those tasks. Clear visibility ensures AI-driven assessments reflect meaningful and actionable data.

Taylor adds that AI makes talent mapping actionable by:

• Analyzing team-wide skills to identify gaps
• Quantifying proficiency to guide staffing and promotions
• Highlighting employees ready for key projects or leadership roles
• Aligning insights with strategic priorities

She then concludes her point on the note that mapping talent reveals latent skills and cross-functional potential, enabling strategic internal mobility and succession planning while ensuring learning investments target high-priority business outcomes. Together, these approaches bridge talent strategy and execution.

Identifying Bottlenecks to Accelerate Workforce Impact

“One of the greatest advantages of assessing skills at scale is being able to pinpoint organizational bottlenecks. We can see where certain skills are concentrated, where gaps exist, and who has the potential to step into mission-critical roles. This enables organizations to deploy talent faster and reduce the time it takes to develop critical capabilities.”

— Taylor Sullivan, Ph.D., Head of Product and Assessments at Workera

Conventional approaches rely on inferred skills, leaving leaders uncertain where gaps truly exist. Taylor notes that AI-powered analytics provide granular visibility across teams, enabling evidence-based decisions for internal mobility, succession planning, and targeted development initiatives.

In her episode, Trish Vassar underscores that visibility alone is insufficient. “If we don’t align skills data with the tasks and goals that matter most to the business, we risk investing in development that doesn’t move the organization forward,” she explains, noting that strategic alignment is essential for ensuring skill development drives real business impact.

Taylor offers a practical AI-enabled bottleneck identification and deployment framework for the kind of strategic alignment Trish espouses:

• Skill Bottleneck Detection: AI analyzes team-wide competencies to reveal areas where critical skills are concentrated or missing, helping leaders see potential gaps before they impact operations.
• Internal Mobility Prioritization: Verified insights guide decisions on which employees can move into mission-critical roles quickly, ensuring readiness for high-priority projects.
• Targeted Role Development: By identifying where skill shortages exist, organizations can allocate learning resources to the areas that will most accelerate workforce readiness.
• Strategic Alignment Mapping: AI insights are paired with business priorities, ensuring that development efforts focus on skills that directly impact organizational objectives.

By combining AI-driven identification of skill bottlenecks with strategic alignment, organizations can deploy talent more effectively, optimize internal mobility, and ensure that workforce investments translate into measurable business outcomes.

Taylor also notes that CEOs and boards are focused on measurable outputs. Skill verification delivers both trust and speed: confidence in data for critical decisions, and faster time to proficiency through personalized learning. She contrasted this with traditional “butts in seats” metrics, explaining that AI enables “time well spent” — employees skip content they already know, reducing wasted training hours and accelerating readiness.

Looking ahead, Taylor describes the rise of skills-based organizations as a shift toward meritocracy. Verified skill profiles allow employees to demonstrate their value based on proven capabilities rather than degrees or connections. She envisions a future where individuals carry a universal “skills fingerprint” — a scorecard of demonstrated proficiencies that travels with them across roles and companies.

The cultural shift moves organizations away from nepotism and toward fairer, data-driven talent decisions. Both Taylor and Trish note in their respective episodes that applying the approach not only speeds up workforce readiness but also delivers measurable ROI on L&D and strategic talent initiatives.