Generative AI: A Game Changer for Corporate Training

Mohsen Yahyaei

A staggering 72% of employees feel current corporate training programs fail to align with their career goals (NEXGROW, 2024). This finding from our Workplace Learning Survey in 2024 highlights a persistent disconnect in workplace learning: despite significant investment, organizations still struggle to bridge skill gaps, combat initiative fatigue, and engage their workforce.

The challenge is clear: the one-size-fits-all approach is no longer effective. But what if we could tailor training to every individual, at scale? This is where Generative AI (Gen AI) emerges as a game-changer, poised to revolutionize corporate training.

Corporate Training Gaps: What the Data Shows

Our 2024 survey, with responses across industries, paints a sobering picture (Figure 1):

• Lack of Personalization: 68% reported that training is not tailored to their needs. As one respondent put it, “We’re often required to sit through hours of training that have no relevance to our daily work. It feels like a waste of time.”

• Low Engagement: Only 35% reported feeling engaged in training.

• Initiative Fatigue: 55% described being overwhelmed by too many programs lacking clear purpose.

• Limited Application to Daily Work: 48% said training rarely translates into practice.

• Lack of Real-Time Feedback: 42% said they seldom receive feedback that helps them improve (NEXGROW, 2024).

Figure 1. Top 5 corporate training gaps (NEXGROW 2024 Survey)

Beyond these gaps, our data shows average scores of 5.3/10 for skill gaps and 4.4/10 for “not reaching potential”—evidence that employees both want and need more from L&D. Many also voiced frustrations at employer underinvestment: 35% reported no training budget at all, forcing half of respondents to spend their own money on development. Figure 2 shows the distribution of employee and employer investment on learning and development based on NEXGROW 2024 survey data.

Figure 2. (a) Employee personal investment and (b) employer investment in learning and development (NEXGROW 2024 survey)

Generative AI in Action: Closing the Gaps

Generative AI addresses these challenges in practical ways:

• Personalized learning paths: AI tailors’ journeys to roles, skills, and career aspirations, ensuring training is relevant and timely.

• Adaptive microlearning: Tackles the #1 barrier—time—by offering flexible, bite-sized content on demand.

• Real-time feedback & adaptive simulations: AI coaches employees through practice and productive mistakes, enhancing retention.

• Automated content generation: Reduces the time and cost of developing engaging materials.

• Scalable delivery: AI makes personalization possible at enterprise scale, where traditional methods cannot.

Market analysts agree. Gartner projects that by 2030, AI-driven platforms will deliver more than 30% of corporate training, while Deloitte highlights AI as the primary driver of growth in the global corporate e-learning market, forecast to reach $44.6 billion by 2028.

The Science of Learning Lens: Why AI Matters

The power of AI is not in technology alone—it lies in how it operationalizes the Science of Learning (SoL) principles.

• Retrieval practice & spaced learning: Generative AI can schedule quizzes and nudges, building on evidence that effortful recall strengthens durable memory (Bjork, Dunlosky, & Kornell, 2013).

• Self-regulated learning (SRL): Platforms can embed Zimmerman’s (2002) SRL cycle—goal setting, monitoring, reflection—into dashboards, helping learners steer their own growth.

• Productive failure: AI can simulate ill-structured, authentic problems, encouraging initial struggle before instruction. Research shows this primes deeper conceptual understanding (Kapur, 2024; Loibl, Roll, & Rummel, 2017).

• Cognitive load management: By chunking content and scaffolding tasks, AI prevents overload (Sweller, van Merriënboer, & Paas, 2019).

• Motivation and engagement: AI can foster autonomy, competence, and relatedness—the drivers of intrinsic motivation (Ryan & Deci, 2000, 2017).

Put simply, Generative AI enables training that is engaging, personalized, and grounded in evidence-based learning science.

Benefits, Risks, and Opportunities

Benefits

• Deliver scalable personalization across entire workforces

• Reduce content development cost and time

• Enhance engagement with interactive, adaptive modules

Risks

• Bias in AI recommendations must be addressed (Holmes & Porayska-Pomsta, 2022)

• Over-reliance on automation risks sterile learning experiences

• Lack of human mentorship if AI is seen as a full substitute

Opportunities

• Blend AI precision with human expertise, ensuring training remains social, contextual, and meaningful (Immordino-Yang & Damasio, 2007; Dikker et al., 2017).

• Develop human-centered AI strategies that foreground equity, transparency, and learner agency.

Conclusion

The NEXGROW 2024 Survey confirms what many feel: traditional training isn’t working. Employees are motivated and ambitious—57% aspire to leadership roles, 27% to entrepreneurship—but they lack the personalized support to reach those goals.

Generative AI offers a way forward: embedding SoL principles into scalable, adaptive systems that close gaps and unlock potential. Not as a replacement for human mentorship, but as an enabler of deeper, more effective learning.

The real question is: will organizations seize the opportunity to transform training into growth?

Is your organization ready to turn training gaps into growth opportunities with AI?

Follow NEXGROW for the next article in this series: “Personalized Learning Paths: Leveraging AI for Tailored Employee Training.”

This article is part of NEXGROW’s thought leadership series on The Future of Corporate Learning. Follow us to explore how Generative AI is reshaping how we grow, upskill, and unlock human potential in the workplace.

References

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