Why Are We Stalling on Generative AI Adoption?

Article Dec 10, 2024

Generative AI (GenAI) is hailed as a game-changer for the workplace, with the potential to reshape up to 40% of global working hours in the next five years, according to the World Economic Forum (WEF). By augmenting jobs, automating routine tasks, and enabling higher-value activities, GenAI could unlock trillions of dollars in economic value. Yet, despite these promises, its adoption has stalled in many organizations.

The issue isn’t GenAI itself—it’s our readiness to embrace it. Organizations are failing to address foundational barriers, leaving them unable to realize GenAI’s transformative potential. If the technology is ready, why aren’t we?

The Challenges Stalling GenAI Adoption
The WEF identifies several barriers that organizations face in adopting GenAI effectively:

1. The Trust Deficit

Only 12% of workers globally report using GenAI tools daily, reflecting low adoption rates driven by mistrust and lack of confidence in its outputs.
Concerns over transparency, bias, and data security remain high. Workers are wary of GenAI’s “black box” nature and skeptical about its ability to deliver reliable results without oversight.

2. Skills Gaps Across the Workforce

Nearly 40% of employers cite the lack of AI-related skills as a major barrier to GenAI integration.
Despite GenAI's accessibility, many employees lack the foundational data and AI literacy needed to leverage its capabilities effectively. For example, 37% of workers globally have never used GenAI for work, and an additional 25% have done so only a handful of times.

3. Cultural Resistance to Change

Organizations with a rigid or risk-averse culture struggle to adopt new technologies. Without a culture that encourages experimentation and failure, GenAI adoption stalls.
Employees fear job displacement rather than seeing GenAI as an enabler. For instance, 47% of employees who have used GenAI express concerns about its impact on their roles.

4. Lack of Proven Use Cases

While early adopters have seen significant gains—such as a 26% increase in task completion by software developers—most organizations fail to systematically track and refine GenAI applications. Without clear metrics of success, skepticism grows, and momentum stalls.

Breaking Through the Barriers: Knowledge as the Catalyst
The WEF report emphasizes a critical truth: Trust, cultural readiness, and skills development are essential for unlocking GenAI’s potential. These factors are deeply interconnected and can only be addressed through widespread knowledge and upskilling in data and AI literacy.
1. Building Trust Through Upskilling

Trust begins with understanding. Employees need training that demystifies GenAI, explaining its strengths, limitations, and ethical considerations. When workers understand how GenAI supports their roles, they are more likely to embrace it.
Knowledge reduces fear. For instance, organizations that implemented robust AI training saw increased workforce engagement and reduced resistance, as employees began to see GenAI as a collaborator rather than a competitor.

2. Driving Culture Change with Data Literacy

Culture change isn’t about technology; it’s about empowering people. Data and AI literacy training equips employees with the skills to experiment confidently with GenAI, fostering a culture of innovation and collaboration.
Organizations that embrace GenAI report enhanced employee satisfaction. Over 25% of workers using GenAI say it has made their jobs more enjoyable by automating tedious tasks and allowing them to focus on creative, value-added work.

3. Closing the Skills Gap

Organizations must map skills gaps across roles and implement targeted upskilling programs. For example, one company reduced task completion times from two weeks to a few minutes after training employees to use GenAI effectively for routine processes.
Strategic workforce planning is crucial. Identifying job adjacencies and creating career pathways helps employees transition into augmented roles, reducing displacement fears.

4. Tracking and Iterating on Use Cases

Early adopters who tracked use cases systematically reported significant efficiency gains. For instance, GenAI reduced task times by 50% for one-third of tasks in certain occupations.
By identifying high-impact areas and refining applications, organizations can create a roadmap of successes that build confidence and accelerate adoption.

The Numbers Speak for Themselves

$7.6% of IT budgets: By 2027, investments in GenAI are expected to grow significantly, accounting for this share of budgets globally.
18% increase in task quality: Early GenAI applications have shown measurable improvements in outcomes, proving the technology’s potential when implemented effectively.
Energy trade-offs: Large language models powering GenAI are energy-intensive, requiring sustainable strategies to offset their environmental impact—a concern for many organizations striving for greener operations.

Organizations are quick to invest in cutting-edge technologies but often hesitate to invest in the knowledge required to make them successful. The data is clear: Without addressing trust, skills, and culture, GenAI’s potential remains untapped.

Imagine a workplace where:

Every employee understands and trusts the AI tools they use.
A culture of innovation thrives, driven by empowered and data-literate workers.
GenAI use cases evolve through collaboration, enhancing both productivity and satisfaction.

This isn’t a far-off vision—it’s an achievable reality. But it starts with investing in knowledge.

The Way Forward: Knowledge Unlocks Transformation

Generative AI isn’t just about efficiency; it’s about rethinking how we work. Trust, culture change, and adoption all hinge on a foundation of data and AI literacy. Only by empowering people can organizations truly harness GenAI’s potential.

The question isn’t whether GenAI will transform work—it’s whether we are ready to transform with it. Are we prepared to prioritize knowledge and upskilling to ensure success? Or will we let hesitation hold us back from one of the most promising technological advancements of our time?

About the Author.

Sharala Axryd is passionate about data driven business transformations & driving data science education in ASEAN. A natural thought leader, she is a highly-sought-after speaker for conferences with topics ranging from analytics to women in STEM.