The biggest obstacle to AI adoption is not technology, but education. While organizations scramble to implement the latest large language models (LLMs) and generate AI tools, there is a deep gap between our technical capabilities and our workforce’s ability to effectively utilize them. It’s not just technical training; it’s about reimagining learning in the AI era. Those organizations that will thrive are not necessarily those with state-of-the-art AI, but those that change the education of the workforce, creating cultures that make continuous learning, interdisciplinary collaboration, diversity and psychological security a competitive advantage.
AI adoption has accelerated significantly – Mcckinsey’s 2024 AI State Report found that 72% of organizations now use AI, up from 50% in previous years, while generated AI has almost doubled in just ten months. As shown in Figure 1.
Meanwhile, the World Economic Forum reported that 44% of workers’ skills will be interrupted over the next five years, but only 50% of workers’ skills have received adequate training. This gap has the potential to limit the potential for AI generation, and LinkedIn’s research confirms that organizations that prioritize career development are 42% more likely to be in AI adoption.
Figure 1: Increased global AI adoption
Source: McKinsey’s 2024 AI State Report
My analysis of all this? The most critical AI literacy skills to develop are business acumen, critical thinking and cross-functional communication skills that can effectively collaborate technically and non-technically.
In addition to technical training: AI literacy is a common business skill
True AI literacy covers the ability to understand how AI systems make decisions, identify their capabilities and limitations, and use critical thinking to evaluate the output generated by AI.
For non-technical leaders, this means building enough understanding to ask questions about AI investment. For technical teams, it involves turning complex concepts into business languages and building domain expertise.
As I pointed out in my recent Anaconda hosting group: “Getting your employees to use new tools with lots of unknowns is a challenge. Being able to blend business acumen and technical expertise is a tough goal.” This mix creates a common language that bridges the gap between tech enterprises.
As McKinsey’s 2023 “Diverity More Important” report noted, cognitive diversity expands these efforts, which found that organizations with leadership diversified reported 57% of collaborations that worked better and innovation stronger. Embrace cognitive diversity – converging different mindsets, educational backgrounds, and life experiences – especially important for AI initiatives, where AI initiatives require creative problem solving and the ability to identify potential blind spots or biases in the system. Artificial intelligence literacy thrives when leaders create diverse learning ecosystems of curiosity.
Self-guided learning revolution: promoting curiosity as a competitive advantage
In this era of AI, self-guided experience learning can help students get ahead of traditional knowledge systems that become faster than ever before.
During Anaconda’s panel discussion, Eevamaija Virtanen, senior data engineer and co-founder of Invinite Oy, highlighted the shift: “Entertainment is something that all organizations should build their own culture. It provides employees with space to use AI tools, learn and explore.”
Forward-looking organizations should create structured opportunities through dedicated innovation time or internal “AI sandbox” where employees can safely test AI tools for proper governance. This approach recognizes that hands-on experiences often exceed formal teaching.
Collaborative Knowledge Network: Reimagining how organizations learn
The complexity of AI implementation requires a variety of perspectives and cross-functional knowledge sharing.
DataStrato’s data engineer and product manager Lisa Cao emphasized this in our group: “Document is the best place: create a common place where you can customize the teaching content for your audience without being burdened with technical details and really customize the teaching content to your audience.”
This transformation treats knowledge as knowledge acquired alone but rather as collectively constructed. Deloitte’s research reveals an optimistic gap between C-Suite and frontline workers in AI implementation, thus highlighting the need for open communication across organizations.
Strategic framework: AI education maturity model
To help organizations evaluate and develop their approach to AI education, I propose an AI education maturity model that identifies five key aspects:
- Learning Structure: From centralized training programs to a continuous learning ecosystem with multiple methods
- Knowledge flow: Transfer from isolated expertise to dynamic knowledge networks throughout the organization
- Artificial Intelligence Literacy: Extending from a technician to a universal literacy with a depth of role
- Psychological safety: Transition from a culture of risk aversion to an environment that encourages experiments
- Learn to measure: From completion indicators to business impact and innovation indicators
Organizations can use this framework to assess their current maturity, identify gaps, and develop strategic plans to drive their AI education capabilities. The goal should be to determine the right balance that aligns with your organization’s priorities and AI ambitions, not just perform well in every category.
As shown in Figure 2, the returns on AI education yields vary over different time frames. Investing in psychological security and collaborative knowledge networks may take longer to show results, but ultimately leads to higher returns. The lack of instant returns can explain why many organizations are struggling with AI education programs.
Figure 2: ROI timetable for AI education.
Source: Claude, According to data from LinkedIn Workplace Learning Report 2025, Deloitte’s enterprise generation AI status in 2025 and McKinsey’s AI status in 2024.
Change your AI education approach
Follow these three actions to make your organization a AI literacy:
- Assess your current AI education maturity Use frameworks to identify the strengths and gaps to be addressed.
- Create a dedicated space for experimentation Employees are free to explore AI tools.
- Lead by example In the process of advocating ongoing learning – 88% of organizations are concerned about employee retention, but only 15% of employees say their managers support their career plans.
Organizations that will thrive not only need to deploy the latest technologies, but will also create cultures where continuous learning, knowledge sharing and interdisciplinary collaboration become the basic operating principles. Competitive advantages come from having the workforce that most effectively utilizes artificial intelligence.
