AI’s Real Impact on Organizations
When I started self-learning data science concepts, the AI revolution was just picking up speed, with GPT models and other advancements catching widespread attention. Initially, I saw AI as simply another wave in the ongoing tech evolution—following computers, internet, digital transformation, and big data. But after two years of diving deeper into AI, I came to realize that this shift wasn’t just about technology; it was about transforming how we think, work, and learn.
For those with data science backgrounds, AI might be straightforward to grasp and scale within an organization. Yet, the true complexity lies in managing the profound cultural change the AI carries, especially in how we train, learn, and reskill talent. This revolution goes beyond technical skills—it reshapes our approach to learning itself.
The Evolving Re-skilling Role
We've seen technological shifts before. Every major disruption has driven organizations to retool and reskill their workforce. Are OD Practitioners Becoming the New AI Educators?
Indeed, we’re stepping into roles that blend technical know-how with educational leadership. Are we becoming the new AI educators, or will reskilling efforts eventually be outsourced to external platforms? Perhaps, more than ever, it’s time to shift from the idea of “knowing it all” to “learning it all.”
In this era, learning agility has become the core skill that AI demands—not because of the technology itself but because of the rapid pace of change it drives. The traditional “Train-the-Trainer” (TOT) model is outdated. Now, the focus is on *learners teaching learners*, fostering a community of continuous growth and collaboration.
Reskilling isn’t a destination; it’s a journey of adaptability. For OD practitioners, guiding this journey will be one of our most critical contributions to our organizations as AI continues to shape the future of work and learning.
But today’s AI-driven landscape introduces unprecedented challenges. Multimodal platforms like YouTube, TikTok, and Vimeo have evolved from entertainment sites into indispensable training resources, now powered by AI to generate, distribute, and assess learning content. This presents us with a key question: how can organizations effectively manage AI-driven reskilling across these diverse channels? The other day, a client asked me if AI can make e-Learning courses? I answered without any hesitation. Off course. AI can make e-courses either for groups or even for individuals. Read that again. Individuals. And in the channel preferred by the individual. Personal learning is here.
An Agile Learning Pathway
Below, I’ll outline a practical approach OD practitioners can use to build reskilling programs that are not only comprehensive but also adaptive and sustainable.
1. Conducting a Skills Gap Analysis
To navigate AI-driven reskilling, start with a robust skills gap analysis:
-Identify Competencies Needed for AI-Enabled Roles. Understand the new skills required and pinpoint those that will complement AI rather than compete with it.
-Gauge Digital Literacy Levels. Assess digital literacy across different employee segments to determine readiness for AI integration.
-Prioritize Skills That Foster Collaboration with AI. Shift the focus to skills that enhance AI capabilities within teams rather than attempting to compete with automation.
2. Designing a Blended Learning Architecture
Blended learning is essential to a balanced reskilling strategy in the age of AI.
-Develop a Core Curriculum. Cover foundational AI concepts in formal, structured courses.
-Incorporate External Learning. Leverage high-quality, curated content from platforms like YouTube as a complement to internal training.
-Use Microlearning for Quick Updates. Supplement core lessons with short, targeted content that reinforces skills in real time.
3. Crafting Personalized Learning Paths
AI-driven reskilling must be relevant to each role, making personalized learning a critical element.
-Tailor Learning Journeys. Customize pathways that align with specific job functions and skill needs.
-Offer Flexible Formats. Allow employees to choose learning modes they’re most comfortable with, whether videos, articles, or hands-on practice.
-Combine Technical and Soft Skills. Include both hard and soft skills in the learning path, with clear progression milestones to track growth.
4. Integrating Learning with Real Work
Re-skilling must move from theory to practice, creating real-world relevance for learners.
-Apply Project-Based Learning. Engage employees with projects that address real challenges within your organization.
-Mentorship for Skill Transfer. Foster mentorship between AI-savvy employees and learners to accelerate practical knowledge sharing.
-Immediate Skill Application. Design projects or roles where new skills can be immediately tested and refined.
-Incorporate Feedback Loops. Continuous feedback and reflection allow for adjustments and improvements in the learning process.
5. Establishing Quality Control Measures
Quality assurance is essential to maintain the relevance and effectiveness of AI-driven learning content.
-Vet External Resources. Ensure all third-party content meets your organization’s standards for accuracy and relevance.
-Keep Content Current. Regularly update courses to stay aligned with the rapid pace of AI advancement.
-Measure Outcomes. Use assessments to track learning progress and gather feedback to enhance course content and delivery methods.
In this forum, Christina “CK” Kerley, a 25-year veteran in disruptions, digital revolutions, and business reinventions, discussed the top three drivers of the AI re-skilling revolution in organizations today. CK’s core message is that the same innovations that power game-changing businesses can also ignite life-changing careers. Stay until the end for ten takeaways for OD practitioners in AI-powered organizations.
The AI Reskilling Revolution – closing the skills gap
NOTES
ODPN Conversations
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