Embracing AI in Learning and Development: A Strategic Advantage

AI is transforming how we create, deliver, and measure learning. In my experience with professional development, the seismic shifts we’re witnessing in this sector aren't just about adopting AI tools. They stem from the rapid evolution of operational structures within education provider organizations. These changes are essential to meet modern learner expectations.

Becoming equipped to use generative AI and AI-enabled technologies is crucial for learning professionals. However, I believe that alone isn’t enough. To be fully prepared, we need to understand four strategic components that are positioning AI-ready organizations as innovation leaders.

1. Evolving Roles

AI is already embedded in many daily tools for L&D professionals. Numerous organizations are also creating private LLMs with their proprietary content in preparation for atomic design. Here, AI will dynamically prepare training aligned with learners' performance gaps and goals.

While AI isn’t yet an instructional designer, it is redefining learning and development team roles. Instead of merely content creators, companies will increasingly seek L&D professionals skilled in content architecture, dynamic learning pathway audits, content-focused GPT training, and learning-data analytics. Collaboration with subject matter experts (SMEs) is evolving too. Professional development organizations are now generating outlines and content drafts for SMEs to edit, rather than asking them to create content from scratch. This marks a significant shift in practice.

These demands call for both upskilling and recruiting for this type of expertise in the L&D field.

2. Portfolio Ratio

As more professionals turn to GPTs for immediate challenges or self-directed learning, I’m noticing a decline in the market value for knowledge base training. If professionals can access self-directed learning through GPT, they likely won’t pay for courses covering that same knowledge base. This trend has led many professional associations to struggle with selling their knowledge base programs, while skill development training remains marketable.

In my company's assessment of professional association and continuing education company learning portfolios, we found that most programs provide foundational information rather than facilitate skill development. Granted, specialized expertise still holds strong value. However, in the current skills-first job market, I recommend that learning teams prioritize skill development in their education portfolios. AI tools can help companies quickly assemble knowledge base learning, allowing L&D teams to focus more on designing and facilitating impactful skilling, upskilling, and reskilling training.

3. Governance

Every organization should have an AI policy, but I recommend creating a subset policy specifically governing the ethical and judicious use of AI for L&D. Consider this: with just a subject expert's photo, voice, gestures, and a narration script, people can generate video training media without needing a studio or virtual recording session. This means that professionals must now consider licensing their likeness and voice for specific uses, in addition to contracting with subject experts for copyright of material.

AI can revolutionize content creation for learning. However, it’s crucial for L&D teams to establish ethical standards and operational structures to uphold them. When crafting governance in L&D, remember to account for:

• Content integrity and quality assurance.

• Bias mitigation.

• Data security.

• AI use disclosure.

• Ethical use guidelines.

  
  

All policy and operationalized governance structures should also consider performance protocols and accountability to mitigate risk and harm.

4. Data Management

While reinforcing data minimization principles is essential—collecting only necessary learner data and employing consent management systems for sensitive data—the future of learning analytics requires new management. Unfortunately, learner data is often decentralized and inaccessible, stored in various platforms like survey tools, Excel files, PDF reports, LMS, and CMS platforms. This fragmentation makes it challenging for team members to query trends, performance, content priorities, and strategic decision-making.

That’s why I recommend consolidating and structuring your existing data. This will inform ongoing learner market analysis and prepare for the AI-powered analytics likely to become the engine behind adaptive learning. Based on my experience, structuring data as market analysis can significantly improve what currently exists for most professional associations and continuing education organizations.

The Importance of Human-Centered Design

As we embrace AI, it’s vital to remember that technology should enhance human experiences, not replace them. The integration of AI must be approached with a focus on human-centered design. This means considering how AI can support learners in achieving their goals while fostering a sense of connection and engagement.

AI can be a powerful ally in creating personalized learning experiences. By leveraging AI, we can analyze learner data to tailor content and pathways that resonate with individual needs. This approach not only improves learning outcomes but also enhances learner satisfaction.

Conclusion

AI can be both an enabler and a disrupter. We are already witnessing challenges and opportunities in continuing education and training as we onboard new technologies into our program development processes. By keeping a strategic eye on where AI-enablement is evolving, you can ensure your organization creates operational structures that are flexible and responsive while remaining grounded in human-centered ethics for learning design.

Embracing AI isn't just about technology; it's about transforming our approach to learning. Let's leverage these advancements to build profitable, sustainable, and impactful education portfolios that stay human-centered.