Embracing AI-Enabled Professional Development

Originally posted by Forbes Business Council September 19, 2025.

AI is changing how we create, deliver and measure learning. But in my experience working in professional development, the seismic shifts we’re seeing in this sector aren't rooted in AI tool adoption alone; they’re rooted in the rapid evolution of the operational structures within education provider organizations that are necessary to meet modern learner expectations.

Becoming equipped to use generative AI and AI-enabled technologies is important for learning professionals. However, I believe that alone is not enough. To be fully equipped, we need to understand four strategic components that are preparing AI-ready organizations to become innovation leaders.

1. Evolving Roles

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

While AI is not yet an instructional designer, it has already begun redefining learning and development team roles. Instead of content creators, I expect more companies will be looking for L&D professionals capable of content architecture, dynamic learning pathway audits, content-focused GPT training and maintenance, and learning-data analytics. Collaboration with subject matter experts is already evolving: Professional development organizations are generating outlines and content drafts for SMEs to edit rather than asking them to create the content from a blank page, which represents a significant departure in practice.

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

2. Portfolio Ratio

As more professionals turn to GPTs to solve immediate challenges or initiate self-directed learning, I’m seeing the market value for knowledge base training decreasing. If professionals can access self-directed learning via GPT, they likely won't pay for courses covering that knowledge base. This has caused more professional associations to struggle to sell their knowledge base programs, but skill dev training remains marketable. In my company's assessment of professional association and continuing education company learning portfolios, we have found that the majority of programs provide foundational information rather than facilitate skill development.

Granted, specialized expertise still holds a strong value proposition; but in the current skills-first job market, I recommend having your learning teams prioritize skill development in their education portfolios. AI tools can enable companies to assemble knowledge base learning quickly so that L&D teams can dedicate more resources to designing and facilitating high-impact skilling, upskilling and reskilling training.

3. Governance

While every organization should already have an AI policy, I recommend creating a subset policy that specifically governs ethical and judicious use of AI for L&D. Consider the fact that with just a subject expert photo, voice and gestures samples, and a narration script, people can now generate video training media without a studio or virtual recording capture session. Instead of just contracting with subject experts for copyright of material, now professionals have to consider licensing their likeness and voice for specific use.

AI can revolutionize content creation for learning, but it's also important that L&D teams establish an ethical standard and operational structure that will uphold it. 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

While it is important to reinforce data minimization principles—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 survey platforms, excel files, PDF reports, LMS and CMS platforms, vendor portals, etc., which can make it difficult for team members to query for trends, performance, content priorities and strategic decision-making.

That is why I recommend consolidating and structuring your existing data to both inform continued learner market analysis and also prepare for the AI-powered analytics that will likely become the engine behind adaptive learning. Based on my experience, structuring data as market analysis can be a huge improvement over what currently exists for the vast majority of professional associations and CE organizations.

Conclusion

AI can be both an enabler and a disrupter. We are already seeing both 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 is creating operational structures that are flexible and responsive while staying grounded in human-centered ethics for learning design.