Transition from content output to feature management
AI isn’t just changing the way we learn. The value of learning work is changing. For years, many L&D teams have been funded and measured based on tangible deliverables such as courses started, completions, content libraries, learning journeys, and activity dashboards. The model was already under pressure. Generative AI can now generate the majority of these outputs in minutes. As content becomes cheaper and faster, L&D faces a test of reliability. If you can generate 10x more assets, will your business perform 10x better? Otherwise, you risk allowing this functionality to “illusion” its own value, mistaking content velocity for increased functionality and control.
This is not about whether AI will “replace” L&D. It all comes down to whether L&D is ultimately measured on what leaders actually care about: better performance, better decision-making, and faster execution of critical roles.
In this article…
When AI executes your “value proposition” in seconds
To be honest, here are some areas where AI already has strengths:
Convert small business knowledge into polished drafts (decks, scripts, modules). Create quiz questions, scenarios, role plays, and job aids. Reduce your policy to microlearning and knowledge checks. Translate and localize your content quickly. Create “personalized” learning paths based on tags and skills.
When a generic model can draft 70-80% of what many teams publish, content output is no longer central to the L&D value proposition. The results are still important, but they are not differentiating. And that raises a harder question: What parts of capability building and management are L&D in a position to own and which AI cannot commoditize?
Here’s a useful way to frame it for executives:
AI improves learning efficiency. Learning value does not automatically increase.
Evidence from field experiments shows that generative AI can improve productivity for certain tasks, often assisting less experienced workers. But increased productivity is not the same as stronger judgment, better leadership decisions, or safer, more ethical execution.
New illusion: “AI utilization” equals “strategic”
Under pressure to demonstrate innovation, many learning teams are racing to make their learning stacks “AI-ready.”
AI search in LMS/LXP. AI content library. AI coaching bot. Automatically generated learning path. Inference and classification of AI skills.
Some of these features are really useful. The risk is something they can cover up. The dashboard will be improved soon, so it will look like this:
More registrations and completions. More content consumption. More “engagement signals” (clicks, likes, time on platform) More stakeholder trust (“We’re future-ready – look, we have AI!”)
But at the C-suite level, these are leading indicators at best. The real question is more difficult.
Four questions for executives to unlock the brightness of AI
Are managers improving their performance conversations? Are their decision-making measurably improving? Are they accelerating time to proficiency in key roles? Can we cite the capabilities we currently “possess” as a competitive advantage?
If the honest answer is “I don’t know,” then the AI layer hasn’t made L&D more strategic. Value now looks more convincing.
Why is the center of gravity of the content wrong?
Most organizations don’t have a content problem. There is a problem with transfer and execution. Decades of research on training transfer shows that what happens after training (manager support, application opportunities, job context, incentives, reinforcement) largely determines whether learning becomes performance. So, if L&D uses AI to create more content without changing forwarding conditions, the results will likely look something like this:
Learn more about “supply”. Same performance friction. Noise within the ecosystem increases. More skepticism from leaders who need results, not libraries.
AI can speed up the “content engine.” The organizational conditions that make learning stick are not resolved.
Human capability gaps that cannot be filled by AI
AI is great at scaling information and drafts. It is much weaker in its ability to determine whether a strategy is implemented in the actual workplace.
Judgment under uncertainty
If you don’t have a prompt with full context. Trade-offs and prioritization
Especially between competing stakeholders. Ethical reasoning and accountability
Not just what can be done, but what should be done. courage of leadership
Holds the line even in moments of pressure. Building trust
Relationship capital that enables execution.
These are not “soft skills”. They are operational capabilities. If your organization is weak, you will be well-informed but poorly prepared, and your organization will be vulnerable. And here lies the L&D risk. If capabilities remain centered around content creation (accelerated by AI), productivity may appear to increase, but businesses may become more vulnerable.
What CLOs Must Have: Capacity Management
To remain resilient, L&D leadership must move from “learning supply” to competency management. It means owning the few questions that are high-stakes for companies.
Four questions about stewardship
Which features will determine performance over the next three to five years? What is the evidence that these capabilities are being strengthened? Where is performance declining beyond what can be course corrected (work design, decision rights, manager habits, incentives)? How can we leverage AI to remove friction and free humans to spend more time on high-effort exercises?
This is not a rebrand. It’s a shift in operating model.
What this looks like in real life (without vendor hype)
When talent management becomes a reality, work will change. This looks more like a performance-oriented design than an “AI-powered course.”
1) A practice environment, not a content catalog
High-impact capabilities are built through practice, feedback, and reflection, especially when situations are complex. Research on simulation-based training supports its value for developing leadership and decision-making skills. AI can help here, but not as a “response engine.” Please use it as a sparring partner.
Role-play a difficult conversation. Determined by pressure test. The surfacing of risks and objections. Generate variations of the scenario and practice repeatedly.
2) Performance data → Workflow redesign
Use AI to detect patterns of performance friction (tickets, quality issues, time delays, customer sentiment, manager behavior). Then, partner with companies to redesign their workflows. That means fewer courses and potentially more courses.
In-flow prompts. Decision making checklist. manager’s routine. Communities of practice. A practice loop tied to real work.
3) Manager development as a primary “learning platform”
If turnover is the problem, managers can also be part of the solution. Please equip the following:
A short observation guide. Coaching prompts. A rubric for “good” performance. A simple practice routine for team meetings. Ethical guidance for using AI in teams.
This is where many learning strategies succeed or fail. This is because managers shape the environment in which capabilities are either strengthened or attenuated.
4) AI supervision as a learnable ability
Once AI is integrated into workflows, people must learn how to monitor it.
Detect when the output is unreliable. Validate based on policy and context. Escalate the risk. Document your decisions. Stay responsible.
Recent reports and research show that many workers spend significant time remediating weak AI outputs, often due to a lack of training and clear guardrails. (This is exactly where L&D can create value.)
A simple scorecard that L&D leaders can follow
If you want your executives to fund your capabilities, measure them. The categories of accomplishments that most senior leaders immediately recognize are:
Time to demonstrate ability
in an important role. Errors, rework, or quality incidents
It is tied to decision making and execution. Improving manager efficiency
Frequency and quality of coaching and quality of performance conversations. customer outcomes
Where relevant (emotions, resolution time, escalation) Bench readiness
Prepare for internal mobility, succession, and promotion.
The goal is not complete attribution. It’s about trusted stewardship, clear results, measurable movement, and a transparent contribution story.
From tool deployment to ecosystem resilience
There is a quiet illusion emerging that if we choose the right AI platform, we can be future-proof. in fact:
Markets will consolidate. Tools change quickly. Regulations and ethical expectations will become stricter. Budgets are getting smaller and smaller.
Resilience is the next strategy.
Design portable, tool-independent learning data models and processes. Maintain ownership of core functional frameworks and success measures. Build internal AI literacy so you can evaluate, switch, and adjust tools instead of relying on one vendor.
Capabilities based on distinct human capabilities can move between tools without losing themselves. Platform-fixed functionality becomes irrelevant with one procurement decision.
L&D leadership challenges in the age of AI
AI forces you to make a choice. L&D can double down on the illusion – more content, more features, more “AI-powered” labels – or they can use this moment to tell the truth.
Access to information is not a competency. Content output is not a performance change. Automation does not eliminate the need for judgment. it increases it.
Winning in the age of AI The L&D department is not the one that generates the most assets. They will be the ones who can clearly define and tangibly develop human capabilities that no model can replace. Even if you can’t name some of the features your learning strategy is enhancing and can’t show evidence that they’re improving, AI can help you create jobs faster that companies no longer fund.
