L&D is operating in a very different environment than it was even a year ago, and data from the 2026 Global Sentiment Survey (GSS) reflects this shift clearly. With insights from more than 3,700 respondents across 105 countries, it’s clear evidence that upskilling and reskilling remain the top people challenges, while AI is now the biggest technology challenge.

At the close of L&D Next 2026, industry experts Donald Taylor (Founder and Lead Researcher, the L&D Survey Series), Ryan Austin (Founder & CEO at Cognota), and Martha Soehren (Executive Advisor and Coach, ex-Comcast Cable) came together to discuss these results in the context of a larger question: what is actually working in L&D today?

Across the conversation, one theme stood out: L&D is entering a more demanding phase, where success depends not on adopting new ideas, but on applying them in ways that clearly support the business.

 

AI is moving from experimentation to operations

AI continues to dominate the conversation, but its role is becoming more practical and less speculative.

While some may interpret the leveling off of “AI fever” as a sign of slowing momentum, the reality is more nuanced. Ryan Austin explained that the first wave of experimentation has peaked, while the next wave is operational. That being said, most organizations are still far from advanced maturity, with “less than 1% operating at what we call an adaptive model. "Donald Taylor also pointed out an interesting re-emergence of priorities: his surveys have shown that over the past 10 years, “content personalization was becoming less and less important, but for the past three years it's gone up. ” This means that stronger and smarter AI tools are making it easier than ever for L&D to embed personalization features in their learning initiatives.

Across more mature L&D teams, AI is increasingly being used to support skills practice, coaching, and performance support. For example, by feeding transcripts of real customer-facing conversations into agentic AI to model good or poor examples, frontline employees can receive real-time guidance from the AI agent on how to improve their performance.Today’s AI tools are helping L&D teams address capacity constraints, expanding what L&D can realistically deliver for learners and for the business.

 

Why proving value feels urgent (and requires a reset from content to capability)

Although technology is always evolving, Taylor notes that L&D’s core purpose has stayed the same: helping individuals and organizations flourish. The issue is that the function has confused its purpose with the means, relying too heavily on content creation. That model is no longer sufficient, especially as AI makes content faster and cheaper to produce. As a direct result, the perceived value of traditional L&D activities is called into question, with the Global Sentiment Survey finding that L&D’s pressure to “show value” is at its highest point in a decade.

Taylor observed that many practitioners attribute this to a fear of losing their jobs to AI, and Austin explains that it’s a broader business reality and shift in how L&D is evaluated. Rather than focusing on outputs like courses and satisfaction scores, Austin and Soehren explained that L&D needs to reframe its value to leaders by shifting from content delivery to building capability systems for the business, and proving ROI with transformational data like productivity, quality, and risk reduction.

Demonstrating value shouldn’t be a separate exercise for L&D, but rather the natural result of aligning work directly to business priorities and measuring what matters to the organization.

 

Execution gaps: data, tools, and discipline

Even with the right mindset, many organizations struggle to translate intent into consistent results. For example, technology was identified in the GSS as a top challenge, but the issue isn’t usually the tools themselves. Austin describes it as more of an “operating model problem”, with the real challenge being misguided focus.

Many teams are spending more time managing tools versus driving business outcomes, often investing in new platforms without clearly defining the problems they are trying to solve. Data presents a similar challenge. Although more data is available than ever, it is often underutilized in organizations that treat analytics as reporting instead of something that drives decisions. GSS responses cited a “lack of trust in AI outputs” and “lack of AI skills in the L&D team” as major barriers to leveraging its full potential.For Soehren, helping users gain confidence and capability using AI is imperative to help the entire organization move forward faster.

The tools, data, and ideas exist—but without clear goals to solve the biggest business problems, even the most sophisticated tech stacks will struggle to deliver value. Turning them into consistent, business-aligned action remains a challenge for many L&D teams.

 

Where L&D goes next

This year’s results from the GSS indicate the drive towards a more mature L&D phase, defined less by experimentation and more by accountability.

The opportunity is significant, but realizing that opportunity requires focus: on capability over content, outcomes over activity, and alignment over autonomy.

Organizations that succeed will not necessarily be those doing the most. They will be the ones doing the right things consistently, with a clear connection to business impact.

For those looking to go deeper into these ideas and hear the full discussion from Donald Taylor, Ryan Austin, and Martha Soehren, get the session replay here.

 

Chris Bondarenko 

CRO at 360Learning