The data on workforce development tells a contradictory story. 85% of companies plan to prioritize upskilling their workforce through 2030. At the same time, 63% of employers still identify skills gaps as the single biggest barrier to business transformation.

The explanation for this is that the model most organizations use to develop their people was built for a slower world, and it hasn’t kept up. Learning and development content needs to get scripted, created, reviewed, localized and published.

Even in large, well-resourced organizations, that process can take weeks. By the time most training reaches an employee, the product it was designed to explain has shipped two new updates. The compliance process it covers has been revised. The sales motion it was meant to reinforce has already been changed by the team in the field.

We’ve all had the experience of sitting through mandated corporate training that felt more of a check-box exercise rather than an experience where we actually learn and retain something. To make that learning and development more relevant, companies are changing both the format and the time to delivery.

The Chief Learning Officer’s new mandate

Jayney Howson, Chief Learning Officer at ServiceNow, is working through what a better model actually looks like. ServiceNow University, the company’s initiative to upskill more than three million people by the end of 2027, was recently rebuilt to be AI-native.

The challenge her team faced will be familiar to most L&D leaders: a business shipping AI products on a continuous cycle, a global workforce that needs to stay current and a content production process that couldn’t move fast enough to serve them.

Howson’s response was to rebuild the infrastructure around AI, including AI-generated video, reducing course production time by roughly ten times.

Her team was able to use Synthesia and produce more than 5,000 videos in 18 months, with programs like Sales Academy for their global sales team and partner enablement running consistently and globally. Learning content now reflects what the business is doing today, not what it was doing a few months ago.

According to Jayney, “It feels like a Netflix experience, where it serves up personalized recommendations for each employee. But it can also see that for the job I’m doing right now, the proficiency level I’ve got on a skill is a one and it needs to be a four. So it serves me up that training, too.”

Production is no longer the constraint

ServiceNow’s experience reflects a shift visible across enterprise L&D more broadly. Our research found that 87% of learning professionals are already using AI in their workflows. 72% say the biggest future gain they expect from AI is more personalized learning delivered closer to the moment of need, not just cheaper production.

Those two things have always been linked. Personalization at scale was the stated goal of corporate learning for years, and also its persistent failure. Building individualized learning paths for thousands of employees is not feasible when a single course takes weeks to produce.

When video content can be created, updated and translated in hours, that changes. Programs can be built for specific roles, regions and points in someone’s tenure, rather than averaged out across an entire workforce and useful to no one in particular.

What changes for learning leaders

Organizations that solve the production capacity problem through AI free up their learning function to focus on harder questions.

Which skills actually drive business performance? What does good look like in a specific role, and how do you build toward it? How do you measure whether learning changed behavior, rather than just which employees clicked through a module?

Those are the questions that connect L&D to business outcomes in a way that completion rates never did. The organizations making progress on the skills gap tend to be the ones where learning leaders have been given permission to rethink the operating model, and where AI is being used to close the gap between when knowledge is needed and when it actually arrives.

For Howson, the infrastructure changes matter, but so does the environment around them. She describes her goal for ServiceNow University in terms that go beyond output to making sure the learning experience itself feels like a place where people can take risks.

“We all can remember being a kid and feeling like we were safe,” she said. “This needs to feel like you’re safe to push yourself and not get it right the first time.”

That combination of learning that’s faster, more relevant, and psychologically safe is what separates the organizations closing the skills gap from the ones still trying to solve a 2026 problem with a 2016 model.

Jensen Huang’s comments at Computex defend the Samsung bonus structure that delivers $400,000 to chip engineers, but land alongside an $80bn Nvidia buyback announced two weeks ago.

Jensen Huang, the Nvidia chief executive, told reporters on the sidelines of Computex in Taipei on Tuesday that workers should be paid “as much as possible,” framing the principle as the response to a question about Samsung Electronics’ new bonus structure that delivers as much as $400,000 to memory chip engineers.

“I pay my employees as much as I can,” Jensen Huang said, before adding, “but it doesn’t make this right,” in an unusual public hedging from a CEO who almost never qualifies his own positions in real time.

The Samsung context is the trigger for the question. The Korean memory firm’s union and management reached a deal earlier this month, after a near-strike that survived an injunction filing from a smaller non-chip union, that allocates 10.5% of semiconductor operating profit to chip-division bonuses, with payouts of up to 600 million won (about $400,000) per memory-division worker contingent on sustained profit targets through 2035.

The arrangement was described in Reuters analysis as the largest single profit-share commitment in major Korean corporate history. Samsung supplies HBM4 to Nvidia for the Vera Rubin platform, which made the question to Huang structurally relevant rather than merely topical.

The harder context is the cash-return commitment Nvidia announced two weeks before Huang’s remarks. The company’s Q1 fiscal 2027 results, released on 18 May, included an $80bn share repurchase authorisation, a quarterly cash-dividend increase from $0.01 to $0.25 per share (a 2,400% lift), and a stated commitment to return at least 50% of free cash flow to shareholders through 2026 and beyond. The company returned a record $20bn to shareholders in the quarter alone.

Nvidia’s $81.6bn quarterly revenue and 85% year-on-year growth comfortably support the return profile, but the arithmetic relationship matters: the buyback alone is larger than Nvidia’s total annual payroll many times over.

The two positions Huang is now publicly holding, that workers should be paid as much as possible and that Nvidia should return half its free cash flow to shareholders, are not strictly in tension. Both can be true; the company has the cash to do both.

But the public framing matters at a moment when corporate AI productivity gains are increasingly accumulating to shareholders rather than to workforces, and when Morgan Stanley’s European-banking forecast last week doubled the projected AI-driven job-loss figure to 20%.

Huang’s instinctive defence of high worker pay, even with the “it doesn’t make this right” hedge, is the closest a major AI-infrastructure CEO has come to publicly acknowledging the labour-and-capital tension the AI build-out is producing.

The other Huang comment from this week’s Computex appearances is worth noting alongside. Huang told a separate audience that Nvidia engineers should be using AI tokens worth roughly half their annual salary every year to remain productive, framing non-use of AI tools as analogous to designing chips with pencil and paper.

That position is, on its own terms, a defence of generous worker compensation in token-purchasing rather than salary terms: if engineers are issued $100,000-$150,000 in annual token budget on top of base pay, the realised compensation package is materially larger than the published salary figure suggests.

It is also a framing that depends entirely on AI-token costs remaining at current pricing rather than continuing to rise on the trajectory Commonwealth Bank’s Matt Comyn flagged this week.

Nvidia employs roughly 36,000 people globally. Average compensation per employee, on the company’s most recent disclosures, runs to several hundred thousand dollars including stock-based compensation. The AI boom has driven Nvidia’s share price up roughly 1,170% over the past five years, which has made meaningful numbers of Nvidia employees who hold restricted-stock units into multi-millionaires through normal vesting cycles.

His remark therefore lands inside an Nvidia compensation reality that has, by the standards of large public technology companies, already been unusually worker-favourable.

Huang is travelling to Seoul this week to meet Samsung Electronics chairman Lee Jae-yong and other Korean industrial leaders. The bonus-structure question will likely come up again. The public position Huang has now taken is harder to walk back than the standard executive non-answer.