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.

The UK government will offer to buy AI chips directly from British technology companies in a bid to keep them in the country. Technology Secretary Liz Kendall will outline plans for “strategic purchases” of semiconductor equipment from UK-based firms at London Tech Week this week, the Telegraph reported. The initiative includes access to taxpayer-backed funding and investment in skills to retain workforces in Britain.

The announcement is part of a broader AI hardware plan that targets 5% of the global chip market, which would translate to roughly £37 billion in revenue and tens of thousands of jobs. The government has already committed £100 million through ARIA’s scaling compute programme, including £50 million for a scaling inference lab where British startups can test and demonstrate that their hardware works.

The urgency is clear. Britain keeps losing its best chip companies to foreign buyers. SoftBank acquired Graphcore in 2024. Qualcomm bought Alphawave IP for $2.4 billion last year. Arm, the UK’s most valuable chip designer, chose New York for its main listing in 2023. Each departure weakened the case that Britain can retain a semiconductor industry of its own.

“This is far too important a technology to depend entirely on other countries, especially in areas like defence, financial services and health care,” Kendall said in a speech at Bloomberg in January, when she announced £1 billion in funding to expand the UK’s AI research compute capacity by 20-fold.

The chip purchases would make the government a customer, not just a regulator, giving British firms guaranteed demand. Six UK companies have already gained access to government-funded supercomputers to advance their AI models, with the government retaining a right of first refusal for future investments. Fractile, a British inference chip startup that recently raised $220 million and is reportedly in talks with Anthropic, is among the firms the strategy aims to support.

The plans also respond to concerns about foreign dependency in government procurement. A recent parliamentary report warned that US firm Palantir should not play such a significant role in the UK public sector, and flagged a growing reliance on Microsoft and AWS. The HMRC’s £175 million AI contract with London-based Quantexa was an early signal of the government’s preference for domestic providers.

Whether strategic purchases alone can prevent the next Graphcore from being sold abroad remains an open question. Britain has the engineering talent and the research base. What it has lacked is the domestic demand and patient capital to keep companies scaling at home instead of selling to SoftBank or Qualcomm at the first serious offer.

Instagram has a well-documented problem with how it treats teenage users, and Meta is finally taking a more concrete step to address it. The company announced it is testing a new feature on Instagram designed to stop teens from being repeatedly served the same type of content in Explore, Feed, and Reels.

The announcement is part of a broader global rollout of tightened teen account protections across Instagram, Facebook, and Messenger.

The evidence against Instagram’s algorithm has been building for years

Researchers set up fake teen profiles on Instagram and found that liking just one fitness-related post was enough to completely change what the algorithm recommended next. The Explore tab quickly filled up with weight-loss tips, extreme dieting content, and images of heavily edited body types. The pattern repeated across profiles mimicking teenagers of different ages and genders.

According to Harvard researchers, Instagram’s algorithm actively draws vulnerable teens into a damaging cycle of negative social comparison, worsening body image, anxiety, depression, and eating disorders.

Most damningly, Facebook’s own internal research found that 32% of teen girls said Instagram made them feel worse about their bodies when they were already struggling. The platform knew this and kept going.

What Meta is doing to protect teens from harmful content

Meta acknowledges that content around nutrition, fitness, or coping with anxiety can be useful, but argues it should be balanced rather than served on repeat. The new feature being tested is specifically aimed at breaking that loop.

Separately, the 13+ content setting, first launched last October, is now expanding globally across Instagram, Facebook, and Messenger. Nine out of ten teens have stayed within that setting since launch.

An independent assessment by online safety firm Alice found that teens in the default 13+ setting saw 68% less mature content than on a leading competitor’s teen experience. Those in the stricter Limited Content setting saw 96% less.

Meta also crowdsourced feedback from hundreds of thousands of parents who rated over 15 million pieces of content. In a survey at the end of April, fewer than 2% of posts were flagged as inappropriate by most parents. The stricter Limited Content setting is also coming to Facebook and Messenger later this year.