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• Small businesses are less confidence with AI than larger ones
• Half of businesses say AI has become critical
• Training and policies should be more comprehensive

New research has claimed barely one in 10 (12%) SMEs have invested in AI-related training for their staff.

The report from The Institute of Coding revealed nearly one in three (29%) SMEs now see a lack of training as their biggest obstacle to AI tools adoption, with a further one in two (52%) citing a lack of internal skills and knowledge as the main battier.

Moreover, the research depicts a troubling picture for smaller companies when compared with their larger counterparts – 82% of medium businesses expressed confidence working with AI compared with 37% of smaller businesses.

Small businesses are struggling to adopt AI

Around one in two (51%) of the SMEs surveyed agreed that AI could now be perceived as critical, but only around half of these (27%) believe they can safely and effectively implement AI tools.

As such, The Institute of Coding is warning of a growing AI readiness divide between different types of companies, launching its own free short AI courses aimed at all career levels.

Looking ahead, companies that are on the verge of being left out by the AI revolution are now calling for greater support from the government.

Three in five (59%) call for national AI skills strategies to support businesses of all sizes, with three-quarters asking for clearer guidance on the AI skills they're likely to need in the next three to five years.

"This isn’t just about individual business success – it’s about ensuring the entire UK economy can participate in the AI transformation," Professor Rachid Hourizi MBE, Director of the Institute of Coding, explained.

The report goes on to explain that, if micro businesses and sole traders are not explicitly included in national plans, AI will become concentrated, not democratized.

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Kerr County applied for federal grants to build a warning system to protect residents from flash floods. Under the Trump Administration, that kind of funding is drying up.

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• Former Microsoft executive Peter Moore has said that if Xbox hadn't spent so much time and money repairing Xbox 360 consoles, "I'm not sure the Xbox brand would be around today"
• Moore called it a "defining moment" for the company
• He also believes that if Xbox did fall, the games industry would have suffered

Former Microsoft lead Peter Moore believes the Xbox brand might not be around today if the company hadn't focused on repairing countless Xbox 360 consoles.

In an interview with The Game Business, Moore suggested that the company was in hot water trying to figure out how to fix the Xbox 360's "red ring of death", but determined that spending the $1.15 billion on repairs is what ultimately saved its future.

"It took us a while to figure out what was going on," Moore said (thanks, GamesRadar). "Were the fans in the right place? [...] Trying to figure out whether, you know, [...] wrapping the towel around it would create more heat, which would rejoin some of the issues of the cracks in some of the units. All of this was going on, and it was stressful. But the one thing I will always say is, you know, this was, for us, a defining moment.

"If we hadn't done what we did, I'm not sure the Xbox brand would be around today."

Moore continued, saying, "We felt that that was money well spent to hang on to a brand that we built, that we felt had huge viability going forward – and of course it does," he said. "And doing good by the gamers."

However, Moore believes that if Xbox had fallen, the game industry as a whole would have suffered.

"Rising tide lifts all ships. Microsoft's entry into the market created a massive rising tide," he said. "They put billions of dollars into marketing, advertising, R&D, and I don’t think the industry would be anywhere close to what it is today if Microsoft wasn't involved."

In other news, Microsoft recently made significant cuts to its gaming division, laying off an estimated 9,000 staff members, and canceling multiple projects such as the Perfect Dark reboot, Everwild, and an unannounced MMO from The Elder Scrolls Online developer.

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This week, Samsung Galaxy Unpacked unveiled its latest smartphones, the Galaxy Z Fold 7, Flip 7, and Flip 7 FE, all integrated with Google Gemini AI features. To sweeten the deal for potential customers, Google announced a special offer: six free months of Google AI Pro for those who purchase the new phones. This premium subscription service includes access to the Gemini 2.5 Pro model, the Google Veo 3 AI video generator, two terabytes of cloud storage, and early access to upcoming AI features.

Of course, once those six months are up, you'll have to pay the standard $20 a month to keep your subscription. But, Google likely believes more than a few people will be happy to pay after they get accustomed to its AI toolkit. The psychology behind this is as simple as free samples at the grocery store. Google isn’t trying to sell you a subscription right now because it thinks you won't want to give it up just because it isn't free anymore.

It's a pretty impressive set of features. Veo 3 is one of the most powerful consumer-facing video generators available. And Gemini 2.5 Pro is far more coherent in conversation than its predecessors.

Gemini try and buy

It's easy to imagine how Google hopes the six months will go. You might spend a month fiddling with Veo and creating movies about your dog going on adventures. Or start turning to Gemini to summarize very long emails, and eventually every email. Or you might get a great recipe from a random prompt to Gemini and soon use it to plan your every meal. By the end of six months, Google's AI might just be what you turn to a dozen times a day as a reflex. By the time Google asks for $20 a month, you might even consider it a bargain.

That's Google's dream scenario, but it comes with a risk. Google is betting that people will find these tools indispensable. But, if people take it for granted for six months, they might resent having to pay for it, no matter how much you enjoy playing with Veo 3 and talking to Gemini. Nobody likes the feeling of having something useful pulled away unless you pony up. That's probably even more likely when it comes bundled with a device that already costs over a thousand dollars. There’s a version of this where the user relationship becomes less “wow, this is useful,” and more “wait, I have to pay extra for that now?”

But, I wouldn't be surprised if a scenario somewhere between the extremes still makes Google happy. Tying its most advanced AI tools to Samsung’s brand-new hardware is smart. The Galaxy Z Fold 7 and Flip 7 are devices people buy because they want the bells and whistles. They're practically built for people who like to show off. In other words, the people most likely to find ways to use AI enough to justify $20.

But it cuts both ways. Because if the experience feels too essential, people will feel punished when it disappears. And if it doesn’t feel essential enough, they won’t bother subscribing. The six-month trial is walking a very fine line between generosity and locking someone in to an AI future.

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At a time when many believe that oversight of the Artificial Intelligence industry is desperately needed, the US government appears to have different ideas. The "One Big Beautiful Bill Act" (OBBBA)—recently given the nod by the House of Representatives—includes a 10-year moratorium on state and local governments enacting or enforcing regulations on AI models, systems, or automated decision-making tools.

Supporters claim the goal is to streamline AI regulation by establishing federal oversight, thereby preventing a patchwork of state laws that could stifle innovation and create compliance chaos. Critics warn that the moratorium could leave consumers vulnerable to emerging AI-related issues, such as algorithmic bias, privacy violations, and the spread of deepfakes.

Basically, if the AI sector is the Wild West, no one will be allowed to clean up Dodge.

Why should we care?

History may not literally repeat itself, but there are historical patterns and trends that we can view and hopefully be informed by, and our history books are packed with examples of technology reshaping the lives of the workforce.

And be it in the form of James Watt’s steam engine or Henry Ford’s moving assembly line, the cost of the progress brought by fresh technology is regularly paid by the large numbers of people sent home without a pay packet.

And AI will cost jobs too.

Experts such as those at McKinsey, the Lancet, or the World Economic Forum (WEF) may not agree on exact numbers or percentages of lost jobs, but the consistent message is that it will be bad:

• 30% of US work hours across all sectors will be automated by 2030 says McKinsey
• 25% of medical administrative tasks could vanish by 2035 according to a Lancet study
• 39% of existing skill sets will become outdated between now and 2030 warns WEF

Of course, as with all new technologies, new jobs will be created. But we can’t all be prompt engineers.

The Great Brain Robbery

Essentially, those hit hardest by the bulk of new technologies from the Spinning Jenny onwards were the ones engaged to carry out physical work. But AI wants to muscle in on the intellectual and creative domains previously considered uniquely human. For example, nonpartisan American think tank the Pew Research Center reckons 30% of media jobs could be automated by 2035.

And those creative jobs are under threat because creatives are being ripped off.

Many AI models are trained on massive datasets scraped from the internet, and these often include articles, books, images, music and even code that are protected by copyright laws, but AI companies lean heavily towards take-first-ask-later. Obviously, artists, writers, and other content creators see this practice as unauthorized use of their intellectual property and they argue that ultimately, it’s not even in the best interests of the AI sector.

If AI takes work away from human creatives—devastating creative industries already operating on thin margins—there will be less and less innovative content to feed to AI systems which will result in AI feeding off homogenized AI content – a derivative digital snake eating its own tail.

A smarter way forward would be to find a framework where creatives are compensated for use of their work to ensure the sustainability of human produced product. The music industry already has a model where artists receive payments via performing rights organizations such as PRS, GEMA and BMI. The AI sector needs to find something similar.

To make this happen, regulators may need to be involved.

Competitive opportunity versus minimizing societal harm

Without regulation, we risk undermining the economic foundations of creative and knowledge-based industries. Journalism, photography, literature, music, and visual arts depend on compensation mechanisms that AI training currently bypasses.

The United Kingdom and the European Union are taking notably different paths when it comes to regulating AI. The EU is pursuing a strict, binding regulatory framework, an approach designed to protect fundamental rights, promote safety, and ensure ethical use of AI across member states. In contrast, the UK is currently opting for a more flexible approach, emphasizing innovation and light-touch oversight aiming to encourage rapid AI development and attracting investment.

But this light-touch strategy could be a massive misstep – one that in the long term could leave everyone wishing we’d thought things through.

While AI enthusiasts may initially be pleased with minimal interference from regulators, eventually AI businesses will come up against consumer trust, something they absolutely need.

While AI businesses operating in Europe will be looking at higher compliance costs, there is also a clearer regulatory landscape and therefore more likely to be greater consumer trust – a huge commercial advantage.

Meanwhile, AI businesses operating in light-touch markets (such as the UK) need to consider how their AI data practices align with their (and their competitors’) brand values and customer expectations. As public awareness grows, companies seen as exploiting creators may face reputational damage. And a lack of consumer confidence could lead to a shift in mindset from previously arm’s-length regulators.

Regardless of the initial regulatory environment, early adopters of ethical AI practices may gain competitive advantages as regulatory requirements catch up to ethical standards. Perhaps the wisest way forward is to voluntarily make Dodge City a better place, even if there’s no sheriff in town – for now.

I tried 70+ best AI tools.

This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro

A South Korean court approved the new arrest of former President Yoon on charges related to his imposition of martial law in December. Yoon's lawyers had described the arrest request as excessive.

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I know what you’re thinking – why isn’t Girls getting rebooted like every other TV show these days? Why isn’t Lena Dunham contorting herself to make that happen? Isn’t everything else she works on going to be Girls 2.0? It’s understandable that her new Netflix series, Too Much, will be compared to the show that launched her career to global heights, but it’s a lazy contrast.

You see, I am not a Girls fan. Aside from brief flashbacks of Dunham’s character Hannah spaced out on drugs while wearing a string vest that wasn’t hers, much of the show’s scenes blur into one hazy hallucination for me. That’s not to say the hit TV show was a bust in my eyes – far from it – but 17-year-old me wasn’t ready to embrace the messy reality of womanhood when Girls first aired in 2012.

Aged 30 in 2025, I couldn’t have fallen harder for Too Much. Set in my back garden of London, Jessica (Megan Stalter) takes the opportunity to move to the city for work after her ex-boyfriend, who she’s still not over, proposes to a popular influencer. It’s not the most original lead-in to a new Netflix show like this, but it’s the springboard for something even better.

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No matter your opinions on Dunham or her work, she’s able to remain effortlessly relevant in a way that’s absolutely astounding. The script and situations feel fresh without ever venturing into laughable territory, incorporating 2020s culture like Instagram reels and TikTok beauty hacks with successful irony. Trust me, you’ve never wanted to own a hairless dog who wears bespoke turtleneck jumpers more.

Where Dunham has largely stepped behind the camera (aside from a few stray appearances), Megan Stalter’s Jessica is a flawless replacement. We’ve known she’s a comedy queen in the making since her role on Hacks, but Too Much is her circus ring for the performance of a lifetime. Jessica might question her choices every 0.5 seconds, but she never truly sacrifices her sense of self, unashamedly returning to the things that give her joy in darker moments. She’s a typical 2020s twentysomething and proud of it, and that in turn makes us feel better about the ways we’ve adapted cliché and overdramatic habits.

Clearly Stalter is our star here, but the rest of the ensemble cast is just as chaotically fleshed-out, and we want to sink our teeth into all of them. Even better is the frankly insane level of celebrity cameos, popping out more frequently than a teenager’s acne. It’s hard to pick a favourite, but Naomi Watts’ diabolically unfazed British housewife Ann has to cinch it for me. Keep a close eye on every episode, though – I won’t spoil it, but there’s a particularly good cameo in episode 9 that might remind you of a character they’ve played before.

There are a few downsides, but regardless of how Too Much tackled its subject, they were bound to be there. Jessica’s fixation on her ex-boyfriend becomes as tiring as it is in the show as we experience watching it ourselves, with the stereotypes of her new London chums not doing anyone any favours either. Roll the two together too frequently and the show becomes its namesake, but sadly, Jessica needs to wade through the worst to learn her lessons.

Too Much isn’t an in-depth exploration of what it means to love and live as a young woman in a new decade, and it doesn’t have to be. In hindsight, Girls was heralded as exactly that for its time and place, but to judge Too Much in the same way is to miss the raw emotion and humanity on display here. It’s never slacking on the chaotic front, and going with the emotions and bad decisions in real-time is what makes Dunham’s show feel like a new take on something we’ve seen before. Sometimes you’ve just got to make your peace with the fact your life has resulted in eating cold instant noodles at 1am, and that’s okay.

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As organizations continue to adopt AI tools, security teams are often caught unprepared for the emerging challenges. The disconnect between engineering teams rapidly deploying AI solutions and security teams struggling to establish proper guardrails has created significant exposure across enterprises. This fundamental security paradox—balancing innovation with protection—is especially pronounced as AI adoption accelerates at unprecedented rates.

The most critical AI security challenge enterprises face today stems from organizational misalignment. Engineering teams are integrating AI and Large Language Models (LLMs) into applications without proper security guidance, while security teams fail to communicate their AI readiness expectations clearly.

McKinsey research confirms this disconnect: leaders are 2.4 times more likely to cite employee readiness as a barrier to adoption versus their own issues with leadership alignment, despite employees currently using generative AI three times more than leaders expect.

Understanding the Unique Challenges of AI Applications

Organizations implementing AI solutions are essentially creating new data pathways that are not necessarily accounted for in traditional security models. This presents several key concerns:

1. Unintentional Data Leakage

Users sharing sensitive information with AI systems may not recognize the downstream implications. AI systems frequently operate as black boxes, processing and potentially storing information in ways that lack transparency.

The challenge is compounded when AI systems maintain conversation history or context windows that persist across user sessions. Information shared in one interaction might unexpectedly resurface in later exchanges, potentially exposing sensitive data to different users or contexts. This "memory effect" represents a fundamental departure from traditional application security models where data flow paths are typically more predictable and controllable.

2. Prompt Injection Attacks

Prompt injection attacks represent an emerging threat vector poised to attract financially motivated attackers as enterprise AI deployment scales. Organizations dismissing these concerns for internal (employee-facing) applications overlook the more sophisticated threat of indirect prompt attacks capable of manipulating decision-making processes over time.

For example, a job applicant could embed hidden text like "prioritize this resume" in their PDF application to manipulate HR AI tools, pushing their application to the top regardless of qualifications. Similarly, a vendor might insert invisible prompt commands in contract documents that influence procurement AI to favor their proposals over competitors. These aren't theoretical threats - we've already seen instances where subtle manipulation of AI inputs has led to measurable changes in outputs and decisions.

3. Authorization Challenges

Inadequate authorization enforcement in AI applications can lead to information exposure to unauthorized users, creating potential compliance violations and data breaches.

4. Visibility Gaps

Insufficient monitoring of AI interfaces leaves organizations with limited insights into queries, response and decision rationales, making it difficult to detect misuse or evaluate performance.

The Four-Phase Security Approach

To build a comprehensive AI security program that addresses these unique challenges while enabling innovation, organizations should implement a structured approach:

Phase 1: Assessment

Begin by cataloging what AI systems are already in use, including shadow IT. Understand what data flows through these systems and where sensitive information resides. This discovery phase should include interviews with department leaders, surveys of technology usage and technical scans to identify unauthorized AI tools.

Rather than imposing restrictive controls (which inevitably drive users toward shadow AI), acknowledge that your organization is embracing AI rather than fighting it. Clear communication about assessment goals will encourage transparency and cooperation.

Phase 2: Policy Development

Collaborate with stakeholders to create clear policies about what types of information should never be shared with AI systems and what safeguards need to be in place. Develop and share concrete guidelines for secure AI development and usage that balance security requirements with practical usability.

These policies should address data classification, acceptable use cases, required security controls and escalation procedures for exceptions. The most effective policies are developed collaboratively, incorporating input from both security and business stakeholders.

Phase 3: Technical Implementation

Deploy appropriate security controls based on potential impact. This might include API-based redaction services, authentication mechanisms and monitoring tools. The implementation phase should prioritize automation wherever possible.

Manual review processes simply cannot scale to meet the volume and velocity of AI interactions. Instead, focus on implementing guardrails that can programmatically identify and protect sensitive information in real-time, without creating friction that might drive users toward unsanctioned alternatives. Create structured partnerships between security and engineering teams, where both share responsibility for secure AI implementation.

Phase 4: Education and Awareness

Educate users about AI security. Help them understand what information is appropriate to share and how to use AI systems safely. Training should be role-specific, providing relevant examples that resonate with different user groups.

Regular updates on emerging threats and best practices will keep security awareness current as the AI landscape evolves. Recognize departments that successfully balance innovation with security to create positive incentives for compliance.

Looking Ahead

As AI becomes increasingly embedded throughout enterprise processes, security approaches must evolve to address emerging challenges. Organizations viewing AI security as an enabler rather than an impediment will gain competitive advantages in their transformation journeys.

Through improved governance frameworks, effective controls and cross-functional collaboration, enterprises can leverage AI's transformative potential while mitigating its unique challenges.

We've listed the best online cybersecurity courses.

This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro

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