News
Artificial Intelligence (AI) is rapidly evolving. Across industries, many organizations are increasingly deploying AI into systems that must run continuously, securely, and at scale.
As AI adoption accelerates, one thing is becoming clear: infrastructure planning cannot wait.
AI workloads are becoming more interconnected, distributed, and operationally integrated across cloud, data center, and edge environments. Infrastructure planning now requires organizations to align compute, networking, software, memory, and operational requirements across increasingly complex environments.
As a result, many enterprises are beginning infrastructure planning sooner rather than later.
The cost of waitingAs AI becomes more integrated into everyday business operations through continuous inference and agentic AI systems, infrastructure demands are evolving significantly.
Modern AI deployments increasingly require:
- Continuous inference running around the clock
- Multi-agent systems coordinating across applications and databases
- Real-time orchestration across cloud, data center, and edge environments
- Strong governance, security, and operational efficiency
These workloads require more than raw compute performance. They require balanced infrastructure where compute, networking, software, memory, and operational workflows work cohesively at scale.
Because of this, enterprises are beginning AI infrastructure planning earlier, recognizing that planning, testing, and Proof of Concepts (PoCs) for complex systems like this take time.
At the same time, the cost of delaying AI infrastructure planning is becoming more apparent. Delays can slow deployment readiness and postpone AI-driven benefits such as productivity gains and operational automation. As AI demand continues to rise, organizations are prioritizing earlier planning to secure the compute capacity needed to support long-term AI growth.
As AI infrastructure becomes more complex, infrastructure planning needs to begin earlier than traditional IT upgrade cycles. Evaluating workloads, validating deployment models, and ensuring scalability across environments takes time and time is of the essence if we want to be ahead of our competitors.
AI is now a systems challengeThe conversation around AI infrastructure often begins with Graphics Processing Units (GPUs). But as deployments scale, AI performance depends not on individual components, but on how the entire system operates together.
Modern AI infrastructure relies on Central Processing Units (CPUs) for orchestration and data movement, GPUs for large-scale parallel compute, high-speed networking for low-latency communication across systems, and open software platforms for portability and scalability.
As AI systems become more distributed and inference-driven, orchestration and system balance become critical. CPUs play a pivotal role in managing workload coordination, memory access, and GPU utilization, ensuring infrastructure operates efficiently under sustained demand.
This shift reflects a broader industry reality: AI is no longer just a GPU problem. It is a full-stack infrastructure challenge that organization must tackle early on.
Planning for distributed AIAI is also scaling in multiple directions at once.
Some workloads are expanding into large, centralized clusters, while others are moving closer to where data is generated – including edge deployments such as in factories or hospitals, and AI-enabled endpoints like the PCs.
For organizations, this creates unique infrastructure considerations around hybrid cloud, on-premises deployments, edge AI, compliance, and latency-sensitive applications.
This diversity underscores the importance of infrastructure strategies designed for modularity, portability, and adaptability that necessitates upfront planning.
Openness and flexibility matter more than everAs AI innovation accelerates, organizations are prioritizing infrastructure flexibility to support rapidly evolving models, frameworks, and deployment environments.
Open ecosystems can reduce integration complexity while supporting broader compatibility across software frameworks, cloud environments, and deployment architectures. They also provide greater flexibility to evolve infrastructure strategies over time while helping avoid the migration costs that can come with highly closed or single-vendor environments.
For many organizations, openness is no longer just a developer preference. It is becoming an important consideration for balancing performance, operational efficiency, cost optimization, and long-term infrastructure investment.
This is another reason infrastructure planning must happen early. Building AI environments that remain scalable, portable, and adaptable over time require long-term thinking around openness and interoperability from the beginning.
Infrastructure readiness will define the next phase of AIThe next phase of AI growth will reward organizations that take a proactive approach to infrastructure planning.
Organizations that delay infrastructure planning may find it more challenging to deploy AI tools down the road, not only due to not having ample time to plan and test, but not securing the compute resources needed early on.
The cost of waiting is becoming ever clearer.
Ultimately, the companies that succeed in the next phase of AI will not necessarily be those with the largest clusters, but those that plan early and build balanced, scalable, and open infrastructure designed to support continuous innovation in an increasingly AI-driven economy.
We've featured the best AI chatbot for business.
This article was produced as part of TechRadar Pro Perspectives, our channel to 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/pro/perspectives-how-to-submit
Suno, the most popular AI music creation tool, spent years making it incredibly easy to generate music. Now it's introducing download limits, watermarking and fingerprinting to stop people flooding streaming services with AI slop.
That suggests something important to me: even the companies building generative AI are starting to realize unlimited AI creation comes with unintended consequences.
Suno CEO, Mikey Shulman, shared a blog post about the company's principles for building the future of music responsibly. He says “AI should help people create something new, not imitate someone else’s work. This philosophy has guided how we’ve built our models and platform from the start.”
(Image credit: Suno)Great music is made by peopleIn a section titled “Our Principles”, Shulman says that “great music is made by people”.
The principles themselves aren't new. What's new is that Suno is now dedicating significant engineering effort to limiting abuse rather than simply enabling creation.
In his blog post Shulman writes “We will soon introduce a new downloads policy designed to limit the ability to mass distribute songs on streaming platforms, while preserving the professional, creative, and personal ways people use Suno. These changes won’t affect the vast majority of our users, but they will make large-scale abuse much harder.”
Schulman continues: “In the coming weeks, we will also be adopting new audio watermarking and fingerprinting technology so we can partner even more closely with distribution platforms on combatting fraud and misuse.”
That evolution fits the broader trend we’ve been covering from the music streaming services like Spotify, Deezer, Tidal, and Qobuz, who have started to fight back against AI-generated music flooding their platforms.
Managing the consequencesStreaming giant Tidal has published a comprehensive AI policy with the strapline "Promoting Fairness and Economic Empowerment in the Era of AI-Generated Music". Tidal will identify it, tag it and crucially, not pay any streaming royalties for it.
Spotfiy has introduced measures to prevent fraudulent streams and AI impersonation . Deezer announced that over half of all new daily uploads to its site are AI — up from 44% in April and just over 30% at the end of last year. It launched a free site to scan your playlists for AI in June. Qobuz has announced that it is taking a human-first approach to its recommendations and “developing detection and monitoring systems to identify AI-generated content and fraudulent streaming patterns.”
This announcement from Suno feels more significant than another AI company publishing a set of principles. It marks a shift in priorities. For the first few years of generative AI, success was measured by how much content these systems could produce. Now it looks like success is being measured by how effectively companies can prevent that content from overwhelming everything else.
The AI music industry is moving from maximizing generation to managing the consequences of generation, and it's about time.
- Swiss government confirms attackers breached BIT’s SharePoint servers
- Investigators suspect exploitation of recent SharePoint flaws
- No sensitive or confidential data is believed to have been stored on the platform
Cybercriminals broke into the IT network of the Swiss government and stole data from roughly 200 accounts. As a result, the Swiss government disconnected some of its servers from the wider internet and launched an investigation.
In an announcement, the Swiss government said that on July 28 2026 its security specialists noticed “abnormalities” in the Federal Office for Information Technology and Telecommunication’s (BIT) SharePoint servers.
Three days later, on July 31, the investigators determined that the attackers accessed data found in around 200 accounts, both user and technical.
Two vulnerabilitiesThe investigation is currently ongoing, the agency said, adding that it is getting support from Microsoft, as well. So far, the identity of the attackers is unknown, and the stolen data has not yet leaked to the dark web.
“No confidential information or particularly sensitive personal data may be stored on the SharePoint platform,” the announcement reads.
While BIT has not yet determined the initial access vector, it suspects it to be one of two flaws in SharePoint that Microsoft fixed last month:
“In mid-July, Microsoft announced several vulnerabilities in SharePoint,” it says in the announcement. “After the publication of the corresponding security updates, the FOITT immediately started work on importing them into its own systems.”
“The cyberattack was carried out by previously unknown actors, which was presumably made possible by exploiting these vulnerabilities in the SharePoint software.” It did not say which vulnerabilities those are, but in its report, BleepingComputer says that it could be one of these two: CVE-2026-56164 (an actively exploited privilege escalation vulnerability), or CVE-2026-50522 (a critical remote code execution flaw later exploited to steal SharePoint machine keys and maintain access after servers were patched).
Given its popularity among businesses of all sizes, SharePoint is a major target for cybercriminals. So far, no threat actors claimed responsibility for the attack, or demanded any ransom in exchange for the stolen data.
Via BleepingComputer
- A new PlayStation 5 firmware update has been released for testing
- The update automatically enables PSSR 2.0 on PS5 Pro, even for games that support the previous version of PSSR
- This means better image quality for titles yet to receive the 2.0 upgrade
Sony has released a new PlayStation 5 firmware update for its beta program that automatically enables PSSR 2.0 (PlayStation Spectral Super Resolution) on PS5 Pro.
That's according to a new ResetEra post, which claims that emails have been sent to beta users allowing them to test out the upcoming features before they're released.
The most significant update is the addition of PSSR 2.0, the latest version of Sony's enhanced-performance technology, built for PS5 Pro. With this beta firmware patch, the 'Enhance PSSR Image Quality' option is automatically enabled by default, but can be toggled on or off in the settings menu.
As per the patch notes (thanks, PushSquare), when players enable this feature, enhanced PSSR is used "even for games that support the previous version of PSSR so you can enjoy a sharper and clearer visual experience."
PSSR 2.0 began rolling out in February, with Resident Evil Requiem becoming the first game to receive the update before a handful of other titles.
"The upgraded PSSR represents another step in our commitment to evolving the PS5 Pro experience," Sony said at the time. "Moving forward, most new PS5 Pro titles will launch with support for this enhanced PSSR, ensuring players continue to see improvements in image quality and performance."
Supported games include Silent Hill f, Monster Hunter Wilds, Dragon's Dogma 2, Dragon Age: The Veilguard, Control, Alan Wake 2, and more.
With the latest PS5 Pro beta update, players will be able to enjoy improved image quality for PSSR-supported games yet to be upgraded to PSSR 2.0.
Alongside this feature, Sony has added an option to turn off Bluetooth in certain countries or regions, in order to manage controller connections at esports venues and similar locations. You can check out the full patch notes below.
- We've turned on Enhance PSSR Image Quality as a default setting. When you update the system software, this setting automatically turns on. When you enable this feature, enhanced PSSR is used even for games that support the previous version of PSSR so you can enjoy a sharper and clearer visual experience.
- To turn off this setting, go to Settings > Screen and Video > Video Output > Enhance PSSR Image Quality.
- This feature is available only on PS5 Pro.
- We’ve changed the following features for PS5 consoles and PS5 Pro consoles in certain countries or regions.
- If you go to Settings > Accessories > General > Advance Settings and select Turn Off Bluetooth, you’re no longer able to put your console into rest mode. Additionally, HDMI link is no longer supported when you select Turn Off Bluetooth.
- The Turn Off Bluetooth function is for managing controller connections at esports venues and similar locations. Do not use this feature for any other purpose.
- We've improved system software performance and stability.
- Kit Connor has reportedly joined Marvel's new X-Men movie
- The Heartstopper star is said to be playing Scott Summers/ Cyclops
- He joins Sadie Sink and Samara Weaving as part of the hotly-anticipated MCU film
Marvel has reportedly found the star it wants to portray beloved X-Men member Scott Summers/ Cyclops in its cinematic universe.
Per Deadline, Heartstopper actor Kit Connor has been hired to play the mutant group's in-the-field leader. Typically, Marvel hasn't commented on the report, but Deadline is extremely reliable when it comes to casting updates. It stands to reason, then, that Connor is the latest star to land a role in the Marvel Cinematic Universe (MCU).
If true, Connor will be the third actor to sign on for the new X-Men movie. Sadie Sink, who was confirmed to be the MCU's version of Jean Grey in Spider-Man: Brand New Day, is all but locked in for a role in Marvel's mutant-starring flick. Meanwhile, Samara Weaving was reportedly cast as popular superhuman Emma Frost on July 31, with Deadline again being the first to break the news of her apparent involvement.
Little else is known about Marvel's X-Men film reboot. According to Variety, though, the comic giant has ambitions on releasing it as the first project of Marvel Phase 7. If true, the untitled flick will precede the Ryan Gosling-starring Ghost Rider and David Jonsson-led Black Panther III, both of which are primed to arrive in 2028.
An ever-evolving line-upSadie Sink should reprise her role as Jean Grey in Marvel's X-Men movie reboot (Image credit: Marvel Studios/Sony Pictures)Marvel may have locked in Sink, Weaving, and Connor for its forthcoming X-Men film, but fans are desperate to learn who else will feature alongside them.
Speculation has been rife about which other stars could appear, and you can catch up on the biggest rumors by reading my guide on everything we know so far about Marvel's new X-Men movie. Nonetheless, myriad new rumors have circulated online since news of Connor's involvement broke — and I wouldn't be surprised if there's some truth to all of them.
For starters, comic book creator Rob Liefeld took to X/ Twitter to write he'd heard that Inde Navarrette had officially signed on for the project. Liefeld's comments come weeks after the breakout star of Obsession confirmed she'd met with X-Men movie director Jake Schreier and, more recently, threw her hat in the ring to play Mystique.
Just gonna get it out of the way say I was also wrong about Pelphrey as Xavier. I heard a different name that’s not Skarsgard and that name is going around already so I’m sure it’ll leak soon but I’ll let it come out on its own. https://t.co/6bmdqgcD4yAugust 7, 2026
Elsewhere, trustworthy industry insider Apocalyptic Horseman has walked back on Tom Pelphrey (Task, Iron Fist) playing Charles Xavier/ Professor X, while another leaker in Caleb Williams has claimed that fan-favorite mutant Gambit won't be part of proceedings.
Meanwhile, on the latest edition of The Hot Mic podcast, the less-reliable Jeff Sneider has suggested Charles Melton (Beef season 2) is set to play Beast. Fellow podcast host John Rocha has also heard that Cailee Spaeny (Alien: Romulus, Beef season 2) has been tapped to portray Rogue, although Sneider believes she hasn't been.
Lastly, Sneider has indicated that Bill Skarsgard (It, Nosferatu), Ethan Hawke (Moon Knight, Blue Moon) and Jonathan Groff (Frozen, Wicked) are all in the running to play Professor X. Rocha, though, has heard Skarsgard isn't being looked at for that highly-coveted position.
As the X-Men casting rumor mill continues to capture everyone's attention, it might not be long until Marvel puts a pin in the speculation.
Indeed, with the 2026 edition of D23 Expo, aka the so-called ultimate fan event for Disney diehards that takes place every two years, being held between August 14 and 16, some fans believe Marvel will announce the film's full roster during said event. Let's hope that comes to pass because, I don't know about you, but I can't take many more months' of conjecture.
Want to see how the latest Spider-Man film tentatively sets up the next X-Men movie? Read my Spider-Man: Brand New Day ending explained piece. Once you're done, find out how to watch the X-Men movies in order.
Apple unveiled the very first Mac Pro 20 years ago today, dubbing the computer “the ideal system for the most demanding user.” It was the last Mac to make the transition to Intel processors and put the final nail in the coffin of its PowerPC predecessor — quite a statement at the time.
In fact, the Mac Pro was so powerful that it doubled the performance of the Power Mac G5 that had launched less than a year earlier. Talk about earning that “Pro” moniker.
Many different iterations followed, but the one constant was the positioning. The Mac Pro was always the Mac you went for if money was no object but power was an imperative. It was, seemingly, a computer for the ages.
Or not, as it turned out. In March 2026, Apple officially discontinued the Mac Pro and said it had no plans for any future models. It was an ignominious end to the most powerful Mac Apple has ever made.
The very nature of the Mac Pro had always made it vulnerable. But that vulnerability proved to be fatal when combined with market conditions that even a giant like Apple is struggling to deal with today. When we look back at why the Mac Pro (just) failed to make it to 20 years old, there’s one very obvious culprit.
A dangerously niche product(Image credit: Joseph Greve / Unsplash)Right from the off, the Mac Pro was designed to be used by people in challenging professions who valued performance above all else. It was made to complement the eye-watering powers of the best Intel chips of the day, which offered far more in the way of output than the PowerPC processors Apple had previously been using.
That meant that the Mac Pro was always a niche product that flaunted its raw power. That was the point of the machine, and although the general public liked to poke fun at its sky-high prices (remember the $400 wheels on the 2019 model?), its intended audience didn’t much care. When your work consists of high-grade film productions, artificial intelligence (AI) data crunching or similarly demanding pursuits, high prices are just the cost of doing business.
But that niche nature meant the Mac Pro was never going to sell on the scale of the MacBook Air or the MacBook Pro. Apple was always taking a calculated risk, determining that it was worth going after a small but loyal audience who craved the Mac Pro’s power and reliability — and would continue to do so over the years.
The problem, though, was that Apple often seemed to take these users for granted. The cylindrical model introduced in 2013 — nicknamed the “trash can” Mac Pro for its tubular shape — went years without an update because, Apple later admitted, the company had “designed ourselves into a bit of a thermal corner”.
Amazingly, the 2013 model wasn’t updated until six years later — a veritable lifetime in computing terms — when the final design iteration of the Mac Pro finally touched down.
The introduction of the Mac Studio also caused a massive problem for the Mac Pro. Its performance was just as good as the Mac Pro, if not better, yet it came in a smaller package that cost far less. The choice for many people was clear.
With the transition to Apple silicon, add-in extras like the Apple Afterburner Card were no longer feasible, meaning the Mac Pro’s only real advantage was its inclusion of a few expansion options like PCIe. But when you’re paying a minimum of $6,999, those returns make less and less sense when compared with a Mac Studio.
Apple had long walked the fine line between infrequent updates and outright abandonment, but still it kept chugging away at the Mac Pro, even after the Mac Studio’s arrival. Instead, it was an external force that finally killed off the Mac Pro — and forced a complete rethink from Apple.
Buried in the dark(Image credit: Apple)On March 26, 2026, news emerged that Apple had discontinued the Mac Pro. The company explained to 9to5Mac that it also had “no plans to offer future Mac Pro hardware,” seemingly sounding the computer’s death knell for good.
The timing might have seemed strange, and it certainly seemed to come out of the blue. But with the benefit of hindsight, we can gain a pretty good idea of what happened and why Apple made the move when it did.
That’s because just three months later, Apple announced bombshell price rises that affected nearly every device it sold.
The Mac range was badly hit, with high-end devices like the Mac Studio coming in for particularly stinging increases. That device saw the single highest price jump by far, rocketing up by a painful $1,300 for the M3 Ultra model. The MacBook Pro, meanwhile, rose $300, the second-highest dollar increase of any Apple product.
These price adjustments weren’t made on a whim. No, they were prompted by the widespread component crisis that’s currently ravaging the entire computing industry.
Parts are getting more expensive by the day thanks to the overwhelming demand from AI data centers. Things are so bad that Apple CEO Tim Cook spoke out, calling the situation “unsustainable” and describing his company’s price increases as “unavoidable”.
(Image credit: Far Chinberdiev / Unsplash)When you see how badly pro users were affected by these changes, Apple’s decision to ditch the Mac Pro starts to make a lot more sense. If the Mac Studio now costs $1,300 more than it did previously — a figure that ballooned by almost 33% — what kind of wince-inducing costs would Mac Pro users be faced with had Apple continued to sell the device?
Going with that same 33% jump, we could have been looking at a Mac Pro that started at around $9,299. That’s a roughly 33% step up over the Mac Pro’s old entry price of $6,999. And we’re not even talking about any upgrades here — no beefed-up CPU or additional storage, not even any ultra-premium wheels. Just the base-level Mac Pro and nothing more.
When you think about it like that, it’s obvious why the Mac Pro was killed off just shy of its 20th anniversary. If Apple waited for the computer’s birthday, it would have been forced to escalate its price to frankly outrageous heights alongside all the other affected products.
And in the cut-throat world of business, that would have been a no-go considering the prices we’re talking about here. The Mac Pro was already selling in vanishingly small numbers. Boost its price by 33% — or even 20% — and its sales would have fallen off a cliff. The only thing a move like that would have generated is a torrent of bad press for very little positive return.
So in the end, Apple didn’t afford the Mac Pro a sentimental 20th birthday celebration. Given the ongoing memory crisis and its wide-ranging effects on the entire computing industry, there was no room to stand on ceremony. The Mac Pro was buried in the dark — before its price could become even more contentious.
Recruitment has long relied on signals that serve as proxies for candidate skillsets: degrees, previous job titles, years of experience, and employer names. Those indicators don’t always show whether someone actually has the skills needed for the role they’re applying for. As AI reshapes work, businesses need clearer evidence of what people can do.
This imperative has led to a drastic shift in hiring norms In the UK, 99 per cent of employers are using skills-based hiring in some capacity, according to research. At the same time, 58 per cent expect more than a third of core job skills to change by 2030. Recruiters now need to hire for current requirements while also thinking about how roles are likely to evolve.
For hiring teams, five changes stand out.
1. Hiring is becoming more skills-firstAI is accelerating the move away from degree-first evaluation. When tools, business needs, and ways of working change quickly, employers can’t just rely on static indicators of academic achievement. They need to know whether candidates have practical, current and job-relevant skills.
Degrees still retain significant signaling value. A degree offers an essential foundation, particularly for critical thinking, communication and domain knowledge. But employers increasingly want additional proof that a candidate can apply those strengths in workplace settings.
For recruiters, job descriptions and selection criteria need to become more skill-oriented. Instead of asking for broad experience in a field, businesses should define the specific capabilities needed for the role: data analysis, AI literacy, cloud computing, cybersecurity awareness, project management, or the ability to interpret AI-generated outputs. A clearer skills profile can also help organizations identify strong candidates who have not followed traditional pathways.
2. AI credentials are changing how experience is weightedExperience still carries weight, but AI is changing how that experience is judged. In fast-moving areas such as generative AI, data, and cloud, a candidate with recent, verified learning may be better prepared than someone with greater experience but outdated skills.
That’s why 42 per cent of UK employers say they would choose a less experienced candidate with a GenAI credential over a more experienced candidate without one. It shows how quickly the value of verified AI capability is rising.
This has practical implications for recruiters. Years of experience shouldn’t be treated as a signifier of readiness. In some roles, recent evidence of applied learning may be more useful. The challenge is to distinguish between candidates who’ve completed primarily theoretical training and those who can show they’re ready to apply what they’ve learned.
3. Verification is becoming more importantAI has made it easier for candidates to produce polished CVs, cover letters and portfolios. It has also made it harder for employers to know which evidence to trust.
This is likely to increase the value of credentials that verify skills to employers. In the UK, 95 per cent of employers say micro-credentials help identify candidates with real-world, applied expertise in areas such as AI, data, and cloud. That matters because recruiters need evidence that can stand up to scrutiny.
The most useful credentials are those that assess applied skills, rather than only content completion. Employers will increasingly look for evidence that a candidate has built something, solved a practical problem or completed a project relevant to workplace needs.
Recruitment teams should reflect this in their processes. CV screening should be supported by practical assessments, structured interviews, and work-sample tasks. This blended approach will make decisions more accurate.
4. Recruiters need to assess human judgement alongside AI skillsAI literacy is becoming a common requirement, but it is insufficient to simply possess technical knowledge, particularly for those deploying AI in non-technical roles. Businesses need people who can work effectively with AI, including knowing when to question it.
As AI becomes embedded in everyday work, candidates will need to show that they can evaluate outputs, check sources, spot weak reasoning, and apply context. A candidate who accepts AI-generated content uncritically will introduce risk, irrespective of technical proficiency.
This adds novel new stages to the assessment process. Recruiters may need to ask candidates to critique an AI-generated response, improve a flawed analysis, or explain what further evidence they’d need before making a decision. These exercises can reveal whether someone has the judgement and domain expertise required to use AI tools responsibly.
For many roles, the differentiator won’t be whether a candidate can prompt a system: it’ll be whether they can turn AI output into sound business action.
5. Skills-first hiring must complement skills-first trainingAI is compressing the shelf-life of skills. If, as expected, over a third of core skills will change by 2030 for many UK employers, hiring alone will not be enough to keep pace. In fact, 74 per cent of tech leaders acknowledge they cannot depend on new hires alone to fill AI skills gaps. That means skills-first hiring needs to become part of a wider talent development framework.
The same approach that helps recruiters identify the capabilities needed for a role can also help businesses map the skills they already have, spot gaps across the workforce, and create clearer routes for employees to build the capabilities the organization will need next.
Some capabilities will still need to be brought in from outside the organization, particularly in fast-moving areas such as AI, data and cloud. But many skills will also need to be developed internally as roles evolve. The strongest businesses will combine skills-based hiring with ongoing upskilling, giving employees clear routes to adapt as roles change.
The new recruitment advantageAI is changing what employers need to know about candidates. Businesses that can identify real capability, verify applied skills, and recognize potential beyond traditional signals will be better placed to hire well. The value of this approach is already visible in workplace outcomes. 92 per cent of employers say entry-level hires with micro-credentials perform better in their first year on the job.
For candidates, the message is just as clear. In an AI-enabled labor market, employability will depend less on what someone once learned and more on what they can prove they can do now, and the evidence they’re still learning.
For employers, recruitment needs to become part of a broader skills strategy. AI may be changing the work, but the hiring challenge remains human: finding people with the skills, judgement and adaptability to help businesses compete and innovate.
We've featured the best website for hiring niche employees.
This article was produced as part of TechRadar Pro Perspectives, our channel to 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/pro/perspectives-how-to-submit