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Once upon a time there was an amazing little voice assistant that ended up being exclusively available in Apple products. It was called Siri and it was ahead of its time.

Because Siri seemed pretty magical when it first hit the iPhone; it would answer requests, find out information, and even do useful things such as taking photographs or naming songs you heard on the radio.

Available in numerous languages and with a range of male and female speaking voices, Siri remains the most widely distributed on-device chatbot in terms of language support. The assistant also scaled well, eventually appearing across Apple’s product lines. But critics and competitors now agree, Siri was too ambitious and failed to keep up with the times.

Back to the future

Cast your mind back to 2010 when Siri first appeared for iPhone. At that time, research into artificial intelligence (AI) and autonomous technologies, ongoing since the launch of the Stanford Artificial Intelligence Lab (SAIL) in the 60’s, really accelerated. Apple led the charge, at least in terms of media profile, and Siri (which the company acquired soon after its introduction) was a leading-edge challenger in the nascent field

What was great about Siri was its fluffy, friendly image. 

Being an Apple product gave the solution access to a huge market of engaged and happy consumers willing to overcome their general concern at the dystopian application of AI to give the friendly little assistant a try. 

Building acceptance one error at a time

Arguably, Apple’s little assistant helped drive acceptance of technologies that have become critical to today’s cutting edge generative AI (genAI) systems, including:

Speech recognition

The idea of intelligent machines

Devices equipped to listen for your commands 24/7

Fast and real-time access to information on spoken request

Andr even real-time transcription.

Siri’s well-publicized errors actually helped build acceptance. After all, if you think about it, the fact that Siri sometimes made mistakes somehow helped humanize it. It is better to think that AI is stupid than to see it as threateningly smart.

This helped a skeptical public come to terms with AI, even while Apple’s assistant embodied a range of concepts people resisted. The logic was that it couldn’t be too bad if machines had this kind of intelligence built inside, right? It’s not as if they are smart enough to fully understand. Did it really matter if the tech listened to you when you thought it was switched off? 

What use would the information picked up be? (The answer: around 81% of UK consumers now claim to have experienced targeted consumer advertising generated by AI.)

Trust in me

We’ve had many debates on these topics since then — debates that show Apple’s commitment to user privacy in AI to be unique, and under attack from competitors and authoritarians alike. It’s almost as if, when some leaders heard Apple CEO Tim Cook warn this is surveillance, they chose to exploit it as an opportunity, rather than protect against it.

All the same, Siri helped people become more capable of placing trust in AI. 

Years later, OpenAI was introduced to a public already more accepting of such tech. That acceptance was to some extent built on the back of Siri adoption, and the appearance of other big name search assistants across the industry.

That acceptance means around 77% of devices in use today have some form of AI, and roughly 90% of organizations are using AI. Investment in the sector is booming, with the tech giants (Apple, Amazon, Google, Microsoft and Meta) spending $92.17 billion on capital expenditures in Q2 2025 alone. That’s up a whopping 66.67% on the year ago quarter, mainly on the strength of big investments in data centers, servers, and AI infrastructure.

Hope, hype, and history

The hype around AI is growing as fast as the investments.

Firms in the space are signing massive multi-billion dollar deals, governments are investing vast quantities of cash and resources to support AI industry development, and consumers are preparing to pay for all this investment come the inevitable industry collapse. 

Consumers will pay? Just look at history. We know this because that’s what happened following the dotcom boom, the South Sea Bubble collapse, and the financial crisis, when unsustainable investments came before a fall. We know this because by the time the highly probable AI industry collapse happens, the tech will be so deeply intertwined in our daily lives we will be told these companies are strategically important, making them “too big to fail.”

So we will bail them out.

What follows Siri? 

I come to bury Siri, not to praise it, because competitors say it was ambitious and failed to keep up with them. But if you’d never experienced Siri, would you have trusted ChatGPT? Perhaps a little, but not as much.

For the future of Siri, ask whether privacy continue to be baked in, or will governments get their way when it comes to data encryption, in which case no one will be private unless they can afford to be.

If governments do get their way and Apple is made to take privacy out of its algorithms, just how much of a threat will Siri become to other AI services, which already seem to respect privacy less? Siri doesn’t seem to know the answer (yet).

Though it probably has quite a lot of data to help it work one out.

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Source:: Computer World

OpenAI has released its first open-weight language models since GPT-2, marking a significant strategic shift as the company seeks to expand enterprise adoption through more flexible deployment options and reduced operational costs.

The two new models — gpt-oss-120b and gpt-oss-20b — deliver what OpenAI describes as competitive performance while running efficiently on consumer-grade hardware. The larger model reportedly achieves near-parity with OpenAI’s o4-mini on reasoning benchmarks while running on a single 80 GB GPU, while the smaller variant matches o3-mini performance and can operate on edge devices with just 16 GB of memory.

“This is a bold go-to-market move by OpenAI and is now really open,” said Neil Shah, VP for research and partner at Counterpoint Research. “This move nicely challenges rivals such as Meta, DeepSeek, and other proprietary vendors both for cloud and more specifically edge.”

Open-weight models provide access to the trained model parameters, allowing organizations to run and customize the AI locally, but differ from traditional open-source software by not necessarily including the original training code or datasets.

Architecture designed for enterprise efficiency

The models leverage a mixture-of-experts (MoE) architecture to optimize computational efficiency. The gpt-oss-120b activates 5.1 billion parameters per token from its 117 billion total parameters, while gpt-oss-20b activates 3.6 billion from its 21 billion parameter base. Both support 128,000-token context windows and are released under the Apache 2.0 license, enabling unrestricted commercial use and customization.

The models are available for download on Hugging Face and come natively quantized in MXFP4 format, according to the statement. The company has partnered with deployment platforms, including Azure, AWS, Hugging Face, vLLM, Ollama, Fireworks, Together AI, Databricks, and Vercel to ensure broad accessibility.

For enterprise IT teams, this architecture could translate to more predictable resource requirements and potentially significant cost savings compared to proprietary model deployments. According to the statement, the models include instruction following, web search integration, Python code execution, and reasoning capabilities that can be adjusted based on task complexity.

“This will accelerate adoption of OpenAI models for research as well as commercial use under Apache 2.0 license,” Shah noted, highlighting the strategic value of the licensing approach.

Total cost calculations favor high-volume users

The economics of open-weight deployment versus AI-as-a-service present complex calculations for enterprise decision-makers. Organizations face initial infrastructure investments and ongoing operational costs for self-hosting, but can eliminate per-token API fees that accumulate with high-volume usage.

“The TCO calculation will break even for enterprises with high-volume usage or mission-critical needs where the per-token savings of self-hosting and open weights will eventually outweigh the high initial and operational costs,” Shah explained. “For low usage, AI-as-a-Service will benefit better.”

Early enterprise partners, including AI Sweden, Orange, and Snowflake, have begun testing real-world applications, from on-premises hosting for data security to fine-tuning on specialized datasets, the statement added. The timing aligns with enterprise technology spending expected to reach $4.9 trillion in 2025, with AI investments driving much of that growth.

OpenAI said that it subjected the models to comprehensive safety training and evaluations, including testing an adversarially fine-tuned version of gpt-oss-120b under the company’s Preparedness Framework. Also, its methodology was reviewed by external experts, addressing enterprise concerns about open-source AI deployments.

According to OpenAI’s benchmarks, the models showed competitive performance: gpt-oss-120b achieved 79.8% Pass@1 on AIME 2024 and 97.3% on MATH-500, while demonstrating coding capabilities with a 2,029 Elo rating on Codeforces. The company reported that both models performed well on tool use and few-shot function calling — capabilities relevant for business automation.

Strategic decoupling from Microsoft

The release has significant implications for OpenAI’s relationship with Microsoft, its primary investor and cloud partner. Despite the open-weight approach, Microsoft is bringing GPU-optimized versions of the gpt-oss-20b model to Windows devices through ONNX Runtime, supporting local inference via Foundry Local and the AI Toolkit for VS Code, the statement added.

Shah noted that “OpenAI with this move smartly decouples itself from Microsoft Azure and developers can now attach the open-weights models they have been working on and host it if they want to in the future on other rival clouds such as AWS or Google or even OpenAI-Oracle cloud.”

This strategic flexibility could pressure Microsoft to diversify beyond OpenAI partnerships while providing enterprises with greater vendor negotiating power. “This also now offers higher bargaining power for the enterprise against other AI vendors and even AI-as-a-Service models,” Shah observed.

Enterprise deployment considerations

The shift represents OpenAI’s recognition that enterprise AI adoption increasingly requires deployment flexibility. Organizations in regulated industries particularly value data sovereignty options, while others seek to escape vendor lock-in concerns associated with cloud-dependent AI services.

However, enterprises must weigh operational complexity against cost savings. While hardware requirements may be more accessible than previous generations, organizations need expertise in model deployment, fine-tuning, and ongoing maintenance—capabilities that vary significantly across enterprises.

The company is working with hardware providers, including Nvidia, AMD, Cerebras, and Groq, to ensure optimized performance across different systems, potentially easing deployment concerns for enterprise IT teams.

For IT decision-makers, the release expands strategic options in AI deployment models and vendor relationships. The Apache 2.0 licensing removes traditional barriers to customization while enabling organizations to develop proprietary AI applications without ongoing licensing fees.

“In the end it’s a win for enterprises,” Shah concluded, summarizing the broader market impact of OpenAI’s strategic pivot toward openness in the increasingly competitive enterprise AI landscape.

Source:: Computer World

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By Jano Costard Imagine drones that map disaster zones today and scout military targets tomorrow. Or seismic activity sensors built for construction that go on to detect submarines underwater. These ideas represent the promise of dual-use technologies that serve both civilian and military purposes. For the first time, the European Commission is explicitly proposing to fund them through programmes such as Horizon Europe. But as we race to embrace dual-use technologies, we face a pivotal choice: continue the old model where military applications drive innovation that civilians later adopt, or turn this paradigm on its head? Technological innovation has long followed a well-trodden…This story continues at The Next Web

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Apple continues to grow in the enterprise, supporting this push with regular enterprise-focused enhancements for its platforms. We can see the results as Mac adoption sets new records. But the company can still do more for enterprise IT. To get a sense of some improvements it could make, I caught up with Apu Pavithran, the CEO of device management vendor Hexnode.

“Apple has already shown that it’s listening closely to IT needs, and the path ahead is promising,” he said, arguing that as more Apple devices embed themselves across the enterprise, it’s “a win for everyone: users, IT, and the company itself.”

Pavithran does have three suggestions that should help IT manage Apple’s platforms better, including granular permissions within apps, more advanced support for shared devices, and new APIs to help manage apps acquired outside of Apple’s App Store.

Granular permissions for Apple Intelligence in apps

“Take, for example, Apple intelligence,” he said. “Apple’s on-device AI and its focus on privacy is aligned with the expectations of security-conscious organizations. But as these tools make their way into enterprise and education, IT needs ways to fine-tune how and where they show up.  He argued that one improvement Apple could make would be to give IT admins the power to allocate Apple Intelligence support on a per-app basis. 

“Not all apps should have access to summarization or generative suggestions, particularly when working with sensitive or regulated data,” he said. “Adding an easier process to toggle Apple Intelligence features on a per-app basis would help organizations maintain control without compromising the user experience.”

Pavithran also looked at personalization and AI. He argued that by tying personalization and context to the authenticated user, not just the device, Apple would be able to better ensure that AI-generated insights are truly relevant. 

That’s particularly important on shared devices, as by making the connection between user and context, Apple would drastically reduce the chance AI responses might carry over between users in environments like hospitals, classrooms, or retail floors. 

Making shared devices even more secure

Apple has built a foundation to support shared devices with tools such as Shared iPad, Return to Service (RTS), and Authenticated Guest Mode, but the Hexnode CEO thinks Apple could go further. “RTS has the potential to offer more granular control over wipe behavior or session persistence,” Pavithran told me.

“Apple could take this even further by unifying shared device behavior across iOS, macOS, and visionOS. The ability to pre-stage apps and configurations based on the next user’s role, define what gets retained post-session, or automate the return-to-service flow based on schedules or events would simplify management and reduce friction.”

Better yet, of course, “for privacy-conscious deployments, session isolation and user sandboxing would round out the experience. This allows IT teams to streamline management for devices that change hands multiple times, such as in healthcare, logistics, or field operations.”

What about securing the apps?

Apple’s App Store might be under attack from regulators eager to open these platforms for all the wrong reasons, but it remains the most stable, secure, and trusted app ecosystem on any platform. That means many enterprises rely on the Apple App Store for the distribution of their employee apps.

Announced at WWDC 2025, one of the big changes coming up in the ’26 series of operating systems will be something called version-pinning for App Store apps. This is a great feature that gives IT precise control over when and how updates roll out across their managed fleets.

The problem — and this is one that will likely worsen as third-party App Stores appear — is that many enterprise apps are still not made available via Apple’s store. “These apps often power essential workflows in healthcare, logistics, field service, and finance, places where reliability and predictability aren’t optional,” Pavithran said.

The snag is that as these apps come from beyond the App Store, it means they can’t be managed by the new version-pinning feature introduced at WWDC. 

There is one way Apple might be able to enable App Pinning on non-App Store Apps, suggests Pavithran. “Apple would likely need to introduce a dedicated framework for enterprises to roll out their apps through the App Store. With the right tooling in place, Apple could give IT teams the same level of granular control for every app,” he said.

What’s interesting about these three pain points is the extent to which they represent how Apple use in the enterprise has become so commonplace that the improvements the company can make represent quite unique use cases – shared devices, proprietary app support, and so on. 

At the risk of being a bit of a broken record, if these are the problems IT now needs Apple to solve, you can read that to mean its platforms are already fit for deployment in your business.

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Source:: Computer World

A public war of words has erupted between cloud infrastructure leader Cloudflare and AI search company Perplexity, with both sides making serious allegations about each other’s technical competence in a dispute that industry analysts say exposes fundamental flaws in how enterprises protect content from AI data collection.

The controversy began when Cloudflare published a scathing technical report accusing Perplexity of “stealth crawling” — using disguised web browsers to sneak past website blocks and scrape content that site owners explicitly wanted to keep away from AI training. Perplexity quickly fired back, accusing Cloudflare of creating a “publicity stunt” by misattributing millions of web requests from unrelated services to boost its own marketing efforts.

Industry experts warn that the heated exchange reveals that current bot detection tools are failing to distinguish between legitimate AI services and problematic crawlers, leaving enterprises without reliable protection strategies.

Cloudflare’s technical allegations

Cloudflare’s investigation started after customers complained that Perplexity was still accessing their content despite blocking its known crawlers through robots.txt files and firewall rules. To test this, Cloudflare created brand-new domains, blocked all AI crawlers, and then asked Perplexity questions about those sites.

“We discovered Perplexity was still providing detailed information regarding the exact content hosted on each of these restricted domains,” Cloudflare reported in a blog post. “This response was unexpected, as we had taken all necessary precautions to prevent this data from being retrievable by their crawlers.”

The company found that when Perplexity’s declared crawler was blocked, it allegedly switched to a generic browser user agent designed to look like Chrome on macOS. This alleged stealth crawler generated 3-6 million daily requests across tens of thousands of websites, while Perplexity’s declared crawler handled 20-25 million daily requests.

Cloudflare emphasized that this behavior violated basic web principles: “The Internet as we have known it for the past three decades is rapidly changing, but one thing remains constant: it is built on trust. There are clear preferences that crawlers should be transparent, serve a clear purpose, perform a specific activity, and, most importantly, follow website directives and preferences.”

By contrast, when Cloudflare tested OpenAI’s ChatGPT with the same blocked domains, “we found that ChatGPT-User fetched the robots file and stopped crawling when it was disallowed. We did not observe follow-up crawls from any other user agents or third-party bots.”

Perplexity’s ‘publicity stunt’ accusation

Perplexity wasn’t having any of it. In a LinkedIn post that pulled no punches, the company accused Cloudflare of deliberately targeting its own customer for marketing advantage.

The AI company suggested two possible explanations for Cloudflare’s report: “Cloudflare needed a clever publicity moment and we – their own customer – happened to be a useful name to get them one” or “Cloudflare fundamentally misattributed 3-6M daily requests from BrowserBase’s automated browser service to Perplexity.”

Perplexity claimed the disputed traffic actually came from BrowserBase, a third-party cloud browser service that Perplexity uses sparingly, accounting for fewer than 45,000 of their daily requests versus the 3-6 million Cloudflare attributed to stealth crawling.

“Cloudflare fundamentally misattributed 3-6M daily requests from BrowserBase’s automated browser service to Perplexity, a basic traffic analysis failure that’s particularly embarrassing for a company whose core business is understanding and categorizing web traffic,” Perplexity shot back.

The company also argued that Cloudflare misunderstands how modern AI assistants work: “When you ask Perplexity a question that requires current information — say, ‘What are the latest reviews for that new restaurant?’ — the AI doesn’t already have that information sitting in a database somewhere. Instead, it goes to the relevant websites, reads the content, and brings back a summary tailored to your specific question.”

Perplexity took direct aim at Cloudflare’s competence: “If you can’t tell a helpful digital assistant from a malicious scraper, then you probably shouldn’t be making decisions about what constitutes legitimate web traffic.”

Expert analysis reveals deeper problems

Industry analysts say the dispute exposes broader vulnerabilities in enterprise content protection strategies that go beyond this single controversy.

“Some bot detection tools exhibit significant reliability issues, including high false positives and susceptibility to evasion tactics, as evidenced by inconsistent performance in distinguishing legitimate AI services from malicious crawlers,” said Charlie Dai, VP and principal analyst at Forrester.

Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research, argued that the dispute “signals an urgent inflection point for enterprise security teams: traditional bot detection tools — built for static web crawlers and volumetric automation — are no longer equipped to handle the subtlety of AI-powered agents operating on behalf of users.”

The technical challenge is nuanced, Gogia explained, “While advanced AI assistants often fetch content in real-time for a user’s query — without storing or training on that data — they do so using automation frameworks like Puppeteer or Playwright that bear a striking resemblance to scraping tools. This leaves bot detection systems guessing between help and harm.”

The path to new standards

This fight isn’t just about technical details — it’s about establishing rules for AI-web interaction. Perplexity warned of broader consequences: “The result is a two-tiered internet where your access depends not on your needs, but on whether your chosen tools have been blessed by infrastructure controllers.”

Industry frameworks are emerging, but slowly. “Mature standards are unlikely before 2026. Enterprises might still have to rely on custom contracts, robots.txt, and evolving legal precedents in the interim,” Dai noted. Meanwhile, some companies are developing solutions: OpenAI is piloting identity verification through Web Bot Auth, allowing websites to cryptographically confirm agent requests.

Gogia warned of broader implications: “The risk is a balkanised web, where only vendors deemed compliant by major infrastructure providers are allowed access, thus favouring incumbents and freezing out open innovation.”

Source:: Computer World

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Source:: The Next Web

Autotrader has evolved from its roots as an automative magazine to become the UK’s largest automotive marketplace, a tech business that is now 100% using Apple products. 

Autotrader isn’t the first to move to Mac; the opportunity to move from a Windows ecosystem to Macs is being seized by a rapidly growing number of enterprises in response to Microsoft’s push to drive customers to Windows 11 following the CrowdStrike disaster.

Accelerating Apple adoption

The company has shifted to Apple products over the last couple of years, after becoming a 60% Mac environment in 2023-24. “User experience is everything to us and is one of the reasons we choose Apple technology,” Lee Skade, enterprise IT engineer at the company, said in a short video clip explaining its embrace of Apple technology. (The video is available here.)

At that time, Autotrader’s own internal data showed that 72% of all business employees would choose a Mac above all other devices, and 78% of millennial employees believe that access to the tech they like makes them more effective. We know data like that reflects findings at companies worldwide, from Cisco to IBM to Rituals, SAP, and elsewhere. With digital experiences becoming the primary employee experiences, these lights show the road.

Autotrader gets this and as a title focused on methods of transport seems to understand that the direction is accelerating to become the norm, which is why since publishing that clip, the company has gone all in and now operates a 100% Mac fleet. 

The transformation of IT

To help manage and support its Mac fleet, Autotrader is now looking for a Senior Mac Engineer, a role in which it intends to help optimize the user and employee experiences Apple tech brings to any company making the migration. This isn’t seen as a reactive role for troubleshooting tech problems, but as a leadership position intended to nurture positive deployments of technology for real business challenges.

This reflects one of the big trends in IT right now, particularly within Apple-based ecosystems in which the culture of IT moves away from support toward enablement. At this stage of the tech evolution, support should be seen as a business investment rather than a cost, Autotrader believes. “Tech’s not an afterthought anymore. Our people are driving what they want out of tech,” says Holly Redman, technology experience partner at Sync, which assisted Autotrader’s deployment.

“We believe in consistent, high-performing, collaborative experiences (and let’s be honest, we love a good trackpad gesture),” said Autotrader Technology Resource Partner Claire Isherwood.

It’s more than trackpad gestures, of course. Autotrader is a solid illustration of a company that understands that well-applied digital technologies can generate major business transformation. In this case, it has helped the title transition from a print business to become an all-points technology service. 

How Mac shapes up for business

The move to Mac implicitly empowers this. Apple Silicon, for example, supports the company’s development teams when building apps and services for its millions of users. These M-series processors easily handle the demands when building and supporting apps for multiple platforms. The video also explains who the adoption of Apple products benefits, how it helps the company in terms of sustainability, and how it enables a better corporate focus. 

Autotrader joins a growing number of companies to have successfully moved from Windows hardware to Macs, from high street brands like Rituals to accelerating deployments at PayPal and Roche. Banks, most recently including Siam Commercial Bank in Thailand, are also migrating to Apple’s products.

When they do so, they benefit from significantly lower software licensing costs and significantly more secure computing experiences. They also get to offer incoming and existing employees’ access to the tech they use at home, boosting productivity and engagement. None of these claims should surprise anyone, of course — they are all reflected in last year’s Apple-sponsored Forrester Research report which identified that the adoption of its products in business boost digital employee experiences, decrease support costs, improve security and bolster productivity.

This is the reality that continues to drive change in enterprise IT.

Long may it continue.

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Source:: Computer World

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In today’s fast-changing workplace, investing in employee development isn’t just a nice-to-have — it’s a strategic necessity. But with hundreds of learning experience platforms (LXPs) on the market, each promising personalized journeys, AI-driven insights, and seamless integrations, choosing the right one can feel overwhelming.

The best LXP for your organization will do more than deliver content; it will align with your business goals, adapt to the skills your teams need most, and foster a culture of continuous growth.

Whether you’re upgrading from a traditional learning management system or implementing a learning system for the first time, making the right choice starts with understanding what really matters — not just in features, but in outcomes. Here’s how to navigate the crowded landscape and find the platform that fits your organization’s unique needs.

What is a learning experience platform?

A learning experience platform is a type of online software that helps employees find, take, and track learning and training content in a way that feels more personal and flexible than traditional training systems.

“Learning experience platforms are digital solutions that personalize the learning and development experiences for individual employees,” says Zachary Chertok, research manager at IDC.

Unlike older learning management systems (LMSes) that mostly just assign required training modules, LXPs focus on giving users more control over what they learn, when they learn it, and how it fits into their jobs.

An LXP suggests courses, videos, or articles based on employees’ interests, roles, or skills needs, and even lets them choose content from many different sources. Many LXPs also include social features, such as discussion forums, leaderboards, and the ability to share knowledge with co-workers.

LXPs make it easier for companies to deliver training, upskilling, and reskilling while people are working, Chertok says. They help keep employees engaged by offering many types of learning, including formal courses, informal learning, learning from co-workers, live sessions, on-demand lessons, and short, bite-size content.

Top trends in LXPs

According to Gartner analyst Travis Wickesberg, AI is increasingly powering several important areas of workplace learning and skills development. One key tool is AI-enabled skills management, which uses various technologies, such as natural language processing and knowledge graphs, to create a constantly updated view of employees’ skills. This allows companies to automatically assess what skills a person has, what a job or task requires, and how employees can continue to grow in their careers, he says.

Another important application is AI-based learning assistants, Wickesberg says. These systems help employees find relevant content, boost their skills, and create personalized learning plans based on their roles, current skills, career goals, and personal interests.

Rather than following a one-size-fits-all training program, employees receive customized recommendations for courses or resources that match their individual needs and help them build the right skills at their own pace.

A third trend is “learning in the flow of work,” which integrates learning directly into employees’ daily activities, Wickesberg says.

Instead of requiring workers to leave their tasks to complete separate training, AI learning assistants or embedded training tools within different platforms, such as Microsoft Teams or Salesforce, can suggest quick lessons or helpful content exactly when employees need it. This makes learning more seamless and immediate.

“[And] generative AI content creation tools help companies modify existing content or create new content using genAI,” he says. This makes it easier to keep learning resources fresh, relevant, and aligned to employees’ evolving needs.

Era Singh, practice director at Everest Group, says a major trend in the learning space is the growing focus on skills-based learning.

“Enterprises are increasingly looking for solutions that can dynamically tie skills to job roles, content, and career development pathways,” she says. “What it means is that these LXP vendors are using skills as the language to understand people and work. They are helping organizations gain visibility into the skills their people have, skills people will need in the future, and providing learning pathways to bridge those skill gaps.”

An additional emerging trend is the integration of learning with broader talent functions. Rather than treating learning as a standalone activity, companies are bundling it with performance management, internal mobility, and other HR tools, Singh says.

The goal is to create a total experience throughout the entire employee lifecycle — from onboarding to growth and retention, so learning is no longer isolated but embedded within other functions.

“Another major trend that I’m seeing is a focus on social learning,” she says. “We are seeing a shift toward gamification, cohort-based learning, coaching, and mentoring to motivate and incentivize employees to learn.”

In addition, data is becoming central to companies’ learning strategies. “The role of advanced analytics to track employee behavior and measure the learning impact on performance and costs is becoming pivotal,” she says.

What to look for in an LXP

“There are not really different types of LXPs,” says Wickesberg. “In fact, the vendor landscape for LXP providers has evolved and shifted in the last few years. With the spotlight on learning experience, many LMS providers have improved their learner experience and learning-journey capabilities.”

This added competition has forced many of the original LMS providers to reinvent themselves or opt to build out new functionality around skills and talent development, he says. Some have also invested in new functionality that supports mentoring, coaching, and talent marketplaces.

“We will likely see new partnerships and continued development in this space as LXP vendors look for new ways to add value for their clients,” Wickesberg adds.

The LXP market is complicated, and as such, there are a lot of different use cases, says Katy Tynan, vice president and principal analyst at Forrester Research.

Although organizations are using LXPs to deliver learning, they might also use them for things like onboarding, assessments, compliance training, and change management, she says.

“So for features and functionality, it’s very much dependent on the use case that the organization buying the platform has,” she says.

For some organizations, customized learning paths will be paramount; others will prioritize integration with enterprise software, advanced workforce analytics, or peer-driven training. It’s important for IT buyers to determine from the outset what features matter most to the business.

Before you shop: Questions to ask yourself and your stakeholders

Wickesberg says these are the basic questions you should ask yourself before you start shopping for an LXP:

What problem are you trying to solve?

What are your business outcomes or goals?

How will the new LXP integrate with and enhance your existing learning tools?

How will you measure ROI?

A clear understanding of goals ensures the selected platform aligns with your company’s business outcomes, rather than simply offering trendy features.

You should also consider who your learners are and what they need. This will help ensure the platform keeps people interested and meets their different skill levels, locations, and needs.

It’s also important to understand the existing systems that your learning platform needs to integrate with. Integration with your human resources information system, customer relationship management systems, or LMS reduces manual work and keeps important learning and performance data secure.

In addition, think about what types of content formats and sources are important for your company. Choosing a platform that supports the right learning formats, such as videos, articles, certifications, or peer-based learning, ensures that it works well with your current and future content plans.

Also consider your budget range, and how your organization’s needs might grow over time. Understanding your budget and how the platform can grow helps you avoid unexpected costs and the need to replace it too soon.

Key questions to ask vendors when shopping for LXPs

Once you know your organization’s needs, it’s time to look at what different vendors provide. Ask these questions to better understand both the basic features and any extras they offer.

What makes your platform different from a traditional LMS?

How does your platform personalize learning for different users?

What types of content formats (videos, articles, podcasts, etc.) does your platform support?

Can your platform integrate with our existing HR, LMS, and other internal systems?

How do you track and measure learner progress, engagement, and skill development?

What reporting and analytics features are included?

Is your platform mobile-friendly and accessible across devices?

How does pricing work (per user, per feature, annual licensing)?

What support, onboarding, and training services do you provide after purchase?

How often is your platform updated with new features or improvements?

How easy is it for administrators to manage users, assign content, and create learning paths?

Does the platform offer skills frameworks, career development paths, or competency mapping?

What are your data security, privacy, and compliance standards (GDPR, SOC 2, etc.)?

Can we curate both internal training materials and external content from third parties?

Can you provide references, case studies, or customer success stories from similar organizations?

10 leading learning experience platform vendors

Here’s a quick summary of the leading LXPs as noted by experts and independent research.

360Learning

360Learning specializes in collaborative learning, allowing employees and subject matter experts to quickly create, share, and improve courses. It combines social learning with traditional training tools. You can create courses, assign hands-on projects, give and get feedback from others, and track how engaged people are. AI suggests learning materials, takes care of repetitive tasks, and keeps track of what skills people need to develop. Pricing typically starts around $8 per user per month for up to 100 users. Larger deployments may have significantly higher costs.

Adobe Learning Manager

Adobe Learning Manager is a flexible platform that helps companies train their employees, educate their customers, and support their partners. It uses AI to recommend the right courses, gamifies learning with badges and leaderboards, and encourages social and collaborative learning. It manages diverse content types and provides a consistent experience across desktop and mobile. Adobe doesn’t publish fixed prices publicly.

Cornerstone OnDemand

Cornerstone OnDemand is a complete learning platform that uses AI to create personalized training paths. It helps employees build skills, stay up to date with required training, earn certifications, and plan their careers, while giving companies detailed insights about their workforce. The user-friendly design enables mobile learning and integrates with major enterprise systems. Aimed at midsize to large enterprises, Cornerstone OnDemand offers customized pricing based on a number of factors, such as company size, selected modules, and specific organizational needs.

Degreed

Degreed focuses on skills-based learning, linking employees to content from thousands of sources, including courses, articles, podcasts, and videos. It provides skills tracking, personalized learning feeds, and LMS/content provider integrations. Degreed supports formal company training and informal self-paced learning where employees can explore topics on their own. Contracts are typically customized based on company size. Degreed is best suited to midsize and large organizations.

Docebo

Docebo combines the structure of an LMS with the flexibility of an LXP, offering an adaptable learning platform. It uses AI to suggest personalized content, create automated learning paths, and handle some admin tasks in the background. Key features include pulling content from different sources, tracking skill development, encouraging team-based learning, and offering training for partners and customers outside the company. Pricing is not publicly available.  

EdCast by Cornerstone

EdCast, now part of Cornerstone OnDemand, is a learning tool that personalizes content for each employee and integrates learning with their everyday work. It pulls together learning materials from inside and outside the company, offers AI-powered recommendations, tracks skills, and integrates with major enterprise tools including Salesforce, Workday, and Microsoft Teams. Pricing is typically customized and varies widely based on company size, industry, and integration needs.

Fuse

Fuse promotes continuous, on-demand social learning. Instead of just relying on formal courses, Fuse lets employees learn by creating, sharing, and talking about knowledge as part of their everyday work. It uses AI and machine learning to highlight expert knowledge from across the company and recommend helpful content when it’s needed. Key features include mobile-friendly access, learning through videos, conversations with co-workers, and detailed insights into how people are learning. Pricing is not available publicly.

Juno LXP

Juno LXP uses AI to create learning experiences that are personalized for each employee. It recommends courses based on each person’s skills and goals, pulling content from more than 120 providers, such as LinkedIn Learning and Coursera. Juno also allows employees to request specific learning content, and managers can oversee budgets and track progress with built-in analytics. The platform also offers group learning areas and guides to help workers plan their career paths. Pricing is not publicly available. Juno LXP is designed to support companies of various sizes, from small teams to large enterprises.

LearnUpon

LearnUpon blends the reliability of a traditional LMS with a modern, easy-to-use design. Suitable for organizations of all sizes, it lets companies train employees, customers, and partners all in one place. Key features include a modern, easy-to-use design; custom-branded learning sites; simple tools to build courses; certificates for completed training; automated workflows; clear reports; and connections to enterprise tools such as Salesforce and Slack. Pricing is not publicly available.

Valamis

Valamis combines the structure of a traditional learning system with the flexibility of newer tools. Aimed at midsize to large organizations, it helps companies connect employee learning to business goals through tools such as skills tracking, custom content, and detailed reports. It supports online and offline learning and integrates with enterprise tools including Microsoft Teams, Salesforce, and Workday. Pricing for smaller deployments typically starts around $19,000 per year, with larger global implementations costing significantly more depending on customization and integration complexity.

More on upskilling and training:

How — and why — to upskill your employees

Within two years, 90% of organizations will suffer a critical tech skills shortage

Upskilling ramps up as genAI forces enterprises to transform

Continuous learning gives U.S. Bank a technology talent edge

9 upskilling tips that pay dividends

How to discover hidden tech talent in your organization

Source:: Computer World

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If you own Microsoft stock, you’ve got to be happy.

Microsoft’s latest financial results for the quarter ending June 30, 2025 were robust: Revenue reached $76.4 billion (up 18% year-over-year), with net income at $27.2 billion (up 24%). The primary driver was robust growth in Microsoft’s cloud and AI businesses. Azure’s revenue, which has finally been separated out in the financial reporting, now stands at $75 billion in annual revenue, representing a 34% surge for the year. 

Put it all together, and you can see why Microsoft has joined Nvidia in the $4-trillion valuation club. 

So much for the good news. Despite those impressive financials, Microsoft has laid off a substantial number of employees this year — about 25,000, to be exact, including roughly 9,100 in July alone (approximately 4% of its workforce). The layoffs have affected a broad range of divisions, including Xbox, engineering, and management layers.

What’s driving these cuts is clearly not poor performance. Instead, Microsoft is betting that its aggressive $80 billion AI investments will pay off. Don’t take my word for it:  Microsoft CEO Satya Nadella recently noted the “incongruence” of laying off employees even as Microsoft is “thriving.” He called this dynamic “the enigma of success” in a tech industry where progress demands constant change, even when things are going well.

“I want to express my sincere gratitude to those who have left,” he added. “Their contributions have shaped who we are as a company, helping build the foundation we stand on today. And for that, I am deeply grateful.”

I’m sure deep gratitude will help keep a roof over their heads.

Microsoft is far from alone in dumping once-valued employees for cheaper AI alternatives. IBM has cut 8,000 jobs so far this year. In particular, IBM is gradually replacing clerical jobs with AI, while its AskHR bot has taken over much of HR’s old workloads. At the same time, Meta is reducing its staff by 5% in 2025 as it switches gears from its metaverse dud to AI. 

Amazon CEO Andy Jassy put an even finer point on where things are headed. “As we roll out more generative AI and agents, it should change the way our work is done.” That means, he said, “We will need fewer people doing some of the jobs that are being done today.”

This isn’t just happening in the US. In India, Tata Consultancy Services (TCS) has cut 12,000 jobs this year. Why? Because its customers are moving to AI, which has sharply cut the outsourcing work the company has historically profited from. Meanwhile, in Japan, Indeed and Glassdoor’s parent company has laid off 1,300 workers, approximately 6% of its workforce, according to CEO Hisayuki “Deko” Idekoba.

“AI is changing the world,” he said. It certainly changed the world of the 130,000 or so tech jobs that have been lost this year, according to estimates by The Bridge Chronicle.

Oh, and by the way, Indeed reports that tech job postings in July were down 36% from their early 2020 levels. 

Is this a great time to be looking for a tech job or what!? But for every fantasy job, like the $100-million AI jobs Meta is supposedly offering, there are hundreds of tech workers who have kissed their six-figure positions goodbye and thousands of clerical staffers who will no longer be making five-figure annual incomes.  

There’s one little problem with all these job cuts. We still don’t know whether AI can deliver the work goods. Sure, some jobs, such as repetitive HR or call-center work, can be largely replaced.  Heck, we’ve seen that happen before. I remember when many call-center jobs were located in the US — before companies figured out that outsourcing such jobs to low-cost countries was cheaper. For jobs like these, AI is just the next step in cost-cutting. 

Companies often use AI like that — to cut costs. Not many are using it to help good workers become great workers. Instead, CEOs are buying the illusion that AI can magically replace highly skilled workers.

I hate to break it to you: It can’t. Take programming. According to the 2025 Stack Overflow Developer Survey, 84% of programmers now use or plan to use AI tools in their workflow. However — and this is a crucial point — 46% of AI-using developers don’t trust AI results. In fact, even as AI developer tools have supposedly improved, programmers are trusting them even less. 

Why? Because they’re spending a lot of their time fixing AI mistakes. AI programming tools make fine interns, but they’re not senior or even mid-level developers. 

Let’s also remember that in the last few weeks, Replit, the leading vibe-coding company, which promises to make “software creation accessible to everyone, entirely through natural language,” wrecked a production database for a senior tech executive who was trying, and spectacularly failing, to build a new application.

The bottom line is that AI is not good enough, not yet anyway, to replace engineers and developers. This will come back and bite companies like Microsoft in the rump. Sure, the market loves companies that cut costs. But if AI doesn’t deliver on its promises, getting all those experienced ex-workers back in the foldwon’t be easy.

Today, AI is great for getting people to believe that groups of bunnies love hopping on trampolines; but doing hard technical work? That’s another question entirely. 

Source:: Computer World