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• 10Web announced a new WordPress plugin
• It allows users to create fully white-labeled websites in minutes
• Users can expect increased ARPU and reduced churn, 10Web says

You can now create a WordPress website in minutes, with the help of Generative AI (GenAI), without needing a third-party website builder or AI tool. Everything can be done in WordPress directly, through a chat interface, and without the website builder’s branding showing anywhere on the site.

This is all courtesy of the website builder platform 10Web, which just announced the launch of its fully white-labeled AI website builder solution. It comes in the form of a WordPress plugin, and allows users to create a website inside their hosting stack without relying on a separate builder platform.

In a press release shared with TechRadar Pro earlier this week, 10Web says the new offering should further increase ARPU, reduce churn, and differentiate through same-day AI website delivery.

“Hosting companies have been stuck selling blank WordPress installs,” said Arto Minasyan, Founder and CEO of 10Web. “With this solution, they can launch fully functional websites under their own brand in seconds. It’s the simplest way to deliver real customer value, without changing how they host or deploy WordPress.”

WooCommerce included

Usually, when a customer buys a hosting service, they get either a blank WordPress dashboard, or one bundled with themes and plugins. However, with the emergence of GenAI, expectations changed, and customers have gotten used to the “describe and build” experience, the company claims.

That being said, it claims “early tests” showed users being 30% more likely to publish their site compared to traditional WordPress onboarding flows. It didn’t say when the tests took place, who was tested, and against what, though.

In any case, 10Web says the plugin is built on its proprietary AI technology which leverages advanced models from OpenAI, Gemini, and Anthropic. The sites are mobile-friendly, fully structured, and based on a “simple business description”.

When users create a site, they will see a branded AI flow that generates the entire website, including WooCommerce integration, if needed. Finally, everything is white-labeled with the hosting provider’s name and logo, and includes a visual editor with AI Co-Pilot.

More from TechRadar Pro

• 10Web claims its new AI tool can help you build an online store in just 10 minutes
• Check out our roundup of the best AI website builders on the market
• Learn more about Wix with our full Wix review

In boardrooms and investor meetings, artificial intelligence is now table stakes. AI tools are everywhere. Analysts are forecasting trillions in potential value. McKinsey estimates that generative AI alone could boost the global economy by up to $4.4 trillion a year.

And yet, in the enterprise? Something’s not clicking.

Despite the hype, most AI projects are still stuck in the sandbox; demo-ready, not decision-ready. The issue isn’t model performance. It’s operationalization. Call it the Enterprise AI Paradox: the more advanced the model, the harder it is to deploy, trust, and govern inside real-world business systems.

The heart of the paradox

At the heart of this paradox, McKinsey argues, lies a misalignment between how AI has been adopted and how it generates value.

Horizontal use cases, notably tools like Microsoft’s Copilot or Google's Workspace AI, proliferate rapidly because they're easy to plug in and intuitive to use. They provide general assistance, they summarize emails, draft notes, simplify meetings, and so on.

Yet these horizontal applications scatter their value thinly, spreading incremental productivity improvements so broadly that the total impact fades into insignificance.

As the McKinsey report puts it, these applications deliver "diffuse, hard-to-measure gains.”

In sharp contrast, vertical applications (those baked into core business functions) carry the promise of significant value but struggle profoundly to scale. Less than 10 percent of these targeted deployments ever graduate beyond pilot phases, trapped behind technological complexity, organizational inertia, and a lack of mature solutions. LLMs are extraordinary. But they’re not enough.

It’s like trying to run a Formula 1 car on a farm track

The real enterprise challenge isn’t building a big, clever model. It’s orchestrating intelligence, across systems, teams, and decisions.

The world’s most innovative companies don’t want a single mega-model spitting out answers from a black box. They want a system that’s intelligent across the board: data flowing from hundreds of sources, automated agents taking action, results being validated, and everything feeding back into an improved loop.

That’s not one model. That’s many. Talking to each other. Acting with autonomy. And constantly learning from a dynamic environment.

This is the future of enterprise AI, and it’s what’s known as agentic.

What is agentic AI, and why does it matter?

Agentic AI systems are different from monolithic LLMs in one key way: they think and act like a team. Each agent is a specialist, trained on a narrow domain, given a clear role, and capable of working with other agents to complete complex tasks.

One might handle user intent. Another interfaces with an internal database. A third enforces compliance. They can run asynchronously, reason over real-time data, and retrain independently.

Think of it like microservices, but for cognition. Unlike traditional generative AI, which remains largely reactive (waiting passively for human prompting) agents introduce something entirely different. "AI agents mark a major evolution in enterprise AI - extending gen AI from reactive content generation to autonomous, goal-driven execution,” McKinsey researchers explain.

This isn’t some speculative vision from a Stanford whitepaper. It’s already happening, in advanced enterprise labs, in the open-source community, and in early production systems that treat AI not as a product, but as a process.

It’s AI moving from intelligence-as-an-output to intelligence-as-infrastructure.

Why most enterprises aren’t ready (yet)

If agentic systems are the answer, why aren’t more enterprises deploying them?

Because most AI infrastructure still assumes a batch world. Systems were designed for analytics, not autonomy. They rely on periodic data snapshots, siloed memory, and brittle pipelines. They weren’t built for real-time decision-making, let alone a swarm of AI agents operating simultaneously across business functions.

To make agentic AI work, enterprises need three things:

Live data access – Agents must act on the most current information available

Shared memory – So knowledge compounds, and agents learn from one another

Auditability and trust – Especially in regulated environments where AI decisions must be traced, explained, and governed

This isn’t just a technology problem, it’s actually an architectural one. And solving it will define the next wave of AI leaders.

From sandbox to system

Enterprise AI isn’t about making better predictions. It’s about delivering better outcomes.

To do that, companies must move beyond models and start thinking in systems. Not static models behind APIs, but living, dynamic intelligence networks: contextual, composable, and accountable.

The Agentic Mesh, as McKinsey calls it, is coming. And it won’t just power next-gen applications. It will reshape how decisions are made, who makes them, and what enterprise infrastructure looks like beneath the surface.

It isn’t simply a set of new tools bolted onto existing systems. Instead, it represents a shift in how organizations conceive, deploy, and manage their AI capabilities.

To really make this work, McKinsey says it’s time to wrap up all those scattered AI experiments and get serious about what matters most. That means clear priorities, solid guardrails, and picking high-impact "lighthouse" projects that show how it's done.

The agentic mesh isn't just a fancy architecture - it’s a call for leaders to rethink how the whole enterprise runs. Because real enterprise transformation won’t come from scaling a smarter model. It will come from orchestrating a smarter system.

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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

Listening to entrepreneurs discuss the potential of AI cybersecurity will give you déjà vu. The discussions are eerily similar to how we once talked about cloud computing when it emerged 15 years ago.

At least initially, there was a surprisingly prevalent misconception that the cloud was inherently more secure than on-premises infrastructure. In reality, the cloud was (and is) a massive attack surface. Innovation always creates new attack vectors, and to say AI is no exception is an understatement.

CISOs are generally aware of AI’s advantage, and for the most part they’re similarly aware that it’s creating new attack vectors. Those who took the right lessons from the development of cloud cybersecurity are right to be even more hesitant about AI.

Within the cloud, proper configuration of the right security controls keeps infrastructure relatively static. AI shifts faster and more dramatically, and is thus inherently more difficult to secure. Companies that got burned by being overeager about cloud infrastructure are now hesitant about AI for the same reasons.

Multi-industry AI adoption bottleneck

The knowledge gap isn’t about AI’s potential to drive growth or streamline operations; it’s about how to implement it securely. CISOs recognize the risks in AI’s expansive attack surface.

Without strong assurances that company data, access controls, and proprietary models can be safeguarded, they hesitate to roll out AI at scale. This is likely the biggest reason why AI apps at the enterprise level are coming out only at a trickle.

The rush to develop AI capabilities has created a multi-industry bottleneck in adoption, not because companies lack interest, but because security hasn’t kept pace. While technical innovation in AI has accelerated rapidly, protections tailored to AI systems have lagged behind.

This imbalance leaves companies exposed and without confidence to deploy at scale. Making matters worse, the talent pool for AI-specific cybersecurity remains shallow, delaying the hands-on support organizations need to integrate safeguards and move from adoption intent to execution.

A cascade of complicating factors

This growing adoption gap isn’t just about tools or staffing—it’s compounded by a broader mix of complicating factors across the landscape. Some 82% of companies in the US now have a BYOD policy, which complicates cybersecurity even absent AI.

Elon Musk’s Department of Government Efficiency (DOGE) has fired hundreds of employees at the U.S. government’s cybersecurity agency CISA, which worked directly with enterprises on cybersecurity measures. This dearth of trust only tightens this bottleneck.

Meanwhile, we’re seeing AI platforms like DeepSeek become capable of creating the basic structure for malware. Human CISOs, in other words, are trying to create AI cybersecurity capable of facing AI attackers, and they’re not sure how. So rather than risk it, they don’t do it at all.

The consequences are now becoming evident, and dealing a critical blow to adoption. It just about goes without saying: AI won’t reach its full potential absent widespread adoption. AI is not going to fizzle out like a mere trend, but AI security is lagging and inadequate and it’s clearly hampering development.

When “good enough" security isn’t enough

AI security is shifting from speculative to strategic. This is a market brimming with potential. Enterprises are grappling with the severity and scale of AI-specific threats, and the demand those challenges created are attracting wider investor interest. Organizations have no choice but to secure AI to fully harness its capabilities. Those that aren’t hesitating are actively seeking solutions through dedicated vendors or by building internal expertise.

This has created a lot of noise. A lot of vendors claim to be doing AI red teaming, while really just offering basic penetration testing in a shiny package. They may expose some vulnerabilities and generate initial shock value, but they fall short of providing the continuous and contextual insight needed to secure AI in real-world conditions.

If I were trying to bring AI into production in an enterprise environment, a simple pen test wouldn’t cut it. I would require robust, repeatable testing that accounts for the nuances of runtime behavior, emergent attack vectors, and model drift. Unfortunately, in the rush to move AI forward, many cybersecurity offerings are relying on this “good enough” pen testing, and that’s not good enough for smart organizations.

The reality is that AI security requires a fundamentally different approach – this is a new class of software. Traditional models of vulnerability testing fail to capture how AI systems adapt, learn, and interact with their environments.

Worse still, many model developers are constrained by their own knowledge silos. They can only guard against threats they’ve seen before. Without continuous external evaluation, blind spots will remain.

As AI becomes embedded across sectors and systems, cybersecurity needs to provide actually suitable solutions. That means moving beyond one-time audits or compliance checkboxes. It means adopting dynamic, adaptive security frameworks that evolve alongside the models they’re meant to protect. Without this, the AI industry will stagnate or risk serious security breaches.

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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

Generative AI is changing the software development game. Beyond its capabilities in IT automation, the tool is also empowering professionals to contribute where it matters most, specifically at the strategic level.

This is the case of developers, who are no longer confined to their expectations of simply building applications but are increasingly becoming more involved with strategic business outcomes.

Gartner predicts that by 2028, 75% of enterprise software engineers will use AI. This figure doesn’t just represent technological advancements and the growing role of generative AI in enterprise software but also serves as a wakeup call for businesses to rethink the role of their developers.

The connecting factor

Organizations need to recognize that developers are the connecting factor between their needs and digital solutions. Those that recognize this early on and harness developer expertise will be those that succeed ahead.

This shift is already underway across multiple industries. In healthcare, for example, developers are addressing clinical needs by designing solutions that reduce operational friction, giving practitioners more time to focus on patient care.

In financial services, they are driving growth in a highly regulated and competitive environment, enhancing fraud detection while making financial services accessible and convenient for customers.

Meanwhile, in the retail sector, developers are elevating the technologies behind customer experiences to meet rising expectations. Across the board, developers are emerging as strategic innovators, leveraging technology, not just to solve problems, but to deliver meaningful business outcomes.

Developers are keepers of insight

Across sectors, businesses are beginning to rethink how they engage with their developers. The conversation is now shifting from basic interaction to empowering them to contribute strategically.

With developers holding a deeper understanding of their business's needs, they are more frequently asking to be heard and consulted upon the innovation strategy to better support business objectives.

Therefore, unlocking the potential of AI will require a mindset shift - one that acknowledges generative AI’s role not just to accelerate development but also elevate individuals behind it. To move forward, organizations need to recognize the value that developers bring to the table; including solving the issues that generative AI alone cannot solve.

Empowering developers: how low-code and AI are redefining complexity

Object oriented low-code and no-code platforms and generative AI have fundamentally changed how developers can leverage their business relevance in their organizations. By eliminating some of the complexity of line-by-line code development, this allows them to move quickly from idea to implementation, creating more room for innovation, experimentation, and collaboration with other stakeholders.

As a result, developers are finding it easier to take a much bigger seat at the table, thereby helping to guide business strategy. Developers bring unique value: they are embedded in systems, close to the problems that need solving, and often have first-hand insight into operational inefficiencies and user frustrations. They understand the organization not just from a technical perspective, but from a business one.

Low-code and generative AI free developers from repetitive, technical tasks and enable them to focus on solving real business problems. As a result, developers are no longer just responding to requirements - they are helping to shape them. This shift gives developers a greater voice in strategic discussions and positions them as key players in driving business success.

Generative AI as a copilot

Generative AI copilots go beyond traditional tools by actively assisting developers throughout the software development lifecycle. Instead of working within rigid frameworks that slow innovation, developers can now brainstorm ideas and instantly generate code, receive intelligent suggestions, automate repetitive routine tasks, like debugging, or documentation. These copilots act as intelligent partners, freeing developers to concentrate on solving high-impact problems faster.

The critical advantage of a DevOps team with time, is their ability to more proactively engage with the overall direction of business. Generative AI amplifies the value of human insight further by enabling developers to focus on the work that matters most including creativity, judgement, and a deep understanding of organizational context. In addition, when generative AI is paired with low-code, developers have a co-pilot aiding them on the journey to create better applications and services for the industry they work in.

Developers delivering value across industries

An industry where the shift in developers offering insights is most apparent is in healthcare. The development of applications in this sector isn’t just about building tools, but more importantly reducing friction for clinical practitioners and returning time to patient care.

Developers who understand the pain points and frustrations clinical staff face, are better equipped to create applications that minimize these complexities. Generative AI and low-code development platforms make it possible to quickly build, iterate, and improve these tools, resulting in better alignment between healthcare technology and frontline needs.

Another telling example is the financial services sector where 75% of financial firms already use AI. Developers are able to redirect their focus from routine tasks and offer value by modernizing legacy systems, streamlining compliance and enhancing fraud detection, all while supporting rapid product innovation.

Building solutions

In a tightly regulated industry, their ability to build secure, efficient and customer-centric solutions is critical. Developers offer real value by creating solutions without compromising safety or security. With AI, developers can move faster, meet regulatory requirements, and deliver personalized experiences that build trust and retention.

In retail, developers are using customer feedback to solve friction points in the shopping journey. They are building tools that personalize the user experience, boost satisfaction and increase sales. With AI and low=code automating routine tasks, developers can focus on innovation, from responding to consumer trends to improving supply chain resilience.

Across sectors, the combination of generative AI, low-code platforms, and developer insight is accelerating innovation and unlocking strategic business value.

Time to push the needle

It is time for businesses to push the needle, not just to adopt generative AI but also to empower developers to lead innovatively. With the use of generative AI and AI-powered low-code, developers can reallocate time that they can then reinvest towards targeting strategic business needs. Thanks to their strong understanding of business needs and pain points, developers are able to shape solutions that align digital solutions with business objectives.

Successful businesses will be those that recognize that AI will not be replacing developers but rather promoting them to more strategic roles.

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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

Here are the answers for The New York Times Mini Crossword for Aug. 13

Investigators say the former president and first lady exerted undue influence on the conservative People Power Party to nominate a specific candidate during a 2022 election.

(Image credit: Jung Yeon-je)