Technology

• Google has shared a teaser video showing a phone in the Pixel 11 series
• You can see it in a gold shade, with a colored light that must be the rumored 'Pixel Glow' feature
• The company has also confirmed that pre-orders open on August 12

We’re now less than a month away from the August 12 announcement of the Pixel 11 series, and Google is in full teaser mode. The tech giant has just posted a short clip showing off a phone in the Pixel 11 series in a gold shade.

You can mostly just see the gold-colored sides of the phone, but the camera block is also visible, and this houses a circular multicolored light. This then, must be the rumored ‘Pixel Glow’ feature, which will presumably be able to light up in different colors — or different patterns of colors — to alert you to different kinds of notifications.

Perhaps, for example, a WhatsApp message would appear in blue shifting to pink, while a calendar event might be a single block of yellow.

A Nothing idea

Glyph lighting on the Nothing Phone 2 (Image credit: Future | Alex Walker Todd)

How customizable this will be, and whether it’s capable of anything else, remains to be seen. But at least now we have a sense for how it looks, with this being a bit reminiscent of Nothing’s Glyph lighting systems, just rather smaller and more subtle.

That makes sense coming from Google, as its phone design is never as flashy as Nothing’s — a brand which almost makes gaming phones look visually tame.

But if you’ve missed the notification light that phones used to have, then this could appeal as a modern upgrade of that. And while it’s probably not going to be super innovative — given that it looks reminiscent of what Nothing handsets have and is an evolution of an old feature — it’s still not the sort of thing you see in most mainstream phones, so it’s nice to see Google try something a bit different here.

Beyond all this, the teaser page also mentions that Pixel 11 series pre-orders will open on August 12 — so presumably right after the announcement. And if you sign up for emails from that page you’ll receive an unspecified discount on the new phones, which is always appreciated, especially with everything getting more expensive lately.

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Business laptops under $600
Business laptops under $1000
Business laptops over $1000

A business laptop is a critical decision that impacts productivity. Let’s just say, I’ve made the wrong choice before, and the results weren’t nice. But don’t worry, I learned from my mistake and made a much better choice. You can also learn from my experience, as since then, I’ve extensively researched and tested the best business laptops.

The best deals ultimately depend on your workflow. For instance, the choice for an office worker using everyday business apps like Google Workspace and Microsoft 365 differs much from a colleague editing 4K video and running CAD and 3D rendering programs. The key factors to consider are performance, storage, portability, security, and connectivity, based on your unique needs.

Whether seeking a mid-range or high-end option, you don’t need to stress to find it. With my long experience running through many business laptops, I’ve compiled the top picks from Dell, Asus, HP, and other known brands, starting from $548.

Best business laptop deals: Quick links

• Amazon: Save on HP, Microsoft, Apple machines and more
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Best business laptop deals: Under $600

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Best business laptop deals: Under $1000

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The Intel ThinkPad E16 Gen 3 pairs a Core Ultra 7 255H processor with 16GB of DDR5 memory, a 1TB PCIe Gen4 SSD, and a 16-inch WUXGA IPS display for demanding workloads.

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Built for demanding workloads, this 16-inch laptop scored 4 stars in our review. It combines desktop-class performance, a huge 99.9Wh battery, expandable storage up to 6TB, advanced cooling, and modern connectivity in a slim aluminum design for professionals on the move.

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Best business laptop deals: Over $1000

Powered by an Intel Core i7 processor and Nvidia GeForce RTX 5060 graphics, this laptop, which held up well in our review, pairs a color-accurate 15.6-inch QHD 165Hz display with 16GB DDR5 memory and a 1TB SSD for demanding creative workloads.View Deal

A clean, professional 16-inch laptop with a touchscreen you'll actually use. As we found when we went hands-on with it, the Intel Core Ultra 9 285H runs 16 cores up to 5.3GHz and includes an NPU for Copilot+ AI features. 32GB of DDR5-5600 in a dual-channel config gives you genuine multitasking headroom — not just enough to get by. View Deal

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This 15-inch Lenovo laptop pairs an Intel Core i7-14700HX processor with Nvidia GeForce RTX 5060 graphics, 16GB RAM, a 1TB SSD, and a color-rich 165Hz OLED display.View Deal

The AMD-powered ThinkPad E16 Gen 3 combines a Ryzen 5 processor with 16GB of DDR5 memory, a 1TB PCIe Gen4 SSD, and a 16-inch WUXGA IPS display for dependable everyday productivity and multitasking.

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Available at Best Buy, HP's 16-inch laptop combines an AMD Ryzen 9 processor, Nvidia GeForce RTX 5060 graphics, 16GB of memory, a 1TB SSD, and a vibrant 2.5K 165Hz OLED display for demanding creative workloads.View Deal

Fortune 500 companies are expected to operate more than 150,000 agents by 2028, according to an analysis from Gartner.

If achieved, it would represent a 10,000-fold increase compared to 2025, when enterprises averaged just 15 agents. Indeed, this shift toward agentic systems is the most significant architectural evolution of our lifetime.

With adoption accelerating so rapidly, governance is rising to the top of the AI agenda. Businesses of all sizes are seeking safeguards and systems of control to ensure they can trust the agents they work alongside, yet current methods often fail.

The data speaks for itself: although almost every global enterprise is using AI agents, only 12% have introduced a centralized governance approach. For most, governance remains piecemeal, exacerbating the chaos it is intended to control.

This disconnect raises a critical question: as AI scales rapidly, how can organizations maintain governance to manage it responsibly? Nearly every leadership team is facing the same problem: they want to harness AI’s full potential but also maintain control.

AI enthusiasm leads to AI sprawl

While most businesses recognize the value of agentic AI, and are experimenting and deploying various agents across their organisation, the risk of AI sprawl is high. The growing mix of customer-built and pre-built agents creates disparate agentic tools with no centralized strategy.

As enthusiastic employees develop their own agents – an approach which is increasingly common as the technology becomes more accessible - AI usage becomes siloed and inconsistent across the business.

This type of AI sprawl drives diverging standards and duplicated efforts, with research showing 94% of businesses are experiencing increased complexity, technical debt and security risk.

The autonomous nature of agentic AI, where systems interact with data and other agents in real time, means a lack of governance in the early stages can see AI sprawl quickly spiral out of control.

Fragmented governance is ineffective

Forward thinking businesses are investing time and effort into new compliance processes and building better safeguards to ensure AI outputs can be trusted. One such method is the use of human-in-the-loop models for key decisions, an approach currently used by 52% of businesses.

However, as AI scales further and more rapidly, new challenges will emerge. There is a risk that human-in-the-loop methods lead to unintended inconsistency, where employees spanning different teams, regions or departments introduce uncoordinated rules for access, security and usage.

Instead of enforcing control, this has the opposite effect. Businesses are then left grappling with governance systems which reinforce the fragmentation they were trying to overcome.

Centralized rules, centralized standards, centralized knowledge

The only effective way to maintain control of scaling AI is to build governance into the AI itself. Governance must be designed as part of the foundations of an agentic system; it cannot be added as an afterthought.

To ensure meaningful control, enterprises require centralized visibility into which agents are running, how they are connected and where their dependencies lie. This represents a major opportunity, given that only 12% of businesses had introduced a centralized governance approach as of January 2026.

Historically, manual governance fails because enterprises cannot keep pace with the speed and scale at which AI agents interact. One of the core governance challenges is coordinating agents so they do not duplicate work or conflict with one another.

This requires orchestrating agents through a central neutral system layer that connects them to a real-time understanding of enterprise architecture and its operational parameters, ensuring outputs remain secure, compliant and coherent.

By ensuring there is a centralized governance layer built into the enterprise, all agents can follow the same rules, comply with the same standards and draw from shared knowledge. The governance system can then evolve and grow alongside the business itself.

Governance doesn’t need to be slow to be successful

The modern enterprise is already shaped by legacy systems, fragmented architectures and technical complexity, but it is not inevitable for AI to follow the same path.

The explosion of agentic tools offers enormous potential for business productivity, even if it does not automatically create coherent, high-quality architecture. The biggest challenge organizations face right now isn't the capability of the models themselves - it's connecting AI to where real work actually happens inside the enterprise.

It is now up to each organisation to pick and choose the agents best suited to their organisation and prioritize their integration in a unified way. This means moving away from old habits – often where governance is added incrementally every time a new agent or application is introduced – to remove the risk of fragmentation and AI sprawl.

The optimal approach is one which works with the unified and agnostic governance layer built into the foundations of the AI system to combat technical debt at scale. Only then will enterprises be able to harness AI’s full long-term potential in a sustainable manner.

Governance should not restrict the speed of AI adoption; it can scale alongside it. The successful enterprise of the future depends on its ability to approach AI with speed and efficiency without compromising trust.

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