When you ask businesses about how they are innovating their ways of working, one word will come up time and again: Agile. Agile delivery. Agile workflows. Agile everything.

The term has been so overwhelmed by buzzwords, supposed best practice guides, and misguided strategy documents that it has almost lost all meaning.

In fact, if many organizations that believe they’re operating in an Agile manner were to take a step back and take an objective look at their ways of working, they would quickly come to realize they have not moved away from legacy command-and-control structures.

In this fog of misunderstanding, a series of myths has taken hold that have made the role of Agile professionals far less clear-cut, making it harder for organizations to put this invaluable methodology into practice.

One root of these problems lies in a tendency for people to narrow Agile’s scope, approaching it as a tick list of processes to adhere to rather than a fundamental shift in how people work and deliver for a business.

It is critical that Agile professionals equip themselves to fact-check these myths as they come up in their work. Every failed project done in Agile’s name diminishes the luster attached to the methodology, increasing the risk that future businesses will reject this powerful way to improve program and project management.

Myth 1 – “Agile delivery leaders are just administrators”

Agile professionals are often seen principally as bureaucratic note-takers, or schedulers – the people who organize everyone else’s work.

This view misses the point: their real value is as facilitators and protectors from distraction. Good Agile professionals create the space for teams to focus, and align delivery with outcomes rather than just activity.

Myth 2 – “Agile professionals are the scrum police”

Some believe Agile delivery operates within a top-down hierarchy, with Agile professionals in place to enforce frameworks and control how team members spend their time.

This is quite wrong: Agile professionals work with teams, coaching them to make better decisions, remove friction, and build confidence in delivery – without resorting to outdated command-and-control methods.

Myth 3 – “Agile delivery must be technical”

It’s a common assumption that Agile delivery leaders require a deep technical background. Technical awareness can certainly help, supporting a deeper understanding of the team’s tasks and challenges, but it isn’t the core of the job.

The real skill lies in understanding people and processes, then creating the conditions for specialists to succeed. In most projects, the need is for strong communication and facilitation across the entire team rather than strategic decision-making or technical troubleshooting.

Indeed, in some circumstances ‘hands-on’ Agile delivery leaders who think they know best can create conflict within teams, damaging morale and motivation in the process. Think of it like athletics: coaches don’t need to run faster than the athletes; their task is to help the athletes run faster.

Myth 4 – “Certification equals capability”

A certificate may prove that somebody knows about an Agile framework, but it does not demonstrate that they’re capable of applying it in practice.

The best Agile professionals succeed through experience, attitude, and ‘soft’ skills. They know from experience how to adapt Agile values and principles to particular circumstances; when to challenge people, and when to simply step back and get out of their team’s way.

Like any profession, being truly effective at the job requires skill, practical experience, and professional characteristics appropriate for the role. A piece of paper doesn’t make you agile; effectiveness with people and process does.

Myth 5 – “Agile delivery leaders are cheerleaders”

Contrary to popular belief, Agile delivery isn’t principally about arranging team socials or keeping everyone upbeat during stand-ups.

The value lies in keeping people aligned and productive under deadline pressure and/or changing priorities; after all, no plan survives contact with the enemy. Agile delivery leaders are there to create stability within an uncertain environment: they help teams stay focused on what matters, and shield them from distractions so they can maximize time spent ‘in the zone’.

That said, there is value in boosting morale – and championing a team’s achievements is one way to do this. According to the “broaden-and-build” theory of psychologist Barbara Fredrickson, positive emotions broaden our thought-action repertoires, enabling us to develop skills and resources that improve our long-term productivity.

One famous study from the "Journal of Labor Economics" showed that happiness at work leads to improved performance, especially in jobs where creativity and initiative are key.

Myth 6 – “Agile delivery leaders must remove all blockers for their teams”

While impediment removal is without doubt a core responsibility for an Agile delivery leader, the reality is that teams solve most of their own issues – perhaps with the assistance of a little coaching or support.

Nonetheless, sometimes a challenge is too big, distracting or political for them to handle, and Agile delivery leaders must step in – often by representing the team with stakeholders and at governance layers: this can be helpful in reducing the ‘noise’ around the team and the number of distracting interactions that take team members away from the core task.

As captured in the proverb: “Give a man a fish, and you feed him for a day; teach a man to fish, and you feed him for a lifetime,” effective Agile delivery leadership is about enabling, not babysitting.

True Agile

So Agile is a much more subtle and responsive discipline than the stereotypes present – and it must be led by subtle and responsive practitioners. The approach can generate huge benefits – saving time, handling change more elegantly, reducing project failure rates and, above all, creating better products and services that more effectively meet customer needs and objectives.

These goals are achieved by working with people – rather than by tinkering with processes – to surface problems early in the development process, resolve them quickly and with a minimum of fuss, and avoid interfering when the team is already functioning effectively.

High-performing teams depend on trust, accountability and continuous learning. Leaders who foster those qualities are more likely to deliver successful outcomes, creating teams that stay focused, take ownership of their work, and continuously improve how they operate.

This is a role that is all about empowerment; when they’re doing their job well, much of an Agile practitioner’s work occurs out of sight. So how do you know when you’ve got a really great Agile delivery leader? When their team runs perfectly well without them.

This may not sound like the smartest business model for the practitioner, but it’s definitely the best outcome for the client’s own business – and that, ultimately, is what Agile is all about.

We've rated and ranked the best productivity tools.

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

• OpenAI expands Daybreak with Blue and Red tiers for defensive cyber work
• New GPT‑5.6‑Cyber model offers high compliance for authorized vulnerability research
• Access remains restricted due to dual‑use risks and reduced safeguard operation

OpenAI has announced two new tiers for its Daybreak dedicated cybersecurity project, each offering a different model with different levels of compliance. It also used the opportunity to introduce a new security-focused AI model, as well.

Hackers and criminals are increasingly abusing AI to improve and speed up the creation of phishing emails and malicious code, and with the introduction of AI agents, they’ve also used it to automate entire attack processes. The AI community responded by placing strong guardrails, making sure their models do not comply with requests to build malware, or hack other companies.

These guardrails ended up being a two-edged sword, because as they slowed down attackers, they also slowed down the defenders.

What is Daybreak?

To give the cybersecurity community the upper edge, companies like OpenAI started creating dedicated cybersecurity initiatives that provide a vetted list of companies state-of-the-art models, free of guardrails. The company also provided them with pre-trained AI agents, as well as access to a pool of shared knowledge.

Initially launched in June 2026, Daybreak originally included GPT.5-5-Cyber (a model optimized for security work), Codex Security (an agent that can analyze codebases, identify vulnerabilities, validate findings, and help develop patches), Patch the Planet (an initiative with Trail of Bits to find and fix vulnerabilities in open-source software), Daybreak Cyber Partner Program (lets approved cybersecurity companies such as Cloudflare or Cisco integrate OpenAI's cyber capabilities into their own products and services), and Trusted Access for Cyber (the governance/access system for organizations doing authorized cybersecurity work with these capabilities).

Now, OpenAI has expanded Daybreak with two access tiers, Daybreak Blue, and Daybreak Red.

The company says Daybreak Blue is “the recommended starting point for most defenders, supporting vulnerability discovery, secure code review, malware analysis, incident response, and patch validation. Companies opting for this tier can expect access to frontier general-purpose models, including GPT‑5.6 Sol, whose safeguards have been tailored to authorized defensive security work.

Daybreak Red, on the other hand, provides access to OpenAI’s “purpose-trained cybersecurity models for authorized vulnerability research, exploit validation, and security testing.” This tier offers the brand new GPT‑5.6‑Cyber, built on GPT‑5.6 Sol and trained to improve capabilities on several specialized cybersecurity tasks such as finding zero-day vulnerabilities and developing exploit chains.

This model is also more compliant and less likely to refuse certain higher-risk, dual-use cyber tasks.

Complying with "dangerous" requests

Request compliance is the name of the game here. OpenAI says the new model addresses feedback from security researchers who “encountered persistent refusals with the earlier model.” General-purpose GPT-5.6 Sol, for example, will comply with just 1.5% of the requests usually given by cyber-defenders working on codebase analysis or vulnerability identification. This percentage increases to 2.0% with Daybreak Blue access.

GPT-5.6-Cyber, on the other hand, completes 95.0% of requests, OpenAI says, up from 57.3% of the previous model, GPT-5.5-Cyber. We weren’t able to independently verify these claims, though.

While it doesn’t outright say it, OpenAI considers these models relatively dangerous to use, which is why they’re locked behind the Daybreak Cyber Partner Program. However, the program is now expanding, allowing these companies to embed the models behind their own products, managed services, or cybersecurity engagements, and offer them to clients of their own.

Those who wish to be a part of the program directly can do so by applying to join online now.

“Models running with reduced safeguards carry risks beyond standard model usage, whether from misuse or misalignment. Despite these risks, we believe that democratizing access to frontier intelligence for defenders is crucial to accelerating and automating cyber defense,” OpenAI said.

“Daybreak Blue and Daybreak Red access are available for approved individuals⁠ and organizations conducting authorized work. We control access through identity verification, account security, monitoring, approved-use restrictions, and legal attestations.”

• Wix launches Symphony, a standalone AI agent platform designed for SMBs
• AI agents work to understand your business, then complete essential tasks on your behalf
• Symphony comes with a built-in quality layer, helping ensure trust.

Wix, one of the world’s biggest website builders, has today launched Symphony, a new platform which aims to provide SMBs with a proactive team of AI agents that are built around their specific business needs, goals, and workflows.

The company says Symphony will allow business owners to move away from working alone to operating with a tailored AI team in just minutes.

What is Symphony?

Wix claims Symphony has been built on the experience and unique data it has accumulated while supporting hundreds of millions of businesses worldwide.

The company highlights how, unlike typical AI tools that simply act on instructions, Symphony learns the business, taking the time to understand its unique needs rather than offering a one-size-fits-all solution.

At the center is Maestro, Symphony’s central AI agent. Maestro understands what is happening across the business, identifies what needs attention next, and coordinates the right agent for each job.

Operating much like a team of humans, Symphony’s AI agents work across approved workflows, executing tasks and connecting to the tools that the business already uses. It then reports back, offering updates, analytics, and flagging key actions for approvals.

Each day your agents will also have a ‘morning meeting’, providing a summary through a simple app of work completed, highlighting what still needs to be completed, and identifying new opportunities based on business activity, customer behavior, and current trends.

Symphony also provides a built-in quality review layer, with a separate agent checking work before it is presented, helping support accuracy.

According to Wix, Symphony is available through a tiered subscription model.

“We built Symphony to be the go-to AI agent orchestrator for SMBs,” said Ronny Elkayam, COO of Wix.

“It is purpose-built for how small businesses operate, combining Wix’s two decades of SMB expertise with a coordinated multi-agent system that can learn how a business works, take action and help owners grow with more speed and confidence. With Symphony, any business owner can have the team they need to scale.”

After testing Geekom's Intel-powered IT13 Max, we found it a good, robust machine that's ideal for office work and light creative tasks. And right now, the Geekom IT13 Max AI mini PC is discounted to $679 (was $799) at Amazon with code TRT13DFF.

This mini PC pairs a genuine Core i9 mobile chip with 24GB of RAM and a 1TB SSD, in a chassis smaller than most external hard drives. In the UK, Geekom's mini PC is down to £637 (was £749) at Amazon.

Should you buy it?

✅ Buy the GMKtec IT13 MAX if...

You want strong CPU performance for office work, coding, or light creative tasks in a tiny footprint. You need to drive multiple 4K monitors from one small box.

❌ Skip the GMKtec IT13 MAX if...

You want to game beyond light, reduced-settings titles — this runs integrated graphics only. It also uses DDR4 RAM, so skip it if you need DDR5 bandwidth for memory-intensive workloads. And if you're on a tighter budget, Geekom's lower-tier i5 configs cost meaningfully less.

Why we recommend it

The GMKtec IT13 Max earned 4-stars in our review, where it proved a capable mini PC. We said, "for office and light creative work the IT13 Max is an excellent option," as well as use as a workshop or production-line machine.

The i9-13900HK is a genuine high-end mobile chip — 14 cores, 20 threads, and clock speeds up to 5.4 GHz — and it's unusual to see it in a mini PC this size and at this price. It handles everyday office work, code builds, and multitasking comfortably, and Geekom's cooling keeps things quiet under sustained load.

24GB of RAM and a 1TB SSD are both generous starting points (but opt for the 16GB RAM model if you want to expand that memory up to 96GB). Connectivity is solid: dual USB4 ports at 40 Gbps, dual HDMI, Wi-Fi 7, and support for driving four displays simultaneously, making it a capable multi-monitor workstation for the price.

Price Context & Historical Value

Geekom lists this configuration with an MSRP north of $800, and it's typically discounted through coupon codes rather than site-wide sales — $679 with code TRT13DFF is a solid price for the 24GB/1TB tier, in line with what this model has landed at during previous promotions. This is the lowest price in the UK that the IT13 Max has been. Worth noting the code expires August 30, so this isn't a standing price.

For more savings, every week we track the best mini PC deals.

The Catch: What to know before you buy

There are two sticking points with the IT13 Max. First, the 24GB RAM model features fixed memory, so you can't upgrade it. If that's what you want, look for the version with 16GB RAM (available direct from Geekom US and Geekom UK).

This lets you expand both memory and storage — RAM to 64GB, plus room for a second M.2 drive and a 2.5-inch SATA bay.

Second, the IT13 uses Intel's Iris Xe integrated graphics, not a dedicated GPU, and it runs on DDR4 rather than DDR5 — a deliberate cost-saving choice that keeps the price down but caps performance in games and GPU-bound creative work.

It'll handle 4K video playback and light gaming at reduced settings, but it's not built for modern titles at high settings. If gaming or GPU-accelerated tasks are the priority.

• See all mini PC deals at Amazon.

• CEVA Logistics hack disrupted European warehouses and exposed some customer data
• Retailers and Valve reported compromised delivery information and service delays
• Clients warned of targeted scams while awaiting fuller incident details from CEVA

CEVA Logistics, one of the biggest shipping and logistics companies in the world, has suffered a major cyberattack, the effects of which are trickling down to many of its clients.

The details of the hack itself, however, are not yet publicly available and what little information is out there came from the affected clients themselves.

CEVA has not yet issued an official statement, or filed a report with the regulators, but confirmed to TechCrunch that the attack most likely started on July 29, 2026, and affected at least eight warehouses across Europe.

Technical details missing, affected customers step forward

CEVA Logistics is a global logistics and supply-chain company and a wholly owned subsidiary of CMA CGM, a French shipping giant. It provides freight forwarding, contract logistics, warehousing and transportation services to thousands of customers, including major companies in the consumer and retail, automotive, industrial and aerospace sectors.

The company operates in more than 170 countries around the world and last year it generated $18.3 billion in gross revenue, so it is a major player and a key target for attackers.

The details about the incident itself are scarce right now. We don’t know how the crooks broke in (via a successful social engineering attack, or by abusing a software vulnerability, for example), how much data they stole, or if they demanded a ransom payment in exchange for deleting the stolen goods. From one of the victims, though, we have learned that some data was most likely compromised.

When the effects of a cyberattack spill into the physical realm (as is the case here with eight affected warehouses) we can speculate the attack was either ransomware, or disruptive malware. Companies shut down parts of their IT infrastructure only when there is no other way to clear an infection or remove malicious outsiders.

In the meantime, a small number of CEVA’s customers confirmed suffering an attack and losing sensitive data.

Among them is Bol, a Dutch online retail company, which said the incident affected two systems used for processing orders from one of its fulfillment

“No bol systems were affected,” it said. “However, data of customers whose orders were processed via this location may have been viewed or copied.”

Bol also said restoring operations at one of CEVA’s locations was taking longer than anticipated. As a result, the assortment stored at the affected location was taken offline, and the products were unavailable for sale. Also, Bol is currently unable to receive goods from suppliers and sales partners at that location.

A similar announcement was given by De Bijenkorf, another Dutch luxury retailer, who said that order processing, returns, and refunds, might take longer, but stressed that its stores remained open. It also said that some customer data may have been compromised, including names, contact details, online orders data and, in some cases, VAT numbers. Payment information, bank account numbers (IBANs), credit card information, usernames, or passwords, were not compromised, it was confirmed.

Valve steps forward

Retail giants aside, PC gaming powerhouse Valve also notified its customers about the incident. It said CEVA ships Steam hardware to its European customers and as such, receives specific delivery-related information from Steam.

This information, which CEVA retains for up to 90 days after the order, was most likely compromised. It includes names, street addresses, phone numbers, email addresses, and the type and price of ordered products.

Valve warned its customers to expect fake messages, either via email, SMS, or phone, that might mention recent hardware orders.

“They may quote your address back to you to prove they're genuine. They may ask you to confirm a delivery, pay a small customs or redelivery fee, or sign in somewhere to “verify” your order. Treat all of them as fake,” Valve warned. The company also stressed that customer accounts are safe and that users need not do anything to secure them.

Other details are missing, not just for the general public, but for the affected CEVA clients, as well. Valve said it was “pressing” the company for the full scope of what was taken and how, and added that it is notifying the relevant authorities, as well.

A spokesperson of the Dutch data protection authority, Mark Schenkel, told TechCrunch the agency so far received 10 incident reports. Given the size of CEVA, it’s safe to assume there will be others.

Epson's EcoTank ink tank printer have consistently impressed us during tests, with the format using bottled ink that's much cheaper than traditional inkjet printer cartridges. For an office upgrade or a new back-to-school set-up, I recommend checking out the Epson EcoTank ET-2800 for $200 (was $240) at Amazon.

If you're sick of buying overpriced ink cartridges, this is the cure. It includes four bottles of ink in the box that Epson says can last up to two years, and it's one of the best-selling cartridge-free printers on Amazon for a reason.

Should you buy it?

✅ Buy the Epson EcoTank ET-2800 if...

You print a steady mix of documents and photos and want to escape the cartridge cycle. You want the lowest-cost entry point into Epson's EcoTank lineup. You're fine with manual double-sided printing and scanning one page at a time.

❌ Skip the Epson EcoTank ET-2800 if...

You need automatic two-sided printing (look at the ET-2850 instead). You regularly scan or copy multi-page documents — there's no automatic document feeder here. You print only occasionally — infrequent use can dry out the print heads.

Why we recommend it

We've tested the best home printers and best ink tank printers, and Epson's EcoTank line has delivered consistent performance results, topping our lists. Quick, cheap and easy refills, with good text and photo printing results.

The EcoTank system is the whole pitch here: instead of replacing small cartridges every few weeks, you refill from ink bottles, and each set is rated for up to 4,500 black-and-white pages or 7,500 color pages. Epson estimates that's roughly two years of ink for an average household.

At $199.99, this is one of the most affordable ways into that ecosystem, and it comes with wireless printing, a flatbed scanner, and a copier built in, so it covers the basics most home users need without extra bulk.

Price Context & Historical Value

$199.99 matches this printer's going rate at other major retailers, including Best Buy, Staples, and direct from Epson, where it's currently listed at the same price after a $40 discount from $239.99.

The cheapest I've seen for the EcoTank ET-2800 is $175, which is typically around big shopping events like Black Friday and the Christmas sales.

At a cent under $200, that makes this less of a rare price drop and more of a reliably good, currently live price, and $40 off is still a meaningful percentage saving on a printer in this range. It's the lowest we've seen this bundle land since January.

The Catch: What to know before you buy

This is a basic model, and Epson kept costs down by omitting a couple of features that some buyers will miss. There's no automatic document feeder, so scanning or copying multi-page documents means feeding them one sheet at a time. It also doesn't support automatic double-sided printing — you'll need to flip pages manually for duplex. If either is a dealbreaker, Epson's ET-2850 adds both for more money. For most home printing — school paperwork, tax documents, the odd batch of photos — the ET-2800 covers it comfortably.

• See all printer deals at Amazon.

AI can already write production code, review pull requests, generate documentation, diagnose bugs, and propose architectural changes. Its influence now extends beyond developer productivity, affecting how engineering teams are organized, how decisions are made, and where technical authority rests.

CTOs, meanwhile, have always acted as orchestrators. Company growth gradually pulled their attention toward hiring leaders, setting architecture, resolving trade-offs, and improving team performance. Their influence flowed through the organization they built and the people they developed.

AI now changes the organization beneath technical leaders. As of May 2026, Claude authored more than 80% of the code merged into Anthropic’s codebase, while the typical engineer merged eight times as much code per day during the second quarter of 2026 as in 2024. Engineers increasingly direct and review AI-generated work, while retaining responsibility for technical judgment, goal selection, and higher-level decisions.

Implementation and debugging can increasingly pass to coding agents, leaving engineers responsible for defining tasks, reviewing results, and deciding when human intervention is required.

The middle management tier consequently faces compression, while the CTO comes closer to execution because agent access, architectural rules, security controls, and release criteria affect the entire company.

The CTO’s responsibility is building a verification machine capable of producing sound technical decisions at high speed.

The middle tier compresses

Traditional engineering organizations have always grown through coordination. A CTO worked through vice presidents, directors, and engineering managers, each translating company priorities into technical plans. Managers assigned work, tracked delivery, resolved blockers, and kept teams aligned.

AI reduces much of this coordination burden. An engineer can describe a feature, ask an agent to inspect the codebase, generate an implementation, write tests, and prepare a pull request. More advanced systems can divide work across several agents and combine their output.

The engineer increasingly manages an AI development team, which compresses roles centered on task distribution and progress tracking. Human leadership retains its value through coaching, conflict resolution, recruitment, and professional growth, while coordination as a standalone function carries less leverage.

Responsibility, therefore, spreads in two directions:

Engineers gain greater ownership because they command far more productive capacity; CTOs become more involved in the systems governing this capacity because one weak permission rule or review gate can expose the entire company.

The distance between technical leadership and execution narrows. A CTO may write little application code, yet needs a precise understanding of how agents create, inspect, test, and deploy it. The role owns the engineering operating system governing people, agents, and software delivery.

The CTO’s verification machine

Verification is now the central technical challenge.

An AI system can generate several possible implementations during the time a human engineer once needed to produce one. This abundance creates a selection problem because companies must identify which implementation fits the architecture, meets security requirements, remains maintainable, and serves the product goal.

The CTO must design a system capable of making these judgments consistently.

Scoped permissions confine each agent to the files, databases, and services required for its assigned task. Automated evaluations test security, performance, and reliability, while agents review one another’s work before sensitive actions reach a human reviewer.

Human approval, however, loses value when engineers face a constant stream of requests that rarely require intervention. After several rounds of autonomous testing and review, most proposed changes arrive in acceptable condition.

Engineers grow accustomed to approving them, attention declines, and the exceptional case becomes harder to detect. Aviation, medicine, and nuclear operations have studied the same effect: repeated exposure to routine confirmations can turn oversight into habit.

Effective verification therefore depends on reducing the volume presented to humans. Routine and reversible actions can pass through automated controls, while unusual, irreversible, or high-impact decisions receive focused review. The interface should present evidence, alternatives, uncertainties, and possible consequences in a form that encourages scrutiny rather than a reflexive approval.

Firm boundaries remain essential. An agent may propose a database migration while execution requires human authorization. It may generate a deployment plan while production credentials remain outside its permissions. It may identify a vulnerability while changes to authentication controls receive senior review.

The system should also measure the quality of oversight through rejection rates, review times, escalation patterns, and the frequency with which human intervention changes an outcome. Human judgment offers the greatest value when attention is reserved for decisions where experience can alter the result.

Audit trails, rollback procedures, and ownership rules complete the machine by making every autonomous action attributable, inspectable, and reversible. These controls determine how an AI-enabled engineering organization behaves and require architectural judgment, security knowledge, product awareness, and an understanding of human behavior under pressure.

The CTO increasingly designs the conditions under which technical decisions emerge, turning individual judgment into a system capable of producing consistent quality without exhausting the people responsible for its highest-risk decisions.

Coding is abundant; judgment is not

AI lowers implementation costs, while software quality still depends on judgment.

A company can now produce more features, integrations, and experiments than its customers need. It can create technical debt faster than any human team and fill a codebase with locally correct changes capable of weakening the system over time.

Planning, testing, and prioritization consequently become the main constraints on engineering output. Strong organizations will know which problems deserve attention, how each feature supports the product, and where technical compromises create long-term costs.

Technical strategy gains value as implementation capacity grows.

Security is more important because agents can act across more systems at greater speed, while testing carries greater responsibility because generated code can appear persuasive while hiding subtle errors. Maintainability is harder as software volume grows faster than human comprehension.

AI commoditizes implementation and raises the value of technical judgment.

The strongest CTOs will turn judgment into repeatable mechanisms by encoding standards into evaluation suites, review policies, deployment gates, and agent instructions.

Privacy, trust, and security

AI systems increasingly retrieve information, make decisions, call external services, modify records, and trigger actions across business systems. Risk therefore depends on authority as much as intelligence.

An agent with access to customer records, payment systems, private repositories, and production environments carries enormous operational power. Prompt injection, compromised model providers, manipulated data sources, and unintended autonomous actions can become entry points into critical systems.

Privacy and trust become architectural concerns. CTOs must define model governance, data permissions, identity controls, and approval requirements. They also decide which information can enter third-party models, which tasks require isolated environments, and which actions always need human confirmation.

Agent identity is essential because companies need to know which agent performed an action, who authorized the task, which data informed the output, and which rules governed the process. Capability provides an incomplete standard because permission determines the level of risk.

After all, a mediocre model with production credentials is more dangerous than a brilliant one in a sandbox.

A strong AI engineering organization treats every agent as an active participant with a defined identity, a limited role, and an auditable history.

Users judge the product

Companies often present AI as a product feature because it attracts attention, although the largest gains may come from using it inside the development process.

Customers judge software by quality, reliability, safety, and speed of improvement. The number of agents contributing to a codebase carries little relevance to their experience. Competitive advantage comes from turning increased engineering capacity into better products while preserving trust.

AI therefore belongs primarily to the production side of the company. It helps engineers explore more implementations, test changes more thoroughly, respond to incidents faster, and improve existing features with greater frequency.

Product design still determines how much complexity reaches the customer and how effectively the software handles permissions, routing, configuration, and other operational decisions.

Customers experience the value of AI through faster product improvement, fewer defects, more responsive support, and software capable of adapting more effectively to their needs. The technology itself can remain largely invisible.

The CTO must ensure increased engineering capacity serves the product and strengthens the customer experience.

CTOs as product leaders

When implementation was expensive, product teams defined requirements and engineering teams built them. Limited development capacity made the division easier to maintain.

AI weakens this boundary because technical capability can influence product direction almost immediately. A CTO can explore several product concepts with agents, create working prototypes, and evaluate constraints before a conventional development cycle begins, bringing engineering into product decisions earlier.

The CTO must decide where automation improves the experience and where human involvement remains essential. Some decisions benefit from speed and consistency, while others require empathy, context, accountability, and careful interpretation of consequences.

These choices combine product judgment with technical judgment. Breadth gains value because AI can help technical leaders acquire deep knowledge of unfamiliar domains quickly, while the advantage comes from connecting engineering, security, customer needs, and business strategy into one coherent system.

Future CTOs will need to understand the complete product environment, including how the company operates, how customers experience it, and how autonomous systems participate in both.

From organization builder to machine builder

Earlier generations of CTOs were remembered for the organizations they created. Their legacy lived in the people they hired, the leaders they developed, the culture they established, and the engineers who continued advancing the company after they left.

AI adds another durable artifact through the agent fleet, permission model, evaluation systems, deployment gates, architectural constraints, and feedback mechanisms left behind by technical leadership. These components determine whether a company can continue producing software safely after senior leaders depart.

The Industrial Revolution expanded physical production by embedding human knowledge into machines and processes. AI can create a comparable expansion in software development by turning parts of technical reasoning into systems capable of continuous operation.

Small teams can build products once requiring entire departments, while established companies can test ideas and improve software at exceptional speed. The outcome depends on the quality of the machinery surrounding the models, because code generation alone can increase software volume while strong verification systems convert AI capacity into reliable innovation.

The next generation of CTOs will be remembered for the machine they leave behind, whose agents, permissions, gates, and evaluations allow humans and AI to keep producing better software long after its architect has gone.

We've featured the best IT automation software.

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

Artificial intelligence has moved from experimentation to everyday business use faster than almost any technology in recent memory.

For small businesses, AI adoption needs no convincing as most already see the benefits. The real challenge is now transforming isolated AI use into consistent business value.

Goldman Sachs found that 76% of small businesses are using AI, and among those users, 93% say it has had a positive impact. Yet only 14% have fully integrated AI into core operations.

That gap is where the next stage of AI adoption will be won or lost.

The question is no longer whether small businesses can access AI. It is how they can make it part of their business.

For me, that means moving beyond AI as a feature list and toward AI as a trusted experience employees can rely on in the flow of work.

The focus now should be on helping small businesses progress from deploying AI in everyday tasks, to reshaping workflows, to eventually inventing new services, business models and revenue streams.

Start where the work gets stuck

The temptation is to start with the technology, but the better starting point is the work itself. A modern AI-ready device, collaboration setup or workplace platform can promise faster content creation, more productive meetings or automated reporting. Those capabilities matter, but the question is more basic: what problem is slowing the business down?

The problem might be missed customer follow-ups, teams spending too much time turning raw information into action, or even just slow response times. AI becomes valuable when it is pointed at a specific bottleneck and measured against a business outcome: time saved, errors reduced, revenue protected, customers retained, or employees freed up for higher-value work.

This outcome-first mindset is critical because the goal should not be to optimize an old process simply because it exists. It should be to ask what the business needs to achieve, then design the workflow and the technology around that result.

Discipline is important because small businesses do not have much room for technology theater. The most useful AI projects are rarely the flashiest. They are the ones tied to work that happens every day.

Redesign the workflow, not just the task

The next step is to move beyond individual productivity. Many employees are already using AI in small, informal ways, with a 156% increase between 2023 and 2025 in shadow AI usage. Shadow AI refers to employees using AI tools without formal approval, oversight or integration into company systems.

Employees ask AI to clean up an email, summarize a document or prepare a first draft. While those use cases can help, they usually create isolated gains. The bigger opportunity comes when AI is built into the workflow itself.

Consider a customer-facing team. AI can draft a response. But the bigger opportunity is redesigning a process around it. Let AI help categorize the request, identify urgency, suggest the next best action, and leave important judgment calls to a person.

For a lean operations team, AI can turn meetings, documents and business data into clearer next steps, helping reduce the manual follow-through that often slows momentum. Over time, this will become less about a single AI tool assisting with a single task and more about groups of AI agents working across connected workflows, with people shaping the strategy, setting the guardrails and orchestrating the work.

This is where many organizations still struggle. McKinsey’s 2025 State of AI research found that 88% of organizations use AI in at least one business function, but only about one-third have begun scaling AI across the enterprise. The same research found that AI high performers are nearly three times as likely as others to have fundamentally redesigned workflows.

In other words, the return comes less from sprinkling AI over old processes and more from rethinking how work should move. That is the difference between deploying AI, reshaping work and ultimately inventing new ways for the business to grow.

Train people to use AI with judgment

It’s not enough to invest in tools. Businesses must also invest in helping employees use them effectively. AI works best when employees understand what it is good at, where it can fail and when human judgment is required. Not every small business needs a large training program, but it does need practical guidance: which tools are approved, what information should stay protected and when outputs need human review.

Clear guardrails allow a business to scale AI with confidence. Goldman Sachs found that small businesses using AI cite data privacy and security concerns, lack of technical expertise and difficulty choosing tools among their top challenges. 73% said they would benefit from more training and resources to implement and evaluate AI successfully.

When employees are trained to use AI responsibly, technology becomes less of a risk to manage and more of a capacity builder that helps small teams work with greater speed, confidence and focus. It also builds the trust employees need to treat AI not as another feature to try, but as a dependable part of how work gets done.

Make AI a capacity builder

AI is often framed as a replacement story. In practice, many small businesses are using it as a force multiplier. The U.S. Chamber of Commerce found that 58% of small businesses use generative AI, up from 40% in 2024 and 23% in 2023. It also found that 82% of small businesses using AI increased their workforce over the past year. For lean teams, AI can create breathing room: less time spent chasing notes or repeating manual tasks and more time spent with customers and employees.

None of this happens automatically. Small businesses need to choose technology with integration in mind, not just features in isolation. They need to understand where data lives, how systems connect and whether employees can use new tools without adding more complexity.

They also need the confidence to seek outside guidance, whether from technology partners, managed service providers, industry peers or local business networks. The right support can help small businesses see where AI should simply deploy, where it should reshape the way people work and where it may create room to invent something entirely new.

The bottom line: AI should change how work gets done

The small businesses that get the most from AI will not necessarily be the ones that adopt the most tools. They will be the ones that ask sharper questions: Where are we losing time? Where are decisions too slow? Where are customers waiting? Where are employees doing work that software could support safely and reliably?

AI has already changed what small businesses can do. The next challenge is changing how work gets done. For small businesses, the real opportunity is not to add AI everywhere, but to apply it with purpose: deploy it where it helps today, reshape the workflows that define the business and invent new ways to create value tomorrow.

We've reviewed, rated, and ranked the best web hosting services.

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

• Microsoft Teams is bringing badges to your profile card
• Badges will include a user's particular awards and certifications
• Microsoft says this will make the experience more consistent across Teams and Outlook

If you're really looking to show off your skills at work, then Microsoft Teams might have the perfect answer for you - however it might not be for everyone.

The online collaboration platform has announced it is working on bringing "Badges" to your Microsoft Teams profile card, allowing users to display all their achievements to anyone who cares to look.

The addition will show Awards and Certifications on a user's profile card, where they will show up as badges, giving you an easy way to show off exactly what you can do.

Badge of honor

"You can now see Awards and Certifications as badges on a user’s profile card, bringing a more consistent experience across Teams and Outlook," a Microsoft 365 roadmap post outlining the feature explained.

The feature is listed as being "in development" right now, with an expected release date of September 2026. Upon release, it will be available to users across Windows and Mac around the world.

Badges have been available to Microsoft 365 users since 2023, designed as a way for users to display their expertise and qualifications (see above example) on specific cards.

The cards are currently available in several programs, including Outlook and the Copilot app, with their expansion to Teams clearly part of Microsoft's push to make the platform more informative.

This includes a recent upgrade to block bad bots with "smarter protection" which will let humans check all participants in a call are who they say they are, including bots in a call lobby, much like a nightclub bouncer.

Microsoft Teams is also working on a feature which will automatically update a user's work location when they connect to an office Wi-Fi network - hopefully meaning less confusion about where workers actually are situated, but bad news for those looking for a quiet day in the office tucked into a corner.

UK organizations are racing to modernize customer experience. AI-powered chatbots, agent assistants, workflow automation, and self-service tools are rapidly becoming standard across customer operations as businesses look to improve responsiveness, reduce pressure on frontline teams, and meet rising customer expectations.

But many organizations are discovering that deploying new technology is the easy part. The harder challenge is making those systems work reliably in the real world.

Too often, businesses are layering AI tools onto disconnected data, siloed systems, and infrastructure that was never designed for real-time customer engagement. On paper, the technology stack looks modern. In practice, customer experience still feels fragmented.

Customers are passed between channels without context. They repeat information multiple times.

Automated systems provide inconsistent answers. Human agents lack visibility into previous interactions. The result is a more complicated customer journey. This is becoming one of the defining challenges of enterprise AI adoption. For years, organizations focused on adding more digital capabilities to customer operations. Now, AI is exposing the operational weaknesses those businesses already had underneath.

The conversation around customer experience transformation has largely focused on speed and innovation. But the more important question is whether organizations can deliver AI-enhanced experiences consistently, accurately, and responsibly at scale. This is where trust becomes critical.

Customers are less forgiving of AI mistakes

In customer experience environments, trust is fragile. A delayed response may frustrate a customer. An inaccurate response can damage confidence entirely. This becomes especially important as generative AI moves into customer-facing interactions.

Unlike traditional automation, generative AI introduces unpredictability. Responses can vary. Outputs may be inaccurate. Systems can generate information that sounds convincing but is completely wrong. In highly regulated or customer-critical sectors such as financial services, healthcare, or public services, the consequences can be significant.

Customers are also often less forgiving of mistakes made by automated systems than those made by human employees. When AI gets something wrong, customers do not simply blame the technology. They blame the organization behind it. This is why many businesses are realizing that deploying AI is not simply a technology decision. It is an operational and governance challenge as well.

The organizations seeing the strongest long-term results are not necessarily the ones deploying the most AI tools. Instead, they are the ones creating environments where those technologies can operate safely, transparently, and with clear oversight. That requires far more than experimentation.

It means understanding where customer data comes from, how decisions are being made, when human intervention is needed, and how systems are monitored over time. Automation still requires accountability.

Disconnected systems create disconnected experiences

Many of the problems businesses face today are architectural rather than technological. For years, organizations approached CX transformation through isolated point solutions - separate tools for chat, analytics, automation, engagement, and workforce management. But disconnected systems inevitably create disconnected customer experiences.

Customers do not care which department, platform, or channel they are interacting with. They expect organizations to remember who they are, understand what has already happened, and resolve issues without forcing them to start again every time they switch touchpoints. That becomes extremely difficult when customer data is fragmented across multiple systems that cannot communicate effectively with one another.

This is why many organizations are now shifting focus away from simply adding more AI capabilities and towards operational consolidation. Businesses are recognizing that customer experience is no longer defined by individual interactions but defined by continuity across the entire journey. Without unified data, integrated orchestration, and consistent visibility across channels, AI risks amplifying operational complexity instead of reducing it.

The next phase of CX transformation will be about maturity, not experimentation

The pressure on organizations to move quickly is understandable. Businesses are dealing with rising service expectations, economic pressure, and ongoing demands to improve efficiency while maintaining customer satisfaction.

AI can absolutely help address those challenges. Used effectively, it can reduce repetitive workload, support frontline employees, improve response times, and deliver more personalized customer experience at scale.

But speed on its own it not a strategy.

The organizations that succeed over the next decade will not necessarily be the ones adopting AI the fastest. They will be the ones building the operational maturity required to make those systems reliable enough for customers to trust every day.

That means investing in integration, governance, data quality, and accountability with the same urgency that businesses are investing in AI itself. Right now, many organizations are still focused on what AI can do. The more important challenge is ensuring customers can trust how it behaves when it matters most.

We've featured the best CRM platform.

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

• Report finds 40% of British-based data centres are in London
• Recent analysis shows the UK has 555 data center installations operational, second only to the USA (4,423)
• Energy costs in the UK could slow development of data centres, following an OpenAI project being put on hold

A number of recent studies indicate that the UK has a strong data center industry, with the majority of facilities sitting within the M25. But as internet backbone speeds increase and planning is easier to get approved beyond London, the rest of the country is seeing an uptick in facilities.

Recent studies – including one by Statista – indicate a total of 555 UK data farms, placing it second in the worldwide charts, slightly ahead of Germany but massively behind the USA, which leads with 4,423.

But there are interesting developments, as while 40% of the UK’s data centres are within the M25, the South East ( the area immediately beyond London) and the North West boast the second and third largest collection of data centres.

A data center near you?

With London offering 40% of the UK’s data center capacity (219 are already operational, with 15 expansions planned), decision-makers are feeling the pressure.

While there is growing opposition to data centres in the US, public feeling in the UK is also growing, with a facility planned for London’s historic Brick Lane, amid concerns for water use, noise, huge structures, and electricity bills.

The British government announcement on July 31, 2026 that the development would proceed was met with some consternation.

Some of these concerns may also explain why data centres in the UK are popping up beyond the capital.

Prime Minister Andy Burnham’s native North West has 39 facilities, with 5 approved and set for completion, making it the most popular spot in England outside of London. In Scotland, 49 facilities are up and running.

Why-I data centres

Demand for AI and cloud processing and hosting is of course driving the development of new data centres, but not all parts of the UK are rushing towards progress.

Slow to add data centres are the largely rural areas of the South West (with 20 locations), and the North East, with just 21 sites. These regions may be slower to approve plans, or may simply have fewer applications to due to proximity to large population centres.

But this may change soon thanks to the Blackstone-backed development near Blyth in Northumberland, one of two approved within the past 18 months.

Challenges to developers and operators in the UK may yet contribute to a slowdown that might placate anti data center protesters. For example, an OpenAI data center intended for Cobalt Park in North Tyneside, was shelved in 2025 following concerns over increased energy costs in the UK, despite being a designated AI growth zone.

The conversation around post-quantum cryptography (PQC) has shifted. Organizations are no longer asking whether they should prepare for quantum computing, but how quickly they can execute a migration that many expect will take years to complete.

As technology providers accelerate their roadmaps, governments introduce new expectations and boards seek greater assurance over cyber resilience, quantum readiness has become a business priority rather than a future technology project.

That urgency is being driven from several directions. Google and Microsoft have both set out roadmaps that point towards 2029 as a significant milestone in the transition to quantum-safe cryptography, while the recent US Executive Order on strengthening national cyber security reinforces the expectation that organizations begin preparing for the post-quantum era.

Together, these developments are shortening the planning horizon and increasing the pressure on CISOs to move from strategy to execution.

That shift from awareness to execution is reflected across the industry. Gartner's 2026 CISO Role-Based Survey: State of the Union found that fewer than one in four organizations have made measurable progress towards quantum readiness and only 8% have a usable cryptographic inventory.

DigiCert’s own Quantum Readiness Outlook research tells a similar story, as most IT and security leaders expect quantum computers to be capable of breaking today's encryption methods within the next three to five years, yet only 7% report that more than half of their digital certificates are already quantum-safe or hybrid.

The greatest challenge organizations face is no longer understanding the risks posed by quantum, but instead about becoming quantum-ready before today's cryptography becomes tomorrow's liability.

Why the risk is already here

Quantum computing has the potential to deliver significant advances across science, medicine and artificial intelligence, but it also threatens the asymmetric cryptography that secures digital identities, software, financial transactions and communications. The concern is no longer confined to the arrival of a cryptographically relevant quantum computer.

Threat actors are widely believed to be adopting a harvest now, decrypt later (HNDL) approach, collecting encrypted data today with the expectation that it can be decrypted once quantum capabilities mature. For organizations protecting information with a long operational life, that changes the timeline completely.

Our data found that 84% of organizations believe at least some of their encrypted data is vulnerable to HNDL attacks. Financial transaction records and banking data were identified as the assets most at risk (58%), followed by cryptocurrency wallets and private keys (53%).

For CISOs, this provides an important starting point because rather than attempting to replace every cryptographic system simultaneously, the priority should be identifying the systems protecting long-lived, high-value information and focusing migration efforts where the business impact would be greatest.

Discovery before deployment

For many organizations, the greatest challenge is not selecting quantum-resistant algorithms, but understanding where vulnerable cryptography exists across the business.

Furthermore, cryptography underpins cloud infrastructure, enterprise applications, connected devices, operational technology, software signing and countless machine identities. Over time, certificates, keys and algorithms become distributed across complex environments, often without a complete inventory of where they are used or which business services depend on them.

This is why discovery should be the first stage of every quantum readiness program. Organizations cannot prioritize risk, assess dependencies or build a realistic migration roadmap without first understanding their existing cryptographic estate. Discovery also enables security teams to identify the systems protecting their most valuable assets, allowing them to focus investment where it will have the greatest impact.

Once that foundation is in place, organizations can begin introducing quantum-resistant algorithms alongside existing infrastructure, testing interoperability, prioritizing critical systems and building the crypto-agility needed to adapt as standards continue to evolve.

Building a practical roadmap

Preparing for post-quantum cryptography is not a single technology upgrade, in fact, it is a long-term business transformation program that requires collaboration across security, infrastructure, application teams and technology partners.

The discovery stage provides the foundation by revealing where cryptography is deployed, exposing hidden dependencies and identifying systems that may otherwise be overlooked. Those unknowns are often the biggest source of delay, making early discovery essential to building a realistic migration roadmap.

With that understanding, organizations can begin prioritizing the systems that present the greatest business risk while assessing whether their wider technology ecosystem is ready for the transition.

That means working with software, hardware and cloud providers to understand their post-quantum roadmaps, identifying platforms that will require upgrades, and reviewing critical infrastructure, including web servers and TLS implementations, to ensure they can support quantum-safe cryptography.

It is important to remember that cryptographic standards will continue to evolve, making automation and crypto-agility essential for managing certificates, keys and algorithms at scale and adapting to future change.

The organizations that succeed will not be those that wait for quantum computing to arrive, but those that begin preparing now. By uncovering the unknowns within their cryptographic estate and building a phased migration strategy based on business risk, CISOs can strengthen resilience today while preparing their organizations for the cryptographic challenges of tomorrow.

We've featured the best business computer.

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

As AI continues to help the world of work evolve, more and more roles are increasingly embracing the technology in a bid to improve efficiency and value for money.

Perhaps nowhere is that more important than procurement - often a neglected area of interest, it plays a vital role for businesses across all industries, and has proved ripe for AI innovation so far.

We spoke to Amy Worth, Director Amazon Business UK and Public Sector Customers EU, to find out more.

• AI is becoming increasingly embedded in procurement. Where are you seeing AI-powered purchasing insights deliver the greatest value for organisations today?

AI is helping procurement teams move beyond simply processing transactions to making faster, more informed decisions. Many organisations face increasing pressure to do more with fewer resources, making data-driven insights more valuable than ever. Yet, a fifth of decision-makers say they lack systems to monitor and manage risk effectively.

AI can help to address that by offering better visibility into purchasing activity to identify spending trends, spot anomalies within the supply chain, and uncover savings opportunities. And by having AI embedded into everyday procurement workflows, teams spend less time searching for information and more time focusing on supplier relationships and strategic decision-making.

• How can better purchasing visibility help organisations identify savings opportunities while strengthening spend governance?

Organisations can't control what they can't see, so having a clear view of purchasing activity is essential for identifying savings opportunities and maintaining effective spend management. Rather than relying on manual reporting, procurement leaders increasingly need real-time insights that highlight spending patterns and flag unusual purchasing behaviour.

Tools such as Spend Visibility, which provides dashboards and visual analysis to easily track orders, spend and compliance can help organisations get a much clearer understanding of purchasing activity and help uncover savings opportunities.

• Many organisations are looking to simplify purchasing and reduce supplier complexity. How can this help build more resilient supply chains?

In order to build resilience in supply chains, it’s important to have the right procurement foundations in place before challenges arise, so businesses have the flexibility to respond quickly when disruptions occur. Reducing unnecessary complexity and simplifying purchasing is key and helps create that agility.

We help businesses streamline processes, improve visibility and make it easier to source from trusted suppliers, while also enabling them to maintain access to a broad supplier network which gives organisations greater choice when circumstances change.

• As business buying continues to evolve, what do you see as the biggest priorities for procurement leaders over the next few years?

Procurement leaders are balancing a wider set of priorities than ever before. That means alongside managing costs and improving operational efficiency, they're also expected to strengthen supply chain resilience, support sustainability goals and provide better insights to help inform wider business decisions.

It’s not as easy to manage all these priorities at once, and having access to reliable data becomes more important than ever, as it allows you to use the technology available to help make the right purchase decisions to meet these competing objectives.

• Looking ahead, what role do you think AI will play in helping procurement teams move beyond cost control to become more strategic business partners?

AI has the potential to fundamentally reshape the role of procurement by moving the focus to more strategic work. At Amazon Business, we see AI as a tool that removes friction and administration rather than replacing expertise, by helping organisations analyse purchasing data more effectively, identify opportunities more quickly and help make better-informed decisions.

With that friction removed, procurement professionals will be able to focus on building supplier relationships, improving resilience and creating long-term value for their organisations.

For much of the semiconductor industry’s history, performance debates have centered on compute throughput and memory capacity. Faster processors, wider vectors, and larger caches have been the primary levers for system architects, often treating the movement of data between them as a secondary concern.

Today, a different constraint is asserting itself as AI data center workloads proliferate, architectures diversify, and systems extend beyond traditional computing into the physical world. Data movement, rather than processing or storage, is increasingly defining the limits of performance, power efficiency, predictability, determinism, and scalability.

This shift around data movement is already visible. Across applications like advanced SoCs, AI accelerators, chiplet-based systems, and now in physical AI applications such as robotics, industrial automation, and intelligent vehicles, it has become clear that transporting the vast quantities of data required by such workloads is more demanding than processing them.

Physical AI does not introduce a new problem so much as it exposes an existing one: when systems must perceive, decide, and act in closed loops under real-time and safety constraints, inefficient or unpredictable data movement quickly becomes the dominant bottleneck.

The implications for both AI in the data center and physical AI extend far beyond any single market. As data center demand increases exponentially and the industry pushes toward increasingly complex, heterogeneous systems in anything from vehicles to industrial systems, data movement has become the primary performance battleground.

Performance limits are increasingly defined by data movement

Modern semiconductor systems integrate an unprecedented number of processing elements, including general-purpose CPUs, AI accelerators, GPUs, DSPs, sensor processors, and domain-specific engines. Still, simply adding compute resources rarely delivers proportional gains. In many advanced designs, performance plateaus long before compute capacity is exhausted.

Compute units are left sitting idle not because they lack capability, but because data cannot reach them efficiently or predictably. Late-stage analysis of high-performance SoCs often reveals that throughput shortfalls stem from contention, imbalanced bandwidth allocation, or inefficient routing within the communication fabric, rather than from compute limitations.

This is especially visible in physical AI systems, where delays introduced by data movement propagate directly into system behavior in closed-loop architectures. Latency or contention in the transport fabric can destabilize control algorithms, reduce accuracy, and even force over-provisioning of compute to compensate for the absence of bounded latency and guaranteed behavior.

While physical AI makes data movement constraints more immediately visible, the same forces are amplified dramatically in the data center. To push enormous volumes of data through extremely wide interfaces, processors increasingly contain hundreds of compute and accelerator instances, press against reticle-scale integration limits, use chiplet-based designs, and interface with multiple stacks of high-bandwidth memory (HBM).

As designs scale, the combined costs of moving data grow rapidly. In this environment, abundant compute and memory bandwidth offer little benefit without a data movement architecture capable of managing data at scale. What is a tight constraint in physical AI becomes an overwhelming one in the data center.

In both the AI data center and physical AI, performance is inseparable from the behavior of the data paths that connect perception, decision, and actuation. As a result, interconnect design can no longer be a back-end integration task. It has become a central architectural decision, on equal footing with compute and memory selection.

On-chip data movement is fundamentally different

Data movement inside a chip operates under constraints that differ radically from those of off-chip networking or board-level interconnect. On-chip transfers are extremely frequent, tightly synchronized with execution, and subject to stringent latency and power budgets.

Traffic is also inherently heterogeneous. A single system may need to carry high-bandwidth AI data streams, cache and coherency traffic, latency-critical control messages, and safety-related signaling, often simultaneously. These flows have very different requirements and cannot be treated uniformly.

Traditional best-effort arbitration models break down quickly under these conditions. Bursty accelerator traffic can interfere with control-plane and real-time data paths, leading to unpredictable system behavior. In mission and safety-critical or physical AI applications, such interference is unacceptable.

As a result, quality of service (QoS), traffic isolation, bounded latency, and determinism are now architectural requirements, not just optional optimizations. The interconnect fabric increasingly functions as an active system component, enforcing policy and guaranteeing behavior, rather than as a passive conduit for bits.

AI, heterogeneity, and physical intelligence magnify the challenge

AI workloads fundamentally change the character of data movement. They generate massive data volumes, irregular access patterns, and asymmetric traffic flows. At the same time, heterogeneous architectures distribute computation across many specialized engines, eliminating any single “center” of the system.

In both data center and edge contexts, this decentralization increases coordination costs. Data replication, synchronization overhead, and inefficient sharing can consume significant power and latency, eroding the benefits of specialized compute. Rather than compute placement, the challenge designers face now lies in how to efficiently orchestrate data movement between diverse compute elements.

Physical AI places additional pressure on these architectures. Unlike batch or best-effort inference workloads, physical systems operate continuously in real time. Sensor data must be ingested, processed, and acted upon within strict deadlines. Feedback loops amplify even small inefficiencies in data movement.

Together, these demands reinforce the broader lesson that heterogeneity without a deliberate data movement architecture leads to complexity and inefficiency, not scalability.

Chiplets turn data movement into a system-level design problem

Chiplet-based multi-die architectures promise yield advantages, faster innovation cycles, and importantly, flexibility. But they also elevate data movement challenges beyond a single piece of silicon.

Cross-die communication introduces higher energy per bit, tighter physical and architectural constraints, and more extra latency than on-die data movement. Interfaces that were trivial within a single die become critical bottlenecks once they must traverse package boundaries.

Successful chiplet systems, therefore, require system-level planning of data movement, encompassing topology, hierarchy, coherency and protocol selection, and physical constraints. Partitioning functionality without an integrated strategy for how data flows between partitions often increases, rather than reduces, overall system complexity.

Control and safety domains may span multiple dies, and failures or delays in inter-die communication directly affect system behavior. Chiplets demand not only modular compute but also modular, predictable connectivity.

Deliberate data movement is a strategic differentiator

Across markets, a clear divide is emerging. Teams that treat data movement as an incidental consequence of integration struggle with rising complexity, unpredictable performance, and costly late-stage redesigns. Those who deliberately architect data movement gain sustained advantages.

Deliberate data movement architecture enables a host of benefits. Bottlenecks in latency, bandwidth, and power can be identified early; system architectures can be made easily repeatable across product generations; and architectural intent, software behavior and physical implementation can be correlated much more closely and reliably.

Additionally, it better enables teams to explore the tradeoffs between alternative floorplans and interconnect topologies (as well as logical & physical partitioning of multi-die products) well before physical constraints harden.

Achieving this at scale increasingly requires automation that is physically aware, yet architect controlled. Modern approaches synthesize interconnect structures directly from connectivity, traffic, and layout constraints, while allowing designers to intervene and iterate rapidly. The goal is not to hide complexity, but to make it tractable.

Equally important is the generation of consistent architectural, software, verification, and implementation views from a unified system description. This top-down correlation reduces mismatch between intent and realization, helping to minimize risk and accelerate time to market.

The new battleground

As systems extend into the physical world, while architectures grow more complex to keep pace with expanding AI workloads the semiconductor industry is entering a new phase. Performance, power, safety, and scalability are no longer determined primarily by how fast data can be processed, but by how intelligently, efficiently, and predictably it can be moved.

Physical AI makes this reality impossible to ignore, but the lesson applies broadly, from data centers to embedded systems. The invisible highways inside chips have become the decisive terrain on which competitive advantage is won or lost. In this environment, data movement is no longer simple plumbing. It is the battleground where the next generation of semiconductor systems will succeed - or fail.

We've featured the best AI tool.

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

AI transformation is a technical challenge, an operational challenge, a governance challenge, a cultural challenge and, above all, a leadership challenge.

Some AI implementations have been relatively straightforward, while others, particularly where generative AI has been deployed too quickly, have encountered setbacks.

The headlines are filled with stories of employee resistance, AI systems behaving unpredictably, unexpected costs associated with AI usage, alongside encouraging examples of organizations delivering meaningful business value.

Despite these mixed outcomes, confidence in AI remains high because organizations are seeing tangible results where implementation has been approached strategically and with strong leadership.

If organizations take the promise - and the risks - of AI seriously, then leadership attention is integral. Leaders need to consider both the immediate and second-order implications across the P&L, workforce, training, culture, customer experience and every part of the organization.

PwC’s latest UK CEO Survey found that 98% expect to make material changes to their business or operating model this year, and 93% say their businesses have now adopted GenAI to some extent. But on the flipside, only 14% of UK business leaders say GenAI has improved profitability over the past year.

Moving quickly

Many businesses have moved quickly from AI curiosity to AI adoption and then found that the hard phase only just begins then. Many organizations are now grappling with the gap between AI adoption and measurable commercial returns.

Leaders now need to decide where AI genuinely changes the operating model, how teams should work differently, and how to maintain trust with employees and customers as automation becomes more embedded. Transformation cannot be delegated to IT alone. It requires leaders to set clearer priorities, redesign workflows, invest in management capability, and create a culture where people understand both the opportunities and the limitations of new tools.

The initial phase of experimentation and early implementation was comparatively straightforward. Many organizations have now entered a more demanding stage and discovered that AI transformation is very much like all other major transformations: it is not simply a technology initiative; it requires thoughtful planning, organizational alignment and sustained leadership.

The larger a business grows the more complex the real ways of working, with business processes and personalities, making successful transformation a test of judgement, prioritization, trust, and many other ‘soft’ skills as much as technical deployment.

Pilots are easy. Broader change isn’t

AI pilots can often succeed precisely because they are contained, deliberately low-risk, and may be owned by enthusiastic teams looking to solve roadmap challenges. The bigger challenges begin when new automations affect workflows, decision-making, customer experience and employee roles in novel ways, forcing the need for leadership.

One common challenge is that organizations layer AI onto existing processes without questioning whether those processes are still the right ones. For example, a sales team may begin using AI to summarize calls - an easy win. The more important question is how that time is then reinvested: how follow-ups improve, how to make customer relationships stronger, how tracked information is used to power insights.

AI only becomes transformational when leaders connect the technology to measurable improvements in how the business creates value. That requires continual reassessment to ensure that the technology, processes, stakeholders and broader business context remain aligned. This is why genuine business transformation cannot be delegated to IT or to any one department.

IT teams are essential to execute, but leadership is better placed to decide the business priorities that technology serves. Those priorities include fundamental questions: which workflows should change, which decisions should remain human-led, what risks are acceptable, and how success will be measured.

There is a strong case for senior leadership teams becoming fluent not only in AI but also in the capabilities that enable its successful application, including critical thinking, strategic planning and storytelling. These skills help leaders understand the broader systemic effects that AI can amplify - positively or negatively.

If AI is managed as a tool rollout then adoption may not optimally raise productivity, profitability or customer value. Achieving meaningful outcomes requires stronger management disciplines, clearer ownership across leadership teams and greater accountability spanning operations, sales, customer service, HR, finance and compliance.

Trust will be the biggest commercial issue

Pipedrive’s recent research, found that 55% of the public do not fully trust AI. More broadly, many consumers remain skeptical of the sales process itself. Getting over this, changing the perception of the business offering means using AI and strong leadership in concert to make internal transformations support raising external confidence.

Both customers and employees need to understand where AI is being used, why it is being used, and where human judgement still matters. And the research was clear: sales and customer experience teams must be able to develop relationships, showing emotional intelligence and concern for the customer if they are to win hearts and minds.

Using AI for faster responses, automated recommendations or AI-generated communications can damage confidence if they feel impersonal.

AI-generated customer communications should always be appropriately supervised, as inaccuracies can rapidly erode trust. Internally, leaders should ensure employees receive appropriate training, clear guidelines and confidence that AI is being used to support them rather than simply replace them. Transparency, sound judgement and consistent communication are essential if meaningful transformation is to succeed.

Leadership is managing continuous change

Leadership has always been about helping organizations adapt and improve. AI raises that expectation even further.

AI is unlikely to be a one-off transformation program. Instead, it marks the beginning of a period of increasingly rapid operational change. Future technologies, from quantum computing to entirely new forms of automation, will continue to reshape both business and society.

Leaders must stay alert to ensure they are improving and demonstrating the qualities needed at any moment: prioritization, external awareness, empathy, workflow discipline, and the projection of confidence.

Ultimately, the organizations that benefit most from AI will not necessarily be those that adopt the most AI or even the newest AI. They will be those whose leaders understand where technology should reshape the business, and who can build trust while leading that transformation.

We've listed the best small business CRM.

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