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The ‘SaaSpocalypse’ narrative continues to dominate investor outlooks, with fears that AI development is threatening the value of software companies. However, I believe that this is not a SaaSpocalypse, but instead a period of natural selection for the software industry – where the strong will thrive.

Widespread AI adoption is raising the bar, so SaaS providers must focus on delivering differentiated and highly valuable outcomes. Providers that are built on broad, undifferentiated offerings and don’t deliver clear, evolving value will soon go extinct and be replaced by simple AI solutions.

In contrast, software companies with true specialization, deep customer understanding, and defensible moats are well placed to strengthen and evolve their position in the AI era.

Deep customer knowledge

In the industrial mid-market, AI development needs to be grounded in practical applications. Manufacturers operate in physical, industrial environments where the priority is building and delivering the parts and products that power the real economy.

We know our customers are not looking to build the software themselves, or experiment with ‘vibe coding’, but instead they want their existing trusted technology partners to level-up their platforms in a secure, structured way.

Deep industry expertise, knowledge of specialized workflows, and decades of intricate data are the critical factors that pinpoint where real value can be delivered to customers. These elements are the most difficult for AI disruptors to attempt to simulate or replicate.

The impact of AI will meaningfully reshape how software is developed and delivered, but providers should continue to rely on established data moats and security frameworks.

The SaaS providers that own the bank of knowledge that existing software provides and capitalize on this deep expertise during AI adoption are the ones that will stand the test of time. Investing in AI platforms and agents that transform that knowledge into autonomous action will deliver exactly what customers need: improved decision making, elevated efficiency through margin expansion, and increased resilience.

One thing that remains constant is the pressure businesses are under to deliver more for less. To succeed at this critical juncture, software companies need to help businesses do just that through tactical and differentiated applications of AI within workflows.

Embedding AI securely with clear governance

Layering AI into workflows is a practical, high-impact way for businesses to scale without increasing expenditure significantly. When AI agents are embedded and trained in systems, they can automate routine tasks, surface actionable insights in context, and guide users through next-best actions. This frees up time for higher-value work.

When embedding AI tools, providers must prioritize governance by establishing clear guardrails, processes, and secure systems. Role-specific training is critical to ensure AI is embedded in workflows effectively while keeping business data secure.

Added AI capabilities play a central role in workforce development. As workflows become more intelligent, employees should be supported to build new skills to deliver higher-value work, while customers benefit from more responsive and capable systems.

Recognizing a natural progression beyond surface level AI integration, focusing on analysis, judgement and continuous improvement. To achieve this, providers can embed AI agents directly into the software environments that customers already depend on. In doing so, end users can enhance capability without introducing unnecessary risk or disruption to core operations.

AI does not replace expertise or SaaS; it raises the baseline and helps teams develop the skills needed to deliver more value.

Protecting margins

The industry is now moving beyond early enthusiasm for AI towards a more grounded understanding of its true cost and impact. As organizations begin to grapple with the realities of implementation, integration, governance and ongoing running costs, the conversation is becoming more balanced.

This shift will further separate those delivering meaningful, embedded value from those relying on surface-level AI features.

In periods of economic uncertainty and volatile market conditions, which is increasingly a global reality, companies need to build resilience through intelligent decision making and forward thinking. Where costs, energy prices, transport, and supplier availability shift quickly, manufactures are often relying on lagging indicators and manual spreadsheets that are outdated as soon as they are shared.

This latency be combatted by AI that is directly embedded into SaaS that can combine internal and external data to inform likely price changes and ideal buying windows.

Crucially, because these insights sit inside the platform customers utilize day to day, predictive recommendations are delivered in context with the right insight at the right moment. Real-time visibility into performance, supported by AI, allows businesses to act with greater confidence in uncertain conditions.

For customers, this translates to stronger margins, better outcomes, and increased business resilience. So, end-point users can experience an intuitive and informative platform that makes their job easier.

Going beyond the “either or” narrative

AI and SaaS are converging. Organizations that invest in both the technology and the capability to use it effectively will see the greatest return. With the right governance and a clear understanding of customer needs, AI becomes a force multiplier for the value SaaS already delivers, not a replacement for it.

The conversation needs to center on the customers using SaaS, who don’t want experimentations with code but technology they can trust delivered with clear process, security and reliability.

Ultimately, this is not the end of SaaS, but an inflection point to differentiate software providers. Those that embed AI meaningfully within trusted products and maintain genuine specialization will emerge stronger through this period of natural selection. Organizations that invest in both technology and the human capability to use it effectively will see the greatest return.

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AI companies use tokens to track usage and bill customers. Now economists are using them to track the spread of AI through the economy.

As AI adoption accelerates, some workers will naturally feel apprehensive. Harvard Business Review found trust in employer-provided generative AI fell by 31% between May and July 2025, despite rising top-down pressure to leverage the technology and boost productivity. This shows a clear gap between leaders’ priorities, and those of their teams.

AI tools have the ability to lighten workloads across business units and drive measurable results: for instance, teams using AI as a core part of their sales functions were 65% more likely to increase win rates.

Yet narratives that frame AI squarely in terms of cost-cutting and headcount reduction are holding some employees back from embracing this technology despite the obvious upside.

The conversation has shifted from speculation to execution, yet many organizations still struggle to move beyond isolated pilots. Closing that gap is a job leadership must prioritize to realize the technology’s potential.

AI initiatives do not fail because the tools themselves are weak or ineffective, but instead because employees do not trust how the technology is being introduced, what new tools mean for their role, or whether they will still have a place once they are embedded.

The real challenge is helping teams overcome their AI apprehension by building their fluency, introducing clear guardrails, carving out time for practical training, and spotlighting use cases that make the solution feel controllable and purposeful, rather than opaque and threatening.

Putting trust and fluency at the forefront

AI is not going to take an employee’s job, but another human who uses it efficiently, and to their own advantage, will.

It will give rise to entirely new roles (prompt engineers, AI ethics officers, AI maintenance specialists) just as digital transformation created functions that barely existed a decade ago. When I worked at a global social media company a decade ago, "social selling" felt abstract. Now it is mainstream, and we are on the same trajectory with AI, only faster.

When leaders talk about AI purely in terms of doing more with less, employees hear threat, not opportunity. People are far less likely to trust AI if they do not trust leadership's intentions behind it. Reassurance cannot come from policy alone, it has to come from behavior.

That starts with a considered view towards building fluency among teams. This is where leadership is responsible for showing employees how AI works in their own roles. Where employees don’t have a clear understanding of how and where they can integrate AI into their workflows, it’s only natural that they will fill this gap with doubt.

Adoption sticks when leaders provide clear guardrails, responsible training and practical examples so AI feels like a technology that amplifies their skills, rather than a threat.

Redesigning roles, not just adding tools

AI adoption cannot succeed if users' roles don’t evolve at the same pace. If people are operating differently to maximize the gains of AI, their individual job scopes can’t be static.

The most effective leaders are proactively redesigning responsibilities to remove low-value drudgery and focus their teams on interpretation, creativity and judgement.

This looks different depending on the business function. In a customer success team, the role must evolve beyond reactive post-sales support, because that wastes the additional business intelligence that AI can deliver. Leaders must recognize that CSMs can do so much more when aided by AI, and reshape their roles so they act as strategic revenue architects.

Using AI to build expertise on specific buyer personas, they can ask sharper, more consultative questions to anticipate their needs and solve problems before a customer realizes they exist. That is a genuine restructuring of the role, not a superficial rebrand.

On the sales side, AI is transforming how managers coach. Rather than sitting beside a rep on a call and writing notes (with all the unconscious bias that can bring), managers can now review an AI-generated brief, apply data-driven scorecards and identify precisely where individuals need support.

They can surface the sales calls where challenging situations were handled best and use real examples as training material. They can see which teams are winning and why, and where processes are breaking down. Coaching stops being as instinctual and subjective and becomes more rooted in data and real outcomes.

What true AI leadership looks like here

While employees might be apprehensive about the implications of AI on their roles, leaders are ultimately being judged on the results their teams drive and moving forward, AI will be a significant multiplier. It’s no longer a question of whether AI can deliver, so the challenge is taking workers on the journey towards understanding how AI will amplify their skills, not replace them.

Encouraging AI adoption in a way that drives real outcomes is one of leaders’ most urgent priorities. Employees do not move from apprehension to agency simply because they are told AI matters. They do it when leaders make the technology understandable, useful and clearly aligned to supporting their roles.

That means demonstrating trust and reshaping how people work, not just demanding that people urgently learn how new tools work.

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Despite years of artificial intelligence (AI) investment, most customer experiences still remain fragmented, reactive and heavily dependent on disconnected systems behind the scenes.

While 88% of businesses now use AI in at least one function, nearly two-thirds remain stuck in pilot phases, according to McKinsey, highlighting the gap between AI adoption and meaningful operational transformation.

Large Language Models (LLMs) have helped businesses create more responsive and personalized interactions, but many organizations are beginning to recognize that conversational AI alone is not enough. As organizations increasingly compete in the experience economy, the focus is shifting toward technologies capable of coordinating actions, workflows and decisions across the entire journey in real time.

That shift is helping drive a new era of agentic customer experience orchestration, where AI systems can move beyond simply responding to requests and instead help execute tasks, resolve issues and coordinate outcomes autonomously across the enterprise.

Large Action Models (LAMs) are emerging as a key part of that transition, helping organizations move beyond conversational intelligence to real-time operational execution.

Moving AI beyond conversation

That shift matters because it fundamentally changes what AI tools can deliver within customer experience.

LLMs brought conversational intelligence into the enterprise, helping AI understand intent and generate more natural interactions. LAMs build on that foundation by turning intent into action: determining the next best steps and executing multi-step workflows in real time, within enterprise-defined guardrails.

Importantly, the rise of LAMs does not signal the end of LLMs. The two technologies work side by side. LLMs remain critical for conversational understanding and contextual reasoning, while LAMs connect that intelligence to coordinated action. This moves AI beyond simply responding to requests, toward orchestrating outcomes across the customer journey.

For example, take a disrupted airline journey in peak holiday season. Until now, even advanced AI agents could usually only explain the delay or point customers toward another support channel.

Agentic virtual agents built by LAMs change that dynamic entirely. These virtual agents can authenticate the customer, rebook flights, update seating, process compensation, coordinate workflows across systems, and proactively send updates before the customer even asks.

That’s the real transformation taking place today: moving from AI that generates responses, to AI that helps orchestrate meaningful outcomes for customers.

The shift toward agentic orchestration

This marks the beginning of a broader shift toward autonomous customer experience driven by agentic orchestration. As AI systems become increasingly capable of reasoning and acting across systems, organizations are beginning to rethink the operating model behind customer experience itself.

Most enterprises were not designed to deliver the seamless, proactive and context-aware experiences we all increasingly expect. We believe closing that gap requires a new operating model for customer experience, one built on orchestration rather than isolated automation. One that can connect journeys end-to-end with shared context, continuity and coordinated execution across channels, systems, teams and AI agents.

This shift is particularly significant, because businesses today no longer compete solely on products or services. Increasingly, they compete based on experience.

Historically, organizations often faced a trade-off between operational efficiency and customer empathy. Improving one frequently came at the expense of the other. AI-powered experience orchestration has the potential to fundamentally change that equation by enabling experiences that are simultaneously efficient, proactive, personalized and emotionally intelligent.

We are already beginning to see early examples of this in practice. Utility Warehouse, one of the first organizations to deploy agentic virtual agents powered by LAMs, has used the technology to support complex customer journeys including billing support and service restoration.

By simplifying its experience architecture and better connecting front- and back-office workflows, the company has more than doubled containment rates while improving both customer and employee experiences.

Organizations best-positioned to succeed in the next era of customer experience will be those that are not simply deploying more AI, but those capable of orchestrating intelligent, connected experiences at scale.

Why governance is no longer optional

However, autonomy without governance creates the potential for risk.

Recent headlines of AI agents deleting databases, misinterpreting instructions and operating outside approved parameters have exposed a growing challenge for companies. The more capable AI becomes, the more important trust and accountability are.

Governance can no longer be treated as something layered on after deployment. As AI systems become more capable of reasoning and acting independently, governance must evolve from static policy into operational architecture embedded directly into orchestration layers.

This is where governance-by-design becomes essential. AI systems require enterprise-grade guardrails and clear operational boundaries to ensure autonomous actions remain trusted and aligned to business policies.

We expect open interoperability standards such as Model Context Protocol (MCP) and Agent-to-Agent (A2A) will also play an increasingly important role in enabling responsible agentic orchestration across the enterprise.

MCP is designed to act as a secure connective layer between AI systems, enterprise tools, data and workflows, helping provide the real-time context and controls AI systems need to operate safely and effectively. A2A can enable AI agents to securely communicate, collaborate and coordinate actions across different platforms and systems.

Together, these standards can help create the foundation for multi-agent orchestration, where AI agents and human teams can work together with shared context, governance and operational oversight to deliver more seamless, outcome-driven customer experiences. For organizations scaling agentic AI across customer experience, we believe this trust will increasingly become a competitive differentiator.

Human oversight remains essential As AI becomes more embedded into everyday work – with 36% of people already using AI tools in the workplace in the UK – conversation is shifting from what AI can automate, to where human judgement matters most.

AI is becoming more effective at handling routine and multi-step processes autonomously, but these systems still require human oversight. As AI takes on more operational responsibility, people will continue to play a critical role in designing the systems, handling exceptions, guiding decisions and stepping in during moments that require empathy and nuance.

We expect that balance will become increasingly important as organizations move toward more autonomous customer experiences. The goal is to enable humans and AI to operate as a coordinated system – each contributing where they are most effective.

The next chapter of autonomous customer experience

As competition increasingly shifts toward the experience economy, customer loyalty is shaped less by products or services alone and more by the quality of the overall experience they deliver. The challenge is no longer simply introducing AI into customer experience, but using it to remove friction, coordinate journeys and deliver outcomes more effectively across the enterprise.

LAMs are helping accelerate that transition by enabling AI systems to autonomously take action across workflows, channels and operational processes in real time – moving beyond reactive support toward more proactive and connected customer experiences.

It is vital that organizations can successfully combine AI, people and operations in ways that make experiences feel effortless for customers in order to compete. That ability to orchestrate intelligent, connected experiences that drive outcomes at scale will become a far more important differentiator than AI adoption alone.

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If your head is still spinning from the first episode of Stuart Fails to Save the Universe, I can't say that I blame you.

Not only were we walked through how comic book store owner Stuart managed to break the device that set off the 'multiverse Armageddon' we've since been thrown into, but a huge Big Bang Theory character has already been killed off... in a manner of speaking.

This week, we're going to officially universe jump for the first time. So, when does Stuart Fails to Save the Universe episode 2 arrive on HBO Max?

What time can I watch Stuart Fails to Save the Universe episode 2 on HBO Max?

For US viewers, Stuart Fails to Save the Universe episode 2 will drop on Thursday, July 30 at 6pm PT/ 9pm ET.

Internationally, you're looking out for these timings:

• US – 6pm PT / 9pm ET
• Canada – 6pm PT / 9pm ET
• UK – Friday, July 31 at 2am BST
• India – Friday, July 31 at 6:30am IST
• Singapore – Friday, July 31 at 9am SGT
• Australia – Friday, July 31 at 11am AEDT
• New Zealand – Friday, July 31 at 12pm NZDT

When do new episodes of Stuart Fails to Save the Universe come out?

(Image credit: HBO Max)

New episodes of Stuart Fails to Save the Universe will make landfall every Thursday in the US and on Fridays everywhere else. Here are the all-important dates you need to know about:

• Episode 1: out now
• Episode 2: July 30
• Episode 3: August 6
• Episode 4: August 13
• Episode 5: August 20
• Episode 6: August 27
• Episode 7: September 3
• Episode 8: September 10
• Episode 9: September 17
• Episode 10: September 24

Western labs still build the highest-scoring AI models. But the strongest Chinese alternatives now sit only a few benchmark points behind, while costing up to 100 times less, depending on the model and deployment scenario.

That changes the competitive question. The winner of the AI race may not be the company with the single smartest model, but the ecosystem that makes advanced AI affordable enough to deploy everywhere. That conclusion comes out of an analysis of 33 models from 15 providers, comparing reasoning performance, cost, and deployment control across the current market.

A Six-Point Gap: The Top of the Leaderboard Has Nearly Closed

On GPQA Diamond, a demanding science reasoning benchmark, the leading models remain American,but Chinese labs are no longer a step behind in a separate tier.

They're inside the same competitive band as the leaders. Claude Mythos 5 leads at 94.4%, followed by Gemini 3.1 Pro at 94.3% and Claude Fable 5 at 94%.

But Qwen 3.7 Max already reaches 92.4%, while GLM-5.2 and DeepSeek V4 Pro score 91.2% and 90.1%.

The benchmark scores still favor the West. What they no longer do is fully explain the business decision.

The Number That Matters More: Up to 100x Cheaper

A lower inference cost doesn't just save money on the same workload - it changes what companies can afford to build.

Lower inference costs mean more queries for the same budget, AI deployed across dozens of internal processes rather than a single premium use case, and cheaper support, analysis, and automation. They also make advanced models accessible to startups and mid-market companies that cannot justify frontier pricing.

Part of that price gap reflects a different safety model. Chinese open-weight systems generally come with lighter default guardrails than Western frontier models, so more responsibility for testing, misuse prevention, and compliance moves to the company deploying them.

A model that is slightly weaker but ten or fifty times cheaper can be commercially more competitive than the benchmark leader, because most business applications don't need the last few points of reasoning - they need a cost structure that scales.

This is a binary choice. The market has already split into two layers, and each follows its own logic.

At the bottom is the mass-market layer: classification, support, content generation, and routine business tasks. Here, “good enough” has already won, because users simply do not notice a difference of a few benchmark points, while every CFO notices a price difference of tenfold or more. Chinese open-source models are already taking this layer. Over the past year, the share of American models in OpenRouter traffic fell from roughly 74% to 20%, while Chinese models grew to almost half. By token volume, this is already their market.

At the top is the frontier: complex agentic tasks, coding, and everything where a model performs dozens of steps in a row without human intervention. Here, a small difference in quality compounds with every step, and over a sequence of 50 steps, a couple of percentage points in error rate can become the difference between completing the task and failing. That is why money behaves in the opposite way to traffic: enterprise budgets are concentrating around a few top models. Anthropic now accounts for around 40% of enterprise API spending, largely because of coding and agents. Tokens flow to the cheap models, while dollars flow to the best ones.

So there will not be a classic winner-takes-all outcome like in search. Models do not have a network effect, switching to another one costs almost nothing, and routing between several models is already a standard production architecture. In the end, we will have an oligopoly of a few frontier labs capturing most of the value at the top, and an ocean of cheap commodity models at the bottom capturing most of the volume. And that boundary will keep moving downward: what is frontier today will be commodity a year from now. That is why the value is not in any specific model, but in the speed of iteration and distribution.

And this brings us back to the original idea about regulation. If the West over regulates its own models, it will hand the entire lower layer to China, because for most business tasks, “good enough” is already enough. This is not a hypothesis. It is already visible in the numbers.

That's the real threat facing Western AI companies. The biggest risk is not that China immediately takes the top benchmark spot. It is that the market decides the top spot is no longer worth the premium.

Beyond Price: Open Weights as a Second Advantage

Several of the strongest Chinese models: GLM-5.2, DeepSeek V4 Pro, and Qwen3.5 397B among them are released as open weights, meaning companies can run them on their own infrastructure rather than renting access through an API.

That gives businesses more control over their own data, less dependence on a single vendor's pricing or policy changes, and the ability to adapt a model's behavior to their own needs.

Chinese labs are not only offering cheaper versions of Western products. They are competing with a different model of adoption, built around lower cost, openness, and control — one that appeals directly to companies wary of vendor lock-in.

That debate has now moved into Washington. In a recent open letter, leaders including Satya Nadella and Jensen Huang argued against restrictions on open AI models, warning that limiting their development could weaken US competitiveness.

When the heads of the world’s most valuable companies all defend something at the same time, the first question is always about money. Open models benefit Nvidia because it sells the hardware to everyone who runs them. It is telling that OpenAI and Anthropic, whose businesses depend on closed models, did not sign the letter at first, and OpenAI joined only after its absence became the main story. Everyone here is defending their own business model, and that is normal. It just should not be confused with concern for humanity.

But on the substance, the signatories are right. This is the same story as open source. Giving software away for free once seemed irrational, and then Linux became the foundation of the entire internet. Closed and open models will coexist, each in its own segment. The more open models there are, the more competition there is, the cheaper the technology becomes, and the faster the entire industry moves.

The real context of this letter is not philosophy, but Washington’s attempt to restrict Chinese open models that have moved close to the frontier.

Why This Pressures Western Labs

None of this means OpenAI, Anthropic, or Google are losing the AI race. Their models still lead on raw capability.

But that lead is becoming harder to monetize across every workload. As cheaper alternatives get close enough for routine business use, Western labs will face more pressure to defend premium pricing task by task rather than relying on benchmark leadership alone.

Six Topics Chinese Models Won't Touch the Same Way

But the same models that offer more economic and technical freedom come with a different kind of constraint. Technical openness doesn't mean political neutrality.

Chinese models can offer more infrastructure control while remaining more politically constrained on China-related topics: Tiananmen Square and 1989, Taiwan's status, Xinjiang and the treatment of Uyghurs, the Hong Kong protests, criticism of Xi Jinping, and even politically sensitive references such as Winnie the Pooh.

Responses may be refused, redirected, or framed in line with the official Chinese position. This operates on two levels: a live filter on the hosted API, and a deeper alignment trained into the model itself, which survives even when a company runs the model on its own servers.

Enkrypt AI found that roughly 91% of DeepSeek R1's responses on China-related controversies still leaned pro-Beijing after standard jailbreak attempts. Self-hosting can provide data sovereignty, but it does not automatically create political neutrality.

What Decides the Next Phase

The West still leads on maximum capability. The next phase of the AI race will not be decided by a few benchmark points alone. It will be decided by which ecosystem can turn advanced intelligence into affordable, scalable infrastructure for the wider market.

China has built a serious advantage on cost and control, but companies adopting that infrastructure will also need to account for its political constraints.

Microsoft has been one of the latest to put an estimated timeline on the arrival of a commercially viable quantum computer, predicting it will be as early as 2029. For businesses, that’s as little as ten quarterly board meetings away.

Every new prediction reignites debate about when Q-Day will finally arrive, but what’s more pressing is the preparation window it gives businesses.

Quantum readiness is not something organizations can achieve overnight. By the time a cryptographically relevant quantum computer arrives, businesses will already need to have identified vulnerable systems, mapped critical data and begun their migration. Q-day isn’t when preparation starts, it’s when preparation will be judged.

Treating quantum as tomorrow’s problem risks repeating a mistake many organizations made with Y2K. Boards need to recognize it as a critical business risk. The debate about timelines is a distraction we cannot afford when we know there may already be adversaries out there collecting data to decrypt at a later date.

How trust in business data could be swept away overnight

For long lived data - such as financial records, intellectual property or identity data - the risk doesn’t just begin when a quantum computer is able to break today’s encryption. The risk already exists through ‘harvest now, decrypt later’ attacks, where adversaries collect encrypted data today with the intention of decrypting it once sufficiently powerful and viable quantum computers become available.

Many businesses brush off this potential risk for one of two reasons. Some assume that even if encrypted data is being harvested, the sheer volume involved limits the threat. But what they might not have considered is the power of AI and quantum working together.

AI has made it possible to sift through terabytes of data almost instantly, helping to identify high-value information at scale. And that’s exactly what bad actors will do once quantum is available to break the encryption.

Others assume they’re not at risk because they have little long-lived data worth stealing. Yet the impact of a compromise will extend beyond the exposed information itself. Once the integrity of data is brought into question, confidence in the systems, contracts, transactions and even the intellectual property built on that data will quickly erode. At that point, the issue becomes a loss of trust.

Why risk doesn’t only fall on the shoulders of the CISO

For many boards, quantum security still sounds like a specialist technology challenge. In reality, it’s a leadership one that touches governance, resilience, compliance and corporate accountability.

The consequences extend as far as regulatory exposure, supplier risk, customer trust, operational resilience and ultimately, confidence in the data underpinning strategic decisions and AI models that many businesses are built on. If sensitive information can no longer be trusted, neither can the decisions, transactions or services built upon it.

As organizations invest heavily in AI tools and automation, trust in data has become a business asset in its own right.

That is why quantum readiness cannot sit solely with the CISO. Security teams play a critical role in identifying exposure and planning migration, but accountability for long-term resilience ultimately rests with leadership.

Good governance means staying ahead of material risks before they become urgent. Delaying PQC migration is one of the most significant technology risks of the decade and boards that act now will be able to demonstrate exactly how important it will be to the future security of an enterprise.

Why migration will start paying dividends now

One reason organizations and business leaders underestimate the challenge ahead is the assumption that quantum readiness simply means replacing one encryption algorithm for another. In reality, cryptographic systems are highly interconnected, meaning changing one component often has implications elsewhere.

Before any organization can claim to be quantum ready, it must understand and have full visibility of its entire cryptographic estate. That includes where encryption is used, how systems interact and therefore which assets would be affected by change. Without that knowledge, it’s impossible to plan an effective migration strategy.

While the journey to quantum readiness isn’t a quick one, the process delivers immediate value. Mapping cryptographic dependencies often reveals existing security weaknesses and governance gaps that organizations can and must address today.

So in many cases, the work required to prepare for quantum threats also strengthens resilience against current ones, offering businesses ROI far quicker than many might expect.

What should boards be doing now?

Boards don’t need to become cryptographers overnight, but they do need to start asking better questions and accept that quantum security is a present-day issue.

They should be asking the right questions of the right people. This includes:

• Who is leading our quantum readiness strategy across the business?
• Do we know how to identify where we are most exposed throughout supply chains?
• Which of our data assets would still be sensitive if exposed in ten or twenty years’ time?
• Where do we rely on cryptography across the organization?
• Do we have a plan for identifying and replacing vulnerable systems and supplier dependencies?
• Do we understand the difference in cost, complexity, competitive position and reputational risk between starting today versus waiting until 2029?

Organizations on the front foot will be the ones that use this warning period to understand their exposure and modernize their cryptography, putting them in a position where they can prove they took reasonable action while they still had time.

The good news is that getting started on PQC migration is much easier than boards may realize. The first step is mapping the business’ entire cryptographic estate. And more than a future investment, this will help to identify current vulnerabilities, too.

The important thing is taking the first step sooner rather than later, as it is a long process that won’t happen overnight. The question boards should be asking themselves now is, “if quantum arrived tomorrow, what would it expose about your organization?”. If leadership can’t answer that with confidence today, then there’s work to be done.

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The B2B sales process has transformed in recent years, spurred on by agentic AI developments.

These always-on systems identify sales cues and act in real-time, alongside automating orchestration and timely cross-business interactions, reducing latency.

Intelligent, critical thinking-led AI debriefs are also helping sales representatives capture customer insight.

In this model, AI underpins a hyper personalized, efficient and timely B2B sales experience which ensures every customer interaction is captured, understood and acted on.

The sales process: why traditional feels transactional

Business-to-business (B2B) sales experience looks and feels dated in this era of hyper personalized consumer selling. Sales representatives continue to record information within CRM platforms after any customer or prospect interaction, but this traditional method means that depth of information is limited to customer names, key dates, completed sales and other basic details.

This process ends up lacking the depth of the human touch and judgement, meaning the result is transactional and functional, and not always successful.

On top of this, deals take months to close, requiring input from multiple decision-makers and internal stakeholders. Sales representatives have to rely on memory and interpretation of previous interactions and also spend hours updating the wider business teams, coordinating activity and chasing for vital updates.

Often, elements of the customer experience, such as delays in delivery or incomplete orders, which directly influence the current relationship and future sales, are often not communicated down the line.

Agentic AI is changing this. Instead of relying on fragmented information and manual coordination, agentic AI presents businesses with an intelligence-led model that supports faster, more informed customer engagement.

How agentic AI is switching on customer engagement

To truly succeed in B2B sales, there must be effective collaboration throughout the business to allow rapid recognition of buying signals and swift, personalized responses. Agentic AI is switched on, constantly monitoring for these signals by tracking product usage, highlighting billing friction, checking email sentiment and, critically, prioritizing these events and orchestrating the response.

By creating quotes, sending updates to colleagues and generating prompts to help B2B revenue teams engage more effectively, agentic AI also plays a critical role in transforming productivity. By automating these tasks, representatives are free to focus on providing a more personalized customer experience, alongside improving the overall timeliness of decision-making and enhancing engagement and outcomes.

The technology can also initiate routine debriefs. Automating this after every sales visit enables the seamless capture of nuance, human judgement and subtle sales cues, for example: colleagues who may influence the decision, customer business issues that will impact the timeline or a billing dispute. All of this information would have likely been missed previously.

The agentic AI also then drives action forward, for example, triggering a pre-brief ahead of the renewal call and creating a customer-ready response and automatic update of the sales forecast in the case of a billing dispute.

It’s time to transform sales performance

Clearly, agentic AI has the potential to completely transform sales performance, capturing and contextualizing every customer interaction and empowering representatives to be more productive and successful. To achieve this, data from across the business must be structured, enriched and pulled together so that it can detect signals, prompt the correct actions and allow the business to make smarter, more timely decisions.

However, technology and the right data alone isn’t enough to ensure success. In fact, recent research has highlighted that agentic AI projects have a high failure rate, with over 40% expected to be cancelled by the end of 2027. But what is the cause for this?

To create a successful agentic AI tool, it requires complex, ground-up builds that take a significant length of time to deliver, challenging the desired Return on Investment. Instead, many organizations are now turning towards pre-built, subscription-based systems, which are changing this by offering rapid deployment and measurable impact within quarters.

As sales cycles become longer and buying committees become larger, the ability to identify signals, coordinate responses and surface insight in real time becomes increasingly valuable. This value isn’t replacing humans, but rather removing the friction that prevents representatives from selling effectively.

By combining human judgement with automated signal detection, orchestration and insight, agentic AI becomes a foundation for a responsive, personalized and truly successful B2B customer experience.

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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

In fact, experts recommend it because it can keep you off the hamster wheel of debt. Here's how to find the money in your budget to put toward savings and credit card payments.

(Image credit: Reina Takahashi for NPR)

• Beyerdynamic announces new DT 275 Pro wired headphones designed for use in "challenging" and noisy environments thanks to passive noise reduction
• The have a frequency range from 5Hz to 24kHz, and weigh under 200g, so they're nice and portable — and their 45-ohm impedance means they're easy for devices to drive
• They launch on July 28th 2026, and are priced at £99 (about $131 / AU$188) — as a user of Beyedynamic's more expensive headphones, I'm intrigued

Beyerdynamic has launched a new pair of on-ear headphones designed for audio and video professionals in "challenging" and noisy environments, such as live music venues, location recording and mobile production. The DT 275 Pro promise to deliver professional audio and noise isolation without a premium price tag: they're just £99 (about $131 / AU$188).

I'm a big fan of Beyerdynamics' studio headphones, so much so that they're my go-to for recording, mixing and mastering music (TechRadar's Audio Editor Becky Scarrott loved the Beyerdynamic DT 72 IE earbuds), but I don't have to do any of my music-making things on location or in noisy environments, and these new closed-back headphones have been designed for exactly that.

Beyerdynamic DT 275 Pro: key features and specs

The Beyerdynamic DT 275 Pro have an impedance of 45 ohms, making them easy to drive, and they have a quoted frequency range of 5Hz to 24kHz.

Maximum sound pressure is 125dB SPL, and they reduce ambient noise by a claimed 22dBA thanks to their closed-back, on-ear design.

Despite the slightly chunky appearance, these headphones come in just under 200g, so they should be comfortable for long shifts, and the supplied cable can be attached to the left or right ear cup and locked into place. The cable is 1.5m (4.9 feet).

The rotatable ear cups are made from sweat-resistant leatherette, which makes sense for mobile working — the velvety material used in many of the firm's studio headphone earcups isn't something you'd want to have on your head in the heat of a venue or club.

(Image credit: Beyerdynamic)

And key components such as the cup cushions are replaceable so the headphones should remain hygienic and last for a long time.

These are very much a niche product, so if you don't need the isolation and toughness I'd recommend one of the more indoor DT Pro pairs.

But if you need accurate audio when you're out and about, in a venue or on location these look like they'll be just the job — and for far less than you'd expect to pay for pro headphones that I use.

The Beyerdynamic DT 275 Pro will be available from 28 July 2026 with a list price of £99.99 / €119.00 (about $131 / AU$188).

Democrats are making the case that now is the time to tackle anti-corruption reforms — to serve as a check on Donald Trump and to overhaul a system voters say is co-opted against their interests.

(Image credit: Dustin Chambers)

It is widely accepted that technology investment is a key driver of innovation and productivity, with businesses of all sizes striving to achieve the optimal balance between modernizing their IT infrastructure with day-to-day operational priorities.

This can be a challenge and is exacerbated by the pressure to spend on AI. Organizations need to approach their IT modernization strategy in a measured way, as unfettered AI spend does not automatically equate to effective progress.

Recent industry research from Deloitte highlights the profound impact that the AI boom is having on technology investment decisions. AI is commanding a growing share of IT budgets, with 74% of organizations prioritizing investment into these areas well above other IT fundamentals such as data management, cloud platforms and enterprise resource planning.

Inevitably this raises some questions about how businesses are approaching their technology investments. With limited budgets, there are concerns that organizations are pursuing AI at a cost to operational necessities. It also highlights how IT strategies may not align fully with business objectives.

The global climate of economic uncertainty and geopolitical change has also compounded these issues. Decision making is more complex and budgets are more stretched than ever before, making it tough for any organization to implement an effective IT transformation.

Why modernization is a fine balance

In reality, it is not a viable option for the majority of organizations to modernize their entire IT infrastructure and systems in one go. Quite simply, this would be too costly and too disruptive to business operations. Instead, organizations most often opt for a more pragmatic, gradual approach that modernizes IT in planned phases.

Taking this path enables any organization to extend the life of its existing infrastructure, integrating new technologies and platforms with legacy IT along the way.

However, one of the pitfalls of this is when businesses want to add new AI technologies. They find that they are not ready to support these in terms of operational maturity or data reliability and are ill-prepared to innovate as and when needed.

This poses the question about how organizations should balance spending on phased technology replacement with achieving predictable long-term value to business operations. Front of mind for businesses is that operational efficiency is a necessity for successful and timely innovation that will stand the test of time, less so a focus on constant technology replacement.

Mitigate against pricing unpredictability

For IT leaders, while pricing concerns are in the spotlight, it is overall cost unpredictability that truly impacts innovation efforts.

Licensing models, support options and hardware pricing can change overnight, so those organizations that reduce their exposure to sudden cost hikes and have kept their options open are in a much stronger position to navigate through these scenarios and continue on their IT investment pathway.

It is not uncommon now for organizations to choose shorter term agreements or more versatile deployment paths. This might increase spending in the near term, but reduces valuable longer-term risk. Broadcom’s acquisition of VMware in the virtualization market demonstrates this point.

With major changes to areas including licensing models and pricing, this spurred many IT leaders to reassess their infrastructure strategy for the longer term.

Together, these considerations are impacting decision-making about infrastructure, and the in-depth investment planning that goes hand in hand with this. This might mean more focus on evidence-driven technology purchasing, more rigorous cases to support ROI and long-term cost exposure before senior leaders sign off on any modernization projects.

Innovation is not purely about technical ambition

Under closer scrutiny, it is no surprise that organizations are becoming more wary of setting false hopes with transformation initiatives, or large-scale replacement projects. Historically these were built on optimistic longer-term assumptions and a stable global economic climate, with little requirement to ensure that business cases were based on thorough due diligence.

Not so now. While the world’s businesses continue to innovate, there has clearly been a shift in mindset about how businesses are modernizing and the way that they are reaching longer-term goals.

Decisions are less driven by sheer technical ambition, and are more firmly grounded in risk mitigation, business resilience and financial predictability. All of these help to lay solid foundations for any new implementation more dependably than blue sky thinking.

Overall, the organizations reaping the most reward in the next three to five years will be those that modernize sustainably while managing stability in core operations combined with careful budget management. Incremental IT modernization is a highly practical approach that allows innovation at the same time as minimizing disruption and the risk of cost spirals.

In a temperamental technology market with no certain change on the horizon, the businesses that understand the inextricable link between innovation and financial discipline, and apply this fast to their modernization strategies, will be the ones most set up for future success.

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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

Dr. Kenneth Ngure, president-elect of the International AIDS Society, says, "We have had very good scientific advances ... yet the funding cuts have also taken us back."

(Image credit: Pablo Porciuncula)

The meeting comes as both face pressures at home. Netanyahu is up for reelection, embattled in part due to his deteriorating relationship with Trump. Trump is under pressure to end an unpopular war.

(Image credit: Alex Brandon)

The summer holidays are here, but for most of us, that doesn’t mean work stops.

Despite booking multiple days off and roping in anyone I can help with childcare, it is inevitable that, at various points over the next six weeks, the kids will be cooped up in my office with me while I try to work.

As someone who likes to plan, I’ve already invested in several items to help keep my little ones happy and occupied whilst I keep serving up insightful content on small business tech.

I’m keen to provide them with retro-style pastimes and give them some options that get them creative and active, without creating too much noise. That said, I am already eyeing up a £300+ gaming console to get myself some quiet time – and it’s only week two (it's all about balance, right?)

Below you'll find the list of products I'm using, or planning to use, to survive the summer holidays. Half are under £20, with the rest requiring a bit more of an investment.

Of course, there is still a chance that by week six, you'll find me in the playpen playing Pokémon whilst my kids run riot.

Under £20

Silvine A4+ Classic Scrapbook

Pritt Glue Sticks

MACMILLAN The Gruffalo and Friends: Amazing Animals Sticker Book

CiaraQ Air Dry Clay

Daimeitec Catching Sticks Game

EarFun Kids Headphones Wireless

JOYIN Rock Painting Kit for Kids

Kiztoys 26 Inches Kids Dart Board Set

Over £20 (but worth it)

Amazon Fire HD 10 Kids Pro Tablet

Nintendo Switch 2 Console

Neuro Wiz Balance Board for Kids

Gupamiga Playpen

Hot Wheels Stunt and Go Transporter Truck

Heromask VR Headset for Kids 5-12

Gemmicc Magnetic Tiles

Logicraft Smart Playpal 17-In-1 Screen-Free Electronic Handheld Game

French President Emmanuel Macron said France was "facing a completely unprecedented fire," describing the situation as France's worst fire-related crisis since World War II.

(Image credit: Emma Da Silva)

As a keen fitness junkie who likes to keep track of my runs and cycles, I was amazed to learn that Amazfit finally officially launched in Australia earlier this month by way of an Amazon AU storefront. The brand specialises in fitness trackers and wearables, and looks to challenge the likes of Garmin, Apple, Samsung and Google with its budget-friendly models that have excellent health tracking.

While the launch headlined its newest Amazfit Active 3 Premium and the Helio Strap — a screenless wearable taking aim at the Fitbit Air and Garmin Cirqa — Amazon has discounted a couple of older Amazfit models for a limited time, making them even more affordable.

The Amazfit Active 2 is my top pick, and is currently 20% off, bringing the price down to justAU$159.20. Even at full price it was a great buy, but this discount makes the already affordable smartwatch even more enticing.

Our Amazfit Active 2 review gave the watch a perfect 5 stars thanks to its classic design, excellent health tracking and 10-day battery life. We’ve also named the Active 2 the best overall cheap smartwatch, beating out the likes of the Apple Watch SE 3 and the Garmin Forerunner 165. Be quick, as there are only a few hours left before the deal ends tonight.View Deal

For less than AU$160, you’ll be getting a 1.32-inch AMOLED screen with 2,000 nits max brightness, downloadable maps, 160-plus workout modes, an AI coach, a heart health tracker, a cycle tracker and nutrition tracking, to name a few. There’s also voice control and message replies, which you can’t find in most Garmin watches.

Our reviewer said they were “frankly astounded” at the amount of features in the Active 2 considering its price, even saying it’s impressive for a smartwatch of any price tag, let alone one with an RRP of less than AU$200. Features like ECG and on-device GPS were highlighted as standout features that are usually found in pricier watches.The review also praised the Amazfit Active 2’s classic stainless steel design that looks more subtle and understated compared to other smartwatches, although they found the fit of the leather strap wasn’t perfect.

One nitpick they had was the absence of NFC for on-device card payments via Zepp Pay, as the feature is only available in the Amazfit Active 2 Premium model, but that’s hardly a complaint for something this affordable — especially with this discount.

Want something even cheaper? The 4-star Amazfit Bip 6 is also discounted at AU$103.20.

Here are the answers for The New York Times Mini Crossword for July 28.

Samsung's 9100 Pro 2TB SSD has dropped to AU$434.47 at Amazon Australia, well below the typical AU$699 pricing it's selling for at other retailers. Even on Amazon it was AU$644.83 before the current discount.

This isn't a deal to snooze on either, with the sale due to end at 12pm on July 29. Just note that it ships from Amazon US, so delivery can take up to two weeks.

AU$434 is still a lot to spend on a 2TB SSD, particularly if you remember how cheap storage was until relatively recently.

But SSD prices have shifted hugely since last year, and these days this is an excellent deal on Samsung's flagship PCIe 5.0 drive, which can reach sequential read speeds of up to 14,700MB/s and writes of up to 13,400MB/s.

The Samsung 9100 Pro 2TB is a great option for anyone after one of the fastest PCIe 5.0 SSDs around, particularly if you regularly work with large files or other demanding workloads.

At AU$434.47, it's a huge, and real, drop compared to the previous price at other retailers. Just keep in mind that it ships from Amazon US, so delivery can take up to two weeks.View Deal

In our review of the 9100 Pro we said, "Samsung's first true PCIe 5.0 drive is the best there is", as well as "This is a very professional-specific drive in ways that previous Samsung Pro SSDs were not. If you're not a professional user, there are better PCIe 5.0 drives out there, but for pros, there's none better than the 9100 Pro."

This is a premium 2TB SSD, so it's aimed at those who can actually take advantage of its performance, whether that's moving huge files, working with demanding creative projects or running workloads that benefit from very fast storage.

But what makes this deal particularly compelling is that the 9100 Pro is currently cheaper than some of Samsung's slower PCIe 4.0 drives. At the time of writing, the 2TB 990 EVO Plus is AU$446 at Amazon, while the 2TB 990 Pro is AU$517.67.

You don't necessarily need a PCIe 5.0 system to take advantage of the deal either. The 9100 Pro is backwards compatible with PCIe 4.0, so it'll work just fine in an older machine, albeit with its performance capped by the slower interface.

That makes it a great buy even if you're upgrading a PCIe 4.0 PC today, or using the drive in a handheld device. Plus, if you do move it to a PCIe 5.0 system later, you can unlock the full performance the drive is capable of.

Just keep in mind that this deal is specifically for the 2TB model without a heatsink as the other versions and capacities of the 9100 Pro aren't discounted.

And yes, AU$434.47 is still frustratingly pricey for 2TB of storage by historical standards, but I think getting Samsung's flagship PCIe 5.0 SSD for less than its slower models makes this an excellent deal considering current prices.

Here are hints and the answers for the NYT Connections: Sports Edition puzzle for July 28, No. 673.