Welcome to this year’s July blog (always well read because people have more time). This summer we thought we would ask, what’s next for tech?
While many take a step back during the summer months, technology doesn’t slow down. In fact, innovation often accelerates quietly in the background, reshaping how businesses operate, compete, and grow.
To help make sense of what’s coming next, we asked several of PwC Luxembourg’s technology leaders to share their perspective on the trends they believe will define the second half of 2026. From artificial intelligence to cybersecurity, digital assets to cloud transformation, their insights highlight what’s changing, but also why it matters now.
This is the question we asked each one of them: What do you see as the next big trend in your area over the coming months, and why is it important for businesses today?
Get ready because here’s what they’re watching closely…
Julie Martin, Advisory Director, Digital Transformation, PwC Luxembourg
“One major trend I see accelerating is the shift from customer experience design to real-time customer experience execution, powered by AI and connected data. In practical terms, this means moving beyond designing journeys on paper to actively orchestrating the next best interaction for each customer, across channels and in real time.
Why it matters: Customer expectations have changed. People no longer compare you to your direct competitors—they compare you to the best experience they’ve ever had. If an interaction is slow, generic, or disconnected, they move on. Businesses that can respond faster, more personally, and at the right moment will win loyalty and revenue.
The real shift is not just AI itself, but how it’s used:
- AI recommends or triggers actions (not just insights);
- Data is connected and usable across teams;
- Experiences are adapted in real time, not planned upfront.
To offer you a concrete example: instead of sending a generic campaign, a company can detect that a customer has visited a pricing page twice and trigger a tailored follow-up or proactive call. This kind of orchestrated interaction significantly improves conversion and retention.
In short, the next competitive advantage is not having more data, but acting on it at the right time, in the right way. Businesses that operationalise this will move from reactive customer management to truly proactive, value-driven relationships.”
Maxime Pallez, Advisory Director, Cybersecurity, PwC Luxembourg
“Frontier AI is not rewriting the cybersecurity rulebook overnight. But it is changing the clock.
The real shift for organisations is speed and mindset. AI can help attackers move faster from a known weakness to a working exploit, compressing timelines from weeks to hours. That does not mean every organisation is suddenly defenceless. It does mean that slow patching, unclear ownership, and weak response processes become more costly.
For boards and leadership teams, this is a business resilience issue, not just a technical one. The questions remain familiar: do we know what we run, where we are exposed, who we depend on, and how quickly we can recover?
Frontier AI simply makes those questions more urgent.
The practical response should be proportionate, not panicked. Reduce your attack surface, prioritise what is exposed to the internet, patch faster based on real risk, and ensure detection and response can keep pace with the threat. As you adopt AI tools, secure them with the same rigour as any other critical system — with clear ownership, third-party risk management and tested fallback plans.
- Assess exposure
- Compress the tempo
- Build resilience
The message is simple: frontier AI does not remove the need for strong cyber fundamentals. It raises the standard for proving they work… at speed.”
Krzysztof Jaros-Kraszewski, Advisory Director, IT Strategy and CIO Advisory, PwC Luxembourg
Six ICT trends reshaping IT strategy and the Target Operating Model.
Technology strategies can no longer be designed as static, multi-year roadmaps focused primarily on infrastructure, applications, and cost optimisation. Artificial Intelligence (AI), platform-based delivery, growing cyber threats, emerging computing technologies, and increasingly complex ecosystems are changing how organisations create value, manage risk, and operate technology.
As a result, the IT strategy and the IT Target Operating Model (TOM) must be redesigned together. The strategy should define how technology enables business outcomes, while the operating model must translate that ambition into clear capabilities, accountabilities, governance mechanisms, and ways of working.
Six ICT trends are particularly important:
1. AI-native enterpriseAI is moving from isolated use cases to becoming embedded in business processes, technology platforms, and everyday decision-making. In an AI-native enterprise, employees work alongside AI assistants and autonomous agents, while applications increasingly generate content, recommendations, decisions, and software code.
This changes the role of IT. Technology functions must move beyond providing AI tools and establish an enterprise capability for scaling AI safely. The IT strategy should address data readiness, AI architecture, model sourcing, integration, infrastructure, and value realisation.
The TOM must introduce clear accountability for AI products, models, and agents. It should also include AI governance, lifecycle management, security controls, human oversight, model monitoring, and AI-specific financial management. Business and technology teams will need to jointly own AI-enabled processes rather than treat AI as a separate technology initiative.
2. Product and platform organisation
Traditional project-based delivery often creates fragmented ownership, temporary teams, and a growing backlog of technical debt. Leading organisations are therefore moving towards persistent product and platform teams aligned with customer journeys, business capabilities, and value streams.
Under this model, teams remain accountable for a product throughout its lifecycle—from design and delivery to operations, resilience, cost, and continuous improvement. Funding and performance management also shift from individual projects towards measurable product outcomes.
The IT strategy should define which capabilities will be managed as business products, shared platforms, or foundational technology services. The TOM should establish end-to-end product ownership, multidisciplinary teams, and clear decision rights between business, technology, risk, and operations.
Success should be measured not only through delivery milestones, but also through business value, adoption, service quality, resilience, and total cost of ownership.
3. Platform engineering and AI-assisted development
Platform engineering is becoming a critical enabler of speed, standardisation and developer productivity. Internal developer platforms provide reusable technology services, approved tools, automated deployment pipelines, and self-service environments that reduce the operational burden placed on delivery teams.
At the same time, AI-assisted development and vibe coding are changing how software is designed, built, tested, and documented. These capabilities can significantly increase productivity, but they can also create new risks related to code quality, intellectual property, security, and uncontrolled technology proliferation.
The IT strategy should therefore position platform engineering as an enterprise capability rather than a collection of technical tools. It should define the role of AI-enabled software development, self-service automation, and reusable technology components.
The TOM must establish platform teams with clear service ownership, product management disciplines, and adoption of targets. It should also introduce engineering guardrails, secure coding standards, automated testing, and governance for AI-generated code. The objective is not simply faster development, but faster delivery within a controlled and reusable environment.
4. Security and resilience by design
Cybersecurity and operational resilience can no longer be added at the end of the delivery lifecycle. Growing dependency on cloud services, AI models, software supply chains, and external providers means that resilience must be designed into products, platforms, and business processes from the beginning.
The IT strategy should connect cybersecurity, technology risk, business continuity, disaster recovery, and third-party risk within a common resilience ambition. Critical services should be designed around defined tolerance levels, recovery requirements, and realistic failure scenarios.
The TOM must integrate risk and security specialists into product and platform teams while maintaining effective independent oversight. Risk ownership should remain with the teams that design and operate technology, supported by common controls, automated evidence collection, and continuous monitoring.
This approach transforms security from a compliance checkpoint into a core design principle and a shared operational responsibility.
5. Ecosystem and multi-cloud governance
Technology increasingly operates beyond organisational boundaries. Companies rely on public cloud providers, SaaS platforms, fintech partners, AI model providers, managed services, and global data ecosystems.
This creates new challenges related to concentration risk, data sovereignty, portability, interoperability, and service continuity. Multi-cloud strategies may increase flexibility, but they can also add complexity and cost when they are not supported by clear architecture and governance principles.
The IT strategy should define where workloads, applications, and data can be hosted, which capabilities should remain portable, and where deliberate strategic dependencies are acceptable. Sovereignty and resilience requirements should be incorporated into sourcing, architecture, and investment decisions.
The TOM must provide consolidated governance across internal technology teams and external providers. This includes clear service ownership, vendor accountability, cloud financial management, exit planning, and end-to-end monitoring of critical technology chains.
6. Quantum computing readiness
Quantum computing is still an emerging technology, but its potential impact on optimisation, simulation, cryptography, and complex decision-making makes it strategically relevant today. Organisations in financial services, pharmaceuticals, logistics, energy, and advanced manufacturing are already exploring where quantum capabilities could create future advantages.
The immediate priority is not large-scale deployment, but readiness. IT strategies should identify potential business use cases, assess when quantum capabilities could become relevant, and determine which data, applications, and security dependencies may be affected.
A particularly important consideration is cryptography. Future quantum systems may weaken some of the encryption methods currently used to protect sensitive information. Organisations should therefore begin assessing their exposure, understanding long-lived data risks, and preparing for a transition towards quantum-resistant cryptographic standards.
The TOM should assign ownership for quantum technology scanning, experimentation, and risk assessment. This may include small centres of expertise, partnerships with technology providers and universities, controlled pilots, and integration with enterprise architecture, cybersecurity, and innovation governance.
The objective is not to invest heavily in uncertain technology, but to avoid strategic surprise and build the capabilities required to respond when quantum computing becomes commercially relevant.
From technology strategy to enterprise operating model
These trends point to one common conclusion: technology can no longer be managed as a support function separated from the business.
The future IT TOM must combine business products, enterprise platforms, AI capabilities, embedded risk management, ecosystem governance, and readiness for emerging technologies. It should enable teams to move faster while maintaining control, resilience, and transparency.
For leadership teams, the central question is therefore not simply which technologies to adopt. It is whether the organisation’s strategy, governance, capabilities, and operating model are ready to use those technologies at scale – and adapt quickly when the next wave of disruption arrives.
Stéphane Zema, Advisory Director, Cloud Leader, PwC Luxembourg
“What’s the next big trend? Smarter cloud.
Summer is often a chance to step back and look beyond the next deadline. And if there’s one thing I’ve noticed, it’s this: cloud conversation is changing.
A few years ago, cloud was the destination. Organisations launched migration programmes, moved applications, and celebrated the number of workloads they transferred. Today, that feels like measuring success by the number of roads you’ve built, rather than where they take you.
The next big trend isn’t more cloud, it’s smarter cloud.
Business leaders aren’t investing in cloud for the technology itself. They’re investing because they want to deploy AI, bring new products to market faster, strengthen resilience, improve customer experience, and give employees tools. Cloud has become the platform that enables those ambitions.
That also changes how organisations make decisions. Instead of asking, “How much can we move?”, they’re asking, “What creates the most value?” The best strategies won’t necessarily move everything to the cloud. They’ll move the right workloads, for the right reasons, balancing innovation, cost, resilience, and increasingly, digital sovereignty. As organisations increase their reliance on cloud platforms, they are also looking for greater confidence in how their data is protected, how critical services are operated and how much flexibility they retain in their technology choices.
To me, that’s the shift. Cloud is no longer an IT transformation; it’s a business strategy. And the organisations that will lead tomorrow won’t adopt cloud. They’ll use cloud more intentionally to innovate faster, adapt with confidence and create competitive advantage. “
Andreas Braun, Advisory Managing Director, Data Science & AI Team Lead, PwC Luxembourg
“For this summer blog, I want to dive back into my days as a researcher. For years, AI in science meant analysing data that a human had already decided to collect and after an experiment was designed. That’s changing.
Scientists now have access to systems that move this one (or several) steps further. AI systems can propose the experiments, explain why they might work, and even start to control the lab equipment that is testing them. Research begins to move along a similar arc as software development in the advent of vibe coding – moving from an assistive function to one that can drive the scientific process.
So why does this matter beyond the research bubble?
- Speed. Formulating the hypothesis and experiment are often the slowest part of research. AI can compress years of trial-and-error into weeks.
- Access. Smaller labs and companies without large research budgets could start punching above their weight. The original idea might be more relevant than the infrastructure.
- Novel research. AI systems are not bound by the disciplined training of us humans – they might identify new, unexpected links between disciplines and come up with new ideas.
All of this does not mean we will have faster-than-light travel and beaming ourselves to the next holiday destination anytime soon. But it does open up new possibilities for research in a time where budgets and teams are shrinking across the board.”
Conclusion
Across these perspectives, one thing is clear: the next wave of technology transformation will not be defined by one single breakthrough, but by several powerful trends that converge. AI is becoming more embedded in decision-making and customer engagement. Cybersecurity and resilience are moving higher on the leadership agenda. Cloud is evolving from a migration destination into a business platform. And emerging technologies, from quantum readiness to AI-driven research, are asking organisations to think further ahead while acting with greater discipline today.
For businesses, the challenge is not simply to keep pace with change, but to understand which technologies can create real value, which risks need to be managed, and which capabilities must be built now. The organisations that will benefit most are likely to be those that move beyond experimentation and start connecting technology choices to business outcomes, operating models, governance, talent, and trust.
As we move through the second half of 2026, these signals offer more than a glimpse of what may come next. They are a reminder that technology strategy is increasingly business strategy, and that readiness depends on the ability to adapt with clarity, confidence, and purpose. Even in summer, when many of us take a moment to pause, the technologies shaping tomorrow are already taking shape today.
Frequently asked questions:
What is the main technology trend businesses should watch?
The biggest shift is not one technology alone, but the convergence of AI, cloud, cybersecurity, resilience, and emerging technologies into core business strategy.
Why does this matter now?
Because technology decisions increasingly affect growth, customer experience, resilience, trust, and competitiveness.
How should organisations respond?
They should focus on practical value: build the right capabilities, strengthen governance, manage risks, and connect technology choices to clear business outcomes.