7 technology trends that will shape the future of IT

AI now touches sensitive data, runs across mixed cloud environments, and influences decisions in regulated or high-precision activities. Left on its own, this quickly turns into a patchwork of local rules that nobody really owns.

The organizations that move faster are usually the ones where governance is shared. CIO, CISO, and CDO form the core, but you also need inputs from workplace and business IT teams, cloud and FinOps. None of these teams has the full picture; together, they do.

In 2026, your goal is to agree on one enterprise-level way to approve and supervise AI. Teams should know who decides, what is checked, and what is non-negotiable: what can be automated, what must stay under human control, how traceability is guaranteed, where AI is allowed to run, and with which level of autonomy. For use cases with very low tolerance for error, data location, model reliability, and logging have to be treated as design constraints from day one.

AI has reached almost every organization, but not yet every employee. Our 2025 Global AI Survey shows that, on average, only about 30% of target users have truly changed how they work thanks to AI. Many companies hit a ceiling: tools are rolled out, but habits do not move.

Your employees see the gap every day. At home, they use powerful open tools. At work, they face stricter or more limited versions. Meanwhile, you worry about uncontrolled costs, security, a proliferation of local agents, and environmental impact. That mix of high expectations and real constraints explains why usage stalls.

Closing this gap starts with roles rather than technology. For each population, you need a small set of concrete AI uses that make sense in day-to-day work. These uses should be reflected in training paths, playbooks, and performance discussions so managers talk about them regularly.

HR and Communications sit at the centre of this shift. They can help shape new skills, adjust incentives, and manage the cultural side so that AI becomes a normal part of how work gets done, not an optional gadget. Measurement has to follow: instead of counting licenses, you look at where AI changes workflows, how often it is used at key moments, and what people stop doing because the tool now does it better.

The first wave of GenAI in enterprises was dominated by LLMs. That made sense to explore the space quickly. But when you look at concrete use cases such as internal search, document summarization or content clean-up, you rarely need the full breadth of a frontier model.

2025 has shown that small and specialized models can do a lot of the work at lower cost, with better latency and more predictable behavior. In 2026, the real challenge is to stop treating model choice as a purely technical topic and to connect it to business and risk. For each family of use cases, you need a clear stance: where you accept the cost and dependency of large managed models, where you prefer lighter or open models you can host and tune, where you need both.

This is also where many organizations will question “one LLM for everything” strategies. Smaller models can act as judges, filters, or policy enforcers around your core systems, while larger models are reserved for the few situations where their breadth really matters. That shift will not only reduce cost, it will also make your AI landscape more legible for security, compliance, and finance.

GenAI has already moved into the developer’s daily tools: assistants suggest code, generate tests, and help keep documentation in sync. The impact is visible in many organizations that now prototype faster and clear some of the repetitive work from their backlogs. In 2026, the question is how deeply to weave them into the delivery chain without losing control.

As GenAI participates in design, build, test and even run, it will influence architecture choices, security reviews and release rhythms. As a tech leader, you will need to treat these capabilities as part of the standard toolchain: define where AI suggestions are acceptable, how they are reviewed, and how to deal with issues such as licensing or hidden vulnerabilities, while keeping a clear view on the productivity and quality gains you actually observe in your delivery teams.

A significant share of recent incidents started outside the core organization. It could be at a supplier, in a subsidiary, or through a service that had been trusted a little too much. With operations now deeply interconnected, this has become the main blind spot.

Trying to secure every external party at the same depth is unrealistic. A more effective 2026 stance is to identify the limited number of suppliers, platforms and entities that could genuinely stop the business if they were compromised, and to raise the bar for those first. That involves tightening identity and integration paths – service accounts, tokens, admin access from outside – and aligning crisis and escalation rules with those partners so roles are clear before an incident, not negotiated in the middle of one. The goal is simple: treat a small circle of critical third parties almost as an extension of your own perimeter, instead of assuming a generic vendor questionnaire will be enough.

Business teams are shipping assistants, copilots and even early agents inside their own tools. It’s good for innovation, but if each of these initiatives chooses its own level of access and logging, the CISO will end up with a fragmented and opaque landscape. The aim for 2026 is to keep the business moving quickly while making sure every new AI use is visible and stays within a small set of guardrails.

Concretely, business units need a simple way to declare what they want to deploy and to receive a clear answer: fully approved, approved under conditions, or not for now. On the security side, every AI use case and agent should sit inside a minimum “envelope”: which data and systems it can reach, when human oversight is required, what has to be logged and for how long. A very pragmatic first step is to list what is already live – most organisations still discover AI initiatives after the fact – and use that mapping to define a baseline. From there, the work is to bring new projects into that baseline rather than multiplying exceptions.

Let’s be real: at the pace AI is scaling, IT can’t treat its environmental impact as a side topic anymore. ESG governance is becoming more “professional” and starting to look a lot like finance. The most mature organizations already think in terms of a carbon budget: they set emission caps and track their “environmental spend”.

This changes the way decisions get made. Every major initiative (deploying a new AI use case, migrating to the cloud, renewing devices…) now comes with an identifiable and measurable footprint. In our CSRD Benchmark 2025, 58% of large companies say they already have a model to calculate the carbon impact of their IT project portfolio.

The real challenge for 2026 is to manage carbon the way you manage cash. Put a “carbon price” on your tech stack (data centers, cloud, apps), turn it into clear numbers, and use it alongside cost, risk, and time-to-market to prioritize projects.

The goal isn’t to kill high-energy initiatives. It’s to make conscious and defensible choices. If you decide to spend a big part of your carbon budget on a critical AI tool, you know you’ll have to save somewhere else to stay on track, and you can explain that logic to the ExCo with the same rigor as a financial budget.

AI governance is maturing fast with the EU AI Act and internal Responsible AI frameworks. On paper, most of these frameworks mention environmental criteria. In practice, this dimension is only starting to weigh on real choices. Our CSR Barometer 2025 shows that 74% of CSR departments are now involved in AI discussions, which is a clear sign that topics like energy use, water consumption and local impact are moving up the agenda.

For a tech leader, the point is not to pretend that AI can be “green” by default. It is to acknowledge that powerful models and large training runs have a cost, and to design with that in mind. In 2026, more organizations will favor smaller or specialized models when they are good enough, mutualize compute resources instead of multiplying isolated clusters, and include carbon and energy impact in AI approval workflows alongside risk and ethics. Keeping CSR at the table for AI decisions helps keep this tension visible: you can still push ambitious AI use cases, but you do it with a clearer view of what they consume and how to mitigate it over time.

Regionalized IT does not mean walking away from hyperscalers. For most organizations, the realistic horizon is hybrid and multi-cloud: global platforms for mainstream workloads and innovation, plus a much smaller perimeter of regional or “trusted” environments for what is legally or competitively too sensitive.

In Europe, trusted coulds such as Bleu, S3NS or NumSpot, and similar offers in other regions, are now almost production ready. 2026 is a good moment to decide which categories of data and applications belong there: strictly regulated platforms, systems that embed core business know-how, AI models that carry trade secrets. From there, you can design landing zones, align identity and logging with your main cloud, and make sure operations teams can run these environments like any other.

The key is to stay selective. Only a fraction of your portfolio needs this level of protection. Moving that part, however, can both reduce extraterritorial exposure and give you more leverage in discussions with global providers, because you have credible alternatives for your most sensitive workloads.

Even if your cloud and SaaS landscape looks balanced, AI can quietly rebuild the same pattern of dependence. Proprietary model APIs, managed AI platforms, GPU services and integrated marketplaces all pull you deeper into one ecosystem. On a daily basis everything feels smooth; over time, it becomes very hard to move a key AI-first process or challenge costs.

Strategic autonomy needs to enter the AI discussion now, not in three years. A simple way to start in 2026 is to look at your current and planned use cases through two questions:

• which ones could in theory run on several environments?
• which ones must stay under a specific jurisdiction or sector regime?

The second group needs durable, compliant hosting options: trusted clouds, controlled regions at a hyperscaler, or internal platforms. Design choices make a real difference here: models that can be re-hosted, architectures that avoid binding critical logic to one provider’s stack, and clear documentation of which chips, clouds and services each use case relies on.

The objective is not to cut ties with large vendors. The objective is to keep dependence chosen, explainable and reversible when strategy, regulation or economics change – instead of discovering too late that you no longer have room to move.

When your platform spans several clouds, on-prem systems, and edge locations, nobody has a full picture anymore. Teams end up stitching together local monitoring tools and reacting to incidents one by one. For AI-heavy environments, that quickly becomes a problem: you need to understand where latency comes from, where models misbehave, and how changes ripple through the stack.

The priority for 2026 is to raise your level of observability, not by buying yet another tool but by clarifying what you really need to see. That usually means converging traces, logs, and metrics into a shared data plane, agreeing on a handful of “golden signals” for key services, and making sure platform and product teams can explore the same data. Once that base is in place, AIOps and automation become credible options instead of marketing promises.

AI workloads change the economics of your infrastructure. GPU capacity, network, and energy are no longer just technical details; they shape how far you can go with certain use cases. If you don’t make this explicit, costs and environmental impact will grow faster than the value you create.

In 2026, a more disciplined approach is needed. You will want simple, shared views of where GPU and accelerator resources are used, how much they cost, and how often they sit idle. Financial management (FinOps) and environmental management (GreenOps) should be plugged into AI projects from the start, not added as an afterthought. This links naturally with your broader sustainability agenda: lighter models where they are enough, shared platforms instead of scattered clusters, and explicit trade-offs when a use case really justifies heavy compute.

Not all systems face the same quantum risk. What matters is a simple mix: how sensitive the data is, and how long it needs to stay protected. A trade secret that should remain confidential for fifteen years, a medical record, a long-term contract, a key used to sign software or firmware, archives of financial transactions, these are exactly the kinds of targets a “harvest now, decrypt later” attacker would care about.

That is why many of the first serious initiatives appear in finance and critical infrastructure: they handle data and transactions that will still matter long after quantum computers become practical. In 2026, your priority is to identify where those long-lived secrets sit in your organization and how they are protected today. You can then sketch a migration path: which keys to rotate first, which protocols to upgrade, which third-party products to challenge.

This work will take months, sometimes years, and it will cut across teams. Starting now is what keeps you out of rushed, risky change later.

Cryptography is the urgent angle, but it is not the only one. Banks, insurers, industrials and logistics players are already funding pilots on portfolio optimisation, risk modelling, routing, or materials research using quantum hardware and quantum-inspired algorithms. Most of these projects are still exploratory and run with specialised partners, but they now come with real budgets, not just innovation slides.

You do not need a full-blown “quantum strategy” in 2026. You probably do need a short list of places where faster simulation or optimisation would really move the needle for you, and one or two concrete experiments with your data and models. That will help you build internal literacy, test partner ecosystems and avoid being caught off guard when the technology matures.

Throughout, post-quantum cryptography remains your backbone: it is the defensive layer that protects today’s assets while you test tomorrow’s possibilities.