Why getting from A to B isn’t simple

The promise of a more sustainable and data-driven logistics sector sounds great on paper, but the day-to-day reality tells a different story. So before diving into what the future could look like, it’s worth taking a closer look at the state of today’s supply chains.

For starters, logistics still relies heavily on single-use carton boxes. They get the job done, sure—but create a lot of waste, especially now that CO₂ regulations are tightening, the pressure for sustainable alternatives is mounting. Then there’s the question of security and transparency. Packages can be opened, tampered with or mishandled, and there’s often no way to know until it’s too late. Data doesn’t help much either: it’s usually collected at a high level (think entire trucks, not individual shipments) and only analyzed afterward. Useful? Sometimes. Timely? Not really.

Meanwhile, investing in new solutions isn’t easy. Many logistics companies are operating on thin margins while dealing with legacy systems, complex tech, and teams who may not be thrilled about changing how they work.

But here’s the interesting part: these challenges might actually be pointing the way forward.

Sustainable, secure and smart

Here’s where things start to look up. A smart, durable and reusable logistics industry is closer than you think, with concrete opportunities already within reach:

1. Reusable, sustainable boxes: Durable, fully recyclable plastic designed for hundreds or even thousands of rotations, cutting down on wasteful, single-use packaging.
2. Real-time monitoring: Sensors track location, temperature and handling from anywhere, giving teams instant visibility into every shipment.
3. Swappable connection modules: If a module fails, it can be replaced without scrapping the entire box, keeping operations running smoothly.
4. Smart locks: Access is only granted after authentication, protecting goods from tampering.
5. Digital platform analytics: Data from every box feeds into a central platform, enabling anomaly detection, automated rerouting and insights to optimize the supply chain.

Walden illustrated how these opportunities play out in practice by creating a system that delivers high-value goods transparently and sustainably. Each shipment is fully trackable, monitored with temperature sensors and cameras, and secured with smart authentication locks. Beyond protecting goods, the system generates metrics that help optimize future operations, proving that sustainable, data-driven logistics is already happening.

From smart boxes to smarter business

The opportunities we’ve seen so far are just the beginning. The future of logistics promises to be even more sustainable and data-driven, opening doors to new business models and revenue streams.

Resource depletion and tightening environmental regulations will push companies to reduce their ecological footprint, making reusable, sensor-equipped boxes more than just a ‘nice-to-have’. As adoption grows, hardware and data costs will drop, allowing these solutions to expand beyond high-value shipments into low-margin, everyday logistics. In fact, innovations in high-value segments, like smart labels and secure tracking, often pave the way for broader industry adoption.

This evolution also brings fresh financial models. Instead of buying boxes outright, companies could pay for the service: transportation plus the smart box. This ‘hardware as a service’ approach shifts responsibility from the customer to the provider, removes upfront investment barriers and keeps balance sheets light and flexible. Sensors not only track shipments, but also enable predictive maintenance, over-the-air updates and digital twin simulations, giving full control over fleets and operations.

Finally, as more stakeholders join the network, anonymized data itself becomes a service. Governments can share planned roadworks for automatic rerouting or use traffic insights for circulation planning. In other words, the logistics of tomorrow aren’t just sustainable, they’re smart, connected and full of untapped business potential.

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What is agentic AI?

Before diving into the risks, it’s important to understand how agentic AI actually works. At a high level, agentic AI tools are designed to take a task, break it down into steps and execute those steps autonomously. By iteratively reading and generating text, the tool aims to mimic reasoning and is able to call upon all kinds of tools to successfully execute its tasks, often using external tools such as APIs, databases or code execution environments.

The more access it has to data and tools, the more capable it becomes. However, these systems are not deterministic, they don’t follow fixed rules but instead make probabilistic decisions about what to do next. Now, the question of how well these tasks can be executed autonomously is food for another discussion. What is often overlooked, though, more autonomy and access also means that the impact of its mistakes, or manipulation by others, can be much bigger.

Where things go wrong

If an agent can act, it can also misact, and this can happen in different ways. Let’s look at 3 concrete risks.

1. Dataset poisoning: With dataset poisoning, the foundational model used as the brain of your agent is already being attacked before it has been trained. Because the model relies on vast amounts of internet data, it’s often collected at scale with limited filtering. The internet is riddled with examples of how things should not be done: in software development, for example, lots of open source codebases are filled with vulnerabilities. But what if a hacker utilizes this to specifically target you? Research funded by Anthropic investigated just that and found that as little as 0.00016% of the training set has to be directly poisoned to be able to introduce a backdoor. These backdoors can be triggered by specific phrases, causing the model to behave in ways defined by the attacker
2. Prompt injection: A more direct and immediate threat is prompt injection. Here, the attacker doesn’t target the model itself, but its input. By manipulating what the model reads, they can influence how it behaves. An attacker could host a legitimate website containing hidden instructions, text invisible to the user but readable by the model. These instructions tell the agent to extract sensitive information (account credentials, banking information, API keys, …) and send it elsewhere. Even with clear safeguards in place, researchers pulled this off using a surprisingly simple approach, making it clear that it’s far easier to exploit than you’d expect.
3. Slopsquatting: Even without direct manipulation, agents can create risk through their own mistakes: They have an inherent tendency to hallucinate. Since LLMs probabilistically produce text (what word would be most probable to follow in the sentence), they do make mistakes and present things that don’t exist. Attackers can exploit this. If an agent tends to invent a dependency name, an attacker can register it and attach malicious code. When someone else’s agent later tries to install it, the system becomes compromised. This technique has been dubbed as slopsquatting, as the AI variant of typosquatting, and it turns model errors into attack vectors.

How to mitigate the risks

All of these vulnerabilities stem from the same underlying issue: the agent can act autonomously. In theory, the safest approach would be to manually approve every action and verify every output. But doing so removes the very benefit agents provide: autonomy. So what are your other options?

Sandbox the agent

Agents should never run directly on your core systems, production servers or sensitive data with unrestricted access. How can you implement it:

• Run the agent in a container, virtual machine or isolated cloud environment.
• Define and limit access to only the directories, files or databases it actually needs for its tasks.
• Prevent it from installing arbitrary packages or executing commands outside its environment.

If something goes wrong, whether due to a model hallucination, a prompt injection attack or an external exploit, the damage is confined to the sandbox, protecting your main systems and customer data. There are also specialized sandbox processes that can wrap around your agent and do this confinement for you.

Control external communication

Limit which websites, APIs or network endpoints the agent can access. You implement it as follows:

• Use egress filtering with a deny-by-default configuration, only allowing connections to trusted domains.
• Require human approval for each request to an untrusted domain.
• Monitor outbound requests to detect unexpected activity.

This reduces exposure to prompt injection attacks or slopsquatting, where malicious content on a web page could trick the agent into revealing secrets or executing harmful commands.

Apply least-privilege access

Give the agent only the credentials, permissions and data it absolutely needs for its task. How to implement it?

• Use user-scoped tokens rather than global or admin credentials.
• Make tokens time-limited so access expires automatically.
• Restrict permissions to the minimum required actions (read-only where possible).

If an agent is compromised, leaked credentials or tokens won’t grant full access to sensitive systems, reducing potential damage.

Keep humans in the loop

Agents can act autonomously, but critical actions should always have oversight. You implement it as follows:

• Require review for any actions affecting production systems, customer data, or financial transactions.
• Log agent decisions and outputs for auditing.
• Where possible, set up a system that allows agent actions to be rolled back.

Humans can catch mistakes or malicious behavior that the agent might miss, while still letting the agent handle low-risk tasks to improve efficiency.

Final thoughts

Agentic AI is powerful precisely because it can act independently. But that same independence creates a new attack surface. Not just when using a (free) agentic AI tool online, but also when building a custom tool for your business. Want to learn more about how we use and build AI tools responsibly? Make sure to reach out to our AI specialists!

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Base imagery © 2023 Hexagon and data partners

A rising demand for high-quality imaging

In today’s imaging industry, the demand for ever-higher image quality is relentless, yet constraints on cost, weight and size continue to challenge engineers. Meeting these requirements often means more complex or larger hardware, pricier components, and increasingly demanding testing, all of which push projects toward higher budgets and longer development cycles.

Meanwhile, AI-driven image enhancement tools are rapidly advancing and becoming more widespread, from image deblurring features in Lightroom and Photoshop to AI upscalers and generative models like Stable Diffusion. This raises a compelling question: Can artificial intelligence remove blur and improve usable image resolution without altering the existing optical hardware? In other words, could we now finally achieve the kind of dramatic ‘image enhancement’ often depicted in popular TV shows in real-world applications?

Rewriting image quality with AI

The answer? Kind of. The big breakthrough is that artificial intelligence can now act as a kind of ‘virtual upgrade’ for optical systems. By training models on images from high-end cameras or sensors, developers can teach AI to reconstruct what a better system would have seen. In practice, this means cheaper, smaller and lighter optics can produce images that look like they came from far more expensive hardware, dramatically reducing overall system costs.

There are two especially promising approaches. The first is to use AI to boost the image quality of cheaper cameras and sensors, allowing low-cost, lightweight hardware to produce results closer to premium systems. This can also be used in constellations of sensors or satellites: a few units carry premium optics, while many others use simpler hardware. The second is retrospective enhancement: applying AI for image deblurring in hardware already deployed in the field, improving quality without any physical upgrades. Together, these approaches show how AI can extend the capabilities of both future and existing optical systems, purely through software.

At its core, this technique is generative: the AI starts with a degraded image and generates a sharper, higher-quality version. This only works well if the distortions and noise of the real optical system are accurately modeled during training, which requires deep technical expertise. A strong starting model is also crucial, using an AI already trained to deblur regular images makes adaptation faster, cheaper and more reliable. This is how you can reuse and fine-tune existing models to turn image enhancement from a research challenge into a practical engineering tool.

Base imagery © 2023 Hexagon and data partners

AI in action: From space to surgery

AI-powered image enhancement is starting to improve real-world systems across the board. Any product that relies on optics can benefit: from drones monitoring traffic and satellites mapping crops and deforestation changes, to smartphones capturing sharper photos without bigger cameras. Even weather alerts and environmental policy enforcement can be more accurate thanks to clearer, AI-enhanced imagery.

In optical systems for life sciences, AI image deblurring can boost standard microscopes by leveraging training on high-resolution systems. This approach offers high-resolution capabilities to millions of conventional microscopes in the field, improving live-cell imaging, digital pathology and microbial detection. It also supports low-cost diagnostic devices and high-throughput drug screening. This will help researchers and clinicians gain clearer, more reliable insights without upgrading hardware, making advanced imaging more accessible, scalable and cost-effective.

Generative constraints and reliability

AI-based image deblurring is powerful, but it isn’t magic. It can’t recover information that was completely lost in the original image. Since it’s a generative process, it can also introduce artifacts. This is inevitable, but in most cases, not a dealbreaker. For example, imagine a blurred photo of a pedestrian holding a small object. After deblurring, the pedestrian becomes clearer, but the artificial intelligence can only make an educated guess about the object. They might reconstruct it as a phone, a coffee cup, or something else entirely, but for tasks like counting pedestrians or detecting presence, this isn’t a problem.

The AI works much like humans do: it interprets shapes and context to reconstruct likely features, but the accuracy depends heavily on the quality and variety of training data. That’s why high-quality images across diverse conditions and blur types are crucial. By understanding these limits, AI deblurring can be used responsibly, enhancing images without creating misleading or false information.

A new era of optics and AI

AI-driven image enhancement is already proving to change the game tremendously. It can boost the performance of cheaper cameras and sensors, and even improve images from hardware that’s already out in the field. Better-quality imagery at lower cost could transform Earth observation, from tracking traffic and deforestation to responding faster to disasters. While the impact will be huge, broad adoption will likely happen step by step. As artificial intelligence continues to get smarter, the line between what optics alone can achieve and what software can deliver will keep blurring, opening new possibilities for how we capture and use images across industries.

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The current cloud landscape

US cloud platforms dominate the global market. According to recent estimates, about 85% of European enterprises rely on at least one major US-based cloud service. This isn’t necessarily because there aren’t any qualitative European alternatives, but because they’ve become deeply embedded in the way many organizations work. Their platforms are convenient, widely adopted and tightly integrated with the tools companies already use (Like Microsoft 365 or Google Workspace). Over time, this convenience has made US providers the default choice for many European organizations.

However, this reliance comes with hidden complexity. The US Cloud Act grants US authorities the legal right to access data held by American companies, even if the data is stored abroad. For European organizations, this creates a direct conflict with stringent local privacy regulations such as GDPR. While compliance frameworks exist, the fundamental question remains: who ultimately controls the data, and under which legal jurisdiction?

Beyond the legal dimension, the world has entered an era of heightened political and economic instability. Trade tensions, cybersecurity threats and geopolitical disputes all have implications for cross-border data storage. In this context, companies relying on US-based cloud infrastructure may face risks they did not anticipate when they first migrated their workloads. The convenience and innovation offered by global cloud platforms now come with increasingly visible strategic vulnerabilities.

Sovereign cloud: A response to rising complexity

Sovereign cloud offers a compelling answer to this dilemma. At its core, sovereign cloud refers to cloud infrastructure that is hosted, managed and legally controlled within a specific country or region. It is designed to comply with local data protection laws and provide organizations with assurance that their data is subject to domestic jurisdiction rather than foreign legislation.

The concept revolves around three key principles:

• Data residency: Ensuring that data physically resides within a defined geographic boundary.
• Legal control: Allowing local authorities and organizations to maintain oversight over data access.
• Regulatory compliance: Aligning infrastructure with local laws and industry-specific regulations.

Several forces are driving interest in sovereign cloud. European regulations are tightening, and companies handling sensitive information (like financial institutions, healthcare providers and government agencies) face mounting compliance obligations. At the same time, concerns over geopolitical volatility are influencing strategic IT decisions. A disruption in the relationship between the US and Europe, for example, could expose companies to unexpected legal or operational risks.

As a result, sovereign cloud is gaining traction. Several European initiatives, both public and private, aim to build cloud infrastructure that remains under domestic control. National and regional governments are increasingly investing in sovereign cloud projects, while European companies are exploring partnerships with local providers to reduce dependency on foreign platforms. This trend reflects a shift in priorities: speed and scale remain important, but regulatory certainty and geopolitical resilience are becoming equally crucial.

Implications for businesses and the cloud market

For European companies, the rise of sovereign cloud signals a shift in the calculus of cloud adoption. Cost and convenience are no longer the only factors: legal compliance, geopolitical risk and long-term strategic resilience are becoming equally important. Companies may need to rethink their cloud strategies, assessing whether relying on non-sovereign platforms exposes them to unnecessary regulatory or operational risk.

The trend also affects cloud providers themselves. US-based giants face growing pressure to localize their infrastructure or establish partnerships with European entities to address sovereignty concerns. Meanwhile, European and other regional cloud providers have a significant opportunity to differentiate themselves through offerings that guarantee local control and regulatory compliance.

From a market perspective, the sovereign cloud movement represents both a challenge and an opportunity. For businesses, it’s a chance to align technology with regulatory realities and risk management strategies. For providers, it’s a chance to innovate and expand services in response to shifting customer priorities. Analysts predict that as political and regulatory pressures increase, adoption of sovereign cloud solutions will accelerate, particularly among organizations handling sensitive data or operating in highly regulated industries.

Rethinking cloud dependence

While it is not a cure-all, sovereign cloud is more than a technical trend, it is a reflection of a broader shift in how organizations think about data, governance and risk. In a world of increasing political complexity and regulatory scrutiny, understanding this shift, and its implications for business strategy, is essential for companies that want to navigate the cloud with both confidence and foresight.

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Rethinking the narrative

It’s not so strange that many people see large language models as a threat. For most, ChatGPT was their first and only encounter with this technology. It arrived overnight and it looked like a powerful chatbot that could easily replace human input. But LLMs are much more than conversational tools. Their true strength lies in decision making: the ability to interpret, connect and act on complex information at scale.

History shows that every technological leap reshapes how we create value, not just through the most obvious applications. The question isn’t whether to adapt, but how to do it in a way that strengthens what already works. For example, one of our clients in the paper industry, a field that might seem distant from digital innovation, is using LLM technology to extend its paper products by building an adjacent digital platform. Features powered by transforming text into structured data capabilities now help transform how users interact with information, turning written material, like notes, into actionable insights. The core product remains the same, but its value expands into new, data-driven territory.

Making AI work for your business

Real business value with AI doesn’t come from following the hype, but from understanding what the technology can actually do. As mentioned earlier, large language models bring a new decision-making capacity to digital systems. Not just processing information, but also interpreting and connecting it. They can add a new layer of intelligence to existing tools, combining capabilities like reading images, extracting data and putting information into context. Even simple implementations can improve workflows, but when these features are combined, they can unlock opportunities that weren’t possible before.

Nevertheless, everyone has access to the same AI platforms, so the key to creating value isn’t the tool itself, but how different models, data sources and methods are combined to address specific challenges. Some companies focus on internal productivity, letting AI handle routine decisions and free up human time. Others make existing products smarter, for instance, a CRM that can understand client messages, automate responses and collect insights for better service.

Choosing the right platform starts with understanding what you need it to do. A handwritten form might require an OCR tool, while light decision-making could work with a smaller, private model. Tailoring a system means orchestrating multiple components, from prompts to context layers to fine-tuning, so the result fits the intended audience. Through this kind of integration, AI becomes a practical partner rather than just another piece of software.

Avoiding scaling, quality and data security pitfalls

Scaling LLM-based tools comes with a unique set of challenges that go beyond the technology itself. Cost and infrastructure are often the first hurdles: some platforms charge per interaction, and storing full conversation histories can quickly become expensive. Deciding what to keep, what to archive and how to link data efficiently requires careful planning.

Cybersecurity and privacy remain critical concerns. As with any digital tool, companies must ensure sensitive information isn’t exposed—especially when using external AI services. Internal tools with proper protocols are safer, but external platforms require encryption and strict usage guidelines. Confidential data should never be fed into systems where control cannot be guaranteed.

Another common worry is “losing control” to the AI. This can be mitigated with two approaches: man in the loop, where humans validate outputs and maintain oversight for critical decisions, and man on the loop, where AI handles routine tasks while humans monitor trends and intervene when necessary. By understanding these pitfalls and planning for them, organizations can scale LLM solutions responsibly, without compromising privacy, quality or operational control.

The next wave of intelligence

The evolution of LLMs has been rapid—from chatbots to retrieval-augmented generation and now more advanced decision-making tools. Companies that wait too long risk missing out: the cost of not experimenting is losing value and falling behind. The next wave of opportunity lies in understanding where these technologies truly fit, combining tools thoughtfully and implementing them so people actually use them. The most exciting potential is the broader impact—how these tools can change workflows, products and even society itself.

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Within this new landscape, over-the-air (OTA) updates emerge as a powerful enabler. Beyond offering an efficient way to deliver timely patches and extend compliance beyond launch, OTA capabilities within software platforms unlock strategic advantages. To explore this further, we sat down with Jasper Bogaerts, Verhaert Digital’s CRA specialist. Let’s dive in!

Why are OTA updates considered an interesting measure to keep hardware with a digital component and software secure under the CRA?

Through OTA updates, the manufacturer has a way to efficiently keep products secure throughout their lifecycle. Even for companies that don’t yet have a fully mature cybersecurity strategy, OTA can be an interesting option. Whenever a vulnerability is discovered after the release, you can deliver patches quickly and at scale. However, OTA should not be seen as a substitute for ‘secure by design’ — rather, it complements a broader, more mature security approach. While a manufacturer needs to put in place the necessary processes to rapidly identify the root causes for these vulnerabilities and develop patches, OTA updates have proven to be an effective approach to disseminate those security updates, minimizing disruption for the end-user.

In short, a typical OTA update works as follows:

How do OTA updates reduce both technical and organizational compliance burdens compared to more traditional update methods?

Traditional updates generally require manual or physical intervention. For example, a maintenance team would need to access each affected device one by one, which, from an organizational perspective alone, can be quite a burden, especially if they were deployed in remote or hard-to-reach locations. Alternative methods involving wired connections may be subject to technical problems, such as faulty wires, or involve technical challenges when disseminating the patches through local networks.

OTA strategies that account for client requirements, operational constraints and security priorities dramatically reduce these burdens. They enable centralized or selective deployments remotely, controlling which devices are updated, when and how. This flexibility is especially valuable in environments where updates can be prioritized or scheduled to minimize disruption and downtime. Using management platforms or orchestration tools, manufacturers can ensure patches are applied efficiently and securely across diverse devices.

Can you give some examples of industries that already rely heavily on OTA updates?

Industries that apply this are becoming increasingly widespread. For instance, in the automotive industry, some regulations require manufacturers to have a software update management system and prove safe OTA update practices. Tesla pioneered this for both features and safety recalls. Volkswagen, BMW and Ford now rely on OTA to comply with international vehicle regulations, too.

The automotive industry is not alone. For quick, at-scale security patches for smartphones, tablets and smartphone equipment, OTA programming is already being considered the most feasible way to apply effective security updates, and many manufacturers have mature processes in place to support this.

Depending on the product and scaling needs, OTA technology can vary, but three interesting models stand out:

• Lightweight OTA for consumer IoT – designed for devices with limited memory and processing power (e.g. smart speakers, wearables), where updates are automated and minimally disruptive.
• Managed OTA for mid-scale fleets – used in sectors like healthcare or smart home hubs, where orchestration tools enable selective rollouts, device grouping, and compliance tracking.
• Industrial-grade OTA ecosystems – applied to critical infrastructure and large-scale industrial systems, with redundancy, scheduling, and strict fail-safes to minimize downtime (e.g. manufacturing equipment, energy networks).

What are the technical prerequisites for companies to implement OTA updates?

OTA updates are not always necessary or feasible: some products lack the required network components options, face strict security constraints or simply do not justify the additional cost and complexity. When OTA is worthwhile, there’s a wide range of technical challenges that a company must address in order to be able to support OTA programming.

First and foremost, the infrastructure must be there to support the volume and distribution of devices while meeting client-specific requirements. From a security perspective, essential measures include the capability to support secure boot (verified firmware), having a good fail-safe, applying robust encryption in transit, mutual verification of device and server identities in the update process, and being able to provide trustworthy audit trails of the complete update cycle, from server to device. Besides this, there is also the question of how the interaction with the consumer will be. Will all updates be automated? Can they be rescheduled? How are they informed? To what extent does the consumer have control over what is being updated and when? Especially when there are multiple similar devices that might be impacted in an industrial setting. All this requires technical support to ensure that an update is delivered in an effective manner. As a consequence, supporting OTA updates may involve tailored approaches depending on the context of the devices, requiring a thorough analysis to determine the optimal way to apply them seamlessly.

In summary, here’s a checklist of some typical technical prerequisites for secure OTA updates:

How can companies ensure that OTA updates don’t introduce new vulnerabilities?

The more features you integrate, the bigger the attack surface becomes, so ensuring OTA updates themselves remain secure is critical. On a technical level, this is typically implemented through digital signatures that verify the patch originates from the manufacturer. This requires a secure supporting infrastructure and strict controls to protect the signing keys.

Besides securing the rollout itself, developers must also rigorously test the patches to prevent introducing additional problems and vulnerabilities. Techniques such as static and dynamic application security testing and penetration testing help identify potential risks. However, these practices should be part of a holistic approach, not only to make them compliant with the CRA, but rather to make any digital solutions produced more secure by design.

Beyond CRA compliance, what strategic advantages do OTA updates offer?

There are several. They enable data-driven improvements, using analytics and telemetry to give insight into usage and tailor updates accordingly. Operational costs drop by reducing returns, recalls and manual maintenance efforts, while rollout across environments becomes simpler. OTA programming can also improve revenue streams, positioning the offering as premium through seamless, reliable updates and enabling value-added services such as prioritized or extended support.

Do you expect the CRA to influence global cybersecurity standards? Will OTA updates become a universal best practice in this?

CRA will absolutely change the way digital products are built. In many ways, it will formalize actions that we have been doing implicitly up until now, and make the security-related decisions more explicit. This benefits both companies through higher-quality solutions and customers who gain secure-by-default devices.

Consider home appliances, which are increasingly connected. Even though they typically come with a two-year warranty, people expect them to last 10 years or more. That’s a long time in the digital world, and a long time for potential hackers to exploit vulnerabilities.

Taking this into account, I can see OTA updates becoming the predominant method of disseminating both security patches as well as any other product updates. Factors like network access, cost and design constraints will continue to influence adoption, but I’m confident that moving forward, the advantages will compel manufacturers to consider the approach whenever they design a digital product.

If there’s one takeaway you want readers to remember, what would it be?

Both the CRA and OTA programming should be a means, not an end, toward reaching certain goals, be it better cybersecurity or better reach toward devices that require updates.

At Verhaert Digital, our focus is on the purpose behind the legislation: creating genuinely secure digital solutions. Compliance is important, but you should aim for a holistic, effective and lasting solution that addresses core challenges and protects devices as effectively as possible.

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Today, nearly every product – digital or physical – connects to the cloud. But without a strong foundation, businesses can’t scale, stay secure or innovate reliably. This is where infrastructure as code (IaC) comes in. By managing infrastructure through code, teams ensure consistency, speed and resilience. Though invisible to end users, IaC is critical to delivering seamless digital experiences.

What is ‘infrastructure as code’?

Infrastructure as code (IaC) is the practice of configuring and deploying infrastructure, like servers, networks and databases, using code instead of manual setup. Think of setting up your infrastructure like building IKEA furniture. Even with a manual, it’s easy to make mistakes or forget how something was put together. Now imagine that you have a magic instruction book that automatically builds furniture exactly the same way every time you open the box. That’s what infrastructure as code does for your infrastructure. You write instructions once like a recipe, and the system builds everything the same way, every time. It saves time, avoids mistakes and makes sure everything’s built the same way.

End users may never see your infrastructure, but they experience the outcomes it supports when it’s done right:

• Sites that stay fast under pressure
• Services that recover instantly after downtime
• Products that scale smoothly without surprise errors

These outcomes aren’t the result of Infrastructure as Code alone, but IaC helps make them possible by ensuring that the underlying systems are consistent, reproducible, and easy to evolve.

Why it’s worth the investment

Managing infrastructure as code brings clear, repeatable advantages over manual configuration:

• Speed: Provision environments in minutes, accelerating development cycles.
• Reliability: Standardized configurations reduce human errors and environment-specific bugs.
• Scalability: Systems adapt to changing demand automatically.
• Traceability: Every change is versioned, auditable, and reviewed.
• Cost efficiency: Resources can be spun up or down based on need.

From bottleneck to accelerator

Before IaC, infrastructure was often a bottleneck: time-consuming, error-prone and hard to reproduce. With IaC, it becomes a scalable asset:

• Teams collaborate better with shared visibility into what’s deployed
• Development, staging and production environments can be created consistently
• Code reviews and version control improve quality and reduce risk

Even better, infrastructure setups become reusable and composable. Teams can build faster by borrowing proven patterns, while still customizing where needed. And because infrastructure is now written in code, all the benefits of software engineering apply: modular design, documentation, peer review, and continuous improvement.

When not everything needs IaC

Not every project requires a full-scale Infrastructure as Code setup from day one, and that’s okay. The key is aligning early on what the project is today and what it might become tomorrow.

For example, a more lightweight or manual approach may make sense when:

• You’re building a proof of concept (PoC) that will be discarded in a few weeks
• It’s a temporary internal demo, not intended for reuse
• The infrastructure is very minimal, and the team is using it for learning or visual exploration

But here’s the nuance: we’ve seen plenty of PoCs turn into pilots, and pilots into products. That’s why we always clarify with our clients early on: “Is this truly short-lived? Or could it evolve?”

If there’s any chance the environment will be reused, shared, or audited, then even a basic, right-sized IaC setup is worth it because it avoids hidden technical debt down the road.

Our rule of thumb?

If the setup is truly short-lived, internal-only, and unlikely to scale, be pragmatic. But if there’s any future ahead of it, even uncertain, we lean on infrastructure you can trust and repeat.

The future of infrastructure as code

Infrastructure used to be a ‘mystery box’ managed by a few experts. Today, it’s transparent, collaborative and accessible to all, forming the backbone of how we deliver better digital solutions together. Looking ahead, infrastructure as code will become even more standard practice, with customers increasingly requesting it as part of their digital transformation.

Looking ahead, IaC will become even more accessible and intelligent:

• Multi-cloud & hybrid-cloud setups will be easier to manage
• AI-assisted infrastructure generation will allow teams to describe what they want and let tools write the code
• Policy-as-code will make compliance and security checks automatic
• IaC will become a core part of internal developer platforms, enabling self-service for product teams

The future of innovation is digital and digital depends on infrastructure.

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Myth 1: UX = UI

When you ask people to define UX (user experience) design, you often get answers like “Designing clean, modern interfaces” or “Making things look good.” But that’s actually UI (user interface) design. Though they are closely related, UX is about much more than just aesthetics, it’s about designing the entire user journey, both digital and physical.

• UI = How it looks
  User interface design refers to the visual and interactive elements of a product, such as buttons, icons, typography, colors, tactile controls, ergonomics and overall layouts. It’s all about how the product looks (aesthetics) and how users interact with it intuitively.
• UX = How it works
  User experience design is about the overall feel of the product and how easily users can accomplish their goals. It involves research, usability testing, wireframing and interaction design to ensure a smooth, efficient and enjoyable experience.

A great example of this came up while we were developing Ordo Key. Initially, the focus was on look and feel, but user feedback quickly revealed that installers found the installation process unintuitive. This insight led to a shift in approach, prioritizing usability improvements to streamline the workflow, proving that UX is not just about aesthetics but about making products truly work for their users.

© Ordo Key

Myth 2: UX is a luxury expense

Thinking of UX as a “nice-to-have” rather than a critical investment can be a costly mistake. Poor UX doesn’t just frustrate users, it can lead to market failure, expensive updates and even brand damage. Some examples? In 2012, Apple Maps launched with inaccurate data, poor navigation and distorted 3D maps. This resulted in Apple’s shares losing 4.5% of their value, price tag: $30 billion. Volkswagen‘s touchscreen-only controls in the 2020s was widely criticized for being frustrating and unintuitive. In the end, the company announced it would bring back physical buttons. In 2018, Snapchat changed the app’s layout in an attempt to make it more user-friendly, but instead, it caused confusion and frustration. The company lost millions of users and stock prices dropped drastically.

If done right, investing in UX becomes a strategic move that generates significant returns. In fact, research from Forrester showed that for every $1 invested in UX, companies can see up to $100 in return. Or to give an example of the value of a product with a great UX, various analysts estimate that the value of Apple’s Airpod business is $195 billion. That is in line with the value of the combined Bel 20, excluding AB InBev (or about twice the value of AB InBev).

Another mistake is thinking UX requires a massive, time-consuming investment, while it actually can be delivered in small, focused sprints, building on a design roadmap defined in initial project brainstorms.

Myth 3: UX is subjective

Let’s set the record straight: UX is not about the designer’s personal preference or ‘what looks good’. It’s about what works for users, based on data and research. UX uses clear, actionable insights gathered from surveys, feedback and real-world usage data. You dive deep into how features are actually being used, identify pain points and understand frequent tasks.

To measure UX success, we rely on hard metrics like task success rate, task duration, NPS (Net Promoter Score), error rates and misclick rates, defined based on the company’s objectives. For Reynaers Aluminium, research was key in finding the right features for their online platform, DigiTrace. As a result, it’s now widely used, has received a lot of positive feedback and has become a key driver for data-driven innovation.

© Verhaert Digital

Based on a mix of human psychology, usability research, industry best practices and accessibility principles, UX also follows established standards. For example, by following common UI patterns and visual cues (affordances), users don’t have to “learn” a new interface, they can rely on what they already know without needing instructions.

Myth 4: More features = Better UX

Actually more correct would be: More features, more problems. Overloading an app with unnecessary features can overwhelm users, while they crave simplicity and intuitive workflows. In many cases, companies focus more on driving business goals and adding features quickly rather than understanding users’ true needs. The key to great UX is prioritizing the right features, and this starts with research before any development begins.

Alberto Savoia put it perfectly when he said: “You have to build the right it before you build it right”. A great example comes from our work with TEO. Their interfaces had evolved based on customer requests, leading to a variety of workflows and a complex architecture. Based on in-depth research, we helped them refine their design, streamlining features and creating a more structured, intuitive app. Another great example is from Loop. These earplugs are super popular for a reason: They’re a simple product that offers a great experience.

© Loop Earplugs

Myth 5: Users will figure it out

According to PwC, 32% of users will leave a brand they love after just one bad experience. That’s not very surprising. People expect products to be intuitive and effortless to use, and they don’t want to struggle or waste time trying to understand how something works. So the idea that users will simply ‘figure it out’ how to use it is a recipe for failure.

Take this door, for example. Would you push or pull? Everyone can recall the frustration (or embarrassment) of pulling a door that you should actually push open. In UX, we call this concept the ‘Norman door’, a design that confuses its users, causes errors or functions counterintuitively. It also highlights that UX goes far beyond digital interfaces and apps, it applies to the workflows of any product, service or system. These kinds of frustrations may seem small at first, but when they happen too often, it’s all the user will remember about the product.

Many companies assume they know enough about their users, but there’s a huge gap between being familiar with a product and being a first-time user. This can lead to poor decision-making because what seems obvious to the creator might be confusing or unintuitive for someone who hasn’t spent hours with the product. Thanks to the previously mentioned user research, UI standards and affordances, even a first-time user can use your product intuitively. Creativity is cool, but usability wins every time.

A strategic approach to innovation

The misconceptions around UX can seriously stifle innovation. When companies make decisions based on incorrect assumptions, they end up with products that frustrate users and fail to provide real value. UX is not an afterthought or a luxury; it’s a strategic tool that directly impacts user satisfaction, engagement, and business success. To truly innovate, companies should regularly challenge their assumptions and ensure they understand the real needs of their users. After all, developing a product that doesn’t offer value to the end user will inevitably fail.

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Why the foundation is cracking

The construction industry has long been the backbone of economic growth, but it now finds itself at a crossroads. External pressures like economic uncertainty, regulatory shifts, and workforce shortages are forcing companies to adapt or risk falling behind. Unlike previous downturns, today’s challenges are structural, requiring long-term strategic solutions rather than short-term fixes.

• Economic uncertainty and inflation have driven up material costs and project financing expenses, putting significant strain on contractors. High interest rates make borrowing more expensive, limiting investment in new projects and slowing industry growth.
• At the same time, changing and often vague regulations complicate project execution. New sustainability and safety standards require companies to adapt, but unclear guidelines create confusion and slow down compliance efforts.
• Perhaps the most pressing issue is the shortage of skilled labor. The European Construction Industry Federation already reported in 2023 that Belgium has 22,000 unfilled construction jobs, Germany 52,000, and the Netherlands 27,000. With an aging workforce and upcoming retirements, the industry faces a critical skills gap.

Despite these difficulties, the industry has an opportunity to turn adversity into progress. Digital innovation can help businesses improve efficiency, streamline operations and services toward their stakeholders and clients, and reduce reliance on scarce labor. Those who invest in new technologies today will shape the future of construction tomorrow.

The digital toolbox of opportunities

While the construction industry faces mounting pressures, innovation is proving to be more than just a way to keep up. It’s becoming an operational differentiator. Many digital technologies that have already transformed other industries can be readily applied to construction, helping companies streamline operations, reduce costs, and build smarter and faster. Let’s go through the digital tools that are reshaping the way buildings are designed and constructed.

To mitigate economic uncertainty and inflation

• AI can use past project data, weather patterns and economic trends for smarter planning and anticipating project delays. Similar predictive solutions already enhance road safety—why not apply them to construction?
• Building information modeling (BIM) enables precise planning of materials, workforce, and equipment, an approach we’ve already implemented for Wienerberger.
• Digital twins allow testing and validating appliance and utility choices based on realistic model simulations.
• By equipping machinery with sensors and predictive maintenance, issues can be detected before breakdowns occur.

To keep up with changing safety & compliance regulations

• AI can streamline permit approvals, automate regulatory checks and predict equipment failures before they disrupt operations.
• AI-powered robots and drones can assess hazardous areas to minimize human exposure to dangerous conditions, while wearable robotics reduce worker fatigue and injury risks.
• Digital twins allow virtual safety drills and risk assessments based on current regulations before construction begins.
• Software platforms can provide real-time documentation, making regulatory audits faster and more accurate. Make sure to check out the platform we helped build for Reynaers.

To address the pressing issue of skilled labor shortages

• AI can predict workforce needs, forecast material demand, automate/optimize resource allocation toward a minimal footprint and prevent supply chain disruptions.
• AI can also capture, collect and share expert knowledge and best practices. For example, we developed a digital platform for Renson that supports installers with product information, tutorials, and troubleshooting.
• A shared digital model ensures all stakeholders, architects, engineers and contractors work efficiently with up-to-date information. Learn more in our webinar on digital twins for buildings.
• Robots can take on physically demanding, repetitive tasks like automated bricklaying, welding and concrete pouring. Technologies that are already being used for prefab car parts, including wiring, could easily be introduced in the construction industry, too.
• Multi-axis robotic arms and 3D printing can create intricate stone carvings, complex concrete forms and custom metal or woodwork that were previously cost-prohibitive with unprecedented detail. Start-ups like Monumental Labs for example are already making this happen.

A future full of possibilities

Digital innovation is not about replacing human expertise but enhancing it. By integrating AI, automation, and IoT into construction, companies can reduce inefficiencies, improve safety, and open new creative possibilities. Rather than sacrificing design for cost and speed, builders and architects can reintroduce intricate details, relying on technology for precision and efficiency. While concerns about high investment costs and implementation challenges remain, the real opportunity lies in applying existing innovations from other industries, like automotive, ship and plane building. With a forward-thinking mindset, the construction sector can embrace these technologies to build faster, more sustainably, and with greater artistic freedom than ever before.

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The misconception of ‘just an app’

Though you can digitalize a physical product by just adding an app, its potential value creation will typically remain very limited. True digital transformation is about creating a digital ecosystem, a group of connected tech tools that work together. If you don’t think about the whole system, you might not even realize what you’re leaving on the table.

The idea of a digital ‘ecosystem’ might sound overwhelming, but it doesn’t have to be. Their complexity can vary though. Often, digital ecosystems involve the interconnection between different systems, ranging from ERP systems to other IT operating systems to online platforms, external data providers, etc.

Put your smartest brains together to think about the potential of an integrated physical and digital value proposition, then develop a feasible development plan, and you can finetune your roadmap over time. For example, you can start by collecting data now and set up tools to analyze it later. In a later stage, you can then also use those analytics to improve the value to your customers as you develop your offering further.

Building the digital ecosystem

A solid digital ecosystem starts with data gathering, governance and connectivity. Once these basics are in place, you can shape the ecosystem however you need. Whether it’s cloud, edge, or a mix, and whether you need an app or an online platform, you can figure out what works best for your company at each step of the process.

The ecosystem needs to integrate seamlessly with your existing processes and systems, but it doesn’t have to be overly complex. To keep it manageable, start by defining system boundaries, identifying potential problem areas, and linking only the necessary parts. This approach avoids creating overly dependent systems that can be hard to manage.

Building a digital ecosystem is a team effort that brings together many parts of the innovation process. From mechanics, firmware and connectivity to data engineering, security, sales and operations, a multidisciplinary team is key to making it work effectively.

Exploring the business case, what’s in it for you

It’s no secret that building a digital ecosystem requires a significant investment. The rewards can be substantial though. It’s not just about making operations more efficient or cutting maintenance and production costs by removing unused components. Digitalization opens doors to new opportunities, like creating in-app services behind a paywall or shifting from one-off sales to subscription- or service-based models. These changes can unlock fresh revenue streams, increase switching costs and hence customer loyalty, and even new markets and customer segments.

However, it’s important to plan for ongoing costs. Maintaining networks, firmware and the ecosystem itself requires dedicated roles and expertise. Plus, implementing these new processes demands effort, from gaining initial customer buy-in to training teams on new workflows. Lastly, anything connected can be hacked, so ensuring state-of-the-art security measures and budgeting for them is non-negotiable.

Conclusion

The technologies driving digital ecosystems are advancing rapidly, just look at the leaps AI has made in the past year. By integrating these tools from the start of your conceptualization process, you unlock their full potential. Taking the leap may feel daunting, but the rewards are undeniable. Dive in, and you’ll always gain something valuable from the journey.

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