The true cost of hesitation

Sticking to traditional, ‘old-school’ methods is becoming a risky gamble. When information is trapped in silos or kept on paper, it creates an ‘innovation gap’ that eats away at profitability. For an industry already operating under tight margins, this lack of standardization is a structural constraint. Companies that wait to explore these digital tools risk losing their competitive edge and becoming entangled in increasingly complex regulations governing sustainability and safety.

From theory to practice: Insights from the field

While the research began as a high-level strategic study, the real breakthrough came from making it practical for the people actually on the ground. During our workshops with HVAC installers and carpenters, we found that many professionals don’t see themselves as ‘tech people’ – they just want to solve daily headaches.

In our sessions with carpenters, we observed a strong desire to use these tools to reclaim time spent on paperwork and to reduce material waste. Meanwhile, HVAC specialists are considering AI as a digital navigator to help them navigate the maze of green energy subsidies and to begin repairing systems before a client even knows there is a fault. It isn’t about the technology itself; it’s about making the workday lighter.

The AI Toolkit bridges the gap between daily construction challenges and technological impact. By intelligently matching real-world needs with the right AI tools, we transform abstract ideas into concrete, stakeholder-focused scenarios for immediate implementation – whether for contractors, developers, designers, or project managers.

– Gerd Van Cauteren, Project Coordinator at Verhaert Strategic Innovation

A roadmap built for people, not machines

Because the jump to digital can feel overwhelming, we developed the AI toolkit designed to be as simple as matching three puzzle pieces. Instead of staring at a blank screen, you start with the human side of the business:

• The challenge: You begin with a real-world pain point, like the hours lost to manual invoicing.
• The impact: You define exactly what you want to achieve, such as a 5% boost in your profit margin.
• The solution: You identify the specific digital co-pilot – like back-office automation – that gets you there.

This process turns a daunting digital transition into a structured, manageable roadmap tailored to your specific goals.

AI Toolkit NL 
AI Toolkit FR

AI as your digital co-pilot

The real strength of these tools lies in their support for human expertise rather than their replacement. Buildwise positions AI as a strategic partner in three main ways:

1. 1. Transforming the office: Streamlining repetitive tasks like email management and tender analysis so you can focus on the core of your craft.
   2. Operational excellence: Using real-time data to monitor progress and catch errors early, which saves both time and money.
   3. Building for the future: Helping to choose the right materials and energy setups before the first stone is even laid.

Strategic steps for long-term success

The transition to a more data-driven industry isn’t about a single ‘magic’ tool; it’s about a strategic shift in how we approach problems. By breaking down your biggest daily challenges and building a structured roadmap, you turn a complex technological trend into a clear path forward.

The future of construction belongs to those who view these tools as a means to work more intelligently and more sustainably. If you are ready to stop guessing and start building your own digital strategy, our toolkit is the perfect place to begin.

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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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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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What’s driving the next innovation cycle? In this fireside chat, Aimée Mugisha and Axel Funhoff unpack the key trends, from emerging technologies like solid-state batteries, humanoid robots and AI to macro changes like near-shoring and the retreat from globalization. The session wraps with Axel’s tips for how companies can position themselves for success in the next chapter.

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The workforce shortage isn’t going away

The labor shortage in warehousing and logistics isn’t just a short-term challenge, it’s a structural shift that’s here to stay. Solving it requires more than temporary fixes, and here’s why.

• First of all, the continued growth of e-commerce might be good news for businesses, this increased order volume also means that companies need to invest in innovative staffing solutions and technology to support this growth.
• Secondly, more and more experienced workers are reaching retirement and are not being replaced by younger workers. This challenge could be a unique opportunity to rethink workforce planning as a whole, introducing new tools and processes that make warehouse work less physically demanding and more rewarding, while transferring valuable knowledge to the next generation.
• Lastly, younger generations gravitate more toward tech-driven careers. This trend highlights the importance of reimagining warehouse roles and making them more dynamic, engaging and aligned with the expectations of the modern workforce.

The result is a widening labor gap that can’t be solved by recruitment alone. It calls for a fundamental rethinking of how work gets done—one where technology plays a central role in keeping operations running efficiently.

Redefining work in warehousing and logistics

In response to these persistent labor challenges, many logistics operations are turning to automation, AI and even humanoid systems. Why? Rather than just replacing people, these technologies support the workforce and shift the nature of the work where it’s most critical. By reducing strain, increasing efficiency and improving job quality, they offer long-term benefits that make the investment worthwhile.

Automation and robotics are taking over some of the most physically demanding and repetitive warehouse tasks, such as manual picking, palletizing, sorting, scanning and transporting inventory. These activities are not only labor-intensive but also prone to human error and injury.
This shift creates new, tech-enabled roles focused on oversight and coordination, including:

• Optimizing robotic workflows and performance
• Configuring task assignments to robots
• Troubleshooting and resolving complex system exceptions
• Maintaining and upgrading robotic systems
• Coordinating human-robot collaboration zones

AI and algorithms are transforming warehouse operations by replacing time-consuming, error-prone tasks like manual scheduling, route planning, inventory checks and basic data entry. Algorithms handle these processes faster and more accurately, freeing up human workers to focus on higher-value tasks:

• Analyzing demand patterns to shape inventory strategy
• Collaborating with teams to improve AI models and outputs
• Investigating anomalies and driving continuous process improvements
• Leading AI-enabled workforce planning for peak efficiency
• Training or tagging data sets

While traditional robotics are built for one task in a fixed environment, humanoids can handle variability and complex movements that machines can’t easily replicate. Think of physically demanding jobs, monitoring hazards and assisting during emergencies. They work alongside people, minimizing injury risks and helping with tasks like training, inventory checks and equipment alerts. New jobs focus on:

• Define tasks and policies to balance workloads between operators and humanoids
• Coordinating hybrid teams and humanoid assistants
• Identifying and resolving edge cases
• Tag-teaming stock replenishment
• Providing feedback or data to improve systems

Building a future-ready warehouse workforce

“At MOVU, I worked on these kinds of solutions, allowing warehouses to automate their inventory and reduce the operator workload to a minimum”, says Thomas, Mechanical Design Engineer at Verhaert On-site Consulting. “Automated warehouse solutions are systems that manage the inventory, optimize the operations and improve efficiency and picking accuracy, using different automated storage, retrieval and picking solutions. This dramatically reduces the workforce needed to fulfill all orders, while increasing efficiency and productivity.”

“But the impact goes beyond efficiency”, the on-site consultant continues. “Automation creates opportunities for upskilling and role diversification, enabling workers to take on more technical, engaging responsibilities. This shift makes warehouse roles more attractive, especially to younger generations seeking tech-driven careers, and helps build a more resilient, future-ready workforce.”

Need expert support to implement or develop advanced warehouse systems? Let’s discuss how Verhaert’s on-site consultants can drive innovation within your R&D team!

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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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Why high-quality data is essential for valuable AI

AI models learn patterns and make decisions based on the data they’re trained on. If the input data is flawed, the output will be unreliable: garbage in, garbage out. Because AI systems require substantial time and effort to build, you’ll want certain assurances it’ll be worth the investment. That’s why high-quality data is essential for ensuring accurate predictions, valuable insights, and effective automation.

And why poor data quality allows for serious risks. Bias in training data can result in unfair or misleading outcomes. At the same time, inconsistencies and missing values can cause AI to misinterpret information, leading to unpredictable results. And outdated data may drive decisions based on obsolete trends. The biggest threat? AI might generate results that appear correct but are fundamentally inaccurate, making it difficult to distinguish between right and wrong. Without a strong data foundation, even the most advanced AI models can fail. So when do you have a strong data foundation?

What about using AI as a data cleaner?

AI isn’t just dependent on high-quality data, it can also help improve it. Intelligent algorithms can fill in missing values for more complete data. Anomaly detection helps identify outliers and inconsistencies that might otherwise go unnoticed. AI can also identify redundant or conflicting entries, streamlining datasets. Finally, normalization and standardization enforce consistency, making data more structured and reliable for AI applications.

However, AI-driven data cleaning isn’t foolproof. If there’s a flaw somewhere in the process, AI may reinforce incorrect patterns rather than fix them. To prevent this, organizations should implement safeguards: either maintain a small, verified dataset for validation or have human experts review AI-refined data. Even with these precautions, AI-driven data cleaning can significantly speed up the process.

Fixing data at the source

Although AI-driven data cleaning can be a powerful tool when you know you don’t have high-quality data, there are other, sometimes better, options.

• Generating synthetic data: Simulations can be a fast solution for data scarcity. By defining specific parameters, simulations can produce endless data, helping identify trends and patterns when real-world data is lacking or insufficient.
• Leveraging external data sources: Both paid and freely available, external data from sources like governments, research institutions, and other companies can enrich your dataset, providing additional context and improving the AI model’s accuracy.
• Using alternative data sources: Gathering data from different systems or creating new collection methods can improve data relevance and accuracy. For example, you might not have a sensor in place that captures when a machine breaks down, but maybe your conveyor belt stops working and sends an alert as a result, which can be captured.
• Improving existing databases and collection methods: A more long-term solution is focusing on improving their existing databases and data collection methods. Even when you’re still building the AI system, better-designed data systems can automate reporting and even simulate basic insights AI might provide in the future.
• Implementing federated learning models: In privacy-sensitive sectors, federated learning allows you to train AI models using decentralized data without compromising privacy, as the raw data is never shared across systems.

From imperfect to powerful

High-quality data is the cornerstone of effective AI, but no dataset is perfect. By leveraging a combination of continuous improvements and proactive data-gathering strategies, businesses can refine their data to unlock their full AI potential. So how do you know which strategy will work best for you? You have to define what you want to do before you define how to get there. Typically, this is why we set up dedicated ideation workshops in which we get to the bottom of why you want to invest in AI and what you want to get out of it. This helps define clear data strategies, assess data quality, set up improved data-gathering processes, or even algorithmically improve your existing dataset. That’s how, with the right approach, AI can transform not only your insights but also the way you manage and improve your data over time.

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Why the shift to automated microscopy?

The transition from manual to automated microscopy addresses a critical need: tackling tasks that are too complex or time-consuming for humans alone, such as counting cells in biotech applications to evaluate medicine effectiveness or measure cell growth. These critical tasks demand precision, and AI’s exceptional pattern recognition capabilities reduce the risk of human error while maintaining opportunities for experts to validate it. Automated microscopy enables faster, more accurate analyses across industries, from identifying anomalies in medical diagnostics to ensuring the safety of food products.

Key use cases

Automated microscopy drives transformative changes across industries, with three major areas standing out. In medical diagnostics, this technology is revolutionizing disease detection.

“New imaging modalities like Stimulated Raman Spectroscopy and infrared spectroscopy enable analysis without the need for staining, enhancing fields such as anatomopathology. These methods reduce reliance on human inspection, paving the way for AI to address the growing demand for automation in image acquisition and diagnosis,” explains Didier Beghuin, CTO of Lambda-X Verhaert High-Tech. “At Lambda-X, we leverage advanced optical systems to enable breakthroughs in diagnostics. This approach not only enhances accuracy but also accelerates processes critical to patient care. For instance, SoundCell’s systems enable rapid antibiotic susceptibility testing, providing crucial insights into combating bacterial and antimicrobial resistance”.

In the food safety sector, automated image recognition enhances the ability to detect contaminants in products, ensuring rigorous safety standards. Recent advancements highlight how these systems efficiently identify microscopic threats, surpassing the limitations of traditional methods and fostering consumer confidence.

In the biotech industry, automated microscopy plays a pivotal role in processes such as cell counting, where AI can measure cell growth or evaluate the ratio of living to dead cells. These measurements are crucial for testing the effectiveness of new medicines or optimizing bioreactor processes, such as growing fish cells for sustainable food solutions. By combining optics and AI development in parallel, these systems achieve optimal accuracy and performance, reducing human error and accelerating innovation.

Beyond these applications, other industries also benefit from automated microscopy. For instance, water treatment facilities employ these technologies to inspect and analyze samples for contaminants, ensuring compliance with safety regulations and delivering clean water. In the energy sector, automated microscopy aids in material inspections for solar panels and batteries, enhancing durability and efficiency. Similarly, automated systems streamline quality control in the semiconductor industry, where precision sample analysis is crucial for defect detection. These diverse applications underscore the adaptability of automated microscopy in tackling challenges across a broad spectrum of fields.

What sets automated microscopy apart?

Unlike traditional microscopes that rely on human interpretation, automated systems combine advanced hardware—such as spectrometers and fluorescence detectors—with AI-driven image recognition. These technologies enable precise, efficient analysis by identifying patterns and anomalies with a speed and accuracy unattainable through manual methods. Automated microscopy excels in applications where human errors are common, such as counting cells or detecting anomalies in samples. Furthermore, these systems allow for human validation, maintaining a critical balance between automation and oversight.

“AI excels in pattern recognition, making it indispensable in applications like cell counting and anomaly detection,” says Niels Verleysen, AI & data engineer at Verhaert Digital Innovation. “By automating complex tasks, we reduce human error while still enabling expert oversight. This integration of AI into microscopy accelerates processes and opens the door to advanced diagnostics and innovative applications across industries.”

Optics and AI development must go hand in hand to maximize performance. The quality of data gathered by the optical system directly impacts the accuracy of AI models, making an integrated approach essential for achieving superior results. This synergy ensures scalable, high-throughput diagnostics while maintaining reliability and consistency across diverse applications.

The bigger picture

By blending AI with optical engineering, industries are achieving breakthroughs that speed up processes, increase accuracy, and open up advanced use cases. Whether it’s enhancing patient care, ensuring food safety, optimizing biotech processes, or exploring potential components in water inspection, this innovation keeps businesses at the forefront of progress.

As industries pivot toward AI-enabled microscopy, the focus isn’t just on what machines can do, it’s on how they enable people to achieve more.

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Sustainability takes center stage

Sustainability was more than just a buzzword at this year’s IFA, it was an essential part of the conversation. Across multiple sessions and workshops on next-gen products, leaders discussed the circular economy, right-to-repair and ecodesign. Many manufacturers unveiled energy-saving features in their household appliances, as well as initiatives around plastic recycling and pilot projects for circular appliances. Notable examples include the Midea Ecomaster AI program, which optimizes energy use, and Miele’s circular vacuum cleaner, which redefines what it means to go green.

AI powers the rise of a truly smart home

AI was a star player at IFA, with big brands showcasing intelligent systems that are taking a smart home to the next level. Companies like Bosch, LG, and Midea are integrating AI to create home assistants that manage everything from energy use to daily chores. These systems offer advanced features like auto-dosing, smart cooking, and self-cleaning, all designed to enhance convenience and efficiency.

Robotics also made a strong showing, with devices for every corner of the home. We saw a new generation of vacuum and window-cleaning robots, garden maintenance bots, and even personal assistants and entertainment companions like LG’s two-wheeled Self-Driving AI Home Hub, a worthy competitor to Samsung’s Ballie.

One trend we couldn’t ignore was the rapid growth in air purification devices. Integrated into fans, heaters, tables and even cat furniture, air purification technology is proving itself essential in our everyday environments. Another is facilitating indoor gardening with products like Balconyfarm and Grovero, which help users grow plants, herbs, and micro-vegetables in the comfort of their homes.

Size matters: from tiny gadgets to massive screens

While household appliances were our primary focus, we couldn’t resist exploring some of the other incredible tech on display. The latest gadgets combine AI with miniaturized sensors and batteries to deliver magical results. Tiny but mighty innovations like the Plaud smart note pin, the Nuance hearing aid, and Vixion’s autofocus glasses showcase how compact technology can deliver immense benefits. Meanwhile, drones like DJI’s Neo are raising the bar in the affordability of cutting-edge recreational tech.

When it comes to screens, IFA remains a playground for the latest in display technology. TVs are getting bigger, image quality is more astonishing than ever, and portable projectors are now small and affordable enough to fit in a pocket, making them perfect for camper vans, kids’ rooms, and beyond. Samsung captivated us with their transparent microLED displays and Hisense with the demo of their AR glasses, which project a virtual 130” screen right in front of your eyes with impressive image quality, given the small and light device.

Focused evolution of technology

While this year’s IFA didn’t showcase many disruptive new ideas, it did provide a powerful reminder of the mindblowing evolution of technology. We’re seeing the refinement of existing trends – like AI, smart health, IoT, sustainability and robotics – with much of the innovation happening beneath the surface. This kind of ‘invisible’ progress is reshaping our lives, and it’s up to manufacturers to harness these trends to create intelligent, data-driven features that increase convenience, improve quality of life and reduce environmental impact.

Key takeaways for the future

For companies inspired by the trends from IFA, here are some actionable areas to focus on:

• User-centered design: understand what users value by combining design research with value engineering to create products that resonate with today’s needs.
• Cost-effective smart tech: invest in technologies that blend low-cost and robust sensors with sensor fusion and onboard intelligence to monitor things like food readiness, clothing cleanliness, and more.
• Intuitive digital extensions: develop user-friendly digital interfaces and scalable IoT solutions to extend product functionality and enhance user experience.
• Sustainable manufacturing: focus on cost-effective manufacturing techniques to maintain affordability in a competitive, price-sensitive market.

If IFA has sparked ideas and inspired you to innovate, reach out! We’d love to discuss how we can work together to turn your breakthrough ideas into reality.

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In previous blogs, we already discussed that a significant portion of respondents across all company sizes and industries are already using AI tools for innovation in some capacity (check out the blog here). We also took a closer look at how AI impacts the R&D process and what the main challenges are (check out the blog here). Now let’s take a closer look at which level AI is being integrated into specific tasks, starting with idea generation.

Overcoming ideation blues with AI

Let’s start with the most obvious application of AI in the R&D process. Almost two-thirds (64%) of respondents are using AI to some degree to generate new ideas. This responds to nearly 40% of respondents indicating they are experimenting with AI to learn about the use, effectiveness and drawbacks and 24% of the respondents say AI is already to a certain extent integrated into their process. A sizable minority (17%) of all respondents are considering using AI for this task, and 3% are waiting for suitable solutions to get started, both signaling growing adoption rates. Only 16% of respondents have no plans to integrate AI.

AI adoption rate for idea generation per business type ©Verhaert

There are some variations in adoption based on company size. Large corporations have the highest overall adoption (68%), closely followed by start-ups (64%). However, when looking at the level of integration, start-ups are the most mature as 46% of them have already integrated AI into their processes compared to 23% of large corporations. It’s clear that AI is a promising area for innovation generation across different companies. Given the potential benefits of brainstorming and idea generation, it’s likely that adoption rates will
continue to rise.

Enabling next-level pitch deck creation

Looking at the adoption of AI for pitch deck creation, there’s a clear interest across all company sizes. About 39% of all respondents use AI, with 29% experimenting and 10% fully integrating it into their processes. 10% is substantial, but quite a small number compared to those who aren’t using it (yet) (61%). Nevertheless, 26% are considering AI integration in the near or long term, and 9% are waiting for suitable solutions, indicating potential future growth. Adoption rates vary again depending on the company size: SMEs (44%) and start-ups (45%) show higher adoption rates than large companies (35%).

AI adoption rate for pitch deck creation per business type ©Verhaert

Why are companies integrating AI for this task? First and foremost, AI can automate
repetitive tasks in pitch deck creation, such as data representation, visualization and formatting. AI helps identify gaps and uncertainties, saving time and money by automatically generating pitch decks from available information. It can also suggest design elements and tailor content for specific audiences, potentially leading to more compelling presentations. Additionally, AI enables data analysis to enhance information visualization and improve the pitch deck’s persuasiveness.

Overall, while AI in pitch deck creation is not yet a global trend, it shows significant potential. While efficiency benefits drive adoption, the reasons behind its slower integration are not entirely clear.

Unraveling user preferences and motivations

The statistics show the use of AI to understand user preferences and motivations. Overall, there’s a noticeable adoption of AI in user research across all company sizes. More than a third (38%) of respondents are already using AI in some way, with 31% indicating they’re currently experimenting with it and 7% having it integrated into their process. About 31% of respondents are considering doing so in the future or are waiting for suitable solutions.

AI adoption rate for insights on user preferences and motivations per business type ©Verhaert

When comparing company sizes, large corporations (62%) and SMEs (76%) show a lower tendency to adopt AI compared to start-ups (45%). This could be due to the more experimental and agile way of innovation of start-ups compared to the other categories.

These findings underscore AI’s potential to understand user preferences, which is crucial for product development and innovation strategies. Companies are leveraging AI for several reasons:

• Efficiency and scalability: AI automates time-consuming tasks like user research, data interpretation, and report generation, allowing researchers to focus on concept improvement and deeper user understanding.
• Deeper user understanding: AI tools can help analyze vast data, create surrogate personas and digitally prototype study effectiveness. Through digital prototypes, they help identify unclear and hidden preferences and motivations.

Moreover, AI supports increased personalization across various touchpoints, leading to higher user satisfaction and better adoption of innovative outcomes.

Testing the waters of virtual personas with AI

It’s clear from the survey results that AI is rarely adopted to validate assumptions through virtual personas. 38% of respondents indicate they have no plans to use AI at all, and only 3% actually integrate it into their process. Nevertheless, about 43% are considering AI in the future or are waiting for suitable solutions, indicating potential prospective growth. Overall, the data, like in other tasks, suggests potential growth in the future. This highlights the potential of AI in reducing costs, improving accuracy, and scaling user testing efforts.

Start-ups (27%) show a higher tendency to leverage AI for this purpose than large corporations (19%) and SMEs (13%). For large corporations, this could be a result of larger budgets and more defined user research practices than SMEs and start-ups. SMEs may not have a dedicated user research team or less mature methodologies.

AI adoption rate for validating assumptions with virtual personas per business type ©Verhaert

By analyzing demographic, behavioral, and preference data from various sources, AI can define key customer characteristics. This information can then create detailed virtual customer personas to interact with prototypes, competing options, UI/UX designs or product marketing. Through virtual persona interactions, companies can identify (initial) underlying assumptions about user behavior and potential problems more easily. Based on those findings, companies can refine their assumptions, improve their product or service, and conduct further testing with virtual personas.

Fast-tracking patent analysis and documentation

Next, let’s explore AI adoption in patent analysis and documentation. Almost half of respondents are already using AI in some way, whether experimentation (28%) or integration (16%). In addition, 35% are considering using AI in this field in the future, indicating there’s still quite some potential growth.

AI adoption rate for accelerating patent analysis and documentation per business type ©Verhaert

Comparing the differences between company types in the figure above, large corporations (55%) show a higher tendency to leverage AI in these tasks than SMEs (31%) and start-ups (27%). Due to larger budgets and a higher need to analyze documents and generate design documentation, the ROI will be more impactful, whereas SMEs and start-ups often deploy a lean R&D process, and don’t have as much documentation and patenting work.

What are the potential benefits for companies?

• Improved efficiency in patenting: AI automates patent research and analysis, allowing engineers and scientists to focus on strategic work. It helps identify relevant patents and trends, keeping companies competitive and innovative. AI also detects potentially infringing patents, helping to avoid legal issues.
• Standardized and automated documentation: AI can automate the creation of documentation, ensuring consistency and reducing the risk of errors. Additionally, AI-powered platforms can facilitate collaboration between engineers and designers by providing a centralized repository for all design information.

Exploring the new normal of coding with AI

When it comes to testing and validating code, there’s clearly a high adoption of AI tools already across all company sizes. About 63% of respondents are already using AI in some capacity, with 36% experimenting and 27% integrating it into their process. In addition, 23% are considering using AI for these tasks in the future or are waiting for a suitable solution. Only 14% of respondents have no plans to do so currently.

AI adoption rate for testing and developing code per business type ©Verhaert

Diving deeper into the different business types, SMEs (94%) and start-ups (73%) have the highest adoption rates, whereas large enterprises show a much lower rate (45%). This could be due to the more complex software product portfolio and the more stringent requirements of their development processes and code quality requirements.

These results aren’t surprising. AI can automate repetitive testing tasks and optimize software performance, freeing up developers to focus on more complex issues, and the tools to do so are already quite on point. AI can also be used to identify potential bugs and vulnerabilities in code, improving code quality and automatically generating test cases, which will improve test coverage. As AI technology continues to advance and companies are becoming more aware of its benefits, we expect the uptake will only increase.

Cooking up better products with AI-enhanced simulations

The statistics below show a relatively low adoption of AI to enhance simulation work. Only 22% are using AI in some form, with 14% in the experimentation and 8% in the integration phase. 40% of respondents have no plans currently to integrate it, and 38% are considering this in the future, suggesting like in many other development tasks that there’s growth potential. When comparing business sizes, large corporations have the highest adoption rate, potentially due to factors like budget or the complexity of a company’s development tasks.

AI adoption rate for surrogate simulations per business type ©Verhaert

For this topic more than others, companies might not yet know of the potential benefits of AI. There might also be a perception that AI for surrogate simulations is too complex or expensive to implement. Regardless, AI can create simulations that mimic real-world conditions, helping to test products or processes before physical prototypes are built and can optimize designs based on simulation results. By using surrogate simulations, companies could reduce the time and cost associated with traditional development processes, as well as improve the technical predictability of product performances.

Harnessing the future of self-learning solutions

Lastly, let’s take a look at the adoption of AI in facilitating adaptive, self-learning products and services powered by advanced algorithms. Overall, 38% of respondents are using AI for this task, 36% are considering it and 26% don’t. Despite the seemingly moderate uptake, like before, these results again show promising outcomes for the future.

AI adoption rate for enabling new product and service features per business type ©Verhaert

Looking at the differences between company sizes, 35% of large corporations have already deployed AI tools, with nearly 20% already integrating AI into their processes. Of SMEs, on the other hand, 44% of respondents have no intent to use AI. When looking at the start-ups, they seem to be the most enthusiastic about AI, with only about 20% not currently using it, an equal amount using it experimentally and almost 40% already having it integrated into their process. It’s clear that start-ups are leading in the development of disruption and highly innovative products using AI, and we see large corporations starting to pick up on it as well.

Why companies choose to develop their own AI

Finally, let’s take a look at which tools companies would prefer to integrate AI into their R&D processes. Free LLMs show the highest adoption rate, suggesting that companies might prefer to experiment with free tools before making big investments. Nevertheless, it’s clear that a lot of companies show interest in developing their own AI models. This tells us that the uptake of those solutions will continue to grow. There are several reasons for this:

• Retaining control over data and algorithms to avoid sharing sensitive information
• Getting a competitive edge with unique insights or processes
• Investing in long-term, cost-effective, specialized solutions

Desired AI solutions ©Verhaert

In conclusion

it’s clear that AI is more than a fleeting trend—it’s a game-changer. From start-ups to large corporations, businesses are harnessing AI to boost efficiency, uncover hidden insights, and create cutting-edge products. While some are just testing the waters, others are already riding the wave of AI-driven innovation. Want to learn more about the tools and features that are already out there? Make sure to check out our perspective on ‘Leading GenAI tools for innovation’! The future is bright and brimming with possibilities, so stay tuned as AI continues to reshape the world of research and development, one smart solution at a time!

The post From ideation to innovation: AI’s magic touch in R&D appeared first on Verhaert Masters in Innovation.

The post From ideation to innovation: AI’s magic touch in R&D appeared first on Verhaert Masters in Innovation.