With growing demand for more agile, sustainable, and localized production, Additive Manufacturing (AM) is shifting from prototyping to full industrial integration.

Additive Manufacturing, also known as 3D printing, is a process where a three-dimensional object is created by adding material layer by layer, based on a digital design. It contrasts with subtractive manufacturing, which involves removing material to form a shape. 

However, the scope of 3D printing today defines more than prototypes or design studies. Currently, Additive Manufacturing provides the reality of industrial series production of components in a tangible way. However, the path forward can be intricate, demanding meticulous planning, deep expertise in the AM domain, processes, and materials, with a focus on execution.

ÎÚŃ»´«Ă˝ joined the AM I Navigator initiative to be part of its holistic maturity model to shape the stages of industrialization in the AM industry, increasing interoperability in additive manufacturing.  By applying the AM I Navigator Maturity Model, we outline the essential stages for industrializing additive manufacturing, empowering companies to chart a strategic path toward integration.

The AM I Navigator emerged through collaboration with leading partners including Siemens, all working together to create a unified approach to 3D printing. This initiative is about embedding additive manufacturing seamlessly into traditional production systems. By leveraging methodology from industry-proven maturity models, we are helping organizations unlock the transformative potential of additive manufacturing at scale.

The AM I Navigator provides a structured, end-to-end methodology that empowers manufacturers to:

–               Assess their AM maturity

–               Accelerate adoption through data and insight

–               Integrate AM into existing digital production systems

ÎÚŃ»´«Ă˝ joined the AM I Navigator initiative to advance the industrialization of additive manufacturing (AM), together. Alongside industry leaders like Siemens, we will collaborate to help organizations unlock the full potential of industrial-scale 3D printing. This collaboration is a shared vision for scalable, interoperable, and automated additive manufacturing – rooted in a maturity model that guides smart manufacturers through every step of their AM transformation.

AM is a critical enabler of intelligent industry. By joining this initiative, we’re reinforcing our commitment to helping clients connect product design, digital thread, and smart factory strategies at scale. Together with our partners, we’re excited to shape the future of manufacturing, where additive is not an exception but a core capability.

, EVP, Chief Digital and Manufacturing Officer, ÎÚŃ»´«Ă˝
, VP Additive Manufacturing, Siemens AG

, Senior Alliance Manager, Siemens AG
, Senior Manager and Lead AM Factory & AM I Navigator, Siemens AG
, CTO, Manufacturing and Industrial Operations, Engineering, ÎÚŃ»´«Ă˝
, EVP, Chief Digital and Manufacturing Officer, ÎÚŃ»´«Ă˝
, VP Additive Manufacturing, Siemens AG

Our long-term partnership with HEC Paris, a premium global business school, helps test our thinking and anticipate future trends, while mentoring the next generation of leaders through inclusion challenges.

At ÎÚŃ»´«Ă˝, our commitment to inclusion isn’t just limited to our people, but extends to supporting the next generation of leaders, ensuring they can build the inclusive workplaces of tomorrow through key partnerships like our long-term collaboration with HEC Paris. 

As a business partner to HEC Paris, we’ve mentored batches of 25 students for a Master’s course on diversity and inclusion (D&I) in the last three years. We help students understand our approach toward inclusion at ÎÚŃ»´«Ă˝ and propose a team challenge, aligned with the latest D&I trends and topics. Students are then given six weeks to complete their research and present their final reports in our Paris office.  

Working with Matteo Winkler, Associate Professor at HEC Paris, who teaches courses on international business law as well as diversity and inclusion, this is an opportunity for us to connect with emerging talent, test new ideas, and anticipate trends while we mentor them through real-world challenges. 

In this blog, Matteo and I take a closer look at this year’s challenge and what we as practitioners can learn from it.

About the partnership

Matteo: When I created this course years ago as part of the Master of Management program of CEMS (previously the Community of European School of Management), which has now evolved into a global alliance of business schools of which HEC Paris is a partner. I looked for a company committed to inclusion, and who could help my students understand the challenges that D&I poses to both corporate leadership and day-to-day operations. ÎÚŃ»´«Ă˝ felt like a natural fit.

The focus for the 2025 student challenge 

Karine: Each year, we set the students a challenge, designed to stretch their thinking – past topics have included understanding the role of ENGs as inclusion activators or discussions on the emerging debate on inclusion versus meritocracy, or whether DEI trainings should be mandatory or voluntary. The topic this year, “2025 – a turning point for DEI,” was set to encourage a focus on emerging trends, even before the US executive orders were issued in January 2025.

Students were briefed to provide a rational analysis of the global situation, factoring in opportunities and risks from a regional perspective, and drawing on concepts developed during Matteo’s classes, and fed with their own academic research. 

Matteo: We’re seeing a global narrative shift: before January 2025, we needed DEI corporate programs to not discriminate against minorities and people from non-privileged backgrounds. Particularly those who were unable to reach positions of power in a corporate setting. 

The Executive Orders in the US tell us the contrary; we now need to dismantle these programs – to not discriminate, following the 2023 Supreme Court ruling ending affirmative action in the US education system.  

In the US, the risks for companies who do not comply with regulations is financially high, compared to Europe. So the narrative has changed, and private organizations (if they have federal funding or not) are in many cases, abiding.  

This team assignment encouraged students to consider how global and local organizations continue to build diverse and inclusive places to work: where the definition of diversity, the collection of people data, work-related policies and benefits and more are directly impacted by local laws around the world. 

The findings from our student teams 

Matteo: As my colleague and panel member Marcelle LalibertĂ© has said, for major global companies, it’s as if you’re steering a global organization through rapidly shifting waters – with political tides in the US pushing back on diversity efforts, in Europe you’re facing regulatory currents that require you to provide details on pay gaps, and in the Middle East you may be aiming to embed global DEI standards into a local context. Against this backdrop, the student teams highlighted several risks:  

• Legal vulnerability – organizations are facing new legal and regulatory challenges pulling in different directions: from executive orders in the US rescinding diversity initiatives to the increased (and evolving) public reporting duties of the EU Corporate Social Reporting Directive (CSRD) and strengthened anti-discrimination laws in APAC.  

• Potential political and cultural backlash for company reputations, as they attempt to strike the balance between supporting marginalized groups and inclusion for all.  

• Slower progress on DEI goals – economic downturns means DEI budgets, roles, and overall progress is at risk, as companies prioritize cost-cutting. 

• Talent exodus – most job seekers consider workplace diversity important when evaluating companies, and may look elsewhere if organizations change their commitments.  

With D&I at a crossroads, the student teams identified some key opportunities in shaping and evolving global strategies: 

• Establishing a flexible DEI framework allows for region-specific modifications while maintaining a unified global commitment. 

• Skills-based hiring and inclusive leadership – continuing merit-based hiring and leadership development enhances diversity while ensuring compliance in restrictive regions. 

• Compliance as a competitive advantage – successfully delivering on ESG commitments can position a company as an industry leader in fair and inclusive practices and continue to attract talent. 

Key reflections for organizations like ÎÚŃ»´«Ă˝ 

Karine: The students navigated an extremely complex topic. Overall, the recommendations from the student teams followed broad themes for global organizations to consider: 

1. Reaffirm and communicate organizational commitment toward the diversity and inclusion ambition, as a fundamental ingredient of their identity and success 
2. Demonstrate agility, repositioning DEI initiatives to make them accessible for all and more engaging for all employees, and partnering with talent, health and safety, or wellbeing programs 
3. Use data-driven insights and employee feedback to guide actions and increase transparency on global and location actions and impact 
4. Leave flexibility for localized DEI initiatives and expertise under a global framework, strengthening leadership pipelines and fostering a culture of belonging through education and mentorship 
5. Build client-focused strategies to strengthen relationships, collaborate, and grow. 

The presentations from the teams were excellent. Inclusion in the workplace is critical for all businesses. We were delighted to hear their views on the complex situation in 2025, building their experiences and helping them to be ready to make an impact in their future. 

Together with HEC Paris, I look forward to another year of our continued collaboration!

Life Sciences & Healthcare organizations are under unprecedented pressure. Against a backdrop of a growing and ageing population, and with care therapies, drugs and diagnostics becoming more complex and expensive, these industries must deliver personalized, cost-effective, and compliant solutions faster than ever.

At the same time, they face global disruption from geopolitical shifts, sustainability mandates, and increasing competition – both from digital-native new entrants and generic alternatives purchased by increasingly savvy consumers.

The sector has a dual challenge: increasing patient value, and bringing down costs – all within a heavily regulated and safety-conscious environment.

At ÎÚŃ»´«Ă˝, we believe there are three critical ways for life sciences and healthcare organizations to meet these challenges: infusing operations with digital technology, upgrading legacy engineering systems, and building globally agile and resilient operations.

All three revolve around a central idea: transforming the digital and physical worlds together.

1. Infuse digital into the physical world

Life sciences companies have long dealt with sophisticated physical systems – complex manufacturing equipment, labs, and regulated medical devices. But much is also legacy-driven. Its many siloed, often paper-based systems create costs and hurdles, resulting in slower and inefficient go-to-market of critical drugs, devices and therapies.

Using digital technologies (AI, software, IoT, Digital twin etc) at scale can deliver improved ways of operating, faster time to market, and lower costs, whilst also delivering sustainability through reduced waste and energy.

Take connected factories and digital twins. These allow for real-time monitoring, simulation, and optimization of physical processes. Pharmaceutical companies can test and refine manufacturing changes virtually before deploying them in the real world, ensuring compliance and accelerating time to market.

Projects abound across life sciences which illustrate such transformation. An example is , a €20 million+ bioproduction initiative involving Sanofi, ÎÚŃ»´«Ă˝, and others, which used micro-sensors, AI, and digital twins to enhance predictive control of bioproduction processes.

Digital also enables people to work together more efficiently. Data platforms and cloud – increasingly with built in supportive AI agents – provide spaces for scientists to collaborate across drug development silos – creating a digital feedback loop that can significantly reduce R&D cycles.

2. Upgrade core engineering

Many life sciences companies are constrained by aging infrastructure and fragmented legacy systems. These block innovation and cost-efficient operations.

While individual upgrades are fine, the key to unlocking transformation at scale is to streamline and standardize these systems, allowing new technologies to be easily integrated, whether digital or physical.

Predictive maintenance on the shop floor is one such example. ÎÚŃ»´«Ă˝ delivered a predictive maintenance solution for a large global biopharma organization which reduced risk of human error by 80%, improving delivery and yield, whilst boosting asset and capacity utilization by 20%. But it was only possible because the correct data foundation had first been put in place to modernize legacy systems.

Standardization can also underpin more radical changes. Some legacy systems are so outdated, and local skills so hard to find, that companies decide to lift the entire function into a more optimized and smarter ecosystem.

This approach is often seen with activities like product sustenance. For global medical OEM leaders, it is a challenge to maintain and update their large portfolios of Class I, II or III regulated medical products, including lines that have been discontinued but still need maintaining. Our experience shows that a standardized engineering platform across the portfolio of products yields large-scale optimization efficiencies in managing and maintaining these regulated products. This standardized approach also allows such activities to be delivered from anywhere in the world – enabling easy outsourcing of cost centers.

Here again, the magic happens when digital solutions are applied to physical engineering – but this time in a whole new context, on the other side of the world.

3. Build agile, resilient operations around the world

Life Sciences companies are grappling with the fact that legacy, monolithic operations are less and less viable in this globalized yet geopolitically fragile world.

The modern world needs agility – allowing it to rapidly deploy products and services across regions, adapt to changing local regulations, and scale engineering operations quickly. This is vital for quickly getting products to the widest possible market, especially in uncertain environments.

One way to achieve agility is through smaller, distributed manufacturing sites and engineering hubs, strategically placed around the globe where they can be close to customers, suppliers, or talent pools, or where transport emissions are minimized. Such operations require an ecosystem of partners, and require digital solutions to manage, similar to those developed to manage global supply chains.

But agility isn’t just geographic, it’s operational. This is not just about physical manufacturing facilities, but centers of excellence which unblock barriers to innovation and production efficiency. Regulatory compliance, commissioning, qualification, verification, or process heavy activities are classic candidates. Employing dedicated specialist teams to deliver these functions, not only saves money but allows organizations to be faster and more agile.

ÎÚŃ»´«Ă˝ has pioneered a concept called Engineering Factories. These â€�Factories’ redefine traditional outsourcing. Each is designed around a specific engineering domain and business goals, such as delivering products at a specific cost or weight; or managing specific operations such as the supply chain, MES, sustainable product design; or delivering capabilities like compliance, validation, or quality assurance. Each factory combines a team of specialists and engineers with operational and digital expertise. They are transversal, working across industries to bring the best of all worlds synthesized together.

Consider a large-scale Manufacturing Execution System (MES) deployment. Normally, rolling out MES solutions across plants would be time-consuming, resource heavy, and associated with high costs and high stakes. In our experience, a focused MES Factory helps clients standardize processes and achieve faster results. For example, one global pharmaceutical company implementing a global MES solution saw a 75% reduction in quality review time and an 80% reduction in deviations.

A similar example is our Commissioning, Qualification and Validation Factory, based out of centers in Portugal and Morocco, serving highly regulated manufacturing sites focusing on compliance, and complex global regulations with a standardized and efficient approach. Another is the Intelligent Testing Factory, based out of India, that provides full lifecycle product management and intelligent testing for global medical device clients, including a human sample testing lab, ensuring global readiness and regulatory alignment.

Such a factory approach creates a centralized hub that can deploy new capabilities in a standardized, agile way, which is often accessed via a front office on the client site. The result is a more resilient, adaptive, cost-effective engineering organization.

Built for both worlds

Of course, these areas all overlap. A successful life sciences and healthcare organization could digitize its entire value chain to optimize digital and physical processes. This could provide a foundation to quickly upgrade operations or move business functions to centers of excellence that take advantage of high tech setups, cost reduction and global talent pools.

As the world changes around us, what sets successful organizations apart is their ability to operate fluently in both the digital and physical worlds. They will embed intelligence into every part of the product and production lifecycle, whilst shifting from isolated physical systems to joined up digital-physical ecosystems. They will quickly take advantage of cost savings and innovation opportunities, whether by optimizing operations at home, or delivering them elsewhere to take advantage of the benefits of smarter factory setups or favorable business and talent environments around the world.

All of this requires agile physical operations with a digital underpinning.

“The fusion of physical assets and digital intelligence isn’t a futuristic trend – it’s quickly becoming the new standard for today’s businesses. With technologies like digital twins, robotics, and advanced connectivity accelerating at an unprecedented pace, the window for businesses to embrace the latest innovations is rapidly closing. Now is the moment organizations must act to secure their spot ahead of their competitors and shape the future.” – Anastasia Karatrantou  

What if your car could automatically adjust its temperature depending on your personal preferences at a specific time of day? What if an urban developer could simulate how a new construction development will impact local traffic patterns years ahead? What if a manufacturer could instantly adapt the output of its fleet of robots to match real-time shifts in demand? 

These scenarios aren’t speculative. In fact, experiences like these are quickly becoming ingrained in the daily lives of businesses and consumers. This convergence of real-world processes and intelligent technology, something we’ve labeled as Whole Lotta Fusion, marks an important shift for industries, operations, and experiences. Powered by digital twins, robotics, and advanced connectivity, we’re entering a world where organizations can use technology to monitor systems, optimize processes, and scale innovation in real time.  

The technologies merging the physical and digital  

This evolution is beginning to take shape in front of our very eyes. At the core of this transformation is a trio of technologies that, together, are helping bring a more advanced, interconnected world to life: 

1. Digital twins are virtual replicas of physical products, systems, or environments that allow organizations to simulate, monitor, and optimize performance without ever making changes in the real world. Whether stress-testing a new product, fine-tuning manufacturing layouts, or evaluating the resilience of a new structure, digital twins enable smarter, more proactive decision making across industries.  

2. Advanced robotics refers to the integration of intelligent, autonomous machines and systems into industrial and operational environments to enhance precision, efficiency, safety, and collaboration. From autonomous mobile robots (AMRs) that streamline warehouse logistics to collaborative robots (cobots) that work side-by-side with humans on high-precision tasks, advanced robotics enables dynamic human-machine interaction. 

3. Advanced connectivity refers to the seamless, high-speed integration of devices, systems, and environments with technologies such as 5G, edge computing, and embedded sensors. Advanced connectivity enables real-time data exchange, supports the deployment of intelligent systems, and ensures that enterprises can communicate, adapt, and respond instantly. 

Individually, each of these technologies is powerful. But combined with physical products and environments, they create connected, intelligent ecosystems where experimentation is risk-free, insights are instantaneous, and change occurs autonomously. This is where the true power of Whole Lotta Fusion lies. 

How organizations are making strides 

Across industries, leading organizations are already merging the physical and digital together. A Taiwanese electronics manufacturer leveraged AI to build digital twins of its factories. Helping drive automation, enhance industrial efficiency, and reduce costs and energy consumption, this initiative is an example of how today’s manufacturers are racing to make their factories more agile, autonomous, and sustainable.  

In the automotive industry, a Swedish car manufacturer and a German commercial vehicle manufacturer recently to develop a software-defined vehicle platform and dedicated truck operating system that enable standalone digital vehicle functions for their automobiles. Unlocking heightened levels of connectivity, safety, and efficiency for consumers, this partnership aims to push the automotive industry into a more interconnected future. 

In Thailand, four organizations to jointly develop the first fully 5G-connected factory in Southeast Asia. Equipped with an array of 5G embedded technologies, including AI inspection systems, automated guided vehicles (AGV), remote-controlled robotic arms, and next-gen operating rooms, this factory is positioned to introduce unprecedented levels of innovation, efficiency, safety, and cost reduction for each of these organizations. 

A turning point for industry 

What unites each of these examples is a shared outcome: smarter, faster, and more resilient operations. When organizations combine technology with real-world processes and environments, they gain the ability to anticipate disruption, respond flexibly to shifting market conditions, and continually optimize in real time. 

At its core, Whole Lotta Fusion marks a turning point for industries. It’s a new way of thinking about how businesses operate, innovate, and scale in our constantly evolving world. As these capabilities merge, organizations face a critical choice: embrace this convergence – or risk being left behind. 

Learn more 

• TechnoVision 2025 – your guide to emerging technology trendsĚý
• Whole Lotta Fusion – a new trend in Process of fly 
• Voices of TechnoVision – a blog series inspired by ÎÚŃ»´«Ă˝â€™s TechnoVision 2025 that highlights the latest technology trends, industry use cases, and their business impact. This series further guides today’s decision makers on their journey to access the potential of technology.

Earlier this year, we highlighted how AI-driven robotics is reshaping the boundaries between humans and machines—ushering into a new era of intelligent, adaptive systems. This evolution is now taking shape through the rise of physical AI. In this blog, we’ll showcase the core components of physical AI and explore how business leaders can take advantage of this transformative shift. 

The last few years have been a boon for AI. We now have foundational models for generating high-quality and reliable text, images, and video. We are also starting to benefit from agentic AI systems that can operate with autonomy, undertake goal-directed behavior, and adapt their behavior based on changing circumstances.  

But what about Robotics? How have we helped robots act more like humans? The answer is that we have made tentative progress. We have systems that enable robots to handle specific tasks within constrained environments.  

However, asking robots to go beyond these confines and act more like us is problematic. The world is built for humans, and changing the environment to suit a new age of robotics would be too expensive. Enabling robots to become more like us requires a nuanced approach – physical AI, where AI is integrated into physical systems for environmental interaction. 

Creating a foundational model for robotics means enabling machines to adapt to our world. We must visualize and codify the millions of tasks we perform daily and use this data to help robots perform tasks collaboratively.  

This is no straightforward task. The journey to a foundational model for robotics will involve many hurdles. Progress will rely on contributions from multiple parties. Let’s consider how your business can get involved and how advances in AI and edge computing can help us build foundational models that support the long-term emergence of physical AI.  

A new state of urgency around robotics 

Robotics is on the verge of a major leap—much like the recent revolution in generative AI. The hardware components, batteries, and actuators, which cost up to 50% of the actual bill of materials, have fallen by double-digit numbers over the past few years.  

This cost reduction means innovations that might have taken up the better part of a century will be completed in a decade or less. We are moving to a future where robots can work alongside humans, bridging the digital and physical worlds. With advanced sensory integration, deep reasoning, and dexterous manipulation, these robots will adapt to their environment, moving quickly and effectively.  

Agile companies in certain regions, particularly China, are already optimizing their supply chains and iterating on robotic innovations. The companies that create value from physical AI will win the race. So, how can your business stay competitive and thrive in this new era? 

Towards general-purpose robotics 

Business leaders must recognize that AI and humanoids are coming together to move robotics beyond structured manipulation and autonomous navigation to general-purpose robotics, where navigation and manipulation seamlessly integrate. 

This co-evolution of AI and humanoids should be your guide to effective competition in the race towards physical AI. AI, rather than hardware, will be the key differentiator that powers the move to general-purpose robotics, and its foundational model will draw on three main engines: 

• Reinforcement Learning – Rewarding robots for achieving a set business goal and not just completing a discrete task. 

• Agents – Using pioneering work on the industrial metaverse and digital twins as the source data to create the simulations that build the foundational model for robotics. 

• Vision Language Action – Combining vision models that detect an object, scene, or visual input with neural networks trained on thousands of actions. 

The huge amount of training in Vision-Language-Action (VLAs) enables robots to perform actions without programming. They can work on the fly to complete actions they might not have previously encountered. These pre-trained robots can collaborate, with their foundational models interacting to complete tasks effectively. 

Asking the right questions about Physical AI 

As we stated at the outset, the journey towards humanoids is challenging. General-purpose robotics helps us to visualize how a foundation model for robotics is a realistic end objective, but we can’t be complacent. 

Let’s take a step back and think about how humans operate: some things we do intuitively, like recognizing a friend’s face in a crowd, and some things require a more deliberate approach, such as solving a math problem. The same process is true with robotics in an age of physical AI: some systems will need fast, reactive responses and can be handled by transformer models; other systems are more analytical and will require huge computer power and pre-trained VLAs to interpret complex scenarios and plan accordingly. 

Cost will be a crucial concern. The foundational models that support generative and agentic AI often rely on the cloud. Pushing processing requirements to the cloud creates huge costs. These costs will be prohibitive in an age of Physical AI, where robots will need to connect to supporting computer systems rapidly and repeatedly. The key to handling these ever-growing computational requirements is edge computing. The Edge will help your business manage data demands cost-effectively by moving compute to where the data resides, reducing the requirement for expensive cloud services, and minimizing data transmission costs.  

At ÎÚŃ»´«Ă˝, we’re not here to give you all the answers. We’re here to help you ask the right questions, because that’s when you get creative, unlock inspirational ideas, and generate new value.  

As we enter the era of physical AI, we can help you explore the possibilities, challenge assumptions, and shape the future of robotics—together. 

“We must start thinking about how we’ll make the foundational model for robotics. Nobody knows exactly when it will happen, but if we think about AI and the progress during the past three years, I think the call for action starts becoming more and more important.”  – Kary Bheemaiah, CTIO ÎÚŃ»´«Ă˝ Invent 

“VLA is the core of the foundational models powering these humanoids. The world is full of actions, and VLA is just a coming together of all this data. It’s an ingenious idea where now you can look at an object and drive an action without programming.”  – Dheeren Velu, Head of Innovation & AI, AUNZ 

“At the intersection of AI, robotics, and computer vision, advanced training methods like reinforcement learning and VLA are driving a profound transformation: automation endowed with arms, legs – and reasoning. No programming is required.  But the true breakthrough isn’t just technological – it’s collaborative. Humans, humanoid robots, and virtual AI agents are about to operate as cohesive teams, elevating efficiency, precision, and safety to unprecedented levels.” – Alexandre Embry, CTIO, Head of the ÎÚŃ»´«Ă˝ AI Robotics and Experiences Lab  

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Navigating the new industrial landscape – ÎÚŃ»´«Ă˝

Robotics and computer vision are two complex fields that have existed for decades. Yet in the past ten years, things have shifted – and continue to evolve rapidly.

Robotics, once limited to basic automation and repeatable motions in isolated environments, is now expanding to address broader challenges. Traditional industrial robots operated at a safe distance, executing predefined tasks in static environments. 

Meanwhile, computer vision, once fragmented into subdomains like image processing, geometry, and optics, has undergone a transformation. The rise of artificial intelligence has unified these domains and propelled computer vision to the forefront of innovation. 

Today, a new convergence is taking shape — one that merges perception, reasoning, and physical action into integrated systems. This is the promise of Physical AI: the ability for machines not only to process information intelligently, but to act upon it in the real world. And at the heart of this evolution lies the rise of Vision-Language-Action (VLA) models — architectures that combine what a robot sees, what it understands through language, and how it decides to move or manipulate its environment accordingly. 

We’re already seeing early signs of this shift. For example, new-generation robots can now interpret a voice command like “pick up the red cable next to the panel,” visually locate the object in context, and perform the action — all thanks to VLA architectures that connect perception to natural language and motor execution. 

In industrial settings, robots once confined to repetitive welding behind safety cages are now operating side by side with humans — navigating busy factory floors, identifying parts, adapting to shifting workflows, and contributing dynamically to production without the need for constant reprogramming. 

Though often treated as separate disciplines, robotics and vision are deeply intertwined. Today’s robotics is no longer just about repetition — it’s about adaptability in dynamic, unpredictable environments and what better way to enable intelligent action than through perception? After all, around 80% of the information processed by the human brain comes from visual cognition. It’s only logical to equip robots with powerful vision systems if we want them to act meaningfully in the world. 

When vision meets movement 

The fusion of sight and motion is redefining how robots interact with the world around them. 

A robot that interacts intelligently and adapts to its environment relies primarily on its ability to perceive, interpret, and understand the world around it. Much like humans reconstruct their environment from limited focal information, vision systems must extract meaning from incomplete, noisy, and ambiguous data. 

In both humans and machines, vision is not passive — it’s an active process of interpretation, selection, and decision-making. And this principle applies directly to robotics. An efficient humanoid robot must incorporate biomimetic principles, enabling it to understand and act upon its surroundings as humans do. 

That’s why giving robots the ability to “see” is not just an enhancement — it’s a requirement for safe navigation, interaction, and decision-making. In collaborative environments, such as modern industrial settings where humans and robots coexist, real-time perception is essential to avoid collisions and adapt to changing conditions. 

We are moving from conventional robotics and siloed vision systems to intelligent robotics powered by integrated perception. Where traditional robots acted blindly within controlled environments, AI-driven robotics must now interpret complex scenes and operate in the real world — fluid, noisy, and often unpredictable. 

Applications across industries 

From factories to farms, vision-powered robots are reshaping work across every sector. 

Thanks to breakthroughs in both robotics and computer vision, it’s increasingly plausible to anticipate radical changes in how we design, manufacture, and operate across countless industries. 

Many tasks that are still carried out manually — repetitive, sometimes non-standard, and often labor-intensive — could be augmented or replaced by intelligent robots. For instance, repetitive part handling is physically demanding and costly. Delegating such tasks to machines allows humans to focus on less exhausting, more meaningful work. 

A more complex case is visual inspection. Today, for each inspection station, there’s a dedicated process — sometimes manual, sometimes automated, often a mix of both. But with computer vision and robotics, we can envision versatile, autonomous visual inspection systems capable of adapting across product types and conditions. 

And these examples extend well beyond quality control in operations: think of hazardous operations, where robotic systems can prevent human exposure to danger, or required round-the-clock tasks, where robots can operate continuously without fatigue avoiding dangerous error. 

From perception to autonomy 

Seeing is just the beginning – true autonomy emerges when machines understand what they see. 

Attaching cameras to a robot and detecting a few objects doesn’t make it autonomous. While the progress in computer vision is undeniable, real autonomy lies in the transition from raw detection to contextual scene understanding. 

Detection allows a system to identify known elements — objects, markers, obstacles — typically in controlled environments. But the real world is rarely so clean. In industrial settings, in cities, or in natural environments, robots face variability, ambiguity, and noise. That’s where true autonomy begins: not just recognizing what’s in front of them, but understanding what it means, how it changes, and what to do about it. 

This shift requires a deeper integration of perception, cognition, and action. For example, in a fulfillment center scenario, a robot must move from: 

• Identifying a box to understanding that it’s fragile and just fell off a conveyor belt 
• Seeing a person to predicting their trajectory and adjusting behavior safely 
• Detecting a machine to interpreting that it’s idle and requires assistance 

It’s about reasoning, prioritizing, and reacting in real time, based on complex visual input. And this isn’t just a matter of better algorithms — it requires: 

• Multi-modal fusion (combining vision with sound, touch, or contextual data) 
• Learning on the edge (to adapt quickly to new situations without retraining centrally) 
• Generalization (being able to apply learned behaviors to unseen environments) 

In other words, we move from reactive systems to proactive agents capable of operating in the unknown. This is especially vital in dynamic or high-stakes environments — from co-working with humans on factory floors to exploring disaster zones or navigating crowded streets. 

Autonomy is not binary — it’s a spectrum. And the closer we get to human-like understanding of space, intent, and consequence, the more fluid, intelligent, and reliable robotic behavior becomes. 

Ultimately, perception is the lens but autonomy is the leap. 

From seeing to thinking and doing: The rise of physical AI 

Perception alone is not enough — intelligent robots must connect vision, language, and action into one seamless cognitive loop. 

A new wave of intelligent robotics is taking shape — one where vision alone isn’t enough. The frontier is now Physical AI: systems that combine what a robot sees, what it understands, and what it does. At the heart of this evolution are Vision-Language-Action (VLA) models, which merge visual perception, natural language understanding, and physical execution into one unified architecture. This enables robots to go beyond detecting objects — they can now follow instructions, understand goals, and adapt their actions accordingly. 

These models open the door to more intuitive, adaptive robotics in factories, hospitals, and homes — creating machines that collaborate, learn, and act in complex environments. While still an emerging field, Physical AI is rapidly becoming the foundation of truly intelligent autonomy. 

Challenges in the loop 

More intelligence means more complexity – and a greater need for safety, ethics, and control. 

With increasing perceptual capabilities come significant challenges. One key issue is robustness: computer vision systems can be vulnerable to variations in lighting, background, and unexpected events. 

There’s also the challenge of trust and explainability. When robots make decisions based on complex visual input, humans must understand why and how those decisions are made — especially in safety-critical environments. 

Additionally, there’s a computational burden: processing high-resolution video streams in real time, running deep models at the edge, and doing so efficiently and sustainably is still an ongoing technical frontier. 

Moreover, and perhaps most importantly from an ethical perspective, we must ask: What tasks should we delegate to machines? How do we ensure that intelligent robots augment human work in responsible ways? 

Shaping the future together 

Empowering the next generation of robots starts with the choices we make today. 

The fusion of computer vision and robotics is one of the most promising frontiers in technological innovation. It offers a glimpse into a future where machines are not just tools but perceptive collaborators. 

To realize this future, organizations must invest not only in algorithms and hardware, but in talent, infrastructure, and governance. It requires cross-disciplinary collaboration — between engineers, ethicists, designers, and decision-makers. 

Those who act now — by embracing intelligent technologies, fostering experimentation, and building trust — will shape the future of robotics not as a distant vision, but as a practical, human-centered reality.