At the Orano Melox site in southern France, a humanoid robot named Hoxo (short for Humanoid melOX Orano) has quietly begun a new chapter in industrial history.

Currently being tested at the nuclear fuel manufacturer’s training school, it moves, perceives, and acts in a way that looks strikingly human, yet it is powered by something profoundly new. Hoxo is not programmed; it is trained.

Behind this achievement stands the collaboration between Orano and ��ѻ��ý. Together, they combine industrial expertise, applied AI innovation, and advanced computing technologies to create intelligent, adaptable, and autonomous physical agents capable of working safely alongside humans in demanding environments.

From programming to learning

For decades, robots have been rigid by design. They excelled at repetitive, pre-programmed tasks but struggled with anything that required flexibility or judgment. Physical AI changes this paradigm.

Instead of encoding every motion and instruction through manual programming, humanoid robots now learn by experience. They absorb data from simulated environments, human demonstrations, and real-world interactions. Over time, they develop a form of embodied intelligence: the ability to perceive, reason, and act in dynamic settings.

At ��ѻ��ý’s AI Robotics & Experiences Lab, engineers have developed a training pipeline that integrates  with advanced Vision-Language-Action (VLA) models. In this environment, Hoxo’s digital twin learns thousands of variations of a task, opening a valve, climbing a ladder, handling a tool, within safe, photorealistic virtual replicas of Orano’s facilities. Once trained in simulation, the robot transfers these skills to the real world with high precision.

This “Sim-to-Real” approach removes one of robotics’ biggest constraints: the need for extensive reprogramming for every new application. Each new mission becomes a learning opportunity, not a coding exercise. As a result, the robot becomes a versatile platform, easy to retrain and repurpose.

Vision, language and action: a unified intelligence

At the heart of this new generation of humanoids lies , a Vision-Language-Action framework that merges visual understanding, linguistic reasoning, and motor control within a single AI system. Orano and ��ѻ��ý are extensively evaluating the GR00T N model as part of their Proof of Concept (PoC) for their Hoxo humanoid.

Through its cameras and sensors, Hoxo perceives its environment, identifies objects, reads labels, and understands spatial relationships. When an operator provides a command such as “Inspect the containment area and report anomalies” the robot translates that instruction into a series of physical actions.

This integration allows it to adapt to changing conditions. If an obstacle appears, it adjusts its path. If the lighting changes or the workspace evolves, it recalibrates its perception in real time. The result is a robot that continuously links perception, understanding, and execution.

NVIDIA Isaac GR00T N dual-system architecture, inspired by human cognition, makes this possible. A high-level reasoning layer interprets goals and scenes, while a fast, low-level control layer converts them into smooth, precise movements. This combination gives humanoid robots the responsiveness and generalization they need to operate autonomously in complex industrial environments like Orano’s.

Teaching by teletraining

Not all learning happens in simulation. Another method developed by ��ѻ��ý is teletraining, where human operators remotely demonstrate actions that the robot observes and imitates.

Using advanced sensors and computer vision, the robot analyzes gestures, trajectories, and timing, refining its movements through reinforcement learning. Over time, it becomes more autonomous and requires less supervision. This approach makes it possible to transfer human expertise directly to robotic systems, preserving operational know-how and amplifying it across multiple sites.

Why the humanoid form matters

In industries built by humans for humans, form is function. The humanoid design allows robots like Hoxo to navigate existing facilities naturally—opening doors, handling tools, operating panels, without the need for redesigning infrastructure.

In the future, robots could enter hazardous zones in Orano’s plants, perform repetitive or physically demanding tasks, and assist teams in maintaining productivity under challenging conditions. Their flexibility allows them to switch roles, move from inspection to logistics, or maintenance depending on operational needs.

Towards collaborative physical AI

What truly sets these robots apart is their integration into a larger ecosystem of intelligent agents. They can exchange information with digital twins, interact with virtual AI systems, and collaborate with human operators in real time.

This convergence of physical and digital intelligence defines the essence of physical AI. It is not automation in isolation but collaboration at scale.

• For Orano, this represents a new phase in industrial innovation: safer operations, greater agility, and enhanced performance.
• For ��ѻ��ý, it brings to life the vision of intelligent industry, where AI extends from data centers to factory floors.
• For NVIDIA, it showcases the power of its AI infrastructure, open models, libraries and frameworks necessary to develop the next generation of AI-driven robots.

The last mile of industrial automation

Physical AI is the final link between digital intelligence and real-world execution. These humanoid robots are not static machines; they are adaptive systems that learn, reason, and evolve.

As models advance and simulations become more realistic, generalist robots like Hoxo will be able to handle an ever-wider range of industrial tasks. They will contribute directly to operational resilience, efficiency, and safety.

The collaboration between Orano and ��ѻ��ý with NVIDIA technology demonstrates what happens when industrial expertise meets advanced AI and computing: machines that no longer need to be told exactly what to do, because they understand what needs to be done. This is the future of robotics, already unfolding on the factory floor.

Few technologies have proven to be as exhilarating or transformative as Gen AI. It is set to revolutionize the way we work now and in the future. But like any transformative technology, there are so many unanswered questions: Will my data remain safe and secure? What are the ethical issues I need to consider? What are the real benefits to my business? Will people adopt it, use it, and embrace the change?

We see many organizations facing difficulties in fully harnessing the business potential of copilots and AI agents because of obstacles related to strategy, implementation, and scaling. Gartner predicts that by the end of 2025.

So, how do you unlock the true potential of Copilot and AI agents?
Successful Copilot adoption isn’t just about turning on a new tool; it’s about transforming how people work.
Here are a few actionable tips to navigate challenges and drive meaningful, lasting adoption of Copilot and AI tools in your organization.

Start with an end goal in mind:

Before embarking on a Gen AI investment, it is essential to clearly outline your goals in alignment with your business objectives. In our experience, a deep understanding of organizational strategic objectives is key to identifying the right use cases.
Define clear business benefits you want to measure and set clear KPIs. Set reporting mechanisms that measure business value and ROI at different points and various intervals. Ensure robust engagement from C-suite stakeholders to secure funding and to prioritize Copilot and agent use cases that provide the greatest business value.

Assess organizational readiness:

A precise comprehension of your organization’s current position is essential for charting the path to the desired destination. Ensure your tech landscape is mature enough to deploy and scale Copilot and AI agents. Evaluate organizational policies on Copilot licenses and configurations (e.g., consider differences between Microsoft 365 Copilot and Copilot Chat). Within the same organization, there may be departments or business lines with varied maturities. Identify and prioritize strategic levers for value realization. Look across application landscape to recognize areas for cost optimization through process automation with agents.

Lay the foundation for implementation:

Based on your assessment, transform core business processes to support Copilot and agent implementation. Set up a strong governance framework with clear rules, procedures, and responsibilities to ensure accountability, compliance, and alignment with business objectives. Before piloting a project, identify the target users across functions and roles that align with your business objectives. Also, ensure end users have the required resources and support.

User enablement and adoption:

To fully harness the capabilities of copilots and AI agents, it is essential for users to be well-equipped to utilize the tools and applications in their daily tasks. Providing training and enhancing the skills of your employees is crucial for accomplishing this goal. Furthermore, consistent communication initiatives, community engagement, resource availability, support systems, and innovative work practices are essential for promoting a change in mindset.

Data security and compliance:

Data security is crucial to ensuring effective use of copilots and AI agents. Recognize the current privacy and data security posture and identify any gaps. Implement robust data governance with data classification and appropriate access controls to avoid data leakages. Establish compliance mechanisms, and frame social codes of usage to comply with local and national laws and data security policies.

Regularly review and audit permissions, monitor usage, and train users on secure collaboration practices. Continuously refine and evolve your data security strategy to reap the benefits of Gen AI investment while protecting sensitive data.

Scaling copilots and agent initiatives:

Copilot deployment is an investment and not a race. Scaling too quickly can introduce various risks that may undermine the overall business impact. Start with a pilot before rolling out a full-scale deployment. The insights and learnings from the pilot will guide the strategy for the possible implementation and expansion of future deployments.

Focus on a limited set of functions per wave, allowing time for exploration of specific use cases and prompts. While defining POCs, target low-risk, high-impact business scenarios. Create custom copilots for specific functions and business lines to maximize business value.

In conclusion, a comprehensive strategy for the adoption of copilots and AI agents that aligns with business goals, and incorporates well-defined processes, governance, data security protocols, and change management, is essential to fully unleash business benefits.

Are you ready to embark on your Gen AI adventure?
At ��ѻ��ý, we help you build the trust you need to go on the Gen AI adventure at the workplace, and we work alongside you to calm concerns and ignite imagination.

Our proven end-to-end approach, combined with deep industry expertise, helps you transform your organization with Gen AI, powered by Microsoft 365 Copilot, Copilot Chat, and AI agents.

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Public sector organizations are allocating 30% of their tech spend to on-demand technologies – with this figure set to rise to 44% in 2026. What’s causing this increase in costs and how can the public sector contain it?

In a new report from the ��ѻ��ý Research Institute (CRI), The on-demand tech paradox: Balancing speed and spend, 82% of technology leaders surveyed across industries have seen significant cost increases for cloud, SaaS, and Gen AI usage. Almost half are considering moving workloads to on-premises environments as a result. The debate surrounding cloud sovereignty is further fueling this trend, as CIOs and their teams aim to avoid compliance risks and data privacy dependencies that could arise from geopolitical challenges.

What’s accelerating public sector cloud adoption?

Cloud is a necessary foundation for delivering modern, joined-up, data-driven services. And, as AI continues to mature, success with it depends on cloud services. That’s because AI only works when it’s given good data, and cloud offers a better way to connect that data.

Yet on-demand cloud services are cited as one of the biggest IT cost drivers. For the public sector, these services increasingly underpin the way public services are delivered at scale, as well as how the sector handles new or periodic service demands, such as during an election, AI-powered chatbots dealing with citizen queries, and platforms for remote working and collaboration.

What is clear is that the power of the cloud, while creating opportunities for streamlining and automating countless processes, also generates one of the biggest challenges. Just as control, transparency, and governance are required to ensure a sovereign cloud, so it is for cloud costs, particularly as on-demand IT resources are scaled.

Cloud use in the public sector – what’s going wrong?

The CRI survey revealed some startling facts about the difficulty public sector organizations face in controlling their cloud spend:

• 67% are unable to accurately forecast cloud budgets
• 68% see cloud waste as a big challenge – significantly higher than the 59% all-sector average
• 61% say their organization’s on-demand tech costs are “a big black hole”
• 53% have faced bill shocks due to unpredictable spikes in cloud usage

More generically, only around 30% of organizations across sectors have achieved their savings target.

Why the public sector needs FinOps

These figures are perhaps not surprising. What’s missing in so many cloud environments are the controls required to keep costs from spiraling as on-demand tech sprawl expands. For example, 58% of public sector leaders said they did not have complete visibility of how many SaaS apps they had.

What’s needed is an effective FinOps program. FinOps is the continual management of both operational and cultural practices that ensures you get the most from your cloud investments, while controlling their costs. The word “continual” is important here because cloud and on-demand are flexible by nature, thus your controls need to flex in tandem.

But that’s precisely the catch. Many organizations are considering FinOps, but few are implementing it effectively. In the public sector, while 53% of the CRI survey respondents said they used cloud cost management tools, only 34% regularly evaluated the performance and impact of those tools and took action based on the insights. 

Furthermore, FinOps is invariably pushed behind cloud strategies (perceived as an afterthought), thus lagging behind the cost explosion. Playing catch-up is much harder than preventing that cost explosion in the first place.

FinOps, like compliance and security, must therefore be part of the cloud strategy from the outset, moving away from the idea of technology first, cost control second.

What is procurement’s role in controlling cloud costs?

So, where do you start? It is vital when developing a cloud strategy that you identify the base costs for each functionality, as well as the cloud’s cost drivers in a design-to-cost methodology. You should also establish a clear usage framework.

With cloud services – especially AI-based automation – becoming more and more prevalent in business processes, their procurement should be managed just as professionally as other purchased goods. This is precisely why purchasing/procurement has always existed as a standalone skill and function. A good industrial purchaser has the technical background to efficiently manage negotiations and supplier management.

But how good is the purchasing department’s expertise in cloud technology? With 67% of the public sector survey participants saying they are unable to forecast their cloud budgets accurately, something is clearly going wrong.

A big clue lies in how established procurement processes have worked in the past. The public sector has historically used fixed price terms and deliverables. So, while cloud environments have been set for most use cases and are waiting to be deployed and used, on-demand pricing is, in general, a huge challenge for public sector procurement. Balancing cloud cost and on-demand agility demands wholly new thinking.

Rebalancing speed and spend

The pattern of cloud/on-demand first, costs second (often after a service has been built and launched) is especially evident in the public sector. Indeed, 65% of public sector participants reported that this was the case, against an all-sector average of 54%. The outcome of this is clear. For example, taking just one element of on-demand budgets, spending on SaaS in the public sector has seen an average over-spend of 12% in the past year.

Meanwhile, 65% of the public sector respondents confirmed this pattern of tech first, cost second thinking, whereas private industry players are addressing this more effectively, with an all-sector average of 54%. The life sciences sector shows a marked contrast at 38%. This presents an opportunity for public sector organizations to tap into already proven best practices from the private sector. This is particularly pertinent in today‘s climate, where the need for digitization is increasing and there is an expectation of a huge rise in funding IT/digital projects in the public sector.

What impact will sovereignty have on cloud costs?

Another aspect of cloud raises cost implications: geopolitical shifts have led to sovereignty becoming more of an imperative in the public sector. 57% of public sector organizations, versus a global average of 46%, are already embedding cloud sovereignty in their overall cloud strategy.

With the potential to add to the cost control challenge, this demands careful decisions about what environment is appropriate for each workload. Sovereign solutions should only be used where the risk management approach really demands them.

How much are organizations willing to pay for additional sovereignty and compliance? This was a key question in the CRI survey, noting that all cloud providers today are providing different offers. Most of these offers come with a premium tariff for additional compliance controls, liability terms, and local/national operations.

An all-sector average of 42% said they would be willing to pay extra for sovereignty, with an average 11% price increase. A further 37% acknowledged a tentative willingness to pay an 11% premium for sovereign cloud.

That some FinOps thinking is already in play is evidenced by 58% of respondents across all sectors saying that they conduct a cost-benefit analysis to balance sovereignty needs and cost efficiency. This will lead to a multi-cloud approach, providing the right cost/function model for different data classification needs.

What should public sector leaders do?

We have seen that the adoption of on-demand technologies, such as cloud, SaaS, and Gen AI, is accelerating. At the same time, there is a corresponding rise in both costs and complexity. FinOps offers a pathway to controlling the cost explosion and increase the value of on-demand technology.

The following steps can help to establish successful, cost-managed cloud adoption:

• Make cost a deciding factor, not an afterthought. On-demand tech decisions should be made with cost management to the fore.
• Bring procurement and IT closer together. Purchasing can then feature at the outset of new IT adoption.
• Build understanding of cloud provision and on-demand services within your procurement function. Purchasers should know what the cost drivers are and how on-demand pricing can be dealt with.
• Design scalable, agile, frugal, and sustainable architecture. Architecture choices should align costs to value, and cost-aware architecture should limit egress charges.

Making public sector cloud work – cost effectively

Cloud is playing a pivotal role in the transformation of public services. Done the right way, it can reduce costs and make digital modernization easier, but smart choices and cost-containment are key.

Organizations that get this right will be able to rely on the cloud effectively and consistently, and by increasing their use of on-demand services, drive real improvements in productivity, innovation, and cost savings.

Find out more

Read The on-demand tech paradox: Balancing speed and spend published by the CRI for more on the current state of cloud investments and on-demand usage. The breakdown of pure cloud expenditures, SaaS, and Gen AI is particularly interesting.

Our report Making it real: Four steps to implementing a sovereign cloud shows howpublic sector organizations can maintain an appropriate level of control over their data, technology, and operations. 

Technology is developing faster than most organization’s strategies. While you can’t slow the pace of change, you can choose to lead it through focused technology adoption.

How do organizations stay ahead while technology development is accelerating?

New technologies emerge at an ever-accelerating speed; traditional operating models and planning cycles are no longer sufficient to keep pace. Nowhere is this more evident than in the field of AI.

Recent data underscores this paradigm shift: Generative AI adoption has increased from 6% in 2023 to 36% in 2025, and AI agent deployment has grown from 4% to 14% over the same period reflecting technology adoption trends visible across sectors. These significant upticks exemplify the broader trend of accelerated technology diffusion within industry.

As this momentum continues, organizations must not simply respond to change, they must anticipate and orchestrate it. The imperative for proactive leadership in navigating techceleration is clear: those able to architect and execute adaptive strategies will define the future of their industries. 

Techceleration refers to the rapidly increasing speed of technological progress. This includes breakthroughs like 5G, generative AI, and quantum computing. These innovations are driving major changes across industries and organizations which must be actively managed.  

Adapting to rapid technological advancement necessitates staying abreast of emerging tools and prevailing trends. But it also requires embedding a strategic, business-aligned approach to technology and evolving how you operate, compete, and create value. 

AI stands in the middle of techceleration and demonstrates how innovation can quickly translate into impact. Instead of advancing incrementally, AI drives comprehensive transformation at an accelerated pace.  

It can streamline operational IT processes to reduce costs and enhance digitization, while also powering real-time, personalized support at customer and employee touchpoints to redefine service and experience. 

From technology adoption to technology anticipation 

To create business value through techceleration, organizations must adopt an innovation-led approach where technology drives strategic goals. This approach should include:

• Implementing a strategic and hands-on technology watch to actively monitor emerging technologies in order to drive innovation.
• Scaling the use of AI to accelerate transformation.
• Leveraging on-demand tech solutions to boost efficiency.

Techceleration calls for a strategy that clearly identifies where value can be created and gives the organization the flexibility to shift direction quickly.

As part of this strategy, the IT function should establish itself as a business partner by proactively identifying technological trends, introducing innovative ideas, and supporting the organization in adopting and scaling these advancements efficiently.

Achieving this transformation necessitates the implementation of a governance and delivery model attuned to business objectives.

Scaling GenAI with technology adoption

Techceleration has rapidly expanded the digital capabilities of organizations, but it has also raised the stakes for how emerging technologies are adopted. 

Gen AI is a prime example. The question is no longer whether to adopt Gen AI, but how to do so effectively and at scale. This shift demands more than technical experimentation. It requires a strategic framework that integrates Gen AI into organizational design, talent development, partnerships, and data infrastructure. 

The potential is substantial. Internally, Gen AI can streamline operational processes such as software engineering and application lifecycle management, reducing IT costs while advancing digital maturity. Externally, it enables more personalized client interactions, moving organizations closer to genuine client-centricity. 

A global manufacturing conglomerate partnered with ��ѻ��ý to address supply chain inefficiencies caused by unpredictable market demand. To solve this, ��ѻ��ý developed a Gen AI-powered chatbot and forecasting engine that provides real-time insights and highly accurate demand predictions. As a result, the client was able to optimize warehouse logistics, reduce inventory losses, and significantly boost profit margins. Following the success of the initial prototype, the solution is now being scaled globally, demonstrating the transformative potential of Gen AI in supply chain management. 

Yet, scaling Gen AI is complex. Transitioning from proof-of-concept to production-ready solutions creates challenges in data governance, availability, and quality. Organizations must define a clear implementation roadmap, supported by credible business cases and ongoing validation of Gen AI outputs. Addressing bias and ensuring transparency in AI-generated content is essential to building trust and sustaining value. 

On-demand tech value 

Techceleration has made the value of on-demand technology more urgent than ever. As technology adoption outpaces traditional planning cycles, the pressure to extract measurable value from digital investments has intensified. This shift has elevated the role of technology in business strategy and has reframed how its value is assessed. In this context, the rise of on-demand technology presents both a challenge and an opportunity. 

To begin with, the flexibility and scalability of on-demand IT, enabled by cloud services and consumption-based models, have become indispensable. Yet, this very flexibility introduces a new layer of complexity. As organizations embrace these models, they must contend with fluctuating costs, fragmented systems, and the need for continuous oversight. 

What was once a straightforward budgeting exercise has evolved into a balancing act between performance, cost, and business need. Organizations that have developed the capability to link technology-spend directly to business value are putting themselves at a clear advantage. The advantage lies not in cost-cutting per se, but in precision. By making data-driven decisions about where and how to invest, they optimize their technology stack as well as their strategic outcomes. This approach transforms IT from a support function into a driver of competitive advantage. 

However, the path to this level of maturity is not without obstacles. As consumption-based pricing becomes the norm, the ability to forecast and monitor total technology spend becomes essential. Without robust governance, organizations risk creating a fragmented landscape. Strong technology adoption strategies keep ownership clear and value visible. Financial discipline alone is insufficient. It is important to foster a culture of accountability that encourages teams to consider both cost and carbon impact. This cultural shift ensures that optimization efforts are sustainable and aligned with broader organizational goals. 

Accelerate with technology adoption strategies

Techceleration demands speed as well as strategy. By embracing innovation, anticipating change, and embedding scalable solutions like Gen AI and on-demand tech, organizations can unlock sustainable value.

��ѻ��ý recognizes the complex challenges organizations face today. Drawing on decades of experience partnering with clients to drive digital transformation, we’re uniquely positioned to provide practical support and strategic guidance as businesses adapt to the rapid pace of technological change. 

In the next blog of this series, we’ll explore how softwarization is reshaping every layer of business.