By combining confidential computing with Nvidia H100 GPUs, 鈥淧rivatemode AI鈥� provides cloud-hosted LLMs with end-to-end encryption of user data.

The AI revolution is transforming our world at unprecedented speed. Just a few years ago, the idea of conversing naturally with a computer seemed more at home in Hollywood or in science fiction than in the workplace. Yet with the rise of generative AI tools like ChatGPT, these technologies have become an everyday reality, embraced by employees, customers and IT users alike.

However, this rapid adoption brings new challenges, particularly for organizations in regulated industries that must maintain high levels of data protection and privacy.  How can those organizations harness the power of GenAI models at scale while also safeguarding sensitive information?

Confidential AI solves the 鈥渃loud versus on-premises dilemma鈥�

The advent of AI has amplified the importance of choosing between cloud and on-premises infrastructure. Traditionally, organizations preferred to process sensitive data on-premises, within their own data center, as it offered maximum control. But given the significant costs of GPU infrastructure and the energy consumption that AI workloads require, on-premises is usually not economical. What鈥檚 more, limited expertise and technical resources for managing AI architectures locally make the cloud 鈥� especially 鈥淎I-as-a-service鈥� offerings 鈥� a more viable option for most organizations.

Yet, when deploying AI solutions such as large language models (LLMs) via a cloud-based service, many parties 鈥� cloud, model and service providers 鈥� potentially have access to the data. Which creates problems for regulated industries.

Figure 1: With standard GenAI services, model, infrastructure and service providers can all potentially access the data.

This is where confidential computing comes into play. While it鈥檚 long been standard to encrypt data at rest and in motion, data in use has typically not been protected.

Confidential computing solves this problem with two main features: runtime memory encryption and remote attestation. With confidential computing-enabled CPUs, data stays encrypted in the main memory, strictly isolated from other infrastructure components. Remote attestation also makes it possible to verify the confidentiality, integrity and authenticity of the so-called Trusted Execution Environment (TEE) and its respective workloads.

Figure 2: Confidential computing provides runtime encryption and remote attestation for verifiable security.

Confidential computing has been a standard feature of the last few generations of Intel and AMD server CPUs, where the feature is called TDX (Intel) and SEV (AMD) respectively. With Nvidia鈥檚 H100, there鈥檚 now a GPU that provides confidential computing 鈥� allowing organizations to run AI applications that are fully confidential.

Figure 3: Confidential AI allows organizations in regulated industries to use cloud-based AI systems while protecting the data end to end.

How 乌鸦传媒 and Edgeless Systems deliver confidential AI together

乌鸦传媒 is a leader in GenAI, managing large-scale projects to drive automation and foster efficiency gains for clients worldwide. The firm has long-standing expertise in delivering AI systems across clouds and on-premises, including critical aspects like user experience, Retrieval Augmented Generation (RAG) and fast inference. (More on these later.)

Data security and privacy are critical aspects of many 乌鸦传媒 projects, particularly those in regulated industries. This means clients are often confronted with the aforementioned 鈥渃loud versus on-premises dilemma鈥�.

The good news: deploying GenAI tools through ough the cloud, with verifiable end-to-end confidentiality and privacy, isn鈥檛 a distant future. It鈥檚 a reality. And 乌鸦传媒 is already bringing it to clients in regulated industries like healthcare, defense, the public sector and the financial sector.

In 2024, 乌鸦传媒 partnered with Edgeless Systems, a German company that develops leading infrastructure software for confidential computing. (See the blog post, Staying secure and sovereign in the cloud with confidential computing.) Edgeless Systems now provides Privatemode AI, a GenAI service that uses confidential virtual machines and Nvidia鈥檚 H100 GPUs to keep data verifiably encrypted end to end. This allows users to deploy LLMs and coding assistants that are hosted in the cloud while making sure no third party can access the prompts.

• Powerful LLMs, e.g., Llama 3.3 70B and Mistral 7B
• Coding assistants, e.g., Code Llama and Codestral
• End-to-end prompt encryption
• Verifiable security through remote attestation
• Standard, OpenAI-compatible API

Together, 乌鸦传媒 and Edgeless Systems are already bringing exciting confidential AI use cases to life.

Case 1: Confidential AI for public administration

In the German public sector, the demographic change will soon lead to many unfilled positions and capability gaps. GenAI applications can support the work of civil servants, automate administrative tasks and help to reduce labor shortages. For example, the IT provider of the largest German state (IT.NRW 鈥� Landesbetrieb Information und Technik NRW) has contracted 乌鸦传媒 to develop an 鈥淎dministrative AI Assistant鈥� to improve productivity for thousands of administrative employees.

The GenAI application helps in several ways, including by summarizing text or supporting research assistants with RAG (Retrieval Augmented Generation). However, there aren鈥檛 enough GPUs available on-premises to support inference (the process whereby an LLM receives and responds to a request) and the public cloud isn鈥檛 an option for sensitive data. Here, the client uses Privatemode AI for confidential inference in the cloud, serving a Meta Llama 3.3 70B model via a standard OpenAI-compatible API. So while all the heavy processing is done in the cloud, all the user data is encrypted end to end.

Figure 4: Hybrid architecture for LLM-based assistants with Confidential 鈥淎I-as-a-service鈥� for inference (blue box).

Case 2: Confidential coding assistants for sensitive applications

As 乌鸦传媒 is one of the largest global custom software developers, it鈥檚 also responsible for protecting code and developing sensitive applications, including for security agencies. Software development projects are handled fully on-premises due to regulations, which makes integrating state-of-the-art coding assistants that require scalable GPU infrastructure a challenge.

Together, 乌鸦传媒 and Edgeless Systems integrate AI-based confidential coding assistants with end-to-end encryption for developing sensitive, proprietary code. With Privatemode AI, 乌鸦传媒 can also improve the experience for developers by allowing them to use modern coding assistants in a sensitive environment.

Confidential AI is the future of AI in regulated industries

It鈥檚 evident that the discussion about digital sovereignty is especially relevant in the context of AI. Critical infrastructures and regulated industries can largely benefit from GenAI applications but also require secure handling of sensitive data to boost innovation and digitalization. The future of AI therefore lies largely in confidential AI. And by enabling use cases with end-to-end data protection at scale, 乌鸦传媒 and Edgeless Systems are leading the way.

GET THE FUTURE YOU WANT

乌鸦传媒 and Edgeless Systems have already implemented confidential AI use cases in critical infrastructures, public administration and healthcare. Let our experience inspire you and bring your data together with AI innovation.

Additional links:

Edgeless Systems:

Privatemode AI:

Nvidia blog post on Privatemode AI (2024):

Edgeless Systems鈥� Open Confidential Computing Conference OC3 with presentation by 乌鸦传媒 and IT.NRW on Confidential AI:

OC3 presentation: Confidential AI in the Public Sector by Arne Sch枚mann (IT.NRW) and Maximilian K盲lbert (乌鸦传媒):

乌鸦传媒’s award-winning LEAP program develops future leaders by providing hands-on training, ensuring a strong pipeline of capable senior leaders.

As companies grow and evolve, the need for capable leaders who can navigate complex landscapes and drive strategic initiatives becomes increasingly crucial.

However, finding and nurturing internal candidates who are ready to transition from middle-management to senior roles is a common struggle.

The importance of developing future senior leaders

This is problematic as competent leaders typically help guide teams and shape their company’s vision, culture, and strategic direction. Therefore, without a strong pipeline of future leaders, organizations risk stagnation and may struggle to adapt to changing market conditions.

Recognizing this, 乌鸦传媒 chose to transform the way it addresses its talent and workforce challenges by proactively addressing its own leadership gaps, ensuring a steady flow of talented individuals ready to take on senior roles across its organization as a result.

Providing hands-on training to help overcome real-world challenges

Developing future leaders requires more than traditional training programs. It involves creating an environment that fosters innovation, creativity, and continuous learning through hands-on training that equips middle-managers with the skills necessary to excel in senior roles.

This training should ideally involve placing middle managers in interactive, practical scenarios that mirror the real-world situations they will face in more senior positions.

This ensures middle-managers will be able to handle the demands that come with more senior roles by enabling them to enhance the skills needed to do so in a safe, controlled environment.

乌鸦传媒’s Leadership Empowerment and Acceleration Program

乌鸦传媒鈥檚 Leadership Empowerment and Acceleration Program (LEAP) boosts enthusiasm, innovation, and effectiveness among talented middle-management leaders by equipping them with the skills, motivation, and confidence necessary to move into more senior roles.

Through onsite, classroom-like sessions that focus on improving English proficiency, LEAP ensures 乌鸦传媒 benefits from an expanding pool of senior leaders, which helps enhance its efficiency, effectiveness, and customer satisfaction scores across LATAM and beyond.

This is why 乌鸦传媒’s LEAP program recently received a Silver award for “Brilliance in Employee Engagement” from HR Brilliance during their latest award ceremony.

Want to learn more? Discover how 乌鸦传媒鈥檚 Intelligent People Operations team puts your people at the center of your HR proposition through digital platforms that give them a connected experience by contacting: rosimeria.kowalski@capgemini.com or thomas.a.zimmer@capgemini.com.

Edge architectures are a strategic business driver, and companies are approaching these solutions from the edge-in rather than cloud-out. This is especially true as the technologies are gaining traction and maturity across various industries. Google Distributed Cloud brings its extensive data and security experience to help companies navigate the edge successfully. 

The market for edge computing is expected to grow significantly, driven by the need for real-time insights and localized computing power. that the worldwide edge computing market will be worth almost $350 billion by 2027, up from $232 billion by 2024.  

Edge computing and distributed cloud architectures are emerging as pivotal technologies that are redefining data processing and management. This has mainly been driven by the exponential growth of data, the need for real-time insights, and the surge of resource-intensive applications like artificial intelligence. Another factor is that companies cannot afford a public cloud outage that shuts down operations. All of this has created a fundamental change in how businesses operate and compete 鈥� and the answer is at the edge. 

Getting comfortable at the edge 

Technological advancements are driving the growth in edge computing, as more companies adopt it and distributed computing architectures in general. The goal is to make operations more efficient, lower costs, and improve user experience, with real-time data processing and localized decision-making. 

Specific industries are already seeing the benefits of working on the edge. For example, manufacturing facilities can get real-time data processing for predictive maintenance locally versus having facilities connected to the same global cloud. For retail, quicker in-store analytics allows for a more personalized experience, as well as better store and inventory management. And the low latency aspect and ability to work offline mean retailers no longer need to be so focused on the strength of every local internet connection. 

Extending Google Cloud to the edge 

The cloud journey is shifting towards more flexibility through hybrid approaches. The roadmap may include public cloud domains, on-premises environments, and the edge. The edge becomes critical because companies need to process vast amounts of data closer to where it is being generated. But edge computing is also about operations resilience. Traditionally, losing an internet connection at an edge location would cause a complete disconnect from the central cloud. 

Google Distributed Cloud (GDC) brings all the benefits of Google Cloud with AI already embedded. And having GDC at an edge location means systems will still operate even if the connection to the central cloud is out. It also means AI models can be run closer to the data source and processed faster. Running AI models on the edge rather than a central cloud means results are near real-time. 

GDC allows you to federate the way data and AI models work together. With operations at the edge, companies can decentralize the data sets and AI models so they are trained with the data specific to the location. Companies can have AI models created for a particular location, versus always being the same AI model or having the exact same output across all locations. A location may need the model tweaked and fine-tuned in a certain way that works for its local requirements, and that can happen at the edge. 

For example, a retail store may wish to purchase inventory that is unique to that location, so volume and customizations are completely different. That store needs the local AI model to understand what鈥檚 happening with that specific inventory. Or a certain location or country could decide to add a menu item that only exists in one place. And it is still possible to get data and modelling based on these differences. 

The critical agentic enabler 

Agentic systems also need GDC, as having agents at the edge is very efficient. These agents can work with edge devices to provide faster responses across multiple applications, and deliver a more personalized experience. 

Multi-agent orchestration and collaboration are the future. Having the power at the edge means more data is available for local modelling and real-time processing. 

Edge computing and distributed cloud architectures are more than incremental improvements. They have the potential to support a jump in transformation that will shape better business outcomes. By strategically embracing these technologies, companies can enhance operational efficiency, drive innovation, and gain a competitive edge. 

Enabling sovereignty and meeting regulatory needs 

With rising demands for secure cloud capabilities that meet the needs of sovereign and heavily regulated industries, an air-gapped implementation of Google Distributed Cloud provides an experience that is completely isolated from Google or public networks, offering providers a fully redundant, high-availability architecture for mission-critical systems. These industries can harness the power of Google public cloud within a secure datacenter environment, enabling innovation and meeting regulatory requirements and national interests. 

乌鸦传媒 and Google Distributed Cloud (GDC) 

In a complex and connected world, GDC excels in artificial intelligence, machine learning integration, and seamless Kubernetes-based application modernization across edge, cloud, and on-premises environments. Its robust security model and support for sovereign deployments and confidential computing make it suitable for highly regulated sectors. 

乌鸦传媒 and Google Cloud can provide the insights and guidance necessary for companies to navigate the edge frontier for informed decisions and capitalize on the full potential of edge computing. With a portfolio that comes in multiple flavors to cater to diverse needs, Google鈥檚 comprehensive answer to distributed cloud seamlessly extends Google Cloud鈥檚 infrastructure, services, and management capabilities to edge locations and on-premises data centers.  

Digital transformation continues, and the adoption of edge computing needs to be a strategic imperative for forward-thinking businesses. It is the path to remain competitive and resilient in a rapidly changing business environment. 

Join us at Google Cloud Next to explore these innovations and unlock the full potential of edge computing.

Think of AI agents as skilled interns joining your software team 鈥� eager, efficient, and ready to take on tasks with minimal supervision. However, like any human intern, they require guidance, training, and direction to ensure quality work. These autonomous digital coworkers are set to transform content creation and workflows, enhancing business operations across the organization 鈥� not just within software product engineering.

How modern software empowers AI Agents

The software industry has continuously evolved, long before generative AI emerged. It has shifted from monolithic applications to scalable, modular architectures like microservices, cloud-native, API-first, headless and data mesh designs. This transformation paved the way for incremental software development, breaking work into smaller, manageable pieces 鈥� just how AI agents like to operate. These digital dynamos thrive on tackling specific, bite-sized tasks, across the software development life cycle.

How AI Agents are revolutionizing software product engineering

Agentic AI represents a significant advancement in artificial intelligence, enabling autonomous agents to reason, plan, and interact with various tools and data sources to solve complex problems. In software product engineering, these agents function within multi-agent systems, each specializing in tasks akin to roles in an agile development team. They automate processes, generate content, and collaborate seamlessly, streamlining the entire software development lifecycle (SDLC). Beyond coding, these digital coworkers refine requirements, create documentation and test artifacts, assist in fixing defects, and help manage releases.

For instance, one AI agent might generate source code, another reviews it for compliance, and a third ensures its executability. If any issues arise, these agents autonomously collaborate to iterate and improve by “chatting” with each other, all within an orchestrated workflow that requires minimal human intervention. This AI-driven approach not only enhances efficiency but also improves accuracy, security, and adherence to organizational policies and templates.

The fun fact 鈥� AI agents are created by software teams to develop and build software. These autonomous agents go beyond 鈥渃hat bot-like鈥� content generation 鈥� they can be programmed to integrate with APIs to update backlogs, publish release notes to wiki pages, utilize build toolchains, access other software applications, and much more.

Ripple Effect Throughout the Organization

AI agents are set to transform software and data-driven teams, boosting productivity and capabilities. We can easily envision multi-agent systems taking on more responsibilities across various industries. Developing, customizing, and mastering AI agents will have a profound impact on operations, transforming business decisions, strategies, and efficiency throughout the organization.

As more software companies embrace product-led growth models, where users experience a product’s value often through free trials before committing, AI agents can play a crucial role in speeding up this transition. They enable faster software product increments and cost-effective hyper-personalization.

AI agents are also likely to influence the build vs. buy decisions by lowering non-recurring engineering (NRE) costs and total cost of ownership (TCO). This allows companies to release custom solutions faster, keep data in-house, and lay the groundwork for digital-native innovation.

The Human Factor: AI Agents as Enhancers, Not Replacements

AI agents aren鈥檛 here to replace software teams 鈥� they鈥檙e here to supercharge them. Think of them as tireless blueprint creators, not decision-makers. Unlike interns or new hires, they won鈥檛 celebrate a big release or stress over a missed deadline. They aren鈥檛 accountable for defects or delays 鈥� that鈥檚 why human oversight is crucial. With 鈥渉uman guardrails鈥� in place, AI streamlines repetitive tasks, freeing developers to focus on learning, creative design thinking, and customer interactions. This allows teams to tackle new challenges head-on while exponentially growing in efficiency and skills.

乌鸦传媒 is at the forefront of Augmented Software Product Engineering

Our Augmented Software Product Engineering offer includes an agent-based asset framework and associated consulting and engineering services to provide a uniquely holistic approach to improving product creativity and quality, development efficiency, and developer experience. It includes software lifecycle accelerators addressing product requirements optimization, code creation, product generation, and code migration.

Peering into the future is a tricky business 鈥� especially in the ever-changing realm of data, analytics, and AI. But if there鈥檚 one thing we鈥檝e learned, it鈥檚 that uncertainty never stopped us from trying. After all, we鈥檙e in a part of the technology profession where predicting the unpredictable is often part of the job description.

We called upon seven of our data-powered innovation movers and shakers to dust off their (frozen) crystal balls and share their visions of what 2025 has in store. Their insights reveal a world where AI balances on the edge of legality, cloud platforms morph into something entirely new, and synthetic data booms with promise 鈥� and no, it鈥檚 not artificial hype. From 鈥渧ertical AI鈥� that digs deep into industry needs to conversational AI that knows what you want before you do, these trends give a glimpse of the fascinating, yet challenging, future of data and AI.

Will their predictions come true? Only time will tell, but one thing鈥檚 for sure: 2025 is shaping up to be a year we鈥檒l be talking about for a long time to come. And data and AI are right in the middle of it.

Let鈥檚 dive in.

AI is not a crime

AI is not a crime, though sometimes it feels like one, given its swift advance beyond legal bounds. While identity theft, deepfakes, and media manipulation emerge from Pandora’s box, many AI ethicists focus on fairness and transparency, skirting around AI’s darker uses. As AI matures and criminal applications surge, discussions will inevitably shift from theoretical ideals to practical realities, especially when organizations face public lawsuits under new AI regulations. This shift will force experts to tackle how AI can harm society directly. So, while AI is not a crime, it certainly invites a compelling conversation about AI and crime. Let鈥檚 face it: When it comes to AI, the real crime would be ignoring the conversation altogether. – Marijn Markus, AI Lead, Managing Data Scientist, 乌鸦传媒 and Data, 乌鸦传媒

Augment my governance

Prepare to be captivated. AI agents are about to revolutionize data management in the upcoming year. They will shoulder burdensome data tasks, enabling companies to reach new pinnacles of productivity and efficiency. With AI seamlessly managing data collection, analysis, and access for us, we can finally focus on something much more crucial: getting value out of data for the business. It’s high time to achieve that, isn鈥檛 it? The future of data management is upon us, and missing the opportunity of augmenting is not an option. Because in this data game, those who augment govern 鈥� and those who don鈥檛 get governed. – Liz Henderson, Executive Advisor, 乌鸦传媒 and Data, 乌鸦传媒

Cloud encounters of the third kind

As we look towards 2025, cloud data platforms apply for a new round of transmitted change. We all recognize the need for high-quality enterprise data as a foundation for relevant, trustworthy AI. Add to that the need to adhere to regulations, data sovereignty, privacy sustainability and cost. It soon becomes apparent that a smart mix of different and diverse cloud approaches for data will play a crucial role in the upcoming year. I expect to see a pendulum swing towards larger investments in cloud data platforms, yet it will be clouds of a different kind. Or, to put it differently, the forecast calls for cloud cover 鈥� but with a whole new kind of silver lining. – Prithvi Krishnappa, Global Head of Data and AI, Sogeti

Let鈥檚 talk better

Conversational AI will continue to be a hot topic in 2025. Contact center transformation, leveraging 鈥渃lassic鈥� AI and generative AI, will help save labor costs by billions and improve customer service significantly. These technologies can handle routine inquiries and provide instant responses, freeing human agents to deal with more complex issues. Imagine a world where you no longer have to press 1 for assistance 鈥� AI will anticipate your every need before you even know you have one. While human agents may become less central, customer satisfaction might reach an all-time high as AI enhances efficiency and personalization in ways we never thought possible. It鈥檒l be the talk of the town in 2025. – Monish Suri, Global Google Partnership Lead, 乌鸦传媒 and Data, 乌鸦传媒

When AI goes vertical

We will see a major rise in domain-specific vertical AI solutions that are finely tuned through rigorous test-driven prompt engineering. These purpose-built AI models will deliver more reliable and precise insights tailored to the unique needs of their industries. For instance, in healthcare, imagine AI predicting patient outcomes like a crystal ball, analyzing vast datasets of medical histories and treatment plans to conjure better patient care and optimized resources. In financial services, AI will become the ultimate fraud-buster, identifying unusual patterns in real time and safeguarding assets with previously unseen precision and confidence. Vertical AI solutions will not only streamline operations but also spark innovation by providing industry-specific intelligence and efficiency. The only way is vertical! – Dan O’Riordan, VP, AI and Data Engineering, 乌鸦传媒

The semantics of confidence

We鈥檝e seen many companies adopting the principles of data mesh and semantics as part of their modern data analytics platform strategy. If nothing else, it鈥檚 needed in 2025 and beyond to comply. For example, the EU AI Act requires close tracking of the purpose of AI models and the underlying data used to train it. This can only be done by enhancing data platforms with semantics, connecting original data sources, forged data products in AI models, and all business dashboards and AI-infused applications. It creates high levels of confidence in both data and AI, next to many new, innovative opportunities to leverage data. The endgame? Nothing less than a full digital twin of the enterprise, a hallmark of data mastery. – Arne Rossmann, Innovation Lead, 乌鸦传媒 and Data, 乌鸦传媒

Synthetic data boom

I predict a boom in synthetic data. But first of all, what is synthetic data? It’s artificially generated but realistic data that mirrors real patterns without using sensitive information. Why is synthetic data crucial? It tackles privacy, security, data scarcity, and control issues. Traditional data sources are hitting their limits. Privacy laws are tightening, and real-world data often lacks the diversity we need. Synthetic data lets companies create datasets that mimic real shopping behavior in retail or complex production processes in manufacturing without exposing sensitive info or being held back by data gaps. I foresee that 2025 will be the year synthetic data moves center stage. Companies ready to leverage it will build powerful, adaptable models faster than ever. The synthetic data boom will be anything but artificial. – Dinand Tinholt, VP, 乌鸦传媒 and Data, North America, 乌鸦传媒

Interesting read?

乌鸦传媒鈥檚 Innovation publication, Data-powered Innovation Review – Wave 9 features 15 captivating innovation articles with contributions from leading experts from 乌鸦传媒, with a special mention of our external contributors from , and . Explore the transformative potential of generative AI, data platforms, and sustainability-driven tech. Find all previous Waves here.

Today鈥檚 industrial challenges are rarely isolated. They鈥檙e interconnected. Productivity, sustainability, digital transformation 鈥� they鈥檙e all part of the same conversation.

But too often, organizations are forced to tackle them separately. At Hannover Messe 2025, we at 乌鸦传媒 will be showing what happens when you take a different approach.

In partnership with Microsoft, we鈥檒l be bringing intelligent industry to life through real-world demonstrations, industry-led conversations, and practical examples of how collaborative thinking can drive better results. It鈥檚 a chance to see how advanced AI, digital twins, and real-time data can drive real progress across manufacturing, automotive, defense, and electric battery innovation.

Come and see it in action

At the center of our stand is the Digital Twin Cockpit 鈥� an interactive experience for production engineering which allows faster ramp-ups and better production design validation while providing complete data scalability and integration for operations use-cases. The Digital Twin Cockpit integrates a Unity viewer with Microsoft technologies including GenAI copilot for intuitive querying of digital twin information.

It鈥檚 fully interoperable, able to plug into different CAD and data sources, and brought to life through a VR-enabled 2D interface 鈥� giving you an immersive, hands-on look at what鈥檚 possible when digital and physical realities come together.

Our experts and will also be sharing insights about new digital shopfloor performance during a live presentation at the Microsoft booth on Thursday April 3 at 10:00. They鈥檒l explore how 乌鸦传媒 and Microsoft are working together to unlock performance, resilience, and scale in today鈥檚 digital factories 鈥� and what that means for your next step forward.

Why it matters

乌鸦传媒 brings together extensive manufacturing expertise with world-class engineering capabilities 鈥� making us a trusted partner for industrial transformation at scale. In close collaboration with Microsoft, and alongside key partners like NVIDIA or Siemens, we combine best-in-class technology with sector-specific insight and hands-on experience. It鈥檚 how we help clients move faster, think bigger, and deliver more 鈥� with a clear path to value.

The organizations that are moving fastest right now are those finding ways to connect across silos 鈥� combining data, teams, and technologies to solve overlapping challenges at once. That鈥檚 what we call Compound Solutions

It鈥檚 a joined-up way of working that helps clients make progress in several areas at the same time. Whether it鈥檚 increasing efficiency, reducing emissions, or modernizing infrastructure, the impact is greater when those goals are tackled together.

It鈥檚 more than a stand 鈥� it鈥檚 a space for real conversations

On Monday March 31 at 17:00, Microsoft鈥檚 and 乌鸦传媒 leaders and , will be speaking live on stage about:

• Major trends currently impacting the manufacturing domain
• Advancements in AI bringing intelligent manufacturing closer to reality
• Real-world success stories from intelligent manufacturing
• Proven ways to overcome industry challenges
• The power of an open, collaborative ecosystem

Our thought leadership sessions will also share practical insights on how clients are applying AI, robotics, spatial computing, and digital twin technologies. You鈥檒l hear stories of what鈥檚 worked, what鈥檚 changing, and how to get ahead.

We鈥檙e recognized for our results

乌鸦传媒 was recently recognized by Everest for its leadership in intelligent industry 鈥� and we鈥檙e already making a real difference across some of the most advanced, high-performing sectors. From helping manufacturers scale transformation to supporting defense clients with secure, intelligent operations, we鈥檝e built a strong track record by delivering where it counts.

Visit 乌鸦传媒 at Hannover Messe

If you鈥檙e attending Hannover Messe, we鈥檇 love to see you.

Come by the 乌鸦传媒 booth to:

• Try the Digital Twin Cockpit demo for yourself
• See how Agentic AI is already transforming industrial operations
• Talk to our experts about the opportunities for your organization
• Explore what Compound Solutions could mean for your business

We鈥檒l also be livestreaming some of our sessions if you are unable to attend in person.

We鈥檙e ready to show you what鈥檚 possible when industry meets impact. Where innovation becomes action. Where you get the future you want.

The struggle to meet rising customer expectations

Customer expectations for financial services firms have never been higher. Whether submitting a mortgage application to a bank or contacting an insurer to file a claim, consumers demand seamless, hyper-personalized, and efficient service at every interaction. However, many financial services contact centers still rely on outdated models that struggle to meet these expectations. Long wait times, fragmented communication channels, and manual processes create frustrating experiences for both customers and agents.

The disconnect between what customers expect and what traditional contact centers deliver is becoming increasingly untenable. According to 乌鸦传媒鈥檚 World Retail Banking Report 2025, only 24% of customers are satisfied with their bank鈥檚 contact center interactions. Customers cite long wait times (61%), inconsistent communication across channels (65%), and gaps in real-time updates between digital and in-person interactions (63%) as major sources of frustration鈥�. These pain points are equally prevalent in the insurance sector, where policyholders frequently encounter delays when filing claims, updating policy details, or seeking assistance during critical life events.

Operationally, these challenges extend beyond customer experience. Many financial services firms continue to operate contact centers where over 80% of an agent鈥檚 workday is consumed by repetitive, manual tasks, leaving little room for value-driven customer engagements鈥�. For insurers, this often means agents spend excessive time manually verifying policyholder information, processing claims, or handling routine inquiries鈥攖asks that could be streamlined through automation. Similarly, in banking, less than 10% of agent time is spent on revenue-generating activities such as up-selling and cross-selling, leading to missed opportunities for business growth鈥�.

The demand for change has never been more pressing. Financial institutions must move beyond incremental improvements and embrace AI and generative AI (GenAI)-powered contact centers that blend automation, real-time analytics, and live agent support. By integrating these capabilities, banks and insurers can significantly reduce operational costs, improve customer satisfaction, enhance compliance monitoring, and strengthen fraud detection efforts. Those that adopt AI and GenAI-based solutions will be well-positioned to turn their contact centers from cost-heavy service departments into strategic hubs of customer engagement, operational efficiency, and revenue generation.

AI-powered contact centers: The key to efficiency and growth

By integrating advanced AI and GenAI technologies, banks and insurers can create smarter, more responsive, and cost-effective contact centers. From real-time insights that improve self-service interactions to automated workflows that streamline post-call processes, these tools are redefining efficiency and service quality.

Improving efficiency through real-time speech recognition

Efficiency is at the heart of a well-functioning contact center, yet traditional workflows burden agents with manual notetaking, post-call documentation, and slow information retrieval鈥攁ll of which extend call durations and reduce productivity. AI-powered real-time speech recognition technology is improving agent workflows by providing instantaneous transcription, automated notetaking, and intelligent response suggestions, allowing agents to focus on engaging with customers rather than administrative tasks.

, a collection of GPU-accelerated multilingual speech and translation microservices, enables firms to create or fine-tune open-source automatic speech recognition (ASR) models to better comprehend sector, function, and firm nuances to generate highly accurate transcriptions at low cost. By leveraging and for scalability, financial institutions can seamlessly deploy instant transcription across large-scale contact centers without disrupting existing workflows.

Bolstering customer satisfaction through deep, real-time insights

Contact centers incorporating GenAI capabilities are revolutionizing self-service by delivering context-aware, human-like interactions that go past scripted responses. Unlike traditional solutions, contact centers powered by capable state-of-art large language models (LLMs), trained on enterprise data, can better understand customer intent, retain long-term context, and provide real-time, personalized support.

With 鈥攁 hybrid state-space and transformer LLM鈥擥enAI can provide advanced sentiment analysis, low-latency response times (<500ms), and deep contextual understanding that adapts as the conversation evolves. 聽can be used to customize the models with domain knowledge. Once in production, model performance can be maintained with NVIDIA庐 NeMo microservices to curate new business data and user feedback, fine-tune and evaluate the model, connect with Retrieval-Augmented Generation (RAG) pipelines, and guardrail the model’s responses. Furthermore, NVIDIA庐 NIM聽can help scale latency and throughput, optimizing聽the delivery聽of GenAI-driven insights.聽

GenAI-powered self-service also ensures seamless omnichannel experiences, enabling smooth transitions between chat, voice, and video interactions while maintaining context. By integrating intelligent automation and real-time insights, banks and insurers can provide faster, more relevant support鈥攂oosting efficiency while strengthening customer loyalty.

With real-time transcription and GenAI-driven insights, agents receive instant customer context and recommended responses, allowing them to resolve inquiries more efficiently. This technology also automates post-call work, generating summaries of key details and next steps鈥攖asks that traditionally take several minutes per interaction. As a result, financial institutions can increase overall agent productivity by an , optimize call handling times, and empower agents to deliver faster, more personalized service.

Simplifying AI and GenAI adoption with a Contact Center-as-a-Service platform

A Contact Center-as-a-Service (CCaaS) platform streamlines the adoption of AI and GenAI capabilities by eliminating the need for complex infrastructure. With plug-and-play integration, firms can rapidly deploy AI-driven real-time speech recognition and GenAI-powered agent assistance without disrupting existing workflows.

can be used to accelerate deployment by orchestrating automation, live-agent interactions, and post-call workflows in a seamless environment. This allows firms to enhance customer engagement and agent productivity without the need for major technology overhauls.

With built-in scalability, Zuqo鈥檚 platform supports multi-region and multi-language operations, enabling financial institutions to expand without technological bottlenecks. Its API-driven architecture ensures effortless integration with existing CRM, compliance, and fraud monitoring systems, allowing AI-powered enhancements to fit naturally within current workflows.

Use case: Rapid fraud resolution in financial services

A customer calls their financial institution鈥檚 contact center after noticing an unauthorized charge on their credit card account. Instead of navigating frustrating hold times or being transferred multiple times, they are quickly connected to a live agent equipped with AI and GenAI-driven support tools.

As the customer explains the issue, real-time speech recognition transcribes the conversation, instantly analyzing intent and retrieving relevant account details. The GenAI-powered system assists the agent by surfacing next-best actions, allowing them to immediately credit the disputed amount while the fraud investigation takes place. With a single click, the agent efficiently cancels the compromised card and issues a new one, ensuring a swift resolution without requiring the customer to call back or complete additional steps.

Once the call ends, AI-driven quality assurance automatically reviews the interaction within 150 seconds, assessing over 60 compliance and service quality indicators to ensure a high standard of support.

The future of contact centers is finally here

The financial services industry is undergoing a profound shift, where traditional contact center models can no longer keep pace with rising customer expectations and increasing operational inefficiencies. AI and GenAI-powered solutions provide the opportunity to transform these challenges into competitive advantages, enabling banks and insurers to deliver faster, smarter, and more seamless customer experiences.

By integrating GenAI-driven self-service, real-time speech recognition, and automated workflows, financial institutions can enhance agent productivity, improve fraud resolution, and ensure regulatory compliance鈥攚hile reducing costs. Contact Center-as-a-Service platforms make this transformation even more accessible, providing a scalable and easily integrated solution that eliminates the barriers to AI and GenAI adoption.

As the demand for efficiency, security, and personalized service continues to grow, financial institutions that embrace contact centers powered by AI and GenAI will position themselves as industry leaders. By modernizing their approach, banks and insurers can future-proof their organizations in an increasingly digital world.

There鈥檚 a crucial need to demystify bio-engineering solutions to demonstrate their value, gain public trust and understanding, and drive adoption and economic growth.

The COVID-19 pandemic was a moment in history, when global governments, private institutions, academia, and society as a whole, coalesced around the need and to benefit global wellbeing. When a vaccine was produced and rapidly approved through extraordinary measures, the collective relief was palpable, but there was an unexpected problem.

Public trust in science, representation, and a lack of bio-literacy around mRNA vaccine technology challenged the progress of the global vaccination program. Governments worldwide invested hundreds of millions in fighting disinformation about bio-engineering solutions.

For a new innovative solution to be adopted, the people it serves must trust that it will work in their best interests, and trust that those developing solutions have their best interests at heart. In the case of such new and emerging deep-tech as bio-engineering solutions, with their implications to natural evolution, building trust and understanding is more important than ever.

There are clear steps we can take to build this trust, to drive adoption of bio-engineering solutions and related socio-economic growth.

Low levels of literacy and trust: policy makers, citizens, and markets must better understand bio-engineering solutions to unlock their potential

Recent data from the 乌鸦传媒 Research Institute highlighted the growing divide between industry innovation and public understanding: 96% of companies reported active engagement with, or plans to develop, bio-solutions. And yet, research by the showed that 76% of UK respondents feel they lack sufficient information to make informed decisions about bio-based products. The same report found that 61% have never heard of engineering biology. Although these findings are UK-specific, similar trends are evident globally. For example, the CRI report also revealed that 65% of startups and corporations view low public bio-literacy as a significant barrier to adoption.

The consequences of this gap are profound. One showed that decades-old public resistance to the distinct domain of genetically modified organisms (GMOs) shaped overly cautious regulatory frameworks that impede advancements in plant genome editing to this day. As a result, solutions to critical challenges, such as food security, continued to be delayed. More recently, hesitancy around mRNA vaccines has shown how a lack of communication can erode trust and create a vacuum for misinformation to grow. Without proactive public engagement, societally-valuable bio-based innovations such as bio-based plastics, precision farming, and sustainable fuels risk similar skepticism, potentially stalling their adoption. Governments and industries must act collaboratively, ensuring consumers feel like active participants in this journey, not passive spectators.

Business and government: shared responsibility to unlock economic growth through engineering biology vision

The challenge of bridging the gap between innovation in bio-engineering solutions and public understanding is a shared responsibility. It demands coordinated action from both businesses in industry and policymakers.

Businesses must go beyond simply creating innovative bio-products 鈥� they should actively engage citizens and a broad stakeholder group throughout the development process by employing user-centered and co-design techniques. This not only builds trust and understanding with consumers but of course ensures stronger market fit and a greater chance of commercial success.

To unlock the economic growth potential of bio-engineering solutions, policy makers also need to engage in similar user-centered and co-design techniques early in the policy making process through techniques like 鈥楶olicy Labs.鈥� Policy Labs help rapidly map 鈥榩olicy whitespace鈥� where a new emergent theme or technology means there are few existing policy ideas or policies to build on. If policy teams become blocked due to a lack of understanding of complex new technologies, this prevents private markets from progressing due to a lack of support or legislation and regulation that unnecessarily blocks development. 

The importance of Policy Labs

Policy Labs are a proven technique to test early policy hypothesis, to ensure policy solutions will work for all stakeholders. For instance, if there鈥檚 to be a proposed investment by the Department of Environment Farming and Rural Affairs (DEFRA) into alternative proteins or GM crops, the farming industry should be engaged to test early policy hypothesis to see whether proposed solutions, services, regulations or legislations are likely to empower farmers with new economic means. Or disempower them by creating new competition in the markets or an even more complex post-Brexit funding landscape.

A Policy Labs approach also ensures better cross-departmental working and inputs from the relevant government bodies that should be involved, across such a broad problem space. For example, in the UK, the Department of Business and Trade (DBT) or Department of Science Innovation and Technology (DSIT) might work with DEFRA to provide a better holistic approach and collaborative understanding to launching new solutions by data sharing agreements. Subject Matter Experts (SMEs) and private sector specialists in Engineering Biology could also be engaged through the policy labs process to ensure policy ideas are technologically viable, operationally feasible, and economically scalable in markets.

Open and honest dialogue

Governments should also be creating an ongoing dialogue with citizens to ask what assurances they need to feel confident about bio-engineering solutions (products and services) and how perceived risks can be addressed. Transparent, accessible communication mechanisms are key, alongside independent oversight to ensure safety and accountability. As the puts it: 鈥楶ublic engagement, improvement of public perception, and building trust are critical factors for the growth of the bio-economy and market.鈥�

A call to action for bio-economy solutions

Engineering biology has the potential to solve some of the world鈥檚 most pressing population scale challenges, from climate change to healthcare. But without better collaboration between governments, industry, and the markets they serve, progress on bio-engineering solutions will stall. By employing co-design both in the product development and policy making lifecycles, we will build better understanding and trust among stakeholders and citizens, while enabling policymakers and businesses to ensure bio-solutions not only innovate but also meet public needs.

At 乌鸦传媒, we provide end-to-end support in Engineering Biology and AI for Science strategy, helping public and private sector clients accelerate bio-solutions while addressing the challenges outlined here. From fostering public bio-literacy and engaging citizens to building transparency into innovation and driving growth through regulatory collaboration, we leverage our insight drawn from practical laboratory bio-engineering, to advise our clients on delivering impactful, trusted, and scalable outcomes. Together, we can draw up a bio-economy strategy that maximizes public trust in the solutions that will shape the world for years to come.

The time is now to act. Together, we can do more than just create bio-solutions; we can create a bio-ready society. Get in touch to explore how we can help accelerate your engineering biology journey.

乌鸦传媒’s Job Fair solution improves CV processing and communication by making the recruitment process more efficient and transparent for candidates.

At one time or another we have all felt the frustration of sending out dozens of job applications, each tailored to a specific job or company, only to receive no response from any HR team whatsoever.

This lack of communication can leave candidates wondering if their application was even considered or how they could improve for future opportunities.

Simplifying CV processing

乌鸦传媒 saw overcoming this problem as a challenge which is why we set out to improve our CV processing capabilities, helping us deliver an intelligent and connected “consumer-grade” people experience to any potential candidate who engages with us.

Our Job Fair solution ensures timely communication with candidates, including those who will not move forward in the recruitment process.

Our process allows HR teams to quickly inform candidates when their recruitment journey has concluded without an offer. These messages are phrased positively to motivate candidates for future efforts while maintaining transparency and efficiency.

This approach helps 乌鸦传媒 build a reputation as a company that cares about career growth, even for those who haven’t worked with us.

Tackling CV processing challenges with precision

乌鸦传媒’s Job Fair solution uses Optical Character Recognition (OCR) and Microsoft’s Power Automate technology to extract key information such as email addresses, and phone numbers from various documents quickly.

Furthermore, our Job Fair solution鈥檚 simplicity and flexibility, combined with its straightforward interface, requires minimal training to operate effectively. This ensures improved CV processing comes with minimal disruption, and enables HR teams to meet a wide range of business needs without developing a new, expensive solution.

Delivering award-winning HR processes

We understand that companies need to focus on bringing their people, processes, and technology together to deal with whatever their business might face, moving them closer to becoming a truly Connected Enterprise.

This mindset is why 乌鸦传媒 recently won a Gold Medal in Brandon Hall鈥檚 Excellence in Technology Awards, 2024. This highlights 乌鸦传媒鈥檚 commitment to implementing effective, easy-to-use applications that address its clients鈥� needs at speed, leaving a positive impact on their businesses.

But that鈥檚 not all. 乌鸦传媒 also won a Silver Award in Brandon Hall鈥檚 HCM program, 2023, which clearly demonstrates that 乌鸦传媒 is among an elite group of exceptional HR service providers.

To discover more about how 乌鸦传媒鈥檚 Intelligent People Operations put your employees at the heart of HR operations, across your talent acquisition, HR administration, payroll, and HR analytics functions, to deliver strong and sustainable business value, contact: alicja.watorek@capgemini.com