Technology is redefining urban living. Rapid urbanization this century has transformed cities into bustling centers of growth and innovation. However, this progress comes with challenges, such as resource management, climate resilience, and efficient governance. In 2025, emerging technologies will play a pivotal role in reimagining how cities function at scale.

With more than half the global population now living in cities, urban areas are under immense pressure to adapt to growing populations and environmental concerns. Smart cities are rising to the challenge, integrating advanced technologies to improve infrastructure, enhance public services, and foster sustainable living. This will also ensure inclusivity, while improving the quality of life for urban dwellers.

Five key trends shaping the future of smart cities

The following insights into the trends shaping the future of our cities reveal that a new chapter in urban living is under way.

The road ahead: Challenges and opportunities

The future of urban living will be defined by how effectively cities adopt and integrate these technological innovations. While the potential benefits are immense—smarter resource management, reduced environmental impact, and improved citizen experiences—success depends on political commitment, societal acceptance, and the ethical use of technology.

In 2025, smart cities will not only focus on innovation but also on creating inclusive, resilient, and sustainable communities. By leveraging the technologies shaping today’s urban transformation, we can build cities that thrive in harmony with people and the planet.

The convergence of biology and technology has unlocked unprecedented scientific breakthroughs. Fueled by data science and artificial intelligence, bio-innovation is reaching new heights. And Generative AI is poised to be a catalyst of this bio-revolution – a transformative force that promises to accelerate discovery, enhance precision, and optimize operations across the pharmaceutical value chain.

For decades, the challenges of drug development have seemed to be set in stone: it takes well over a decade and to bring a new drug to market. Even then somewhere along the way. But what if we could rewrite this equation?

Reimagining Drug Discovery: AI as the Co-scientist

At the heart of every breakthrough medicine is a molecule—a tiny structure with the power to change lives. Finding the right molecule, however, has traditionally been a laborious process of trial and error, relying on time-consuming screening, costly experiments, and unpredictable outcomes.

GenAI is redefining drug discovery with deep learning models trained on vast chemical and biological datasets that predict promising candidates as well as identifying drug targets with unprecedented accuracy. These AI-driven systems don’t just analyze known compounds; they can design entirely new molecules, simulate their interactions, and flag potential failures before they reach the lab.

For pharmaceutical innovators, this means not only shortening R&D timelines but also expanding the pipeline of high-quality drug candidates, reducing the risks associated with late-stage failures. In an industry where speed and accuracy are everything, AI is shifting the balance from guesswork to data-driven certainty.

Revolutionizing Clinical Trials: Smarter, Faster, More Predictive

Clinical trials remain a bottleneck in drug development. Recruiting the right patients, ensuring trial adherence, and managing vast amounts of regulatory data all contribute to delays and rising costs. Here, too, AI is proving to be a game-changer.

AI-powered models can now identify ideal patient subpopulations by analyzing real-world data, ensuring trials enroll individuals who are most likely to respond positively. This not only improves success rates but also lays the groundwork for precision medicine, where treatments are tailored to specific genetic or biomarker profiles.

Meanwhile, AI-generated synthetic data is reducing dependence on traditional control groups, allowing trials to run faster and with greater statistical power. GenAI-assisted automation is also transforming the regulatory process—drafting protocols, ensuring compliance, and streamlining interactions with health authorities.

For pharma executives, this means fewer trial failures, faster regulatory approvals, and a clearer path to market success.

â€�The promise of AI in the life-sciences is to transform it from an industry focused on hunting for ever-smaller needles in ever-larger haystacks, to one where new therapies are purposely designed and engineered with precisionâ€� – Dr Mark Roberts, CTO Applied Sciences, ÎÚÑ»´«Ã½ Engineeringâ€�

Beyond the Lab: AI-Optimized Manufacturing and Digital Therapeutics

While much of AI’s promise lies in discovery and trials, its impact extends into pharmaceutical manufacturing and patient engagement.

AI-driven predictive analytics are optimizing production processes, reducing waste, and improving scalability, making drug manufacturing leaner and more sustainable. Given the growing emphasis on ESG (Environmental, Social, and Governance) initiatives, AI-driven efficiency gains are not just about cost savings—they’re also about meeting global sustainability targets.

At the same time, the rise of digital therapeutics (DTx) is redefining how we think about patient care. AI-powered applications are enabling personalized health interventions, from managing chronic diseases to real-time medication adjustments. As pharma companies explore hybrid models that combine traditional therapeutics with AI-driven digital health solutions, new revenue streams and business models are beginning to emerge.

The AI-Powered Pharma Enterprise: What Comes Next?

Despite the promise of GenAI, pharma organizations must take strategic steps to unlock its full potential. Investing in AI-first R&D strategies, curating high-quality data ecosystems, and fostering AI-literate teams will be critical to long-term success. Regulatory frameworks must evolve alongside AI capabilities, ensuring ethical AI adoption and transparent validation of AI-driven discoveries.

The question is no longer if AI will transform pharma R&D—it already is. The real challenge is how quickly organizations can adapt. In the life-sciences, and other complex industries, autonomous and agentic systems will soon start to challenge existing norms and shorten value chains. Those who act now will define the future of medicine, setting new standards for speed, precision, and impact.

AI isn’t just changing the way we develop drugs—it’s reshaping the very fabric of healthcare. Are we ready to embrace this transformation?

to read the research paper.

About AI Futures Lab 

We are the AI Futures Lab, expert partners that help you confidently visualize and pursue a better, sustainable, and trusted AI-enabled future. We do this by understanding, pre-empting, and harnessing emerging trends and technologies. Ultimately, making possible trustworthy and reliable AI that triggers your imagination, enhances your productivity, and increases your efficiency. We will support you with the business challenges you know about and the emerging ones you will need to know to succeed in the future.Ìý ÌýWe create blogs, like this one, Points of View (POVs), and demos around these focus areas to start a conversation about how AI will impact us in the future. For more information on the AI Lab and more of the work we have done, visit this page: AIÌý³¢²¹²ú. 

ÎÚÑ»´«Ã½’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, ÎÚÑ»´«Ã½ 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.

ÎÚÑ»´«Ã½’s Leadership Empowerment and Acceleration Program

ÎÚÑ»´«Ã½â€™s 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 ÎÚÑ»´«Ã½ 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 ÎÚÑ»´«Ã½’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 ÎÚÑ»´«Ã½â€™s 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.

What is your regulatory reporting telling you about your upstream processes?

As with anything in life, you get out what you put in. Higher quality inputs lead to better outputs. The same principle also applies to firms’ regulatory reporting, upstream processes, and risk management.

Where trouble begins

In our experience working with clients on regulatory reviews, many reporting issues originate in upstream processes and risk management. These problems often begin at trade booking and ripple all the way down to what is reported to regulators.

Many firms operate across multiple booking and risk systems, including those for collateral cash management and valuations. This complexity often comes from business growth, where new or “bolt-on� systems are added to existing architecture, avoiding the need for updates in the short term, but causing more trouble down the road. Information barrier controls – designed to separate private-side MNPI from public-side activity – further fragment systems and data flows.

Firms that handle this well usually have a consolidated data lake. However, not all firms have that capability, or they haven’t done it in a way that supports effective regulatory reporting. Furthermore, there are multiple end users of this collated data and the reporting data is a really good gauge of how data sits ‘upstream’ and will be used by different parts of the firm. If the quality is bad coming into the reporting layer, it will likely be bad upstream.

Inconsistencies pass through the system

Regulators now look for signs of weak internal controls. Investigations often reveal inconsistencies in data lineage – from upstream bookings and risk systems to the final reported figures. Tolerance for these gaps is decreasing, especially when they impact the accuracy of reports.

It is critical for firms to have a clear view of their risk and positions across all systems, and regulators expect this visibility to be reflected in regulatory reports.

Investing in a review

With the wave of regulatory rewrites slowing in 2025, now is a good time for firms to invest in a review of operational processes. Taking a closer look at trade processing flows can reveal underlying issues that may be compromising report accuracy.

An investment in understanding your data lineage and looking at the design of your internal data models has a track record of delivering operational alpha and ROI. The development of an industry standard data model, the ‘Common Domain Model’ (CDM) presents an opportunity to upgrade your internal data models in a way that will offer efficiency gains in data transparency and interoperability with other industry participants. In addition to regulatory reporting, there are other regulatory benefits to ensuring smooth data flows, such as supporting the upcoming requirements of the EU and UK T+1 accelerated settlements cycle. 

 A number of recent fines have mentioned that fined firms are bringing in a third party to review processes – an approach that is viewed favorably by regulators. What we typically find is that firms with an accurate and reconcilable reporting data set often have smoother upstream operations. To promote excellent regulatory compliance in the long term, upstream processes deserve a careful look.

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 – 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’s 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. 

ÎÚÑ»´«Ã½ 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. 

ÎÚÑ»´«Ã½ 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’s comprehensive answer to distributed cloud seamlessly extends Google Cloud’s infrastructure, services, and management capabilities to edge locations and on-premises data centers.ÌýÌý

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.

Peering into the future is a tricky business – especially in the ever-changing realm of data, analytics, and AI. But if there’s one thing we’ve learned, it’s that uncertainty never stopped us from trying. After all, we’re 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 – and no, it’s not artificial hype. From “vertical AI� 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’s for sure: 2025 is shaping up to be a year we’ll be talking about for a long time to come. And data and AI are right in the middle of it.

Let’s 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’s face it: When it comes to AI, the real crime would be ignoring the conversation altogether. – Marijn Markus, AI Lead, Managing Data Scientist, ÎÚÑ»´«Ã½ and Data, ÎÚÑ»´«Ã½

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’t 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 – and those who don’t get governed. – Liz Henderson, Executive Advisor, ÎÚÑ»´«Ã½ and Data, ÎÚÑ»´«Ã½

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 – but with a whole new kind of silver lining. – Prithvi Krishnappa, Global Head of Data and AI, Sogeti

Let’s talk better

Conversational AI will continue to be a hot topic in 2025. Contact center transformation, leveraging “classicâ€� 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 – 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’ll be the talk of the town in 2025. – Monish Suri, Global Google Partnership Lead, ÎÚÑ»´«Ã½ and Data, ÎÚÑ»´«Ã½

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, ÎÚÑ»´«Ã½

The semantics of confidence

We’ve seen many companies adopting the principles of data mesh and semantics as part of their modern data analytics platform strategy. If nothing else, it’s 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, ÎÚÑ»´«Ã½ and Data, ÎÚÑ»´«Ã½

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, ÎÚÑ»´«Ã½ and Data, North America, ÎÚÑ»´«Ã½

Interesting read?

ÎÚÑ»´«Ã½â€™s Innovation publication,ÌýData-powered Innovation Review – Wave 9Ìýfeatures 15 captivating innovation articles with contributions from leading experts from ÎÚÑ»´«Ã½, with a special mention of our external contributors from, and.ÌýExplore the transformative potential of generative AI, data platforms, and sustainability-driven tech. Find all previous Waves here.

Today’s industrial challenges are rarely isolated. They’re interconnected. Productivity, sustainability, digital transformation – they’re all part of the same conversation.

But too often, organizations are forced to tackle them separately. At Hannover Messe 2025, we at ÎÚÑ»´«Ã½ will be showing what happens when you take a different approach.

In partnership with Microsoft, we’ll 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’s 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 – 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’s fully interoperable, able to plug into different CAD and data sources, and brought to life through a VR-enabled 2D interface – giving you an immersive, hands-on look at what’s 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’ll explore how ÎÚÑ»´«Ã½ and Microsoft are working together to unlock performance, resilience, and scale in today’s digital factories – and what that means for your next step forward.

Why it matters

ÎÚÑ»´«Ã½ brings together extensive manufacturing expertise with world-class engineering capabilities – 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’s how we help clients move faster, think bigger, and deliver more – with a clear path to value.

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

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

It’s more than a stand – it’s a space for real conversations

On Monday March 31 at 17:00, Microsoft’s and ÎÚÑ»´«Ã½ 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’ll hear stories of what’s worked, what’s changing, and how to get ahead.

We’re recognized for our results

ÎÚÑ»´«Ã½ was recently recognized by Everest for its leadership in intelligent industry – and we’re 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’ve built a strong track record by delivering where it counts.

Visit ÎÚÑ»´«Ã½ at Hannover Messe

If you’re attending Hannover Messe, we’d love to see you.

Come by the ÎÚÑ»´«Ã½ 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’ll also be livestreaming some of our sessions if you are unable to attend in person.

We’re ready to show you what’s 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 ÎÚÑ»´«Ã½â€™s World Retail Banking Report 2025, only 24% of customers are satisfied with their bank’s 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​. 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’s workday is consumed by repetitive, manual tasks, leaving little room for value-driven customer engagements​. For insurers, this often means agents spend excessive time manually verifying policyholder information, processing claims, or handling routine inquiries—tasks 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​.

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—all 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 —a hybrid state-space and transformer LLM—GenAI can provide advanced sentiment analysis, low-latency response times (<500ms), and deep contextual understanding that adapts as the conversation evolves.  can be used to customize the models with domain knowledge. Once in production, model performance can be maintained with NVIDIA® 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® NIM can help scale latency and throughput, optimizing the delivery of GenAI-driven insights. 

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—boosting 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—tasks 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’s 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’s 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—while 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.