We are proud to share that Capgemini has been recognized as a Leader in Avasant’s Cybersecurity Services 2025 RadarView™ – an achievement that reflects our relentless commitment to helping clients build secure, resilient, and future-ready enterprises. 

This recognition is more than a milestone – it’s a powerful validation of our ability to deliver continuous cyber resilience through our robust cybersecurity portfolio that is aligned with our clients’ evolving business and regulatory needs.

Avasant’s comprehensive evaluation of global service providers is based on innovation, capabilities, and industry impact, and placed Capgemini at the forefront. Our leadership position is a direct result of our strategic investments, innovation-led approach, and ability to scale cyber defense solutions globally. 

Empowering clients with continuous resilience 

At Capgemini, cybersecurity is foundational to continuous business resilience. Our end-to-end security services are designed not only to protect, but to enable our clients to anticipate, withstand, and rapidly recover from disruption – ensuring continuity and confidence in an unpredictable world. 

Avasant’s assessment highlights our strengths in zero trust architecture, secure cloud transformation, AI-driven threat intelligence, and our global cyber defense center networks. These capabilities power an integrated and proactive security approach that ensures organizations stay secure and resilient – always. 

Sector-specific cyber innovation 

Our differentiated approach includes industry-specific solutions tailored to the complex needs of highly regulated and high-impact sectors: 

• OT/IoT security in manufacturing, energy and utilities: Securing manufacturing environments from design to deployment, including implementing industrial-grade frameworks across 300+ sites with IEC 62443 alignment. 

• Financial services: Leveraging a best-of-platform approach to drive security consolidation and compliance automation. 

• Connected healthcare and automotive: Ensuring secure innovation across medical devices, vehicles, and 5G ecosystems. 

• Aerospace, oil and gas: Establishing 24×7 SOCs, improving cyber maturity by 95 percent, and delivering integrated IT/OT threat intelligence. 

We’re also shaping future-ready security through pioneering engagements – like our quantum cryptography roadmap for a European bank, developed with our Quantum Lab and Cambridge Consultants. 

The road ahead 

Our promise to clients is simple: cybersecurity that enables sustainable transformation and continuous resilience. Every investment we make, every partnership we build, and every capability we evolve is designed to deliver on that promise. 

This leadership ranking from Avasant reinforces our purpose. As threats grow in complexity and the pace of change accelerates, we will continue to be the trusted partner that helps clients move forward with security, agility, and confidence. 

Click here to read the excerpt. 

Contact Capgemini to understand how we are uniquely positioned to help you structure cybersecurity strength from the ground up.  

As our digital and physical worlds grow more intricately connected, we find ourselves at the start of the next chapter of user experience – You Experience.  

“Spatial computing, digital twins, and AI are blurring the line between the physical and digital. As more businesses look to deliver the hyper-personalized experiences their customers want, they’re turning towards next-gen technologies that carry the potential to drastically transform user experiences for the better.” – Alexandre Embry 

In this world, digital interactions no longer consist of just humans using machines. Thanks to advancements in AI, interfaces, and digital twins, these interactions are traversing their technological confinements and impacting our physical world in ways we’d previously only dreamed of. The result? Businesses are becoming faster, smarter, and greener.  

Striking a balance 

With the evolution of user experience comes great responsibility. Human-like agents and cognitive twins are quickly evolving, and to access their full potential, businesses must balance the benefits of hyper-personalization, automation, and efficiency while prioritizing privacy, empathy, and human-touch.  

 
Despite their ability to deliver speed and precision, it takes the right approach to avoid the misuse of these new technologies and ensure they’re used sustainably. Over the years, many trends have aimed to bring businesses closer to successfully balancing the forces of innovation. This year, two new trends promise to bring them closer than ever before: “Face to Interface” and “You’re Something Spatial”.  

Connecting the digital and physical 

Recent years have shown an uptick in the volume of human and AI interactions, presenting an opportunity for businesses to craft these interactions in ways that feel more natural. New AI agents, designed to look, act, and behave more like humans, are making this possibility a reality.  With the ability to collaborate, converse, and connect with people, connections with AI are now designed to feel more engaging – resulting in technologies being viewed increasingly as partners as opposed to just tools. But that’s only the beginning.  

Advancements in spatial technologies are also transforming the way we design user experiences. By combining digital twins, real-time 3D (RT3D), and AI-powered vision, this convergence of technology is strengthening the connection between the physical and digital, enabling immersive insights, enhanced decision making, and hyper-personalization. Everything from shopping to the design of factory floors is being uplifted by these technological advancements, leaving businesses across industries eager to leverage them within their value chains.  

Next steps for businesses 

How can businesses navigate this new era of experience? Embracing AI and spatial technologies is a necessary first step in improving personalization and designing interactions that feel more human. By integrating AI-driven systems, large vision models, and spatial computing, businesses will realize benefits like improved training, collaboration, and competitiveness.  

The adoption of digital twins and cognitive agents will also be vital to the successful evolution of user experiences. Enabling organizations to improve human and AI collaboration, automate complex tasks, and reduce errors, these technologies will bridge virtual and physical environments and empower organizations to optimize innovation cycles and drive down costs. 

Ensuring innovation remains in-line with sustainability must also be a top priority. Organizations will need to walk the tightrope between generating business value and meeting their environmental targets. Doing so will enable them to achieve their goals while also delivering long-term value for the planet.   

What the future holds 

As this new era begins to take shape, the integration of next-generation technologies will offer organizations an immense opportunity to redefine what it means to create user experiences. By leveraging AI, digital twins, cognitive agents, and advanced spatial technologies, businesses will achieve levels of personalization, efficiency, and engagement that were previously unobtainable. The next chapter of experience is here, and it’s time to embrace it.  

Learn more 

• TechnoVision 2025 – your guide to emerging technology trends 
• You Experience – One of the seven containers of TechnoVision 2025 
• Voices of TechnoVision – a blog series inspired by Capgemini’s TechnoVision 2025 that highlights the latest technology trends, industry use cases, and their business impact. This series further guides today’s decision makers on their journey to access the potential of technology. 

AI is reshaping the way enterprises operate, but one fundamental challenge that still exists is that most applications were not built with AI in mind.

Traditional enterprise systems are designed for transactions, not intelligent decision-making, making it difficult to integrate AI at scale. To bridge this gap, MongoDB and Capgemini are enabling businesses to modernize their infrastructure, unify data platforms, and power AI-driven applications. This blog explores the trends driving the AI revolution and the role that Capgemini and MongoDB play in powering AI solutions.

The challenge: Outdated infrastructure is slowing AI innovation

In talking to many customers across industries, we have heard the following key challenges in adopting AI:

• Data fragmentation: Organizations have long struggled with siloed data, where operational and analytical systems exist separately, making it difficult to unify data for AI-driven insights.
  
  In fact, according to the Workday global survey, 59 percent of C-suite executives said their organizations’ data is somewhat or completely siloed, which results in inefficiencies and lost opportunities. Moreover, AI workloads such as retrieval-augmented generation (RAG), semantic search, and recommendation engines require vector databases, yet most traditional data architectures fail to support these new AI-driven capabilities.
  
• Lack of AI-ready data infrastructure:The lack of AI-ready data infrastructure forces developers to work with multiple disconnected systems, adding complexity to the development process.
  
  Instead of seamlessly integrating AI models, developers often have to manually sync data, join query results across multiple platforms, and ensure consistency between structured and unstructured data sources. This not only slows down AI adoption but also significantly increases the operational burden.

The solution: AI-ready data infrastructure with MongoDB and Capgemini

Together, MongoDB and Capgemini provide enterprises with the end-to-end capabilities needed to modernize their data infrastructure and harness the full potential of AI.

MongoDB provides a flexible document model that allows businesses to store and query structured, semi-structured, and unstructured data seamlessly, a critical need for AI-powered applications. Its vector search capabilities enable semantic search, recommendation engines, RAG, and anomaly detection, eliminating the need for complex data pipelines while reducing latency and operational overhead. Furthermore, MongoDB’s distributed and serverless architecture ensures scalability, allowing businesses to deploy real-time AI workloads like chatbots, intelligent search, and predictive analytics with the agility and efficiency needed to stay competitive.

Capgemini plays a crucial role in this transformation by leveraging AI-powered automation and migration frameworks to help enterprises restructure applications, optimize data workflows, and transition to AI-ready architectures like MongoDB. Using generative AI, Capgemini enables organizations to analyze existing systems, define data migration scripts, and seamlessly integrate AI-driven capabilities into their operations.

Real-world use cases

Let’s explore impactful real-world use cases where MongoDB and Capgemini have collaborated to drive cutting-edge AI projects.

• AI-powered field operations for a global energy company: Workers in hazardous environments, such as oil rigs, previously had to complete complex 75-field forms, which slowed down operations and increased safety risks. To streamline this process, the company implemented a conversational AI interface, allowing workers to interact with the system using natural language instead of manual form-filling. This AI-driven solution has been adopted by over 120,000 field workers, significantly reducing administrative workload, improving efficiency, and enhancing safety in high-risk conditions.
• AI-assisted anomaly detection in the automotive industry: Manual vehicle inspections often led to delays in diagnostics and high maintenance costs, making it difficult to detect mechanical issues early. To address this, an automotive company implemented AI-powered engine sound analysis, which used vector embeddings to identify anomalies and predict potential failures before they occurred. This proactive approach has reduced breakdowns, optimized maintenance scheduling, and improved overall vehicle reliability, ensuring cost savings and enhanced operational efficiency.
• Making insurance more efficient: GenYoda, an AI-driven solution developed by Capgemini, is revolutionizing the insurance industry by enhancing the efficiency of professionals through advanced data analysis. By harnessing the power of MongoDB Atlas Vector Search, GenYoda processes vast amounts of customer information including policy statements, premiums, claims histories, and health records to provide actionable insights.
  
  This comprehensive analysis enables insurance professionals to swiftly evaluate underwriters’ reports, construct detailed health summaries, and optimize customer interactions, thereby improving contact center performance. Remarkably, GenYoda can ingest 100,000 documents within a few hours and deliver responses to user queries in just two to three seconds, matching the performance of leading AI models. The tangible benefits of this solution are evident; for instance, one insurer reported a 15% boost in productivity, a 25% acceleration in report generation – leading to faster decision-making – and a 10% reduction in manual efforts associated with PDF searches, culminating in enhanced operational efficiency.

Conclusion

As AI becomes operational, real-time, and mission-critical for enterprises, businesses must modernize their data infrastructure and integrate AI-driven capabilities into their core applications. With MongoDB and Capgemini, enterprises can move beyond legacy limitations, unify their data, and power the next generation of AI applications. For more, watch this TechCrunch Disrupt session by Steve Jones (EVP, Data-Driven Business & Gen AI at Capgemini) and Will Shulman (former VP of Product at MongoDB) to learn about more real-world use cases. And discover how Capgemini and MongoDB are driving innovation with AI and data solutions.

Read more about our collaboration with MongoDB here.

The more public services are provided online, the more digital accessibility becomes a fundamental design principle for public sector organizations. So, why are so many disabled people and those with neurodiverse conditions still encountering barriers?

The European Union has a target for key public services to be 100% online by 2030. While this is an admirable ambition, it is important that no-one is excluded from these digital services due to a disability. Additionally, the more accessible government and local authority websites and mobile apps are for everyone, regardless of their visual, hearing, motor, and cognitive abilities, the more effective and cost efficient the delivery of public services becomes. .

In the following interview, Emma Atkins, software engineer and accessibility expert at Capgemini UK, gives her personal perspective on the current accessibility picture in digital public services.

Is the EU’s 2030 digital target realistic for disabled people and those with neurodiverse conditions?

No! At least not yet. Of course, it is good to have an ambition to include everyone but, in my opinion, it is beyond the realm of current technology. It doesn’t consider those so severely disabled they cannot speak, leave their bed, or even tolerate light – how would they access these services? So, while I welcome the EU’s 2030 digital target, that ambition is only the start. The most disabled people with the most complex needs will be those for whom the most work needs to be done. To create citizen-centric services that work for everyone, government bodies must think accessibility first, design second.

What digital access barriers do disabled people and neurodivergent citizens still face?

They face numerous access barriers every single day, in both the digital and real world. This can be anything from a visually impaired person unable to use a screen-reader with a website to a neurodivergent person facing inaccessible language in an app. Or it might be someone with access needs who is completely digitally excluded being asked to make a phone call to get accessible information, ignoring the fact that many people can’t easily use a phone!

What impact can digital accessibility have on government policy, as well as on the inclusivity of public information and services?

It’s all about money really! Digital accessibility could save governments a lot of money in the long term. How? By allowing citizens to self-serve information and services, rather than needing direct contact with an advisor to do the same thing. Not to mention that inclusivity allows for greater reach of government information to the wider community, thus maximizing the impact of policies, as well as complying with digital inclusion laws.

What needs to change – e.g. what’s stopping investment in digital accessibility?

Personally, I feel it’s mostly down to ableism! Either intentionally, or out of ignorance. Some people are unsure of how to make their services accessible and believe it to be more difficult than it is. Others simply don’t care, believing disabled people to be unimportant, subscribing to rhetoric along the lines that we don’t work, or do not contribute to society in any way. There is an urgent need to educate non-disabled people about the value of more inclusive thinking and approaches. To achieve the EU’s 2030 target, government and public service agencies should promote an inclusive workplace culture where staff are trained in digital accessibility and the topic is anchored in the department’s mission statement.  

Can you give us some real-life examples of accessible design and co-creation?

The HMRC Mobile App on which I worked achieved full compliance with accessibility standards for two years in a row. This was achieved by putting accessibility first and design second. Simply put, if it wasn’t accessible, we didn’t include it.

For example, we intended to introduce a component to the app that allowed part of the screen to be hidden and revealed at the push of a button, but I had concerns that this would not be suitable for screen reader users. I found ways to ensure this was fully accessible, and we did not include it in the app until it was. As well as drawing on my own expertise as an accessibility expert, we took feedback from disabled users before a professional audit was undertaken by the Digital Accessibility Centre (DAC).

How are AI and other technologies creating new possibilities?

The key difference AI is making to me, and disabled programmers like me, is making programming more accessible. More disabled programmers can only be a good thing, as this is likely to lead to more awareness of accessibility needs, a greater focus on accessibility and thus, more accessible services! Not to mention, for non-technical people with access needs, the ability to convert language into plain, easy to understand language for themselves at the push of a button.

More broadly, AI and other GovTech solutions are beginning to create a more inclusive public sector. For example, there are technological tools available, such as screen readers, magnification software, image description tools, apps that convert text into speech, and AI-supported solutions that interpret visual content and convert it to text or speech. All of these are designed to empower citizens through digital accessibility to public services, creating new possibilities for inclusive citizen-centric government.

What one digital accessibility action do you want all governments to take right now? 

To listen. Listening to disabled people and understanding our needs is the only way change will happen. Understanding that we are real individuals, with real lives, dignity and rights, that deserve equal access to services. And then, of course, acting on that.

So, what action is needed right now? I’ve co-authored a point of view on this, called Public means everybody. We offer recommendations on how to make digital public services work for everyone. We draw on monitoring and research exercises across the EU public sector and show how GovTech is being used to address inaccessible online content and website structures. From proactive engagement with disabled citizens to working with innovative startups in the GovTech sector, we set out a systematic, scalable approach to transforming online government services.

For more, read Public means everybody: Accessibility first, design second in citizen services.

How Capgemini and Airbus partnered to explore the potential of quantum computing in advancing materials science for aerospace innovation.

With their focus on innovation and long-term strategic advantage, Capgemini’s Quantum Lab (Q Lab) and Airbus collaborate to explore how quantum computing could be applied to complex materials science challenges. One such challenge was modeling the atomic-scale processes that govern surface reactions in metallic environments – an ideal test case for quantum-enabled computational chemistry.

Corrosion is a well-known challenge across a wide range of industries, from manufacturing to infrastructure, with estimated global costs exceeding $2.5 trillion. Understanding the fundamental processes of corrosion remains an important area of materials research – especially as the aerospace industry continually seeks to improve performance, longevity, material efficiency and decrease In aerospace, corrosion often leads to significant barriers to growth like reduced efficiency, decreased aircraft lifespans, and increased maintenance costs.

A deep dive into how materials behave at microscopic level

Over time, chemical reactions take place between materials and elements in their environment, such as exposure to oxygen and moisture, gradually degrading them and compromising their integrity and underscoring the need for high-performance surface protection solutions. Accurately modeling these processes provides insight not only into degradation mechanisms but also into material stability and performance. For aerospace, where materials like copper-rich aluminum alloys are widely used for their lightweight and structural properties, such insights can inform the development of next-generation components and coatings.

Current preventive measures, such as aircraft maintenance and corrosion stage assessment, are reliant on experimental data and computational predictive models. These models break corrosion into different levels that span its multi-scale nature: microscopic, mesoscopic and macroscopic.

The most challenging layer to model is the microscopic level. Accurately modeling the chemical reactions that occur on this scale requires a deep knowledge of atomic processes, fine-tuned calculations, and highly complex and expensive equipment. This is particularly true for the oxygen reduction reaction (ORR), which plays a vital role in the corrosion of aluminum alloys and is notoriously difficult to measure experimentally.

Taking on the oxygen reduction reaction

Capgemini’s Q Lab and Airbus focused their efforts on this reaction, with the aim of developing a hybrid quantum computing workflow to assess the ORR at the molecular level. Studying the initial step of this reaction would bring aerospace organizations a step closer to building more accurate predictive models. Considering that the aluminum alloys that are most relevant for the aerospace industry are rich in copper, the research team decided to model the ORR on a copper slab. They then used a combination of quantum chemistry methods to identify the critical geometries and pathways necessary to explore the reaction using quantum computation.

The research team conducted a detailed quantum resource estimation to assess the role quantum computers will play in tackling similar problems in the field of materials science. This research provided an overview of the technological requirements necessary to explore similar use cases using quantum computing, including the hardware, algorithms, and qubits needed for such models and calculations.

A new horizon for quantum computing

This hybrid quantum computing workflow was the first of its kind. As a result of these collaborative efforts, Capgemini and Airbus established an essential foundation for applying quantum computation to atomistic modelling, highlighting its potential to address complex, business-relevant challenges in aerospace and materials science.

Though this research represents a big step forward for organizations, it also underlines the need for significant advancements in quantum hardware, algorithms, and error-correction techniques to make quantum computation viable for business use.

As industries look ahead towards the future of quantum computation, it’s clear that now is the time to determine how quantum computing can make a difference for companies across industries.

You may access the complete research here.

Over the last few years, a growing middle class has led to steep growth in the number of mass affluent customers across the world.

This segment of wealth customers is described as those having investable assets in the range of $250,000 to $1 million. They account for about 40% of global wealth and are expected to replace the middle class as growth drivers in the coming decade. As per a report from Global Data Analytics, the US mass affluent wealth band alone is expected to account for upwards of $US 42 trillion of wealth by 2025.

However, despite their significant scale and the immense potential of the mass affluent segment, it has thus far not been a top priority segment for Wealth Management (WM) firms. Capgemini’s 2023 affluent customer survey found that 47% do not receive the required value-added services from their WM firms.

In recent years, several different FinTechs have seized this opportunity, and begun to offer cost-effective solutions to help clients reach their investment goals. While traditional banks recognize the promise of this segment, they’re not sure about how to approach it, and as a result, it has remained underserved by traditional players. These customers are financially and digitally savvy, fee-sensitive, and inclined to shop around for various options, often spreading their assets across providers. Therefore, a generic cookie-cutter approach is unlikely to create much stickiness in the relationship. However, their investable wealth levels do not justify the traditional, personal one-to-one wealth advisor model. Consequently, the more economical digital self-service models offered by FinTechs have seen significant adoption.

Given this background, traditional firms must consider new ways to attract and retain clients from this segment. These include:

1. Leverage actionable data for insights. Develop a client-centric strategy to create cost-effective yet bespoke offerings with an optimal balance of digital and personal interactions. Mass affluent clients’ aspirations have significantly evolved from basic vanilla products of the past, and hence hyper-personalized offers and service would be key to attracting them.
2. Investment in new age solutions. To optimally serve this segment, it is imperative for firms to leverage the latest technology to differentiate themselves, deliver an exceptional experience, and remain competitively priced. Furthermore, with the great wealth transfer expected to result in over $120 trillion being passed on to next generation heirs by 2048, it would be critical for firms to engage with their younger clients in new ways. The expectations of these beneficiaries – regarding engagement channels, investment opportunities, interest in sustainable products, and preference for alternative asset classes – differ significantly from those of previous generations. Therefore, it becomes essential for firms to invest in the right technologies to effectively serve these new age clients.
3. Invest in an agile operating model. Having a modular architecture centered on an aggregation layer leveraging capabilities from legacy systems, as well as partner components and third parties, will allow WM firms to better leverage their ecosystem. It will also enable them to be better prepared for an expanding product universe consisting of not just traditional asset classes, but also newer ones such as alternatives, private markets, various digital assets (such as cryptos and NFTs), and ESG investments.

Firms are taking new and innovative measures to attract the mass affluent client base. JPMorgan has introduced an innovative “financial center” branch concepts aimed at mass affluent clients. Instead of traditional teller windows, these branches feature library-style sitting rooms and concierge bankers who provide personalized services such as stock purchasing assistance, retirement planning, and help with credit card fraud. Meanwhile, HSBC has relaunched its “Premier” wealth banking brand in Britain, providing a fee-free product that will offer 24-hour-a-day customer service, financial planning tools, as well as travel, international and lifestyle benefits to its mass affluent clients.

As the size of this segment and its investable wealth continue to grow over the next few years, competition between banks and wealth management firms will intensify. Banks will need to differentiate themselves through the relevance of their offerings – advising what is best for the client rather than pushing specific products – along with competitive pricing and the ability to tailor solutions based on the client’s lifecycle stage. Additionally, the capability to identify retail banking clients who may soon join the mass affluent segment, and to start engaging with them early, will position banks to build relationships from the inception of their first portfolios. As these clients’ wealth grows, so too will the potential business for the banks that have earned their trust.

As highlighted in our Top Tech Trends of 2025 report, generative AI remains a critical focus for businesses today and through its application brands are now able to provide ultra-personalized and contextual responses to their clients.

Notably, we are seeing its profound impact on purchasing behaviors as well as a shift from traditional SEO to Generative Engine Optimization (GEO).

Online search is shifting from traditional search engines to systems based on generative AI

After heavily investing in SEO (Search Engine Optimization), brands are venturing into a new era: GEO (Generative Engine Optimization), where content is optimized for generative artificial intelligence. Is this a liberation or an additional constraint for them?

This is a quiet revolution, but one that promises to make a big impact. Having already transformed productivity at work, large language models (LLMs) are profoundly changing purchasing behaviors. According to a 2024 study by YouGov for Zendesk, a quarter of French consumers already planned to use AI for their Black Friday and holiday shopping.

If consumers are turning away from the search engines, they have relied on for so many years, it is because generative AIs, such as ChatGPT, Gemini, or Perplexity, go further. They no longer simply provide a list of results but offer ultra-personalized and contextual responses based on individual preferences, usage context, and purchase history.

A radical change for brands

To support this profound transformation in purchasing behaviors, brands must now shift from SEO, focused on keyword optimization for search engines, to a new paradigm: GEO. In this emerging model, a brand’s visibility depends on how its content is integrated into the corpora of generative AIs.

Consider the concrete example of a consumer looking for an evening dress. With traditional SEO, results depend primarily on generic keywords such as “luxury evening dresses.” The most well-known brands, which invest the most to be well-referenced, naturally occupy the top positions.
In a world dominated by GEO, the response provided by an autonomous agent will more comprehensively integrate the user’s complete profile: their age, measurements, tastes, and social context. The response will no longer be just a well-referenced brand but a statistically optimal and personalized answer.

GEO: A new dynamic for brands

Is this shift to the GEO era a liberation or an additional constraint for brands? The answer is nuanced.

Certainly, this evolution allows brands to escape the hegemony of search engine players and to become known to their target audiences by sharing ultra-personalized information with autonomous agents. A new brand, for example in the cosmetics sector, would benefit from focusing its digital investments directly in GEO, thus bypassing the astronomical costs of traditional SEO which is already dominated by industry leaders.

However, for brands in other sectors, the advent of GEO necessitates a complete overhaul of their content production processes. They will first need to define their personas with unprecedented precision, creating extremely detailed customer profiles to meet the specific expectations of autonomous agents. Beyond traditional keywords, brands will need to provide comprehensive responses rich in contextual and comparative data. Finally, they will need to continuously test their visibility within GenAI tools and the relevance of their content within the results generated by LLMs, to constantly adjust and improve their strategy.

Towards new performance indicators

For brands historically anchored in intensive SEO strategies, this shift represents a new budgetary and technical constraint, requiring new skills in data analysis, content generation, and cloud technology.

With GEO, the number of page views will gradually lose its importance in favor of success indicators related to the effective and relevant presence of a brand in the recommendations generated by LLMs.

In the coming years, specific tools and common benchmarks should emerge, allowing brands to precisely measure their “AI visibility score,” thus facilitating rapid adaptation to this new information economy. The shift from SEO to GEO marks a decisive turning point in the evolution of the web and how brands reach their consumers. Only those capable of anticipating these changes will be able to stand out

Generative AI is rapidly revolutionizing the world of software engineering, driving efficiency, innovation, and business value from the earliest stages of design through to deployment and maintenance. This explosive development in technology enhances and transforms every phase of the software development lifecycle: from analyzing demand and modeling use cases in the design phase, to modernizing legacy code, assisting with documentation, identifying vulnerabilities during testing, and monitoring software post-rollout.

Given its transformative power, it’s no surprise that the Capgemini Research Institute report, Turbocharging Software with Gen AI, reveals that four out of five software professionals expect to use generative AI tools by 2026.

However, our experience and research find that to fully realize the benefits, software engineering organizations must overcome several key challenges. These include unauthorized use, upskilling, and governance. This blog explores these challenges and offers recommendations to help navigate them effectively.

Prevent unauthorized use from becoming a blocker

Our research indicates that 63% of software professionals currently using generative AI are doing so with unauthorized tools, or in a non-governed manner. This highlights both the eagerness of developers to leverage the benefits of AI and the frustration caused by slow or incomplete official adoption processes. This research is validated in our field experience across hundreds of client projects and interactions. Often, such issues arise from an overly ‘experimental’ versus programmatic approach to adoption and scale.

Unauthorized use exposes organizations to various risks, including hallucinated code (AI-generated code that appears correct but is flawed), code leakage, and intellectual property (IP) issues. Such risks can lead to functional failures, security breaches, and legal complications.

Our Capgemini Research Institute report emphasizes that using unauthorized tools without proper governance exposes organizations to significant risks, potentially undermining their efforts to harness the transformative business value of generative AI effectively.

To mitigate unauthorized use, organizations should channel the curiosity of their development teams constructively and in the context of managed transformation roadmaps. This approach should include consistently explaining the pitfalls of unauthorized use, researching available options, learning about best practices, and adopting necessary generative AI tools in a controlled manner that maintains security and integrity throughout the software development process.

Upskilling your workforce

Upskilling is another critical challenge. According to our Capgemini Research Institute findings, only 40% of software professionals receive adequate training from their organizations to use generative AI effectively. The remaining 60% are either self-training (32%) or not training at all (28%). Self-training can lead to inconsistent quality and potential risks, as nearly a third of professionals may lack the necessary skills, resulting in functional and legal vulnerabilities.

A consistent observation from our field experiences is that alongside the issue of training is a correlated barrier to making sufficient time available for teams to apply training in practical ways, and to evolve the training outcomes into pragmatic, lasting culture change.  Because generative AI is such a seismic shift in the way we build software products and platforms, the upskilling curve is about far more than incremental training.

Managing skill development in this new frontier of software engineering will require an ongoing commitment to evolving skills, practices, culture, ways of working and even the ways teams are composed and organized.   As a result, software engineering organizations should embrace a long-term view of upskilling for success.

Those that are most successful in adopting generative AI have invested in comprehensive training programs, which cover essential skills such as prompt engineering, AI model interpretation, and supervision of AI-driven tasks. They have begun to build organizational change management programs and transformation roadmaps that look at the human element, upskilling and culture shift as a vital foundation of success.

Additionally, fostering cross-functional collaboration between data scientists, domain experts, and software engineers is crucial to bridge knowledge gaps, as generative AI brings new levels of data dependency into the software engineering domain. Capgemini’s research shows that successful organizations realizing productivity gains from AI are channeling these gains toward innovative work (50%) and upskilling (47%), rather than reducing headcount.

Establishing strong governance

Despite massive and accelerating interest in generative AI, 61% of organizations lack a governance framework to guide its use, as highlighted in the Capgemini Research Institute report. Governance should go beyond technical oversight to include ethical considerations, such as responsible AI practices and privacy concerns.

A strong governance framework aligns generative AI initiatives with organizational priorities and objectives, addressing issues like bias, explainability, IP and copyright concerns, dependency on external platforms, data leakage, and vulnerability to malicious actors.

Without proper governance, the risks associated with generative AI in software engineering — like hallucinated code, biased outputs, unauthorized data & IP usage, and other issues ranging from security to compliance risks, can outweigh its benefits. Establishing clear policies, driven in practice through strategic transformation planning will help mitigate these potential risks and ensure that AI adoption aligns with business goals.

Best practices for leveraging generative AI in the software engineering domain

Generative AI in software engineering is still in its early stages, but a phased, well-managed approach toward a bold, transformative vision will help organizations maximize its benefits across the development lifecycle. In following this path, here are some important actions to consider:

Mitigate risks

• All productivity gains must be balanced within a risk management framework. Generative AI introduces new risks that must be assessed in line with the organization’s existing risk analysis protocols. This includes considerations around cybersecurity, data protection, compliance and IP management. Developing usage frameworks, checks and quality stopgaps to mitigate these risks is essential.

Support your teams

• Providing comprehensive training for all team members who will interact with generative AI is crucial. This training should cover the analysis of AI outputs, iterative refinement of AI-generated content, and supervision of AI-driven tasks. As our Capgemini Research Institute report suggests, organizations with robust upskilling programs are better positioned to improve workforce productivity, expand innovation and creative possibilities, and mitigate potential risks.

Implement the right platforms and tools

• Effective use of generative AI requires a range of platforms and tools, such as AI-enhanced integrated development environments (IDEs), automation and testing tools, and collaboration tools.
• However, only 27% of organizations report having above-average availability of these tools, highlighting a critical area for improvement.  Beyond the current view of Gen AI as a high-productivity assistant or enabler, we strongly encourage every organization in the business of software engineering to look beyond the ‘copilot mentality’ and over the horizon to what Forrester recently deemed “The Age Of Agents”.  The first wave of Gen AI and the popularity of these technologies as assistive tools will be a great benefit to routine application development tasks.
• For the enterprises that are building industrialized, commercial software products and platforms – and for the experience engineering of the next generation, we believe that the value and even the essentials of competitive survival depend on adopting and building a vision of far more sophisticated AI software engineering capability than basic ‘off the shelf’ code assist tools deliver.

Develop appropriate metrics

• Without the right systems to monitor the effectiveness of generative AI, organizations cannot learn from their experiences or build on successes. Despite this, nearly half of organizations (48%) lack standard metrics to evaluate the success of generative AI use in software engineering. Establishing clear metrics, such as time saved in coding, reduction in bugs, or improvements in customer satisfaction, is vital.
• We believe that organization-specific KPIs and qualitative metrics around things like DevEx (Developer Experience), creativity, innovation and flow are vital to consider, as the power of the generative era lies far more in the impact these intangibles have on the potential of business models, products and platforms than on the cost savings many leaders erroneously focus on. This is absolutely an inflection point, in which the value of the abundance mindset applies.

In conclusion

Generative AI is already well underway in demonstrating its potential to transform the software engineering lifecycle, improve quality, creativity, innovation and the impact of software products and platforms – as well as streamline essential processes like testing, quality assurance, support and maintenance. We expect its use to grow rapidly in the coming years, with continued growth in both investment and business impact.

Organizations that succeed in adopting generative AI as a transformative force in their software engineering ethos will be those that fully integrate it into their processes rather than treating it as a piecemeal solution. Achieving this requires a bold, cohesive vision, changes in governance, the adoption of new tools, the establishment of meaningful metrics, and, most importantly, robust support for teams across the software development lifecycle. 

At Capgemini Engineering Software, we are ambitiously transforming our own world of capability, vision, approach, tools, skills, practices and culture in the way we view and build software products and platforms.  We’re here for you, to help you and your teams strike out on your journey of transformation in the generative software engineering era.

Download our Capgemini Research Institute report: Turbocharging software with Gen AI to learn more.

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