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 , 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

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  We 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. 

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 ÎÚÑ»´«Ã½ 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 ÎÚÑ»´«Ã½ 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 ÎÚÑ»´«Ã½ 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. ÎÚÑ»´«Ã½’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 ÎÚÑ»´«Ã½ 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 ÎÚÑ»´«Ã½ 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.Ìý 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 .Ìý 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 ÎÚÑ»´«Ã½ 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 ÎÚÑ»´«Ã½ Research Institute report: Turbocharging software with Gen AI to learn more.

Check out other blogs in the series:

Applications

Boosting productivity in software engineering with generative AI

Jiani Zhang

Apr 16, 2025

Beauty products can now be both glamorous and green. The EcoBeautyScore aims to make the industry’s environmental impact visible

ÎÚÑ»´«Ã½ Invent is proud to support the official launch of the EcoBeautyScore – a major step forward in enabling the cosmetics industry to transparently communicate to consumers the environmental impact of their products and monitor it within the competition landscape.

Developed through unprecedented industry collaboration, this science-based and user-friendly digital tool empowers beauty brands (large and small) to evaluate, compare, and improve their product footprint across the entire lifecycle.

The scoring system is due to be launched by the EcoBeautyScore Association in Q2 2025 thanks to the collaboration of over 70 cosmetics companies and trade associations from four continents over three years. It provides a harmonized environmental scoring system based on the European Commission’s Product Environmental Footprint (PEF) methodology and is tailored specifically to the unique features of cosmetic products.

From vision to action: building a global sustainability alliance

ÎÚÑ»´«Ã½ Invent has played a pivotal role in shaping the EcoBeautyScore initiative since its inception in 2021. Acting in a startup-like, agile environment, the ÎÚÑ»´«Ã½ team supported the design, launch, and scale-up of the EcoBeautyScore initiative, helping build the EcoBeautyScore consortium’s operational backbone, technical governance, and digital foundations.

From defining the consortium’s vision and recruiting global stakeholders to coordinating technical working groups, leading communication and branding efforts, and guiding IT tool development and prototyping, ÎÚÑ»´«Ã½ Invent has been at the heart of this three-year journey in collaboration with EcoBeautyScore partners.

“We are extremely proud to have accompanied this transformative initiative from the ground up. EcoBeautyScore is not just a tool, it’s a new way of thinking about sustainable product design and consumer transparency.�

Claire Lavagna, VP | Consumer Product Industry, ÎÚÑ»´«Ã½ Invent

Plug-and-play access to environmental scoring

Initially covering four beauty categories (shampoo, conditioning hair treatments, body wash, and face moisturizers and treatments), the EcoBeautyScore enables brands to input product data in a user-friendly tool and instantly receive detailed impact results across 16 PEF indicators, including climate change, water usage, and land use. It provides actionable insights that inform eco-design strategies and facilitate benchmarking against comparable products.

Critically, the methodology has been reviewed by independent lifecycle assessment experts and is being validated by E&H, part of the certification group, EcoCert. This is to ensure alignment with ISO standards and PEF.

Empowering consumers, driving change

Today, more conscientious consumers want detailed cosmetic ingredients analysis. Recognizing this, ÎÚÑ»´«Ã½ and the EcoBeautyScore Association joined forces to deliver on higher expectations. By the end of 2025, consumers in Europe will start seeing EcoBeautyScore on their cosmetic products. The score offers a transparent, standardized reference to help consumers make more informed, sustainable choices. The initiative aims to progressively expand its cosmetic analysis to additional beauty product categories and geographies, establishing a new global reference point for sustainability in the beauty sector.

About EcoBeautyScore Association  

The EcoBeautyScore Association is a not-for-profit organization whose primary goal is to develop a common environmental impact scoring system for cosmetic products, thus enabling consumers to make more informed purchasing decisions. Moreover, the Association aims to enable the industry to anticipate emerging regulatory changes, as well as foster a culture of eco-design among the members and beyond.

You’re something spatial – building a more intelligent future with advanced spatial computing, digital twin, and AI

“How businesses integrate spatial computing into their digital transformation will determine their competitive edge and ensure long-term success in a world where virtual and physical realities are increasingly interconnected.â€� – Monika Underwood 

Imagine this: you’re a surgeon amid a complex operation. Every second counts, and each decision you make has the potential to dramatically alter the course of the surgery you’re performing. But you’re not alone.  

You’re wearing a virtual reality (VR) headset that allows you to connect with experienced surgeons from around the world who’ve performed the exact same procedure before. Your headset also enhances your vision, provides real-time data, and enables you to view surgery footage and CT scans simultaneously.  

This is the future being enabled by spatial computing, digital twins, and real-time 3D (RT3D) technology. Impacting every industry from healthcare to manufacturing, these technologies are ushering in a new chapter of efficiency, cost reduction, and intelligent decision-making – and it’s all happening right before our eyes.  

The next digital revolution 

This convergence of technology is enabling a new world of possibility for businesses across industries. Spatial computing blends the digital and physical using technologies like VR and augmented reality (AR) to deliver seamless, immersive experiences to users. Digital twins are virtual replicas of physical assets that enable users to monitor, simulate, and optimize processes in real-time. RT3D technology helps create immersive simulations by powering the immediate development of dynamic 3-D environments.  

Each of these technologies wields its own special power. Together, they’re helping create the most advanced integrated digital ecosystems that our world has ever seen – transforming how humans interact with technology along the way.  

Spatial computing is often mistaken for just AR, VR, mixed reality (MR), or extended reality (XR), but it’s far more expansive. Fueled by AI, advanced optics, and miniaturized sensors, it represents the next evolution of computing – one that blends the digital and physical seamlessly. As these technologies converge, spatial computing will scale beyond niche applications to become a transformative force across industries and everyday life.  

This includes radical progress in optics, the miniaturization of sensors and chips, and the ability to authentically portray 3D images. These innovations, supported by significant breakthroughs in AI, will make spatial computing increasingly compelling for businesses on a grand scale in the years to come. 

Uplifting industries 

A leading example of this technology is the scenario depicted at the beginning of this blog. Surgeons recently headsets during laparoscopic surgeries to consult with specialists, magnify surgical views, and review surgical footage and CT scans simultaneously during procedures. The VR/AR headset has drastically improved the confidence and performance of the hospital’s surgeons.  

The healthcare industry isn’t the only one to benefit from this wave of technology. A leading Dutch airline is harnessing to develop an advanced training application. By enabling trainees to fully immerse themselves in high-quality, customizable training scenarios, this application delivers greater training volume, flexibility, and efficiency to future pilots who know that every hour of training counts. Another airline is with spatial computing to transform pre-flight damage check procedures. By boosting efficiencies by 900% and drastically reducing flight delays while promoting increased safety, this intersection of technology is having a profound impact on the airline’s business.  

What the future holds 

These are only a handful of the current applications of these technologies. Organizations across retail, manufacturing, education, and more are already reaping the rewards of spatial computing, digital twins, and RT3D tech. With benefits like personalized experiences, predictive analytics, faster go-to-market, and reduced operational costs readily available, more businesses will seek to leverage these technologies within their enterprises.  

Learn more

• TechnoVision 2025 – your guide to emerging technology trends
• You’re something spatial – new trend in user experience
• Voices of TechnoVision – a blog series inspired by ÎÚÑ»´«Ã½â€™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.

Face to interface – How human-like AI agents are changing the way we interact with technology 

Imagine a world where everyone has access to technology, where interacting with technology feels as natural as chatting with a friend, where there are no barriers for individuals, and societies across the globe can access advanced tools that create new opportunities that enhance human life. In this world, technology may look human, sound human, and even behave just as humans do. Though this may sound like some distant, sci-fi future, these interactions are already becoming a reality. 

“Human-AI interaction is accelerating at a pace we have not seen before. We are at crucial point where we have the responsibility to open access to technological developments and create fair and safe opportunities for everyone across individual, societal, and organizational levels. New AI agents designed with a human-centric approach are enabling this future, engaging with us in ways that feel more natural and help us achieve our goals.â€� – Carolina Sanchez Hernandez 

From tool to ally 

Over the last few years, we’ve seen advanced AI tools be made accessible to all. We’ve come to know AI as a tool that can accomplish tasks like brainstorming ideas, drafting emails, and generating code – all this responding to requests made in natural language. Now, we are in a new wave of development with the rise of agentic AI. This AI is more of a partner – one that, when designed with a human-centric approach, can drive profound change across businesses and societies. 

AI agents are software systems that use AI to complete tasks on behalf of users. Built in single or multi-agent configurations, agents can achieve the goals assigned to them using different decision paths, something which open many potential applications across industries including manufacturing, fintech, and consumer services to name a few. As we advance in this field, the way we design agent interfaces and human-AI agent interactions is critical for their safety and success. Now more than ever, AI needs to be designed as a sociotechnical tool guided by responsible principles. This responsible, human-like design is what will enable us to access the profound potential of agents to transform organizations and societies. 

A real-world impact 

Businesses are already benefiting from the latest evolution of AI. Earlier this year, ÎÚÑ»´«Ã½ developed a prototype for a global life sciences organization which involved an AI-driven agent that would be triggered by external events to initiate and automate procurement and logistics tasks. It proactively reached out, created a step-by-step plan, and worked with human experts to execute the process. The agent performed its own analysis, made recommendations, and automated pre-approved tasks while seeking human approval for critical decisions – ensuring a balance of automation and expertise. This system optimized efficiency by automating routine tasks and allowing humans to focus on higher-value work. 

ÎÚÑ»´«Ã½ also developed a solution for a global quick-service restaurant organization. The approach focused on using an AI agent to empower managers to handle minor equipment issues by leveraging AI-driven troubleshooting. Instead of relying solely on engineers, the AI assistant provided step-by-step repair guidance using structured workflows, images, and videos. By integrating equipment manuals and real-time diagnostics, the system enabled managers to identify problems and attempt fixes before escalating to an engineer. 

These advancements are breaking the barrier to entry when it comes to AI, making the technology accessible to all – not just tech experts. Now, any user can interact with an AI agent using plain dialogue to achieve their goals. As more people from all backgrounds begin to understand and utilize the vast potential of agentic AI, organizations will benefit from increased task efficiency, enhanced decision making, and faster innovation that enriches human-AI collaboration.  

The path to tomorrow 

The future of human-AI relationships is already taking shape. As interactions between people and AI evolve to feel more natural, both the technology and its potential business applications will also undergo an evolution. We have the power to shape this future, enable seamless and trustworthy interactions with technology, and deliver a transformational impact for all.  

Learn more

• TechnoVision 2025 – your guide to emerging technology trends
• Face to interface – new trend in user experience
• Voices of TechnoVision – a blog series inspired by ÎÚÑ»´«Ã½â€™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.

As highlighted in our Top Tech Trends of 2025 report, cybersecurity remains a critical focus with new defenses and emerging threats reshaping the landscape.

One of the most pressing concerns is the quantum cyber threat, which demands immediate attention and action

While less visible than artificial intelligence, quantum computing is advancing just as rapidly. In early 2025, Microsoft and Amazon both unveiled quantum processors with self-correcting capabilities, marking a decisive step towards stable, industrial-grade machines. The horizon for quantum computing is becoming clearer and closer, bringing with it the reality of threats that we must now seriously prepare for.

Cryptography in Danger

One of the strengths of quantum computing is its ability to perform massive calculations in parallel, significantly reducing the time required. This could enable, for example, the creation of highly targeted deepfakes from minimal data, which could be a formidable weapon in the wrong hands. However, the most significant threat concerns cryptography. While the security of commonly used asymmetric encryption algorithms today relies on the fact that it would take classical computers thousands of years to break them, a quantum machine could do it in just a few hours. This means all our data, communications, and authentication systems would become immediately vulnerable. In the post-quantum world, no identity, communication, or transaction can be guaranteed if it remains encrypted as it is today.

This risk is not a fantasy. The algorithms that underpin it have been ready for a long time, and their performance has been mathematically demonstrated. What is missing today are sufficiently stable and industrialized quantum machines. Recent announcements show that they will arrive not in ten, fifteen, or twenty years, but much sooner. And once available, they will be accessible to everyone via the cloud, as current quantum processors, despite their limitations, already are. Hackers are ready, the entry cost will be low, and as soon as the platforms are available, the risk will materialize massively and immediately.

An Already Present Risk

The threat is therefore major and imminent. It is even already present, as malicious actors can store encrypted data they collect today to decrypt it when they have the capability. In five years, most long-term strategic information, financial assets, health data, industrial, diplomatic, or military secrets will still be of great value. The issue is similar in all sectors whose products have a long lifespan and incorporate digital technology: defense, aerospace, transportation, energy, health… This strategy, known as “harvest now, decrypt later,” is a proven reality, with many states acknowledging that they practice it in long-term judicial investigations.

At some level, all organizations will be affected, from SMEs to multinationals, from local authorities to government ministries. In France more than elsewhere, very few have yet realized this, and even fewer have started to implement appropriate measures.

Overhauling Trust Systems

We can, and must, prepare now for this major cryptographic challenge, which will require nothing less than a complete overhaul of all trust systems: directories, APIs, certificates, storage, networks, application development… It will be a considerable project with major operational impacts that cannot be improvised in an emergency. It is necessary to start by scrutinizing the IT system to identify and assess risks, then prioritize interventions, allocate budgets, implement, test, and deploy solutions, manage change, coordinate with partners and suppliers… And it will be impossible to compress all this into a few weeks or months when the threat materializes.

Fortunately, technological countermeasures are being put in place. After a long competition, the NIST (National Institute of Standards and Technology) has approved encryption algorithms (five to date) capable of resisting quantum computers. Or rather, they are resistant based on current knowledge, which means that a certain cryptographic agility will be necessary in case they too are eventually broken. In France, several startups and large companies offer solutions in this area. It is also possible to implement hybrid encryption solutions to protect data against both today’s classical threats and tomorrow’s quantum threats. Finally, another area of study, still experimental, concerns the physical security of communications with QKD (Quantum Key Distribution), which provides absolute certainty that exchanges have not been intercepted.

For once, action must be taken without waiting for explicit regulatory pressure. While ANSSI has been warning about the quantum risk since 2022, these warnings are not yet accompanied by any obligations, not even for OIVs. However, texts like NIS 2, DORA, or GDPR hold leaders accountable without specifying the technical nature of the threats. In other words, an organization subject to such regulations will have no excuse if its data is stolen and decrypted by a quantum computer. In the face of the quantum threat, denial, skepticism, or inaction are no longer acceptable, especially in the current security and geopolitical context.

Click here to know more about our Quantum Lab.

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.

Software engineers may have once stated that software doesn’t write itself. That’s not true anymore. Generative AI is perfectly capable of taking on at least some of the simple tasks involved in coding, as well as other aspects of the software development life cycle. In fact, research published in our new ÎÚÑ»´«Ã½ Research Institute report, Turbocharging software with Gen AI, shows that organizations using generative AI have seen a 7–18% productivity improvement in software engineering.

So, what does this mean for those working in the software industry? It would be reasonable to expect some fear of change, after all, status quo is a well-documented human behavior. But our research data – which involved both developers and senior executives – shows that software engineers and their employers expect generative AI to enhance the profession and deliver increased value with software quality and the daily workload of software engineers, as companies demand ever more complex software across all parts of their business and product lines.

Let’s look in more detail at some of these key benefits.

Accelerate faster with greater accuracy

The old idea that moving too fast opens the door to mistakes can be turned on its head with the careful use of generative AI during software development. Because generative AI can automate some simple tasks, and complete them more quickly, it can help speed up a whole host of non-safety-critical processes, leaving more time to spend on complex software development.This can include paying extra attention to safety-critical systems, where human oversight will still play a crucial role in rigorous oversight to maintain the highest safety standards.

Of course, generative AI is not a ‘magic bullet’ that can just be told what to do and automatically produce the result you want. It will need a well-defined architecture and effective rules for how to ‘prompt’ it to generate code that is repeatable and maintainable, and which meets company needs and compliance rules.

But with the right processes in place, Gen AI clearly holds great promise, and these fundamental benefits are widely acknowledged among software developers. Our research indicates that its use is projected to grow significantly, with over a quarter of all work in software design, development, testing, and quality expected to be augmented by generative AI in two years. By 2026, we anticipate that more than four of every five software professionals will utilize generative AI tools.

Make room for talent to shine

Improved speed and accuracy are only part of the picture. They are very much enablers for other key advances, most notably allowing software engineers to spend the time required to develop the complex code they were hired to create.

Software engineers possess a wealth of talents that extend beyond writing quality, complex code. However, these talents can be stifled if they spend the vast majority of their time on the more mundane – even repetitive – aspects of coding. By freeing them of these tasks, tools like generative AI can unlock engineers’ creativity, enabling them to be creative, think of new ways of addressing problems, or imagine entirely new aspects of a software solution.

The challenge of balancing mundane tasks with creative thinking is not unique to software engineers. People in many professions often find that their most profound or innovative thoughts emerge when they are not immersed in the more day-to-day aspects of their work.

However, software engineers still need to spend time writing code, and time must be allocated for it. By automating those everyday tasks, generative AI can free up more time for innovative thinking and creative problem-solving – like allowing software engineers to spend more time thinking through the user experience. Software professionals are aware of this, and we found they see multiple pathways for creativity to emerge. We found that 61% of software leaders have already seen the benefits of generative AI in enabling innovative work, and 36% have seen benefits in collaborative work.

Advantages like this can be experienced across many different job grades. One technical leader told us, “While senior professionals are leveraging generative AI combined with their domain expertise for product innovation, junior professionals see value in AI process and tool innovation, and in automation and productivity optimization.�

Increase job satisfaction and retention

Despite initial fears, firms are not seeing that generative AI is reducing the software engineering workforce. Instead of considering generative AI as a standalone team member, the prevailing view is to use it as a tool to empower team members and enhance their effectiveness.

When we examined how firms plan to utilize the productivity gains they reap from generative AI, we discovered that only a mere 4% intend to reduce the workforce. The overwhelming majority are committed to enhancing more meaningful work opportunities for their software professionals, such as innovation and new feature development (50%), upskilling (47%), and focusing on complex, high-value tasks (46%).

This is not really surprising. The reality is that most engineering companies cannot hire anywhere near the number of software engineers they need. So, far from reducing headcount, generative AI is more about allowing the existing software workforce to get closer to what the company dreams it will deliver.

Our research found that 69% of senior software professionals believe generative AI will positively impact job satisfaction. When we asked software professionals how they see generative AI, 24% felt excited or happy to use it in their work, and an additional 35% felt it left them assisted and augmented. These factors can also benefit staff retention: people who are happy in their work are less likely to look at moving on.

In conclusion

It is still very early days for generative AI in the software development life cycle. Still, we have already found that it is being leveraged to speed up development time, enhance products, free up software engineers to move from the mundane to more innovative work, and in doing all this, boost both productivity and job satisfaction. With uptake predicted to grow significantly over the coming few years, we expect exciting things for developers, their products, and their customers.

Download our ÎÚÑ»´«Ã½ Research Institute report Turbocharging software with Gen AI to learn more.

As we get ready for ServiceNow Knowledge 2025, it’s an opportune time to reflect on the insights and discussions we recently had on the Data-powered Innovation Jam RightHere, RightNow podcast.

Automation is a cornerstone of innovation. On the Data Powered Innovation Jam RightHere, RightNow podcast, , Senior Advisory Solution Architect at ServiceNow, shared invaluable insights on how automation is reshaping industries and unlocking new possibilities. Hosted by ÎÚÑ»´«Ã½â€™s Ron Tolido, Weiwei Feng, and Robert Engels, this episode delved into the transformative power of automation and ServiceNow’s pivotal role in this journey. 

The evolution of ServiceNow  

ServiceNow has come a long way from its origins as a ticketing tool. Toby Isaacson elaborated on how the platform has evolved to encompass a comprehensive suite of automation capabilities. He emphasized that while ServiceNow still excels in IT Service Management (ITSM), its true potential lies in automating diverse business processes across HR, CRM, supply chain, and more. This evolution reflects ServiceNow’s commitment to driving efficiency and innovation in every aspect of enterprise management. 

Automation and AI 

A significant portion of Toby’s discussion focused on the integration of AI and automation within ServiceNow. He highlighted the importance of AI in enhancing productivity by automating repetitive tasks and allowing employees to focus on more valuable work. Toby shared examples of how AI agents and process mining tools are used to identify bottlenecks and optimize workflows, ultimately leading to more efficient and effective operations. 

Unified data and process management

One of the standout points from Toby’s talk was the emphasis on unifying data and process management. He explained how ServiceNow’s single data model and workflow data fabric enable seamless integration and orchestration of data from various enterprise systems. This unified approach ensures that businesses can leverage accurate and consistent data to drive their workflows and AI initiatives, fostering a more cohesive and efficient operational environment. 

Industry-specific solutions   

ServiceNow’s commitment to providing industry-specific solutions was another key highlight. Toby discussed how predefined workflows and best practices tailored to industries like healthcare, manufacturing, and retail can help businesses quickly adopt automation without reinventing the wheel. These solutions not only streamline processes but also allow companies to focus on innovation and differentiation in their respective fields. 

The future of work  

Looking ahead, Toby painted a picture of a dynamic and fluid future of work, driven by automation and AI. He emphasized that the goal is not just efficiency but also creating a work environment that is autonomous and perfectly timed. ServiceNow’s platform is designed to support this vision, ensuring that businesses can navigate the complexities of modern work with ease and agility. 

Toby Isaacson’s insights on the Data Powered Innovation Jam podcast underscore the transformative power of automation and AI in today’s business landscape. As ServiceNow continues to innovate and expand its capabilities, it remains a crucial partner for enterprises seeking to enhance their operations and drive meaningful change.Ìý

Join us at ServiceNow Knowledge 2025, to embrace our theme “Intelligence, meet experience. Welcome to your agentic-powered business,” highlighting how our intelligent solutions are designed to enhance customer experiences and empower businesses with agentic AI capabilities.

Listen to the RightHere, RightNow podcast episode now!