“Everyone is obsessing over agentic AI, and rightfully so. When it comes to operational agility, autonomous agentic systems are set to deliver game-changing benefits to enterprises. In the coming years, the successful integration of these systems won’t just be a good idea, it’ll be the defining factor that separates industry leaders from the rest of the competition.” – Rajesh S. Iyer 

In our world, there are many kinds of agents. Travel agents help us book travel plans, with everything from flight bookings to hotel reservations falling under their jurisdiction. Sports agents help professional athletes navigate the legal and business side of sports, enabling clients to maximize their career and financial opportunities. Secret agents typically deal with top-secret matters.  

What about AI agents? Regarded for their intelligence and ability to tackle business challenges with flexibility and precision, AI agents have quickly become a hot topic for business leaders. The same goes for autonomous AI systems, which are growing increasingly more prominent within organizations.  

While the terms agentic AI and autonomous AI are often used interchangeably, these systems have distinctive qualities that set them apart. Autonomous AI refers to systems that can operate independently within predefined parameters, like self-driving cars or factory robotics. On the other hand, agentic systems are equipped with a deeper sense of agency. These systems are designed to actively pursue goals, dynamically adapt strategies, and make context-dependent decisions. In short, all agentic AI is autonomous, however not all autonomous AI is agentic.  

As more organizations look to integrate AI agents and autonomous AI systems into their operations, a new kind of partnership between people and technology is emerging – one that’s pushing businesses to learn and evolve. 

Making a real-world impact: from education to finance 

The benefits of AI agents and autonomous AI systems are already materializing across industries. In an effort to enhance its learning experiences, a US-based non-profit education company recently started leveraging an that autonomously supports educators and students. Providing teachers with an online teaching assistant and students with an online learning coach, this system helps break complex educational goals into actionable tasks – completely revolutionizing the classroom experience.  

The financial sector is also reeling in the benefits of autonomous agentic systems. In the US, a major bank is using to autonomously handle tasks like interest rate queries, account openings, and fund transfers, drastically improving operational efficiency. Across the globe in India, a leading digital lending and savings platform is leveraging an to automate its customer support services. Since integrating the agent into their operations, the platform has managed to automate 70% of its support tickets in multiple languages, delivering a vast reduction in costs and faster ticket resolution times. 

As organizations continue to leverage these systems and the technology itself continues to develop, benefits such as those mentioned here are just the beginning of a much broader transformation.  

Looking forward: a bright future ahead  

Agentic systems are at the forefront of the next wave of automation and AI. Representing a powerful shift for enterprises, these systems are positioned to improve operational efficiency, workplace collaboration, and customer satisfaction – transforming how organizations across industries pursue their strategic objectives.  

Though the benefits of agentic systems are certainly apparent, human oversight and the continuous adaptation of these systems are paramount for their success. Collaboration between humans and technology must remain at the core of any agentic system to build trust, safeguard privacy, and ensure resilience. As challenges like missing data, system outages, or other unexpected conditions arise, businesses must be able to adjust their systems at speed. Addressing this confluence of factors will dictate whether organizations successfully integrate autonomous agentic systems into their value chains. 

These agents aren’t just tools, but rather catalysts for change capable of unlocking new levels of productivity, personalization, and innovation. The path forward is full of promise for those who are ready to embrace the next chapter of AI-powered business operations. As humans and machines continue to collaborate, the possibilities are only beginning to unfold. 

Important Definitions 

Agentic AI  

Agentic AI refers to AI systems that can act and reason autonomously, collaborate with humans, adapt to changing environments, and use enterprise tools. These systems are designed to act with goals in mind, and are capable of making decisions, taking initiative, and carrying out complex tasks to achieve specific outcomes. 

Autonomous AI  

Autonomous AI refers to AI systems that can operate and process data without human interaction or oversight. These systems perform tasks independently and continuously learn from input data to become more efficient over time. 

Learn more 

• TechnoVision 2025 – your guide to emerging technology trends 
• Autonomous Agent Alliance – a new trend in We Collaborate 
• Voices of TechnoVision – a blog series inspired by �䲹�貵�𳾾��Ծ�’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. 

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.

”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 precision” – Dr Mark Roberts, CTO Applied Sciences, ��ѻ��ý Engineering”

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.   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: AI��������. 

The idea of deploying generative AI (Gen AI) in software for safety critical systems may sound like a non-starter. With , it’s hard to imagine it playing a role in the safety-critical or embedded software used in applications like automatic braking, energy distribution management, or heart rate monitoring.

Engineering teams are right to be cautious about Gen AI. But they should also keep an open mind. Software development is about much more than coding. Design, specification, and validation can collectively consume more time than actual coding, and here, Gen AI can significantly reduce overall development time and cost. It could even improve quality.

Incorporating Gen AI in safety-critical environments

Before we come onto these areas, let’s quickly address the elephant in the room: Gen AI coding. AI code generation for safety-critical software is not impossible, but it would need extensive training of the AI algorithms, rigorous testing processes, and will bring a lot of complexities. Right now, Gen AI should never directly touch a safety-critical line of code. But we should certainly keep an eye on it, as Gen AI code writing as it advances in other sectors.

However, other areas – from specification to validation – are ripe for Gen AI innovation. Our recent ��ѻ��ý Research Institute report, Turbocharging software with Gen AI, found that software professionals felt Gen AI could assist with 28% of software design, 26% of development, and 25% of testing in the next two years. In the report, one Senior Director of Software Product Engineering at a major global pharmaceutical company was quoted as saying: “use cases like bug fixing and documentation are fast emerging, with others like UX design, requirement writing, etc. just around the corner.”

Software design

Let’s consider how the software development journey may look, just a few years from now. Let’s say you are designing a control system for car steering, plane landing gear, or a medical device (pick a product in your industry).

Right at the start, you probably have a project brief. Your company or customer has given you a high-level description of the software’s purpose. Gen AI can analyze this, alongside regulatory standards, to propose functional and non-functional requirements. It will still need work to get it perfect, but it has saved you a lot of time.

However, you want to go beyond technical requirements and ensure this works for the user. Thus, you ask Gen AI to develop a wide range of user stories, so you can design solutions that pre-empt problems. That includes the obvious ones you would have come up with, Gen AI just writes them more quickly. But it includes all the weird and wonderful ways that future customers will use and abuse your product, ways that never would have occurred to a sensible software engineer like you.

In most cases, this is about improving the user experience, but it could also prevent disasters. For example, many of Boeing’s recent troubles , which led to two crashes. While the software was a technically well-designed safety feature, its implementation overlooked pilot training requirements and risks from sensor failures. This is the sort of real-world possibility that Gen AI can help identify, getting engineers who are laser-focused on a specific problem to see the bigger picture.

Armed with this insight, you start writing the code. While the AI doesn’t have any direct influence on the code, you may let it take a hands-off look at your code at each milestone, and make recommendations for improvements against the initial brief, which you can decide whether to act upon.

Test and validation

Once you have a software product you are happy with, Gen AI is back in the game for testing. This is perhaps one of its most valuable roles in safety-critical systems. In our CRI report, 54% of professionals cited improved testing speed as one of the top sources of Gen AI productivity improvements.

Gen AI can start the verification process by conducting a first code review, comparing code industry standards (eg. for automotive, for aerospace), to check for errors, bugs, and security risks. You still need to review it, but a lot of the basic stuff you would have spent time looking for has been sorted in the first pass, saving you time, and giving you more headspace to ensure everything is perfect.

Once you are satisfied with the product, you want to test it. Your Gen AI assistant can quickly generate test cases – sets of inputs to determine whether a software application behaves as expected – faster and more accurately than when you did it manually. This is already a reality in critical industries, as Fabio Veronese, Head of ICT Industrial Delivery at Enel Grids noted in our report that his company uses generative AI for user acceptance tests.

And, when you are confident your software product is robust, Gen AI can help generate the ‘proofs’ to show it works and will function under all specified conditions. For example, in the rail industry, trains rely on automated systems to process signals, ensuring trains stop, go, or slow down at the right times. Gen AI can look at data readouts and create ‘proofs’ that show each step of the signal processing is done correctly and on time under various conditions – and generate the associated documents.

In fact, as you progress through these processes, Gen AI can expedite the creation and completion of required documentation, by populating predefined templates and compliance matrices with test logs. This ensures consistency and accuracy in reporting and saves engineering time.

Automating processes

Gen AI can also help you automate many laborious processes that can be so mundane that human brains struggle to stay focused, thus creating the risk of error.

Take the example of the process used in the space industry for addressing software defects. When a defect is discovered, developers must create a report documenting this defect, develop a test to reproduce the defect, correct the defect in a sandbox, put the updated software through a verification process, reimplement the corrected code back into the main project, and finally test it in within the product.

A five-minute code fix may take hours of meetings and tens of emails. This is exactly the sort of task Gen AI is well suited to support. Any organization writing safety-critical software will have hundreds of such tedious documentation and procedural compliance processes. We believe (in some cases) that as much as 80% of the time could be saved in such processes by deploying Gen AI for routine work.

Don’t just take our word for it. Speaking to us for our report, Akram Sheriff, Senior Software Engineering Leader at Cisco Systems notes that, “One of the biggest drivers of generative AI adoption is innovation. Not just on the product side but also on the process side. While senior professionals leverage 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.”

Managing the risks to get the rewards

Despite all these opportunities, we must acknowledge that this is a new and fast-moving field. There are risks, including the correctness of outputs (Gen AI can hallucinate plausible but wrong answers), inherited risk from underlying models, and bias in training data. But there are also risks of not acting out of fear, and missing out on huge rewards while your competitors speed ahead.

Gen AI needs safeguards, but also a flexible architecture that allows companies to quickly adopt, test, and use new Gen AI technologies, and evolve their uses as needs demand.

In our report, we propose a risk model (see image 1). It states that any use of Gen AI requires (a) a proper assessment of the risks and (b) that – where mistakes could have serious consequences – you have the expertise to assess whether the outputs are correct.

For now, safety-critical code creation will fall into ‘Not safe to use’, because the consequence of error is high, and the expertise needed to assess the code would probably be more of a burden than starting from scratch. However, testing would fall into ‘Use with caution’, because it would provide valuable insights about software behavior, that experts can assess.

Finally, a key part of managing risks is comprehensive user training to understand how Gen AI works and its strengths and weaknesses. In our research, 51% of senior executives said that leveraging Gen AI in software engineering will require significant investment to upskill the software workforce. Yet only 39% of organizations have a generative AI upskilling program for software engineering.

There is a real risk of becoming overly reliant on, or trusting of, Gen AI. We must ensure that humans retain their ability to think critically about the fundamental nature of software and safety. Software engineers must be well-informed and remain actively engaged in verification and decision-making processes, so they can spot problems and be ready to step in if Gen AI reaches its limits.

In conclusion

While Gen AI won’t be building safety-critical software on its own anytime soon, it has the potential to enhance development, documentation, and quality assurance right across the software development lifecycle. In doing so, it can not only save time and money, and speed time to market, but it can even improve safety.

Companies like ��ѻ��ý can help shape achievable, phased roadmaps for Gen AI adoption. We guide organizations to integrate AI carefully, following sensible adaption and risk management frameworks and deploying appropriate training, ensuring both its potential and limitations are carefully navigated.

Download our ��ѻ��ý Research Institute report Turbocharging software with Gen AI to learn more.

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 �䲹�貵�𳾾��Ծ�’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. 

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 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 ��ѻ��ý 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.

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

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 ��ѻ��ý 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 ��ѻ��ý 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 ��ѻ��ý 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. ��ѻ��ý’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 ��ѻ��ý 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 ��ѻ��ý 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 .  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 ��ѻ��ý 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 ��ѻ��ý Research Institute report: Turbocharging software with Gen AI to learn more.

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

��ѻ��ý 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

��ѻ��ý Invent has played a pivotal role in shaping the EcoBeautyScore initiative since its inception in 2021. Acting in a startup-like, agile environment, the ��ѻ��ý 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, ��ѻ��ý 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, ��ѻ��ý 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, ��ѻ��ý 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.