Our long-term partnership with HEC Paris, a premium global business school, helps test our thinking and anticipate future trends, while mentoring the next generation of leaders through inclusion challenges.

At 乌鸦传媒, our commitment to inclusion isn鈥檛 just limited to our people, but extends to supporting the next generation of leaders, ensuring they can build the inclusive workplaces of tomorrow through key partnerships like our long-term collaboration with HEC Paris. 

As a business partner to HEC Paris, we鈥檝e mentored batches of 25 students for a Master鈥檚 course on diversity and inclusion (D&I) in the last three years. We help students understand our approach toward inclusion at 乌鸦传媒 and propose a team challenge, aligned with the latest D&I trends and topics. Students are then given six weeks to complete their research and present their final reports in our Paris office.  

Working with Matteo Winkler, Associate Professor at HEC Paris, who teaches courses on international business law as well as diversity and inclusion, this is an opportunity for us to connect with emerging talent, test new ideas, and anticipate trends while we mentor them through real-world challenges. 

In this blog, Matteo and I take a closer look at this year鈥檚 challenge and what we as practitioners can learn from it.

About the partnership

Matteo: When I created this course years ago as part of the Master of Management program of CEMS (previously the Community of European School of Management), which has now evolved into a global alliance of business schools of which HEC Paris is a partner. I looked for a company committed to inclusion, and who could help my students understand the challenges that D&I poses to both corporate leadership and day-to-day operations. 乌鸦传媒 felt like a natural fit.

The focus for the 2025 student challenge 

Karine: Each year, we set the students a challenge, designed to stretch their thinking 鈥� past topics have included understanding the role of ENGs as inclusion activators or discussions on the emerging debate on inclusion versus meritocracy, or whether DEI trainings should be mandatory or voluntary. The topic this year, 鈥�2025 鈥� a turning point for DEI,鈥� was set to encourage a focus on emerging trends, even before the US executive orders were issued in January 2025.

Students were briefed to provide a rational analysis of the global situation, factoring in opportunities and risks from a regional perspective, and drawing on concepts developed during Matteo鈥檚 classes, and fed with their own academic research. 

Matteo: We鈥檙e seeing a global narrative shift: before January 2025, we needed DEI corporate programs to not discriminate against minorities and people from non-privileged backgrounds. Particularly those who were unable to reach positions of power in a corporate setting. 

The Executive Orders in the US tell us the contrary; we now need to dismantle these programs 鈥� to not discriminate, following the 2023 Supreme Court ruling ending affirmative action in the US education system.  

In the US, the risks for companies who do not comply with regulations is financially high, compared to Europe. So the narrative has changed, and private organizations (if they have federal funding or not) are in many cases, abiding.  

This team assignment encouraged students to consider how global and local organizations continue to build diverse and inclusive places to work: where the definition of diversity, the collection of people data, work-related policies and benefits and more are directly impacted by local laws around the world. 

The findings from our student teams 

Matteo: As my colleague and panel member Marcelle Lalibert茅 has said, for major global companies, it鈥檚 as if you鈥檙e steering a global organization through rapidly shifting waters 鈥� with political tides in the US pushing back on diversity efforts, in Europe you鈥檙e facing regulatory currents that require you to provide details on pay gaps, and in the Middle East you may be aiming to embed global DEI standards into a local context. Against this backdrop, the student teams highlighted several risks:  

• Legal vulnerability 鈥� organizations are facing new legal and regulatory challenges pulling in different directions: from executive orders in the US rescinding diversity initiatives to the increased (and evolving) public reporting duties of the EU Corporate Social Reporting Directive (CSRD) and strengthened anti-discrimination laws in APAC.  

• Potential political and cultural backlash for company reputations, as they attempt to strike the balance between supporting marginalized groups and inclusion for all.  

• Slower progress on DEI goals 鈥� economic downturns means DEI budgets, roles, and overall progress is at risk, as companies prioritize cost-cutting. 

• Talent exodus 鈥� most job seekers consider workplace diversity important when evaluating companies, and may look elsewhere if organizations change their commitments.  

With D&I at a crossroads, the student teams identified some key opportunities in shaping and evolving global strategies: 

• Establishing a flexible DEI framework allows for region-specific modifications while maintaining a unified global commitment. 

• Skills-based hiring and inclusive leadership 鈥� continuing merit-based hiring and leadership development enhances diversity while ensuring compliance in restrictive regions. 

• Compliance as a competitive advantage 鈥� successfully delivering on ESG commitments can position a company as an industry leader in fair and inclusive practices and continue to attract talent. 

Key reflections for organizations like 乌鸦传媒 

Karine: The students navigated an extremely complex topic. Overall, the recommendations from the student teams followed broad themes for global organizations to consider: 

1. Reaffirm and communicate organizational commitment toward the diversity and inclusion ambition, as a fundamental ingredient of their identity and success 
2. Demonstrate agility, repositioning DEI initiatives to make them accessible for all and more engaging for all employees, and partnering with talent, health and safety, or wellbeing programs 
3. Use data-driven insights and employee feedback to guide actions and increase transparency on global and location actions and impact 
4. Leave flexibility for localized DEI initiatives and expertise under a global framework, strengthening leadership pipelines and fostering a culture of belonging through education and mentorship 
5. Build client-focused strategies to strengthen relationships, collaborate, and grow. 

The presentations from the teams were excellent. Inclusion in the workplace is critical for all businesses. We were delighted to hear their views on the complex situation in 2025, building their experiences and helping them to be ready to make an impact in their future. 

Together with HEC Paris, I look forward to another year of our continued collaboration!

Digital Twins & Smart Factories: The next evolution

At Siemens Realize LIVE 2025, we hosted a dynamic panel discussion with experts from AWS, NetApp, and Siemens to explore how cloud and AI technologies are reshaping automotive manufacturing. Led by , VP of Digital Continuity at 乌鸦传媒, the session offered insights into emerging trends, real-world applications, and the transformative potential of AI.

Generative and Agentic AI: The next frontier

The panel explored the future of generative AI and agentic AI, technologies that will revolutionize product design and manufacturing operations. These tools enable faster iterations, smarter automation, and higher-quality outcomes, especially in the automotive sector.

As , Senior Solution Architect at AWS, explained, these tools are transforming rigid, manual processes into adaptive, autonomous systems. Today鈥檚 challenges鈥攍ike coordinating changes across engineering, production, and supply chain systems often depend on sequential workflows and human oversight鈥攍eading to delays and errors.

Agentic AI changes this. It uses autonomous agents to manage cross-system processes in parallel, reducing bottlenecks and improving accuracy. Now more than ever, agentic AI is becoming integral to manufacturing鈥攆rom data and insights to self-optimizing systems that adapt in real time.

鈥淕enerative AI and agentic AI are changing the way we engineer products. From optimizing manufacturing tasks to improving product design, these technologies are making a significant impact.鈥�
鈥� Rex Lam, AWS

Real-world impact: Success stories from the field

Rex Lam, shared how cloud and AI technologies are transforming every part of the automotive value chain – from design and development to manufacturing, sales, and customer service. Key trends driving this transformation include software-defined vehicles, smart manufacturing, and engineering Innovation.

Rex shared impactful success stories that support the tangible benefits of cloud adoption:

1. Smart Manufacturing: Cloud platforms unify factory data to optimize production. AWS-powered cloud platform connects 120+ plants, aiming to cut factory costs by 30% and reduce supply chain waste.
2. Engineering Innovation:  High-performance cloud computing accelerates design cycles. AWS helped an automotive customer moved its engineering workloads to the cloud and saw a 66% increase in software speed, improved availability of compute resources, and enhanced collaboration. As a result, this customer was able to test new concepts and bring new designs to market more quickly.

These real-world solutions prove how cloud platforms can deliver powerful ROI in a short time.

Accelerating digital transformation with AI

, Head of Cloud Product Management at Siemens, spoke about the rapid pace of AI-driven transformation in the automotive sector.

鈥淭he automotive space is transforming to digital very quickly, from design to production and service. AI plays a major role in cutting development cycles and delivering internal efficiencies.鈥�
鈥� Dimitrios Dovas, Siemens

This shift is part of a bigger comeback in manufacturing. A recent 乌鸦传媒 Research Institute report shows that more companies plan to bring manufacturing closer to home 鈥� rising from 60% to 75% in the next three years. Global investments in modernizing factories are expected to grow from $3.4 trillion in 2024 to $4.7 trillion over the next three years.聽

“With onshoring and nearshoring of manufacturing set to increase significantly over the next 3 years, it is driving investments in reindustrialization initiatives. We see companies increasingly make investments in their digitization of manufacturing, intelligent automation, predictive maintenance & energy management initiatives.”
鈥� , Vice President Digital Continuity & Convergence, Group Offer Leader, 乌鸦传媒

Cloud agility in action

, Solutions Architect for AI, HPC, & Data Lakes at NetApp, emphasized how cloud infrastructure enables agility and continuous innovation. Jesse explained that in manufacturing鈥攚here 24/7 uptime is critical鈥攁 secure, scalable hybrid data architecture is essential. This architecture ensures operational continuity while unlocking access to cloud-based tools that drive innovation.

Jesse Lafer shares how cloud brings agility, on-demand access to the latest and greatest technologies, and lessons learned from how other customers have solved similar business challenges using technology.

One of these examples includes collaboration for globally distributed Siemens Teamcenter end-users. This was accomplished through NetApp鈥檚 data caching capabilities between multiple on-premises locations and AWS regions. Data was shared securely and consistently across multiple locations to support both Windows and Linux end-users.

鈥淚n the cloud, you’re always going to have access to the latest and greatest technology. The cloud brings agility, allowing companies to move much faster.鈥�
鈥� Jesse Lafer, NetApp

AI as a copilot for change

Digital Twins are quickly becoming a must-have tool in modern manufacturing. A 乌鸦传媒 study found that companies aim to boost system performance by 25%鈥攅ither by designing more efficient systems or improving operations. Adoption of Digital Twins is expected to grow by 36% over the next five years, powering smarter factories and better decision-making.

The session wrapped with a forward-looking discussion on how generative AI will support change management:

鈥淕enerative AI will help accelerate change, reduce errors, and drive innovation. It will act as a copilot, assisting humans in managing change more efficiently.鈥�

Partnering for digital continuity with Siemens

At 乌鸦传媒, we help businesses navigate their digital transformation journeys. Our deep expertise in cloud technology and AI enables us to deliver customized solutions that drive efficiency, innovation, and growth. Together with Siemens, we deliver end-to-end digital continuity through integrated business and IT/OT solutions. Our 20+ year partnership spans:
Software development

• Software development
• Requirements engineering
• Process control and instrumentation
• Advanced analytics and AI integration

Let鈥檚 drive the future together

It鈥檚 clear that cloud and AI technologies are unlocking new levels of efficiency and innovation in automotive manufacturing. Ready to accelerate your digital transformation journey? Connect with us to explore how we can help you lead the way.

鈥淓veryone 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鈥檛 just be a good idea, it鈥檒l be the defining factor that separates industry leaders from the rest of the competition.鈥� 鈥� 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 鈥� one that鈥檚 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 鈥� 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 鈥� 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鈥檛 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 鈥� your guide to emerging technology trends聽
• Autonomous Agent Alliance – a new trend in We Collaborate 
• Voices of TechnoVision 鈥� a blog series inspired by 乌鸦传媒鈥檚 TechnoVision 2025 that highlights the latest technology trends, industry use cases, and their business impact. This series further guides today鈥檚 decision makers on their journey to access the potential of technology.聽

The idea of deploying generative AI (Gen AI) in software for safety critical systems may sound like a non-starter. With , it鈥檚 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鈥檚 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 鈥� from specification to validation 鈥� are ripe for Gen AI innovation. Our recent 乌鸦传媒 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: 鈥渦se cases like bug fixing and documentation are fast emerging, with others like UX design, requirement writing, etc. just around the corner.鈥�

Software design

Let鈥檚 consider how the software development journey may look, just a few years from now. Let鈥檚 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鈥檚 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鈥檚 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鈥檛 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 鈥� sets of inputs to determine whether a software application behaves as expected 鈥� 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 鈥榩roofs鈥� 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 鈥榩roofs鈥� that show each step of the signal processing is done correctly and on time under various conditions 鈥� 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鈥檛 just take our word for it. Speaking to us for our report, Akram Sheriff, Senior Software Engineering Leader at Cisco Systems notes that, 鈥淥ne 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.鈥�

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 鈥� where mistakes could have serious consequences 鈥� you have the expertise to assess whether the outputs are correct.

For now, safety-critical code creation will fall into 鈥楴ot safe to use鈥�, 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 鈥楿se with caution鈥�, 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 乌鸦传媒 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 乌鸦传媒 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 鈥� You Experience.  

鈥淪patial 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鈥檙e turning towards next-gen technologies that carry the potential to drastically transform user experiences for the better.鈥� 鈥� 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鈥檇 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鈥檙e 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鈥檙e 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 鈥� resulting in technologies being viewed increasingly as partners as opposed to just tools. But that鈥檚 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鈥檚 time to embrace it.  

Learn more 

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