Gen Garage is redefining innovation by harnessing AI to build real-world solutions in areas such as disaster management, sustainable farming, and environmental risk mitigation

By fostering talent transformation and embracing cutting-edge technologies, we empower tomorrow鈥檚 professionals to shape the future through hands-on impact. Step into Gen Garage, 乌鸦传媒鈥檚 乌鸦传媒 & Data innovation hub 鈥� where visionary minds, guided by expert mentors, craft transformative AI-driven solutions at the intersection of talent and technology. Fueled by generative AI, machine learning, and automation, Gen Garage accelerates operational excellence and delivers innovations that drive efficiency, inclusivity, and sustainability. As businesses lean into data-powered insights, our solutions stay aligned with evolving needs 鈥� helping organizations stay future-ready, while making a difference today.

DisasterX: AI-Powered Faster and Smarter Response for Disaster Management

DisasterX is a cutting-edge AI-powered application designed to revolutionize disaster response by enabling real-time, adaptive decision-making in high-pressure environments. Leveraging agentic AI, DisasterX overcomes the challenge of delayed responses by autonomously analysing vast amounts of real-time data from multiple sources. This allows for optimized rescue efforts, efficient resource allocation, and enhanced recovery strategies. By continuously learning from past disaster scenarios, DisasterX improves prediction accuracy, minimizes human error, and accelerates response times鈥攗ltimately saving lives and reducing economic and environmental impact. Its autonomous capabilities ensure scalability and resilience, seamlessly adapting to both local and large-scale emergencies. As the rise of agentic AI reshapes automation, DisasterX stands at the forefront of intelligent, proactive disaster management, delivering greater efficiency and reliability in crisis situations.

Picture 1 : An AI agent (Image credit 鈥� Pixabay)

KisanGPT: AI-Driven 乌鸦传媒 for Smarter, Sustainable Farming

KisanGPT is an AI-powered platform designed to revolutionize farming by providing real-time insights on crop health, weather forecasts, and sustainable agricultural practices. Using advanced language models, satellite data, and weather analytics, it offers personalized recommendations to help farmers optimize yields, conserve resources, and tackle climate challenges. The platform supports multilingual access and speech recognition, ensuring inclusivity for farmers across diverse regions. By integrating market trends, government policy updates, and best farming practices, KisanGPT enhances decision-making, boosts profitability, and promotes eco-friendly agriculture. This AI-driven solution not only improves efficiency but also fosters a more resilient and sustainable farming ecosystem.

The Green Horizon: AI-Powered Vegetation Hazard Management

The Green Horizon is an AI-powered solution that detects and manages vegetation hazards near power lines, preventing wildfires, outages, and safety risks. Using satellite imagery, machine learning, and weather forecasting, it provides real-time monitoring, predictive insights, and proactive risk mitigation. With an intuitive chatbot and geospatial analytics, it empowers organizations to optimize resources, reduce costs, and ensure safer, more sustainable infrastructure. By automating hazard detection and integrating user feedback, it enhances decision-making for utility companies and environmental agencies. This innovative approach not only improves operational efficiency but also supports long-term sustainability and infrastructure resilience.

Market Trends / Key Opportunities and Developments:

Gen Garage strategically aligns its initiatives with prevailing market trends to address pressing societal and business needs.

The increasing investment in AI for disaster management presents a significant market opportunity for DisasterX to deliver innovative and data-driven solutions. With the rise of smart cities and the widespread adoption of IoT sensors in disaster-prone areas, vast amounts of real-time data can be leveraged for predictive analytics and rapid response. Gen Garage is at the forefront of this transformation, utilizing AI to enhance disaster preparedness and resilience. As climate change intensifies the frequency of natural disasters, the demand for intelligent, automated response systems continues to grow, positioning DisasterX as a key player in optimizing disaster mitigation and emergency management strategies.

KisanGPT taps into the growing demand for AI-driven agricultural solutions. By leveraging real-time analytics and precision farming techniques, Gen Garage maximizes market opportunities, helping farmers and agribusinesses adopt smarter, data-driven strategies. With advancements in AI and increasing support for sustainable farming practices, the platform positions itself as a game-changer in modern agriculture, driving innovation and long-term growth in the sector.

The Green Horizon initiative taps into the growing need for AI-driven environmental risk management. By integrating geospatial intelligence and predictive analytics, Gen Garage maximizes market opportunities, enabling utility companies and agencies to adopt smarter, data-driven strategies for sustainability and infrastructure resilience.

Gen Garage is where innovation gets hands-on 鈥� and where emerging talent learns by doing. By combining mentorship with real-world problem-solving, we鈥檙e helping young professionals grow into AI changemakers while delivering solutions that matter. From climate-smart farming to disaster response, the Garage proves that AI for good isn鈥檛 just a concept 鈥� it鈥檚 a daily practice. The challenges may be big, but with the right mix of curiosity, code, and collaboration, we鈥檙e building something that lasts. Stay tuned in the next edition of the Data-powered Innovation Review for more recent cases!

Start innovating now 鈥�

Empower Future Talent

Get involved in innovation projects that enhance AI skills and leadership capabilities, preparing young professionals for real-world challenges.

Leverage AI for Social Impact

Adopt AI solutions to drive sustainability, inclusivity, and efficiency across industries, from disaster management to smart farming.

Stay Ahead of Market Trends

Engage with cutting-edge technology and AI-driven insights to maintain a competitive edge in an evolving digital landscape.

Interesting read? 乌鸦传媒鈥檚 Innovation publication, Data-powered Innovation Review – Wave 10 features more such captivating innovation articles with contributions from leading experts from 乌鸦传媒. Explore the transformative potential of generative AI, data platforms, and sustainability-driven tech. Find all previous Waves here.  Find all previous Waves here.

Climate change isn’t a distant threat鈥攊t’s a reality to deal with now.

Businesses need to rethink how they operate, especially when it comes to supply chains, which are crucial for global trade. Just like in the movie Interstellar, where survival depended on data, AI, and adaptability, today’s supply chains need to be flexible and smart to handle disruptions and climate challenges. AI-powered insights and actions are like the movie鈥檚 robot TARS: helping predict risks, optimize logistics, and reduce waste. Data ensures that every decision is as precise as a gravity equation. AI enhances precision in supply chains by analyzing vast data in real time, predicting risks, and optimizing logistics. It鈥檚 the key to transforming supply chains into smarter, greener, and more resilient systems that balance profitability with ecological responsibility.

Supply chains aren鈥檛 just stretched 鈥� they鈥檙e under siege. Disruption is no longer the exception; it鈥檚 the norm. That鈥檚 why resilience 鈥� the ability to anticipate, adapt, and recover fast 鈥� has shifted from nice-to-have to non-negotiable. A recent report from Institute delivers the reality check: 80% of organizations faced supply chain disruptions last year, most more than once. That鈥檚 an uptick despite better planning 鈥� proof that we鈥檙e still reacting more than we鈥檙e preparing. Meanwhile, sustainability pressures are mounting. With supply chains responsible for over 60% of global carbon emissions, according to the World Economic Forum, they鈥檙e no longer just operational engines 鈥� they鈥檙e climate liabilities too.

Let鈥檚 face it鈥攚hat we鈥檙e doing right now isn鈥檛 cutting it. The cracks in our supply chains are showing, and incremental fixes won鈥檛 be enough. It鈥檚 time for bold moves. If we want supply chains that can truly withstand shocks and stay ahead of the curve, we need to lean into smarter, faster, more adaptive solutions. That鈥檚 where AI steps in鈥攏ot just as a tool, but as a game-changer. With its ability to forecast disruptions, optimize operations, and accelerate response times, AI is shaping the supply chains of the future. To stay ahead, companies must embrace green supply chain management (GSCM), where sustainability is built into every step. AI supercharges this shift, turning GSCM into a smart, data-driven engine. From cutting carbon to driving circular economies, AI enables supply chains that are not just efficient, but truly green.

Resilience, Not Yet Autonomous: Supply Chains Still Heavily Rely on People

Supply chains are navigating a perfect storm: geopolitical instability, extreme weather, shifting consumer expectations 鈥� and growing uncertainty in global trade. Disruptions are no longer outliers; they鈥檙e part of the operating environment. While many organizations are embedding risk management into supply chain strategy, execution is still stuck in manual mode. Too much effort goes into collecting, cleaning, and stitching together data 鈥� leaving little room for insight, foresight, or speed. AI and machine learning are still underused, and critical response actions often rely on human intervention alone. The result? Slow reactions, mounting workloads, and talent focused on firefighting instead of forward-thinking.

What鈥檚 missing? Technology that doesn鈥檛 just capture and store data, but actively turns it into prescriptive insights and clear, actionable recommendations. Unfortunately, most tools in the market today still fall short of that promise. Instead, businesses are left stitching together manual processes and siloed teams to make sense of a rapidly changing environment. To build truly resilient supply chains, we need to shift from reactive, human-heavy models to intelligent, tech-augmented systems. The future isn鈥檛 about replacing people鈥攊t鈥檚 about empowering them with tools that amplify their decision-making, speed up response times, and free them to focus on what matters most.

Greening the Chain: How AI and Data are Changing the Game

Data and AI are at the core of this transformation, delivering unmatched insights, predictive accuracy, and optimization potential. By leveraging real-time data and predictive analytics, AI can identify potential risks鈥攕uch as supplier delays, extreme weather, or geopolitical issues鈥攂efore they impact operations. This early warning capability allows businesses to proactively mitigate threats through alternative sourcing, dynamic rerouting, or inventory adjustments. AI also enables scenario modeling, helping organizations test various disruption scenarios and build contingency plans with data-backed confidence. As a result, companies can maintain continuity, reduce downtime, and ensure customer satisfaction, even in the face of unexpected challenges. In today鈥檚 volatile global environment, AI is no longer a luxury but a critical enabler of resilient and future-ready supply chains.

AI-enhanced supply chain resilience framework

The AI-enhanced supply chain resilience framework strengthens supply chain agility and robustness by harnessing advanced AI technologies. It integrates real-time data from IoT devices into a centralized system for comprehensive analysis. Through predictive analytics and machine learning, the framework forecasts demand and detects potential risks鈥攍ike supplier disruptions or market shifts鈥攅nabling proactive risk mitigation and smarter decisions in areas like inventory and logistics.

AI-driven communication tools improve collaboration with suppliers and stakeholders, ensuring seamless, transparent information flow. Continuous monitoring and adaptive feedback loops allow the supply chain to respond swiftly to changing conditions, driving ongoing improvement and innovation. By adopting this framework, businesses gain end-to-end visibility, reduce vulnerabilities, and ensure operational continuity鈥攗ltimately building a more resilient and high-performing supply chain.

Leveraging AI enables businesses to streamline operations, improve efficiency, cut costs, and elevate customer experiences. One powerful application is demand forecasting, where AI analyzes historical data to accurately predict customer needs. This leads to smarter inventory management鈥攎inimizing overstock and stockouts while optimizing capital use. Another key use case is route optimization. AI-driven tools evaluate factors like weather, traffic, and transport costs to determine the most efficient delivery paths. This reduces time and expenses while ensuring faster, more reliable service that meets growing customer expectations.

How organizations can harness it effectively:

According to the , 55% of Forbes Global 2000 OEMs are projected to have revamped their service supply chains with AI and by 2026, 60% of Asia based 2000 companies will use generative artificial intelligence (GenAI) tools to support core supply chain processes as well as dynamic supply chain design and will leverage AI to reduce operating costs by 5%.鈥疶his鈥痵ignifies a widespread adoption of AI to improve efficiency and gain a competitive advantage in supply chain management. Further, Generative AI can be harnessed to monitor global events and proactively identify emerging risks. It can automatically generate risk assessments, simulate scenarios, and suggest strategic mitigation plans鈥攅mpowering supply chain teams to manage risks more effectively. Its conversational interface enhances user experience and accelerates response times. Over time, this evolves into a system-guided, data-driven approach, drawing from a rich library of scenarios and mitigation strategies to deliver contextual, timely responses to risk events.

Considering all of the facts

The fusion of data and AI isn鈥檛 just a tech upgrade 鈥� it鈥檚 a strategic shift for building supply chains that can bend without breaking. Organizations that embed intelligence into their operations now won鈥檛 just survive the next disruption 鈥� they鈥檒l lead the transition to greener, faster, more adaptive ecosystems. By 2025, global supply chains will be reengineered out of necessity and powered by innovation. AI won鈥檛 just help companies 鈥� it will help nations stay resilient, competitive, and climate-conscious. It will redefine how we make, move, and manage everything. And like TARS in Interstellar, the most effective systems won鈥檛 just follow instructions 鈥� they鈥檒l anticipate, adapt, and act as true copilots. What supply chains need now isn鈥檛 just visibility. It鈥檚 vision.

Start innovating now 鈥�

Give Your Supply Chain an AI-enabled Sixth Sense

• Plug your supply chain into real-time feeds鈥攆rom IoT sensors to storm trackers鈥攁nd let AI act like your all-seeing oracle. Spot trouble (like delayed shipments or political curveballs) before it hits the fan

Make Generative AI Your Strategic Co-Pilot

• Leverage Generative AI to generate real-time risk assessments, simulate disruption scenarios, and recommend mitigation strategies, all in a conversational interface

Build a Digital Twin鈥擸our Virtual Supply Chain Lab

• Think of it as a flight simulator for your supply chain. A digital twin lets you mirror operations in a virtual space to test 鈥渨hat-if鈥� scenarios鈥攆rom port delays to carbon constraints鈥攚ithout breaking a sweat in real life.

Interesting read? 乌鸦传媒鈥檚 Innovation publication, Data-powered Innovation Review – Wave 10 features more such captivating innovation articles with contributions from leading experts from 乌鸦传媒. Explore the transformative potential of generative AI, data platforms, and sustainability-driven tech. Find all previous Waves here.  Find all previous Waves here.

How robots that look like us are reshaping the workplace.

Once confined to science fiction, humanoid robots are stepping onto factory floors 鈥� not to replace workers, but to work alongside them. With the convergence of AI, robotics, spatial computing, and digital twins, enterprises now face a profound shift: automation with arms, legs, and reasoning skills. These human-shaped machines can adapt to existing environments, learn new tasks, and scale operations without disruption.

But the real breakthrough isn鈥檛 just technical, it鈥檚 collaborative. Humans, humanoids, and agentic AI systems are about to become one team. So how do we do it?

More flexibility, less disruption, better scaling.

Until recently, human-shaped robots 鈥� complete with a head, arms, and legs 鈥� were only speculative. But the first production models are now a reality from companies such as California-based Figure AI, which is building a factory expected to manufacture 12,000 humanoid robots per year. These will be game-changers for enterprises across multiple sectors 鈥� including manufacturing, life sciences, automotive, aerospace, defense, energy, utilities, and consumer products.

Robots and automation are nothing new on the factory floor, but legacy deployments involved purposebuilt machines and dedicated assembly lines. When manufacturing changes were required, a company would have to specify new robot designs and rebuild factories 鈥� which translated into significant financial investment and production disruptions.

Humanoid robots address these drawbacks by making the robot as adaptable as a human worker 鈥� capable of mimicking human gestures thanks to the rapid development of sensors and other hardware. What鈥檚 more, they are no longer just machines: humanoids are autonomous and adaptable physical reasoning agents equipped with cognitive capabilities. Artificial intelligence merges with robotics to take a physical form, making this the next big thing in AI and leading to the ultimate stage of automation on the shop floor.

Humanoids can be deployed to automate brown field operations without rebuilding, and humanoids can be easily integrated into existing industrial operations to perform undesirable, dangerous, labor-intensive, or repetitive tasks. Organizations can start small with a few humanoids working on focused activities, and then simply add more robots to scale up over time.

And when production needs change, companies will no longer have to retool. Instead, they can retrain both human employees and humanoid robots to undertake new operations in flexible workspaces.

Investigating the pathways to true convergence

That said, there鈥檚 work to be done to unlock the full potential of humanoids. These robots consist of an incredible package of sensors, controllers, motors, processors, and other hardware. At 乌鸦传媒鈥檚 dedicated AI Robotics and Experiences Lab, we鈥檙e exploring how to apply our expertise in agentic AI and LLM, computer vision, digital twins, data analytics, robotics, and sector-specific industrial processes to humanoid robots. Our goal is to help shape the technical convergence of human-centered, digital physical interactions between humans, systems of robots, AI-agents.

The hub of this convergence is a new virtual space in which most of the digital-human interactions will happen. In this space, unified and pre-trained data models, AI agents, edge AI, and digital twins merge with an interaction layer that leverages technologies such as real-time 3D and spatial computing. Data comes from an aggregation layer linking various data sources 鈥� including IS/IT/OT systems, sensors, and machines.

Agentic AI and physical AI then enable autonomous digital-physical interactions, with virtual AI-agents being able to act in the real world by interacting with AI agents housed in robots.

The challenge is how to best leverage technology enablers to unlock the productivity gains of humanoid robots with minimal operational disruption. To address this, 乌鸦传媒 is adding agentic AI-enabled decision-making capabilities to a network of interconnected digital twins that replicate a physical industrial environment. These enhancements apply several key concepts in robotics and AI research:

路 Vision language action models, which enable agents to understand and execute natural language instructions in a visual environment

路 Reinforcement learning and simulation to real transfer learning, which enable the sharing of knowledge from various data sources, such as videos or motion capture, to train in virtual environments and then translate into real-world scenarios

路 Teletraining, which enables a human to remotely control the agent to demonstrate desired actions

路 System 1 and system 2 reasoning models, which are dual-process architectures that mimic a human brain鈥檚 ability to both generate fast, reactive responses and engage in slower, deliberate planning.

Collaboration in action

On the factory floor of the very near future, 乌鸦传媒 envisions humans, AI-robots, and multi-agent systems working as a team to enhance operational efficiency, precision, and safety. Each participant will make important contributions to this, leveraging their unique abilities.

Human operators will bring their expertise and adaptability to bear upon the production process. Using spatial computing, they鈥檒l supply guidance and oversight. They鈥檒l also handle complex tasks that require fine motor skills and decision-making. And because humans can quickly identify and address unexpected issues, they鈥檒l provide a level of problem solving expertise that enhances automated systems.

Humanoid robots will ensure precise handling and placement of parts, reducing the risk of damage and ensuring consistency. They can also perform repetitive tasks quickly and accurately, increasing throughput and reducing cycle times. And by taking over potentially hazardous tasks, robots will enhance workplace safety by reducing the risk of injury to human operators.

Agentic systems will continuously monitor the operational processes and environment to ensure each step is executed correctly. They will identify deviations, alert the team, and suggest corrective actions to help maintain workflow integrity and prevent errors. They can also make real-time decisions about whether operations require additional action or reinforced quality inspection process. Eventually, robots driven by virtual agents will be able to perform some of these actions.

Hybrid workforces will address pressing challenges

Industrial organizations today face pressing global challenges, including the need to contain costs, attract and retain talent through better employee experiences, and improve sustainability. Emerging solutions that build upon ongoing digital transformations 鈥� such as a properly designed and executed strategy to enhance workforces with humanoid robots 鈥� are essential to address these pressures. Companies that move first stand to gain the greatest rewards.

Start innovating now 鈥�

Establish your foundation. Leading companies are already undertaking complete digital transformations of their operations. This is essential for deploying humanoid robots 鈥� so make completing these transformations a priority.

Don鈥檛 delay. Humanoid robots are here today, and will soon be deployed in real-world environments 鈥� across multiple industrial sectors 鈥� to provide early adopters with significant competitive advantages. Enterprises can鈥檛 afford to wait.

Support, don鈥檛 replace, humans. The best outcomes will be achieved if humanoids are deployed as part of teams that also include human workers and multi-agent systems. Now is the time to determine how each team member鈥檚 strengths can best be leveraged.

Interesting read? 乌鸦传媒鈥檚 Innovation publication, Data-powered Innovation Review – Wave 10 features more such captivating innovation articles with contributions from leading experts from 乌鸦传媒. Explore the transformative potential of generative AI, data platforms, and sustainability-driven tech. Find all previous Waves here.  Find all previous Waves here.

CPR is a tangible world 鈥� but Kushal Dastenavar says that when customers want it all, businesses need to take advantage of the cost, time, quality, and value benefits that digital can bring to the physical

Life is straightforward in consumer products and retail. Said no one, ever.

Because that鈥檚 never been true, has it? And these days, it鈥檚 not just the nature of the challenges in CPR that is daunting 鈥� it鈥檚 their scale, too, and the pace at which they鈥檙e moving.

Now more than ever, markets are global and constantly morphing. Customers know what they want and when they want it, and so organizations need to find ways to streamline processes and increase efficiency. Technology can help them develop new business models, but it can be difficult to identify strategies that genuinely add value 鈥� especially when customers are pulling them in opposite directions.

How are shoppers pulling two ways? By simultaneously seeking eco-friendly options and low cost. As a result, CPR companies are obliged to achieve and maintain a three-way equilibrium between sustainability, affordability, and quality.

Let鈥檚 get digital 鈥� and physical

It鈥檚 a difficult balancing act 鈥� but a practical way forward is to address these challenges holistically. It鈥檚 not a case of simply throwing tech at it, any more than it鈥檚 only about radically overhauling physical operations.

Instead, CPR organizations need to find ways to bring together these two elements 鈥� traditional engineering and digital technology 鈥� so they can redraw departmental lines; align specialisms; make traditional processes more efficient, accurate, and scalable; improve sustainability without compromising on quality or margins; and offer new products and services that take advantage of strengths in both the physical and digital worlds.

Real-world examples: here鈥檚 one鈥�

Let鈥檚 look at how these challenges are being tackled in the real world, and in particular how digital technology is being used to optimize physical performance.

A major multinational brewer has been looking to implement data and analytics transformation to achieve exactly this goal. In recent years it has introduced a number of different solutions across its infrastructure, all of which have been developed based on business needs and majority custom built. Some of them have grown in a non-homogeneous way, without a robust and standardized architecture, and with overlapping functionality. Furthermore, they have been costly and difficult to maintain, and present challenges to scale. What鈥檚 more, these disparate solutions provide information only, rather than a cohesive means of acting upon it.

All of this is why the company is now working with 乌鸦传媒 on the implementation of a CAP-cloud platform (based on Azure) to bring contextualized process data from its breweries, filling, and packaging lines into a consolidated manufacturing data platform.

This approach will provide access to reliable real time data that serves as single source of truth for the supply chain. It will enable the company to set targets and monitor business performance through KPIs; to develop multiple use cases at scale, leveraging AI/ML to improve industrial performance and sustainability. It will accelerate solution deployment with minimized effort and investment across a global manufacturing footprint 鈥� and it will be easier and more cost-effective to maintain and to embed improvement evolution.

鈥� and here鈥檚 another

A global CPR leader wants to optimize its manufacturing systems, reduce downtime, and increase platform efficiency. The challenges are considerable: there are more than 60 manufacturing applications supporting over 3000 manufacturing lines globally, providing a 24×7 support model with a product-centric approach.

The organization is currently working with 乌鸦传媒 on a comprehensive new digital manufacturing operations solution, encompassing global operations and multiple international centers of excellence. A detailed transformation road map has been created, covering all aspects of operations, technical upgrades, and business outcomes. We鈥檙e collaborating with key product vendors on a cost-optimized delivery model that harnesses the best tools and GenAI to deliver benefits that are expected to include a reduction of downtime worth $6-8 million; site reliability engineering (SRE)-driven AI operations to reduce downtime by 34%; standardized service operations and monitoring; and a 25% reduction in (mean time to recovery (MTTR), once again via GenAI.

Two key takeaways

There are two factors implicit in the examples I鈥檝e provided above.

The first of these is the usefulness of having an experienced partner. A knowledgeable solutions provider with a solid track record can provide valuable insights, best practices, and support during the transformation process.

The second key takeaway is the importance of a can-do attitude. CPR organizations working in collaboration with trusted partners can create a virtuous circle of energy and enthusiasm, maintaining and building momentum, and taking advantage of the power of digital to deliver lasting value in the physical world.

Major engineering and R&D-intensive (ER&D) businesses recognize the importance of combining digital and physical change. We recently published a report examining the views of ER&D leaders on the challenges they face and the solutions they propose. You鈥檒l find the report here

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

乌鸦传媒 Invent is proud to support the official launch of the EcoBeautyScore 鈥� 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鈥檚 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

乌鸦传媒 Invent has played a pivotal role in shaping the EcoBeautyScore initiative since its inception in 2021. Acting in a startup-like, agile environment, the 乌鸦传媒 team supported the design, launch, and scale-up of the EcoBeautyScore initiative, helping build the EcoBeautyScore consortium鈥檚 operational backbone, technical governance, and digital foundations.

From defining the consortium鈥檚 vision and recruiting global stakeholders to coordinating technical working groups, leading communication and branding efforts, and guiding IT tool development and prototyping, 乌鸦传媒 Invent has been at the heart of this three-year journey in collaboration with EcoBeautyScore partners.

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

Claire Lavagna, VP | Consumer Product Industry, 乌鸦传媒 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, 乌鸦传媒 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.

Increased competition means companies must understand their customers like never before. Using Agentic AI to harvest insights and drive marketing KPIs is game-changing, but marketers need the right plan to take full advantage of it.

Enterprises must continually improve their understanding of audiences and how to engage them to effectively respond to increasing competitive pressures. It鈥檚 critical for a firm鈥檚 marketing experts to take advantage of every tool to inform smarter decisions.

New, Multi-AI Agents can deliver the insights that drive winning campaigns, but marketing departments must be prepared to take full advantage of these powerful tools. It starts with the right roadmap and strategic technology partner.

Challenges for every marketing pro

In my conversations with Chief Marketing Officers, I鈥檝e identified several common goals for improvement. These include:

• Converting contact center data into valuable insights that help to design effective, customer-centric strategies
• Minimizing customer churn
• Using market intelligence to increase customer conversions

A company鈥檚 own data is an important source of the information required to help CMOs, Chief Experience Officers, and other marketing professionals achieve these goals. Unfortunately, legacy business intelligence systems often fail to deliver, for several reasons:

• Analytics systems rarely support strategic foresight and transformative innovation, instead providing business users with yet another dashboard.
• The results are often, at best, a topic for discussion at the next team meeting. This is not sufficient for a decision-maker to act upon immediately and with confidence.
• Systems typically fail to personalize their output to provide insights contextualized for the person viewing them, instead offering a generic, unsatisfying result.
• Systems often aggregate data within silos, which means their output still requires additional interpretation to be valuable.

In short, many legacy systems miss the big picture, miss actionable meaning, miss the persona and miss the point.

Based on my experience, I recommend an organization address this through multi-AI agent systems.

The Gen AI Strategic Intelligence System by 乌鸦传媒 bridges the gap between the old way, and a value-driven future. This system converts the vast amounts of data generated by each client across their enterprise, into actionable insights. It is agentic, meaning that it operates continuously and is capable of independent decision-making, planning, and execution without human supervision. The solution examines its own work to identify ways to improve it rather than simply responding to prompts. It鈥檚 also able to collaborate with multiple AI agents with specialized roles, to engage in more complex problem solving and deliver better results.

How would organizations potentially go about doing this?

A well-crafted plan for Agentic AI-powered insights

First, organizations must establish a clear roadmap to take full advantage of Agentic AI-enabled decision-making. This should align technology with business objectives.

It starts by identifying the end goals, the core business objectives and associated KPIs relevant to the marketing team. These are the basis upon which the team contributes to the organization鈥檚 business value. Strengthening them is always a smart exercise. The good news is that even small improvements to any of these KPIs can deliver enormous benefits.

The roadmap should take advantage of pre-existing AI models to generate predictive insights. It should also ensure scalability, reliability, and manageability of all AI agents, not just within the realm of marketing and customer experience, but throughout the organization. And it should be designed to leverage domain-centric data products from disparate enterprise resource planning and IT systems.

Finally, the roadmap must identify initiatives to ensure the quality and reliability of the organization鈥檚 data by pursuing best-in-class data strategies. These include:

• deploying the right platform to build secure, reliable, and scalable solutions
• implementing an enterprise-wide governance framework
• establishing the guardrails that protect data privacy, define how generative AI can be used, and shield brand reputation

The right partner delivers more than technology

Second, the organization must engage the right strategic partner 鈥� one that can provide business transformation expertise, industry-specific knowledge, and innovative Agentic AI solutions.

乌鸦传媒 uses its technology expertise, partnerships with all major platform providers, and experience across multiple industrial sectors to design, deliver, and support generative AI strategies and solutions that are secure, reliable, and tailored to its clients unique needs.

The solution draws upon the client鈥檚 data ecosystem to perform root-cause analysis of KPI changes and then generates prescriptive recommendations and next-best actions that are tailored to each persona within the marketing team. The result is goal-oriented insights aligned with business objectives, ready to empower the organization through actionable roadmaps for sustainable growth and competitive advantage.

*Applying agentic AI to customer experience

Here鈥檚 a use case that demonstrates the potential of an agentic Gen AI solution for customer experience.

A marketing department wants to leverage its contact center data to improve customer experience, boost operational efficiency, and manage costs. This requires a comprehensive view of contact center operations, including insights into customer interactions, interaction channels, and outcomes.

An analytics solution powered by agentic generative AI can deliver hierarchical views of customer service level and KPI metrics, conduct near real-time (NRT), around the clock trend analysis for service level agreements, highlight correlations between dependent KPIs for continuous improvement initiatives, provide early warning systems for emerging customer experience challenges, and enhance churn prediction.

The impact can include a 10 percent boost to upsell closure, and a 20 percent improvement to customer satisfaction. 乌鸦传媒 enables this use case through an AI CX insights 360 solution offered for the Gen AI Strategic Intelligent System by 乌鸦传媒.

Just imagine this agent working 24/7 on your behalf. They don鈥檛 sleep, they don鈥檛 get tired, they don鈥檛 take vacation, and they鈥檙e completely autonomous. 

Meaningful, actionable results  
With the right implementation and support, the potential benefits include better access to market intelligence, as well as significant opportunities for growth through cross-selling, up-selling, and capitalizing on both marketing white spaces and competitive insights. 

乌鸦传媒鈥檚 modeling suggests such a solution would accelerate the speed and rate of customer acquisition by 75 percent, while lowering the cost. It would also boost customer satisfaction scores by 20 percent and increase customer conversions by more than 50 percent.

Given the direct relationship between customer-experience excellence and revenue generation, those are meaningful advantages that cannot be ignored.

^*Results based on industry benchmarks and observed outcomes from similar initiatives with clients. Individual results will vary.

The Gen AI Strategic Intelligence System by 乌鸦传媒 works across all industrial sectors, and integrates seamlessly with various corporate domains. Download our PoV here to learn more or contact our below expert if you would like to discuss this further.

Harnessing Financial Operations for Smarter Cloud Transitions

Technology is transforming every organization and business, driving them to surpass economic development. Many enterprises are continuously adopting and shifting workloads to the public cloud, expecting numerous benefits such as flexibility, scalability, agility, and cost savings. However, with the myriad of options available for cloud adoption, there is also a risk of uncontrolled expenditure. It is quite common for enterprises to express that they are not receiving the benefits they anticipated from the shift from data centers to the cloud. The following section of this article delves into those key challenges in detail.

When an organization decides to move to the cloud, it starts with migration planning. Often, gaps in migration planning, inadequate assessments, lack of cloud-ready staff, complex designs, failed migrations, rework, and app or tool dependencies extend migration timelines beyond expectations. Businesses pay for their existing on-premises infrastructure while incurring new expenses for cloud migration, leading to a migration bubble. Additionally, migrating only a portion of the infrastructure while leaving other components on-premises prevents businesses from enjoying the full benefits.

Our practical experience shows that merely migrating workloads from on-premises or co-located data centers to the cloud is not enough. Regardless of the chosen hyperscaler, issues arise when clients overlook cloud best practices, leading to challenges in cloud governance and cost management. It is evident that many enterprises are still approaching cloud adoption with a data center mentality and are hesitant to embrace essential cloud features like autoscaling, on-demand provisioning, and self-service, which have the potential to drive significant innovation.

The shift from data centers to the cloud has also disrupted traditional procurement processes by empowering developers with greater purchasing authority. It enables engineers to spend company funds with just a click of a button or a line of code, bypassing the lengthy conventional procurement procedures including purchase requisitions, calling tenders, vendor scouting, and purchase orders.

Due to these challenges, monthly bills from hyperscalers can spiral out of control, extending the payback period for investments and negating the benefits of cloud transition. Therefore, it is crucial to develop a comprehensive migration strategy with operational governance controls to avoid potential pitfalls and adhere to cost optimization goals, commonly referred to as FinOps. This approach helps free up budgetary funds and accelerates the shift to the cloud. Enterprises must ensure their personnel are cloud-ready and have strong procedures to analyze expenditures and identify key cost drivers. Assessing available cloud resources is also advisable for optimization.

The primary goal of every organization is to lower technological costs, and the cloud is no exception. As companies continue to invest more in the public cloud, recurring cloud run costs will increase. This trend underscores the growing importance of FinOps as a recognized financial management discipline.

Life Sciences & Healthcare organizations are under unprecedented pressure. Against a backdrop of a growing and ageing population, and with care therapies, drugs and diagnostics becoming more complex and expensive, these industries must deliver personalized, cost-effective, and compliant solutions faster than ever.

At the same time, they face global disruption from geopolitical shifts, sustainability mandates, and increasing competition 鈥� both from digital-native new entrants and generic alternatives purchased by increasingly savvy consumers.

The sector has a dual challenge: increasing patient value, and bringing down costs 鈥� all within a heavily regulated and safety-conscious environment.

At 乌鸦传媒, we believe there are three critical ways for life sciences and healthcare organizations to meet these challenges: infusing operations with digital technology, upgrading legacy engineering systems, and building globally agile and resilient operations.

All three revolve around a central idea: transforming the digital and physical worlds together.

1. Infuse digital into the physical world

Life sciences companies have long dealt with sophisticated physical systems 鈥� complex manufacturing equipment, labs, and regulated medical devices. But much is also legacy-driven. Its many siloed, often paper-based systems create costs and hurdles, resulting in slower and inefficient go-to-market of critical drugs, devices and therapies.

Using digital technologies (AI, software, IoT, Digital twin etc) at scale can deliver improved ways of operating, faster time to market, and lower costs, whilst also delivering sustainability through reduced waste and energy.

Take connected factories and digital twins. These allow for real-time monitoring, simulation, and optimization of physical processes. Pharmaceutical companies can test and refine manufacturing changes virtually before deploying them in the real world, ensuring compliance and accelerating time to market.

Projects abound across life sciences which illustrate such transformation. An example is , a 鈧�20 million+ bioproduction initiative involving Sanofi, 乌鸦传媒, and others, which used micro-sensors, AI, and digital twins to enhance predictive control of bioproduction processes.

Digital also enables people to work together more efficiently. Data platforms and cloud 鈥� increasingly with built in supportive AI agents 鈥� provide spaces for scientists to collaborate across drug development silos 鈥� creating a digital feedback loop that can significantly reduce R&D cycles.

2. Upgrade core engineering

Many life sciences companies are constrained by aging infrastructure and fragmented legacy systems. These block innovation and cost-efficient operations.

While individual upgrades are fine, the key to unlocking transformation at scale is to streamline and standardize these systems, allowing new technologies to be easily integrated, whether digital or physical.

Predictive maintenance on the shop floor is one such example. 乌鸦传媒 delivered a predictive maintenance solution for a large global biopharma organization which reduced risk of human error by 80%, improving delivery and yield, whilst boosting asset and capacity utilization by 20%. But it was only possible because the correct data foundation had first been put in place to modernize legacy systems.

Standardization can also underpin more radical changes. Some legacy systems are so outdated, and local skills so hard to find, that companies decide to lift the entire function into a more optimized and smarter ecosystem.

This approach is often seen with activities like product sustenance. For global medical OEM leaders, it is a challenge to maintain and update their large portfolios of Class I, II or III regulated medical products, including lines that have been discontinued but still need maintaining. Our experience shows that a standardized engineering platform across the portfolio of products yields large-scale optimization efficiencies in managing and maintaining these regulated products. This standardized approach also allows such activities to be delivered from anywhere in the world 鈥� enabling easy outsourcing of cost centers.

Here again, the magic happens when digital solutions are applied to physical engineering 鈥� but this time in a whole new context, on the other side of the world.

3. Build agile, resilient operations around the world

Life Sciences companies are grappling with the fact that legacy, monolithic operations are less and less viable in this globalized yet geopolitically fragile world.

The modern world needs agility 鈥� allowing it to rapidly deploy products and services across regions, adapt to changing local regulations, and scale engineering operations quickly. This is vital for quickly getting products to the widest possible market, especially in uncertain environments.

One way to achieve agility is through smaller, distributed manufacturing sites and engineering hubs, strategically placed around the globe where they can be close to customers, suppliers, or talent pools, or where transport emissions are minimized. Such operations require an ecosystem of partners, and require digital solutions to manage, similar to those developed to manage global supply chains.

But agility isn鈥檛 just geographic, it鈥檚 operational. This is not just about physical manufacturing facilities, but centers of excellence which unblock barriers to innovation and production efficiency. Regulatory compliance, commissioning, qualification, verification, or process heavy activities are classic candidates. Employing dedicated specialist teams to deliver these functions, not only saves money but allows organizations to be faster and more agile.

乌鸦传媒 has pioneered a concept called Engineering Factories. These 鈥楩actories鈥� redefine traditional outsourcing. Each is designed around a specific engineering domain and business goals, such as delivering products at a specific cost or weight; or managing specific operations such as the supply chain, MES, sustainable product design; or delivering capabilities like compliance, validation, or quality assurance. Each factory combines a team of specialists and engineers with operational and digital expertise. They are transversal, working across industries to bring the best of all worlds synthesized together.

Consider a large-scale Manufacturing Execution System (MES) deployment. Normally, rolling out MES solutions across plants would be time-consuming, resource heavy, and associated with high costs and high stakes. In our experience, a focused MES Factory helps clients standardize processes and achieve faster results. For example, one global pharmaceutical company implementing a global MES solution saw a 75% reduction in quality review time and an 80% reduction in deviations.

A similar example is our Commissioning, Qualification and Validation Factory, based out of centers in Portugal and Morocco, serving highly regulated manufacturing sites focusing on compliance, and complex global regulations with a standardized and efficient approach. Another is the Intelligent Testing Factory, based out of India, that provides full lifecycle product management and intelligent testing for global medical device clients, including a human sample testing lab, ensuring global readiness and regulatory alignment.

Such a factory approach creates a centralized hub that can deploy new capabilities in a standardized, agile way, which is often accessed via a front office on the client site. The result is a more resilient, adaptive, cost-effective engineering organization.

Built for both worlds

Of course, these areas all overlap. A successful life sciences and healthcare organization could digitize its entire value chain to optimize digital and physical processes. This could provide a foundation to quickly upgrade operations or move business functions to centers of excellence that take advantage of high tech setups, cost reduction and global talent pools.

As the world changes around us, what sets successful organizations apart is their ability to operate fluently in both the digital and physical worlds. They will embed intelligence into every part of the product and production lifecycle, whilst shifting from isolated physical systems to joined up digital-physical ecosystems. They will quickly take advantage of cost savings and innovation opportunities, whether by optimizing operations at home, or delivering them elsewhere to take advantage of the benefits of smarter factory setups or favorable business and talent environments around the world.

All of this requires agile physical operations with a digital underpinning.