ÎÚÑ»´«Ã½ Norway /no-no/ ÎÚÑ»´«Ã½ Wed, 09 Apr 2025 07:59:48 +0000 en-US hourly 1 https://wordpress.org/?v=6.7.2 /no-no/wp-content/uploads/sites/23/2022/11/cropped-favicon.png?w=32 ÎÚÑ»´«Ã½ Norway /no-no/ 32 32 192805502 Trouble upstream? Identifying and stopping risk from the reporting layer /no-no/insights/expert-perspectives/trouble-upstream-identifying-and-stopping-risk-from-the-reporting-layer/ Fri, 04 Apr 2025 13:14:49 +0000 /no-no/?p=857036&preview=true&preview_id=857036 The post Trouble upstream? Identifying and stopping risk from the reporting layer appeared first on ÎÚÑ»´«Ã½ Norway.

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Trouble upstream? Identifying and stopping risk from the reporting layer

Harris Stevenson-Robb
04 Apr 2025

What is your regulatory reporting telling you about your upstream processes?

As with anything in life, you get out what you put in. Higher quality inputs lead to better outputs. The same principle also applies to firms’ regulatory reporting, upstream processes, and risk management.

Where trouble begins

In our experience working with clients on regulatory reviews, many reporting issues originate in upstream processes and risk management. These problems often begin at trade booking and ripple all the way down to what is reported to regulators.

Many firms operate across multiple booking and risk systems, including those for collateral cash management and valuations. This complexity often comes from business growth, where new or “bolt-on†systems are added to existing architecture, avoiding the need for updates in the short term, but causing more trouble down the road. Information barrier controls – designed to separate private-side MNPI from public-side activity – further fragment systems and data flows.

Firms that handle this well usually have a consolidated data lake. However, not all firms have that capability, or they haven’t done it in a way that supports effective regulatory reporting. Furthermore, there are multiple end users of this collated data and the reporting data is a really good gauge of how data sits ‘upstream’ and will be used by different parts of the firm. If the quality is bad coming into the reporting layer, it will likely be bad upstream.

Inconsistencies pass through the system

Regulators now look for signs of weak internal controls. Investigations often reveal inconsistencies in data lineage – from upstream bookings and risk systems to the final reported figures. Tolerance for these gaps is decreasing, especially when they impact the accuracy of reports.

It is critical for firms to have a clear view of their risk and positions across all systems, and regulators expect this visibility to be reflected in regulatory reports.

Investing in a review

With the wave of regulatory rewrites slowing in 2025, now is a good time for firms to invest in a review of operational processes. Taking a closer look at trade processing flows can reveal underlying issues that may be compromising report accuracy.

An investment in understanding your data lineage and looking at the design of your internal data models has a track record of delivering operational alpha and ROI. The development of an industry standard data model, the ‘Common Domain Model’ (CDM) presents an opportunity to upgrade your internal data models in a way that will offer efficiency gains in data transparency and interoperability with other industry participants. In addition to regulatory reporting, there are other regulatory benefits to ensuring smooth data flows, such as supporting the upcoming requirements of the EU and UK T+1 accelerated settlements cycle. 

 A number of recent fines have mentioned that fined firms are bringing in a third party to review processes – an approach that is viewed favorably by regulators. What we typically find is that firms with an accurate and reconcilable reporting data set often have smoother upstream operations. To promote excellent regulatory compliance in the long term, upstream processes deserve a careful look.

Meet the experts

Rory Lane

Director

Kitty Khamchanh

Portfolio Manager

Harris Stevenson-Robb

Manager

Paul Grainger

Portfolio Manager

    Related research and insights

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    Proven businessÌýstrategies for implementing trade promotion software /no-no/insights/expert-perspectives/proven-business-strategies-for-implementing-trade-promotion-software/ Thu, 03 Apr 2025 12:59:30 +0000 /no-no/?p=857024&preview=true&preview_id=857024 The post Proven businessÌýstrategies for implementing trade promotion software appeared first on ÎÚÑ»´«Ã½ Norway.

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    Stay out of the red: Proven businessÌýstrategies for implementing trade promotion software

    Charlie Boyns and Owen McCabe
    Apr 03, 2025

    The days of relying on price inflation for growth are over. Retailers and brands are shifting back to proven strategies, with trade promotions standing out as the most critical lever, especially when considering cost as a percentage of sales. When executed effectively, promotions can expand category usage, encourage repeat purchases, and maximize basket sizes. However, when executed poorly, they can erode value.

    In this article, we share our learnings on how brand owners can leverage advanced data analytics, trade promotion software (TPx) and other shopper engagement tools to stay out of the red and into the black, as the CPG industry enters a new cycle of category-led growth.

    Trade promotion spending is a significant cost item in the consumer-packaged goods sector (as much as 25% of gross sales), yet many companies fail to fully measure the effectiveness of their trade promotions.

    This is where trade promotion software can play a massive role in reducing wasteful spending, also known as Red Sales (using a traffic light system to vet which promotions drive real value).  With a predicted market size of $2.3 billion by 2026, promotion tools are a game changer in the industry – but only if implemented properly. Drawing on our experience deploying TPx solutions at scale, let’s explore what helps make these implementations successful from both an operational and business perspective.

    Key learnings in implementing a trade promotion tool deployment programme at scale

    1. Balancing autonomy with alignment

    To achieve the alignment required to drive the adoption of the trade promotional software, you must leave room for some autonomy from the teams on the ground. The effective execution of these programs relies heavily on balancing effort and empowerment across a variety of roles.

    From our experience, we found that the critical above-market roles that drive a successful trade promotion program include the following roles:

    Delivery Director

    Holds overall responsibility for the project’s successful execution.

    The Delivery Director must be firm but fair, serving as the highest authority within the programme. This authority is particularly valuable when the team needs a ‘bad guy’ to enforce difficult decisions – acting like a parent saying, “Sorry, but this is the rule,†and giving others a diplomatic way to deflect pushback.

    Product Lead

    Validates market needs and ensures adoption across the organisation.

    This product leads are the gatekeepers for change requests, balancing the needs of local markets with the requirement for global standardisation. We learned that their most important skill is stakeholder management, as they are consistently engaging with markets and aligning priorities.

    Deployment Lead

    Serves as the key point of contact between markets and Product teams.

    The Deployment Lead ensures the deployment plans align to the expectations of markets while adhering to the overarching goals. The Deployment Lead must be organised as they manage critical milestones, such as timelines, budgets, and deliverables, and oversees the User Acceptance Testing (UAT) phase as well as post-go-live support.

    Engineering Team

    Owns technical solution and defines what’s achievable with current infrastructure.

    A key learning for us was the importance of their counsel and the weight it was given from leadership – when they said “no,” it was a firm “hard-no,” and working with them to determine what was feasible became essential for success.

    PMO

    Coordinates across teams, manages risks and keeps everything on track.

    What we learned is that the PMO’s role cannot be underestimated and is as crucial to project success as any other role. They are the central point of the project, conducting the various workstreams and driving overall execution. They are the colleague everyone turns to for the latest information.

    2. No free passes: the ‘Bouncer’ approach

    One of the key takeaways from our experience was the importance of effective change management and a tough love approach. Scaling trade promotions globally introduces unique challenges, with markets varying in needs, regulations, and consumer behaviors. Markets frequently submit Change Requests (CRs) ranging from must-have issues to nice-to-haves. While aligning with the global vision is crucial, flexibility is key -some markets have stronger business cases for their changes, which allows them to secure larger budgets or have more influence.

    To better control this process, we implemented a ‘bouncer’ step before the review by the Change Control Board (CCB). At this stage, the Global Business Plan Product Lead acts as the first line of defence – much like a bouncer screening guests at the door of a club. Once approved, the markets meet with the ‘club owner’ at the bar, the Delivery Director, who makes the final call on whether the request is granted. If the process is designed well enough, this should be a bureaucratic step rather than a point of contention. This ensures that requests are legitimate, and markets cannot simply present exaggerated claims, such as cost savings from reduced staff, without providing actual evidence.

    3. Shared objectives and an ability to balance short-term KPIs with longer-term objectives

    One of the key learnings from our TPx implementations is that clients who embed shared objectives across functions into their performance management framework, such as OKRs, achieve faster and more successful adoption. In contrast, when targets are broken down into siloed KPIs without shared objectives, adoption tends to be slower and less effective.

    Another key learning related to “you get what you measure†is that the traditional short-term KPIs of sales uplift, margin maintenance, and in-period market share gain are not sufficient on their own to define a successful implementation.  Longer-term metrics allow for the success of the implementation to be evaluated objectively, offering a retrospective view on whether we have seen the expected decline in the percentage of Red Sales (margin dilutive) has occurred, along with an aggregate positive impact on the 6-12 month rolling average market share.

    AI raises the bar even further

    Integrating AI solutions into trade promotion platforms is the future – but it’s not a cure-all. It’s not just about the technology itself; it’s about building the right governance structure to ensure its success. Without proper oversight and alignment, even the most powerful AI solutions can fall short of their potential.

    1st key lesson: Start small

    Begin with a single market and use it as a pilot. While this may seem obvious, it’s surprising how often we’ve seen a proof of concept designed and then rolled out without being tested.

    2nd key lesson: Bring the markets along for the journey

    Although the pilot may focus on just one market, it’s not enough to develop a solution tailored solely to that market’s needs. The solution must be scalable and applicable across regions. Success hinges on aligning the technology with both local and global business objectives. This requires early collaboration with market teams to understand their unique needs and challenges.

    3rd key lesson: Managing expectations is essential

    When implementing AI solutions, third-party vendors often present bold promises about the technology’s capabilities. While these solutions can be powerful, it’s crucial to maintain a realistic perspective. While the market teams may be enthusiastic about the potential of AI and envision significant results, it’s the engineering team that must act as the gatekeepers. They ensure that the promises made by vendors align with the technical realities and the company’s existing infrastructure. This balancing act – between excitement about new capabilities and the limitations of what is technically feasible – is key to avoiding overpromising and underdelivering. Once the core processes are identified and understood, AI can significantly optimize trade promotions.

    So, is your trade promotion strategy protected by the best bouncer in town?

    Just like a great bouncer keeps the right people in and the troublemakers out, a well-governed trade promotion system ensures seamless execution and long-term success. The right mix of structure, governance, and expertise makes all the difference—not just during implementation, but in keeping things running smoothly long after go-live.

    At ÎÚÑ»´«Ã½, we bring together the right players, frameworks, and processes to ensure your trade promotion software is implemented effectively and managed with precision.

    Ready to transform your trade promotion strategy that works today and scales for tomorrow?
    Contact us now, and let’s start building the future together.

    Authors

    Charlie Boyns

    Management Consultant, ÎÚÑ»´«Ã½ Invent
    Charlie Boyns is a Management Consultant at ÎÚÑ»´«Ã½ Invent. With six years of experience in ÎÚÑ»´«Ã½’s Intelligent Industry practice, he specializes in large-scale technical implementations for the Consumer Packaged Goods (CPG) industry. Charlie has significant expertise in TP software implementation.

    Owen McCabe

    Vice President, Digital Commerce – Global Consumer Goods & Retail, ÎÚÑ»´«Ã½
    Owen is ÎÚÑ»´«Ã½â€™s Global VP for eCommerce. He previously led the Digital Commerce Practice at Kantar and held senior marketing and sales roles at both Procter & Gamble and Nestle. He has domain expertise in eCommerce, digital marketing, brand marketing, route-to-market strategy, and category management. Owen’s passion for digital commerce came about after a private equity assignment in an online travel business.

      The post Proven businessÌýstrategies for implementing trade promotion software appeared first on ÎÚÑ»´«Ã½ Norway.

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      Seven predictions for 2025 /no-no/insights/expert-perspectives/seven-predictions-for-2025/ Tue, 01 Apr 2025 12:54:43 +0000 /no-no/?p=857017&preview=true&preview_id=857017 The post Seven predictions for 2025 appeared first on ÎÚÑ»´«Ã½ Norway.

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      Seven predictions for 2025
      What’s hot in data, analytics, and AI?

      Ron Tolido
      April 1, 2025

      Peering into the future is a tricky business – especially in the ever-changing realm of data, analytics, and AI. But if there’s one thing we’ve learned, it’s that uncertainty never stopped us from trying. After all, we’re in a part of the technology profession where predicting the unpredictable is often part of the job description.

      We called upon seven of our data-powered innovation movers and shakers to dust off their (frozen) crystal balls and share their visions of what 2025 has in store. Their insights reveal a world where AI balances on the edge of legality, cloud platforms morph into something entirely new, and synthetic data booms with promise – and no, it’s not artificial hype. From “vertical AI†that digs deep into industry needs to conversational AI that knows what you want before you do, these trends give a glimpse of the fascinating, yet challenging, future of data and AI.

      Will their predictions come true? Only time will tell, but one thing’s for sure: 2025 is shaping up to be a year we’ll be talking about for a long time to come. And data and AI are right in the middle of it.

      Let’s dive in.

      AI is not a crime

      AI is not a crime, though sometimes it feels like one, given its swift advance beyond legal bounds. While identity theft, deepfakes, and media manipulation emerge from Pandora’s box, many AI ethicists focus on fairness and transparency, skirting around AI’s darker uses. As AI matures and criminal applications surge, discussions will inevitably shift from theoretical ideals to practical realities, especially when organizations face public lawsuits under new AI regulations. This shift will force experts to tackle how AI can harm society directly. So, while AI is not a crime, it certainly invites a compelling conversation about AI and crime. Let’s face it: When it comes to AI, the real crime would be ignoring the conversation altogether. – Marijn Markus, AI Lead, Managing Data Scientist, ÎÚÑ»´«Ã½ and Data, ÎÚÑ»´«Ã½

      Augment my governance

      Prepare to be captivated. AI agents are about to revolutionize data management in the upcoming year. They will shoulder burdensome data tasks, enabling companies to reach new pinnacles of productivity and efficiency. With AI seamlessly managing data collection, analysis, and access for us, we can finally focus on something much more crucial: getting value out of data for the business. It’s high time to achieve that, isn’t it? The future of data management is upon us, and missing the opportunity of augmenting is not an option. Because in this data game, those who augment govern – and those who don’t get governed. – Liz Henderson, Executive Advisor, ÎÚÑ»´«Ã½ and Data, ÎÚÑ»´«Ã½

      Cloud encounters of the third kind

      As we look towards 2025, cloud data platforms apply for a new round of transmitted change. We all recognize the need for high-quality enterprise data as a foundation for relevant, trustworthy AI. Add to that the need to adhere to regulations, data sovereignty, privacy sustainability and cost. It soon becomes apparent that a smart mix of different and diverse cloud approaches for data will play a crucial role in the upcoming year. I expect to see a pendulum swing towards larger investments in cloud data platforms, yet it will be clouds of a different kind. Or, to put it differently, the forecast calls for cloud cover – but with a whole new kind of silver lining. – Prithvi Krishnappa, Global Head of Data and AI, Sogeti

      Let’s talk better

      Conversational AI will continue to be a hot topic in 2025. Contact center transformation, leveraging “classic†AI and generative AI, will help save labor costs by billions and improve customer service significantly. These technologies can handle routine inquiries and provide instant responses, freeing human agents to deal with more complex issues. Imagine a world where you no longer have to press 1 for assistance – AI will anticipate your every need before you even know you have one. While human agents may become less central, customer satisfaction might reach an all-time high as AI enhances efficiency and personalization in ways we never thought possible. It’ll be the talk of the town in 2025. – Monish Suri, Global Google Partnership Lead, ÎÚÑ»´«Ã½ and Data, ÎÚÑ»´«Ã½

      When AI goes vertical

      We will see a major rise in domain-specific vertical AI solutions that are finely tuned through rigorous test-driven prompt engineering. These purpose-built AI models will deliver more reliable and precise insights tailored to the unique needs of their industries. For instance, in healthcare, imagine AI predicting patient outcomes like a crystal ball, analyzing vast datasets of medical histories and treatment plans to conjure better patient care and optimized resources. In financial services, AI will become the ultimate fraud-buster, identifying unusual patterns in real time and safeguarding assets with previously unseen precision and confidence. Vertical AI solutions will not only streamline operations but also spark innovation by providing industry-specific intelligence and efficiency. The only way is vertical! – Dan O’Riordan, VP, AI and Data Engineering, ÎÚÑ»´«Ã½

      The semantics of confidence

      We’ve seen many companies adopting the principles of data mesh and semantics as part of their modern data analytics platform strategy. If nothing else, it’s needed in 2025 and beyond to comply. For example, the EU AI Act requires close tracking of the purpose of AI models and the underlying data used to train it. This can only be done by enhancing data platforms with semantics, connecting original data sources, forged data products in AI models, and all business dashboards and AI-infused applications. It creates high levels of confidence in both data and AI, next to many new, innovative opportunities to leverage data. The endgame? Nothing less than a full digital twin of the enterprise, a hallmark of data mastery. – Arne Rossmann, Innovation Lead, ÎÚÑ»´«Ã½ and Data, ÎÚÑ»´«Ã½

      Synthetic data boom

      I predict a boom in synthetic data. But first of all, what is synthetic data? It’s artificially generated but realistic data that mirrors real patterns without using sensitive information. Why is synthetic data crucial? It tackles privacy, security, data scarcity, and control issues. Traditional data sources are hitting their limits. Privacy laws are tightening, and real-world data often lacks the diversity we need. Synthetic data lets companies create datasets that mimic real shopping behavior in retail or complex production processes in manufacturing without exposing sensitive info or being held back by data gaps. I foresee that 2025 will be the year synthetic data moves center stage. Companies ready to leverage it will build powerful, adaptable models faster than ever. The synthetic data boom will be anything but artificial. – Dinand Tinholt, VP, ÎÚÑ»´«Ã½ and Data, North America, ÎÚÑ»´«Ã½

      Interesting read?

      ÎÚÑ»´«Ã½â€™s Innovation publication,ÌýData-powered Innovation Review – Wave 9Ìýfeatures 15 captivating innovation articles with contributions from leading experts from ÎÚÑ»´«Ã½, with a special mention of our external contributors from, and.ÌýExplore the transformative potential of generative AI, data platforms, and sustainability-driven tech. Find all previous Waves here.

      Meet our authors

      Marijn Markus

      AI Lead, Managing Data Scientist, ÎÚÑ»´«Ã½ and Data, ÎÚÑ»´«Ã½

      Liz Henderson

      Executive Advisor, ÎÚÑ»´«Ã½ and Data, ÎÚÑ»´«Ã½

      Prithvi Krishnappa

      Global Head of Data and AI, Sogeti

      Monish Suri

      Global Google Partnership Lead, ÎÚÑ»´«Ã½ and Data, ÎÚÑ»´«Ã½

      Dan O’Riordan

      VP, AI and Data Engineering, ÎÚÑ»´«Ã½

      Arne Rossmann

      Innovation Lead, ÎÚÑ»´«Ã½ and Data, ÎÚÑ»´«Ã½

      Dinand Tinholt

      VP, ÎÚÑ»´«Ã½ and Data, North America, ÎÚÑ»´«Ã½

      The post Seven predictions for 2025 appeared first on ÎÚÑ»´«Ã½ Norway.

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      857017
      Fast tracking rail transformation /no-no/insights/expert-perspectives/fast-tracking-rail-transformation/ Tue, 01 Apr 2025 12:50:54 +0000 /no-no/?p=857008&preview=true&preview_id=857008 Learn how rail can combine digital & physical infrastructure to become more digital, innovative & sustainable, whilst also cutting costs & time to market.

      The post Fast tracking rail transformation appeared first on ÎÚÑ»´«Ã½ Norway.

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      Fast tracking rail transformation

      Sophie Vallot
      Apr 1, 2025
      capgemini-engineering

      How can the rail industry become more digital, innovative, and sustainable – whilst also cutting costs and time to market?

      Neither a wise man nor a brave man lies down on the tracks of history to wait for the train of the future to run over him.

      – Dwight D. Eisenhower

      The rail industry stands at a crossroads. Once the backbone of modern transportation, rail travel faces mounting pressures from all directions.

      On one hand, more people and goods than ever rely on trains to move them across the country, stretching networks to their limits, which demands expansion and rail infrastructure upgrades. At the same time, vehicle innovations could threaten rail’s long term growth prospects – as digitized and connected cars provide both comfort and privacy, and autonomous fleets offer cost-effective door-to-door freight delivery.

      Rail has many advantages. It is a fast and efficient way to transport large numbers of people and goods. It is the greenest form of long distance transport. And it allows passengers space and time to be productive. But if rail is to remain a preferred choice, it must evolve rapidly.

      Passengers expect seamless digital booking, reliable connectivity, and onboard services that rival airlines, and more and more their own cars. Freight companies want digital services to track and manage their goods. Trains need efficient modular designs and clean, quiet propulsion systems. Operators need to get more use out of existing (and often quite old) rail networks, all without compromising safety.

      But herein lies the challenge: these innovations must come at a time when the costs of rail – material, labor, maintenance – are spiraling. That leads to fares rising, potentially making rail less competitive. Both operators and OEMs are under pressure to reduce costs while keeping networks safe and reliable.

      What’s more, the pace of change must accelerate. The rail sector is understandably built around safety rather than speed. But it needs to become faster and more agile in embracing technological advancements – from hydrogen and battery-powered trains to real-time passenger apps – without, of course, compromising the rigorous safety standards upon which its reliability is built.

      Rapidly building a future-proof, customer-focused, and eco-friendly railway – all while cutting costs – seems a near-impossible balancing act. But new technologies, combined with new ways of thinking, could yet deliver.

      Bringing together the digital and the physical worlds

      Modernizing decades-old (sometimes centuries-old) assets and infrastructure will mean bringing together the digital and the physical worlds, in some cases merging 19th and 21st century technology. This, of course, needs to be carefully managed, navigating regulatory barriers, the sheer technical challenge of integration, and the need to keep assets operational as much as possible.

      In our recent Engineering and R&D trends report, we surveyed 300 senior decision-makers at global engineering and R&D-intensive companies, including (but not limited to) rail. Through their responses, we identified four overlapping strategic imperatives, each involving a mix of strategy, digital technologies, and modern skills to transform different parts of the physical organization. These range from the end product, to the processes that get it designed and made, to its environmental impact.

      We will look at how each applies to rail.

      1. Accelerate value through digital technologies

      Firstly, new digital technologies will directly transform the entire rail ecosystem, from rolling stock to signaling systems. These can deliver rapid and widespread value, from lowering costs via predictive maintenance and optimized train routes, to boosting customer numbers and generating new revenue streams through the onboard digital services.

      This change is underpinned by a raft of enabling technologies, from 5G, to AI, IoT, and software that will need to be deployed into trains and rail infrastructure, as well as the backend IT to join them up.

      These enable high value digital products and services, from smart ticketing for multi-modal journeys, to predictive maintenance that learns from data across the whole rail network. They underpin digital twins of complex systems that allow route optimization, virtual training, and low-risk simulations of ideas, from new rail routes to onboard services. And those are just two examples of the many ways digital technologies will transform rail.

      In our cross-industry survey, respondents said embracing digital technologies would deliver significant value to their organization, including reduced development and after-sales costs (51%), improved market share and revenue (50%), and improved customer experience and quality of the product (37%).

      However, it is unlikely they can do all this alone. Companies will need to access digital talent that they are not used to recruiting and may not be available near their current locations (access to talent was identified as a significant barrier to delivering these benefits in our survey). They will also need to access technology partners who can provide the tools of transformation – cloud, data capture and analysis, silicon chips, and so on.  It is, therefore, unsurprising that 59% said they planned to solve this by partnering with engineering service providers.

      2. Reduce core engineering costs without compromising quality or innovation

      Second, by modernizing traditional engineering practices, companies can achieve greater efficiency across their organization.

      Our survey found that optimizing core engineering to be more cost-effective was considered key to the future of engineering businesses. And, whilst reducing cost is the obvious benefit of core optimization (highlighted by 38%), nearly as many said it was essential to profitability, accelerating time to market, and as a competitive advantage.

      This makes sense, given that making and deploying trains and infrastructure is expensive and slow, and rail companies are under pressure to improve both.  A new rolling stock project tailored to specific train design requirements takes roughly five years from the initial design to qualification – the same as a car took twenty years ago. But automotive manufacturers have since compressed that to two years, largely thanks to digital design, development and testing. Rail could do the same.

      To deliver these cost and time reductions, rail companies must embrace new optimized design and production approaches for both new and existing product lines, like Design-to-X (design to weight, design to cost, to sustainability, etc.).

      They also need to cut costs and time by utilizing the latest efficiency-enhancing digital tools. These range from those that automate physical manufacturing processes, to virtual trial environments for evaluating systems without physical setups, to 3D simulations that provide location-agnostic employee training on new equipment, to exploring how Gen AI use cases can deliver future value.

      Such big technological transformations can be hard in existing factories with legacy systems and cultures. So, many companies are embracing a new type of innovative outsourcing, where designs of physical products are outsourced – not to a carbon copy of their factory in a low cost location – but to cutting-edge factories that can apply whole new approaches to making products more efficiently. These are designed around the latest technologies, located to embrace global workforces with the right skillsets, and built within collaborative ecosystems that ensure constant cross-fertilization of ideas between similar industries. In an era of rail transformation, this exposure to ideas from other digitalizing industries (and access to ecosystems of partners that can deliver such ideas to rail) will be critical to delivering rapid innovation.

      Again, our survey showed that industrial companies can’t do this alone and are exploring a mix of strategies to improve their core engineering, from in-house modernization programs (65%) to partnerships with engineering service providers (67%), hyperscalers (32%) and platform providers (51%).

      3. Reconcile business growth with improving the planet

      Third, while trains are the most sustainable mode of long distance transportation, companies are constantly seeking ways to become greener. This includes developing more eco-friendly product designs, alongside conversion to alternative power sources, like battery or hydrogen. In our survey, 54% said sustainability was key to maintaining market share, and 100% of respondents believed the sustainability imperative would transform their industry within a decade.

      These new approaches often require new capabilities outside of rail’s core skillset. Many are looking to partners to explore eco-design principles during bid and development phases, conducting life-cycle assessments to evaluate the environmental impact of rail components, and undertaking digital simulations and the design-to-X approach to optimize weight, carbon footprint, and energy consumption.

      This is an area where ÎÚÑ»´«Ã½ itself is investing. We aim to deliver a 90% reduction in carbon emissions by 2040, and learn hands-on lessons from this that we can deliver for clients. For example, our Energy Command Center leverages digital technologies to manage energy consumption across all our offices in India, and has provided valuable lessons in delivering emissions reductions at scale across diverse and distributed assets.

      4. Build an agile organization fit for a fast-changing world

      Last but not least, the rail industry’s reliance on physical assets (like rolling stock, signaling systems and infrastructure) makes it hard to quickly adapt to change. Nonetheless, rail must learn to thrive in a faster-paced world, where it will have to make more frequent upgrades to trains and infrastructure, from upgrading to clean propulsion systems, to adding new onboard services as new technologies appear. Many trains will need to operate across borders, networks, and company boundaries – to provide users with seamless journeys, even as trains move between different physical and digital infrastructure.

      Newer trains will become more modular, and with more standardized parts, to speed time to market. At the same time, they must be designed to make future upgrades easier, for example, through digitization of components so that trains can evolve through software updates to meet new needs.

      Doing this may need a change in thinking. Rail companies will have to embrace concepts, like rapid digital prototyping of new designs, and agile and automated approaches to rapidly trialing non-safety critical products, such as bookable onboard ‘workpods’ where commuters can join business calls in private. Some of this work might need to tap global talent pools to bring in new skillsets and ways of working.

      Companies with agile operations and access to flexible global talent pools will be better able to seize opportunities and adapt to change. Industry leaders in our survey recognized the need for more agile engineering practices, including improved responsiveness to market changes, and better access to global talent pools. Many of these ways of working may be new to rail and numerous respondents said they were using partners to improve agility, from outsourcing low value costs to free up resources (84%) to working with technology service providers to ramp up skills as needed (62%).

      A rail industry fit for the future

      Rail is a mode of transport that is efficient and sustainable, removing congestion from roads, reducing transport emissions, and giving drivers back the gift of time. Despite this, it must become more attractive to users – with more efficient, connected, greener, and more comfortable designs that fit around customer needs. And it must do all this whilst cutting costs.

      That will require new digital technologies to be applied across trains and rail networks, engineering teams and manufacturing and maintenance facilities, and backend IT. But it will also require organizations to think differently, bringing modern agile working practices into conservative and often siloed organizations.

      Our cross-industry Engineering Trends survey shows few established companies expect to do this alone – 71% told us that they intend to increase their use of outsourcing partners for engineering.

      The rail industry of the future will be run by those who successfully combine the digital and the physical. Rail companies hoping to play their part in that future will need to build an ecosystem of strong partners, who understand how to combine the physical and digital worlds, in order to safely deliver that transformational change. The sector’s success depends upon it.

      ÎÚÑ»´«Ã½ Engineering brings deep experience and access to ecosystems of partners, in both physical and digital domains, combined with long standing engineering expertise in rail engineering, rail digitalization, and other safety-critical industries. Contact us to discover how we can support your digital and physical rail transformation.

      Rail Infrastructure and Transportation

      Rapid urbanization combined with moves to sustainable transport point to increased demand for rail transportation linking major urban hubs

      Meet the author

      Sophie Vallot

      Vice President Rail Industry, ÎÚÑ»´«Ã½ engineering
      Graduate of Sciences Po Toulouse, Sophie’s professional journey spans over 20 years, across diverse sectors like Defense & Space and Automotive. An expert in addressing customers’ strategic business priorities, she brings a wealth of experience in industry transformation and has been making an impact at ÎÚÑ»´«Ã½ for nearly five years.

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        Trends in 2025 for Security and Justice /no-no/insights/expert-perspectives/trends-in-2025-for-security-and-justice/ Thu, 27 Mar 2025 07:49:32 +0000 /no-no/?p=857103&preview=true&preview_id=857103 The post Trends in 2025 for Security and Justice appeared first on ÎÚÑ»´«Ã½ Norway.

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        Trends in 2025 for Security and Justice

        ÎÚÑ»´«Ã½
        Vanshikha Bhat, Anne Legrand, Conrad Agagan, Nick James, Pierre-Adrien Hanania
        Mar 27, 2025

        A focus on justice reform, restorative practices, and addressing systemic inequalities is reshaping the way societies approach crime and punishment. Coupled with new threats posed by cybersecurity risks, geopolitical instability, and climate change, this will significantly impact both national security priorities and global cooperation in the coming years.

        Data has become a core strategic asset for organizations today and plays a vital role in improving our public safety, law enforcement and judicial systems. By collecting and analyzing available data, law enforcement organizations are making informed decisions, enabling them to detect and prevent crime. Additionally, data has a part to play in improving the efficiency of police and justice as well as enhancing the citizen experience. For example, data is the bedrock of assisted case management for citizen queries and is being used for predictive analytics in courts, whereby legal practitioners can use historic data to predict (and manage) outcomes.

        There is also a matter of how data should be shared. Initiatives such as the promote data sharing, not just within a department but also across borders. So, we are seeing the development of interoperable systems that allow for the seamless exchange of data between countries’ border agencies gaining pace, improving the flow of information about people and cargo across borders.
        At the same time, while data is an undeniable asset, to ensure its value, security organizations must use and protect their own data (and mitigate risks), as well as help other organizations with their data management security requirements. All organizations must balance the need to use data to improve decision making or outcomes while complying with privacy and security standards. The goal is to ensure that data remains both a strategic asset and a protected resource.

        The next generation of forensics is a multifaceted field that blends advanced physical and virtual methods. It utilizes advanced tools, techniques, and methodologies to address the challenges of modern-day investigations. These advancements are crucial as cybercrime becomes one of the fastest-growing criminal activities, with data breaches and digital fraud continuing to rise alongside traditional physical crimes.

        While traditional techniques remain important, the rise of cybercrime, advanced data storage, and complex digital evidence requires continuous adaptation of tools, techniques, and strategies. According to a 2023 report by Cybersecurity Ventures, global cybercrime costs are expected to reach year, up from $3 trillion in 2015. The integration of AI, cloud computing, mobile forensics, and data recovery tools is reshaping how law enforcement and investigators approach crime-solving in an increasingly digital world.
        In the age of generative AI (Gen AI), biometrics are becoming more sophisticated, combining traditional biometric modalities with advanced AI techniques to create more secure, accurate, and user-friendly authentication systems. Using biometric and digital identity systems, governments are developing stronger cybersecurity measures to prevent data breaches and unauthorized access.
        Facial recognition technology is another rapidly growing component of physical forensics. The global facial recognition market was valued at and is projected to grow at a 17.8% CAGR from 2023 to 2030. According to Statista, facial recognition systems were used in over 60% of law enforcement agencies in 2023 worldwide for identification and investigation.
        The implementation of (automated fingerprint identification system) has revolutionized fingerprint analysis. As of 2023, there were more than 500 million fingerprint records in AFIS databases worldwide. The use of AFIS systems is now standard in most countries, and their accuracy has improved significantly with AI and machine learning algorithms, reducing human error and increasing identification speed.

        New technologies continue to shape the future of law enforcement, enhancing crime prevention, improving investigation efficiency, and ensuring better accountability and public safety. These technologies range from AI-driven analytics to advanced crime detection tools and digital forensics.

        Reports also suggest that the will grow from US$ 12.02 billion in 2023 to US$ 99.01 billion by 2031. This is driven by applications such as predictive policing, facial recognition, crime pattern analysis, risk profiling and AI-assisted investigations.
        Predictive policing using AI: AI is being used to analyze patterns in historical data, social media activity, weather, and other factors to anticipate where and when crimes are likely to occur. For example, US police agencies use AI-driven predictive policing tools like PredPol, HunchLab, and Palantir to forecast crime hotspots and resource allocation. The AI in predictive policing is expected to grow by
        Smart policing: Connected devices (e.g., sensors, smart vehicles, and wearable technology) are enabling real-time data sharing and smarter resource deployment.
        Cybersecurity & fraud detection: AI is increasingly used for detecting financial crimes, such as money laundering and fraud. The global market for AI in cybersecurity of US$ 25.35 billion in 2024 is expected to grow at a.
        AI-powered decision making: AI can be used to analyze large datasets, such as travel records, biometric data, and other intelligence sources, to identify trends or patterns indicative of illegal immigration, human trafficking, or drug smuggling. This allows for more efficient decision making, improving how border agents allocate resources.
        AI for risk profiling: AI systems can use historical data, behavioral analytics, and patterns from databases like Interpol or FBI records to predict potential risks. For example, AI models can assess whether an individual is likely to engage in illegal activity based on their travel patterns, previous encounters with law enforcement, or other risk indicators.
        While the added value of such technology is clear, ethical standards will be key to assure compliance with frameworks, such as the EU AI Act. Security and justice organizations have started to look at solutions and organizational set-ups, especially as their use cases can often fall under the scope of high-risk categories. Tools exist, such as ÎÚÑ»´«Ã½â€™s EU AI Act Compliance platform, and a more programmatic approach is being developed, helping polices and home affairs ministries to monitor where they stand in regards to compliance with explainability, human supervision or bias detection standards.

        Countries worldwide are grappling with border security concerns, seeking ways to combat illegal immigration, drug smuggling, and human trafficking. Many are leveraging technology to improve the efficiency, security, and management of border control processes. However, all of this requires a coordinated, multi-faceted approach that combines advanced technologies, effective policies, and law enforcement capabilities. Border agencies must also focus on balancing humanitarian concerns with national security and border management.

        Technologies such as drones, sensors, AI, facial recognition, and advanced detection systemsoffer border management officers powerful tools to enhance their capabilities, while policy reforms and collaborative strategies help address the systemic challenges of illegal immigration and drug trafficking. The following strategies and technologies are among those playing a vital role in addressing these challenges:

        • Biometric identification and digital identity: Biometric systems are being used at border crossings to identify individuals and verify their identities. These systems can be integrated with international databases to track those attempting illegal border crossings. Some countries are implementing digital identity programs that allow travelers to authenticate themselves securely using biometric data on smartphones or other digital platforms, making it harder for migrants to cross illegally using counterfeit documents.
        • Cybersecurity and data protection: As more data is collected and shared across borders (e.g., biometric data, travel information, migration records), securing this sensitive data becomes crucial. Robust cybersecurity protocols and privacy regulations are necessary to prevent misuse or exploitation of personal data while maintaining effective border security.Advanced fraud detection systems are also needed to identify fake documents, including forged passports, visas, or identity cards.

        The justice sector is increasingly using Gen AI technologies like natural language processing (NLP), predictive analytics, and machine learning. These technologies have the potential to improve efficiency, reduce costs, and even contribute to fairness in legal processes. AI is projected to improve court system efficiency, reducing administrative work by 40-50% and automating case management, with the legal AI market expected to .
        AI is also being used to improve access to justice by automating basic legal services. According to the American Bar Association (ABA), over cannot afford legal representation, and AI tools are helping fill this gap. Platforms like have handled millions of cases to date, providing free legal services to underserved populations.

        Law enforcement professionals face unique stresses and risks, such as exposure to trauma, long hours, dangerous situations, and the pressure of public scrutiny. Technology can play a pivotal role in addressing mental health issues and providing better mental health support, early intervention, and improving resilience in high-stress environments.

        According to the , 82 per cent of respondents had experienced feelings of stress, low mood, anxiety, or other difficulties with their mental health or wellbeing, the same rate as in 2022, but up from 77 per cent in 2020.
        Another highlighted several areas that require proactive measures from law enforcement agencies for the goodwill of police officials. Of note, 43 per cent indicated that excessive workload contributed significantly to their poor work-life balance and stress levels and 35 per cent reported that job-related stress affected their personal relationships and family life.
        Tech tools, such as wearables like smartwatches or biosensors, can monitor physical and physiological indicators of stress. AI tools are able to process data from wearables or mental health screenings to identify patterns that suggest an officer is at risk of mental health issues, such as PTSD, anxiety, or depression. Telehealth and self-help solutions can also play a vital role in managing the stress of the officers.

        Redefining future operations

        The trends shaping security and justice in 2025 reflect a complex interplay of technological innovation, social change, and global challenges. As advancements in AI, cybersecurity, and surveillance technologies redefine how law enforcement operates, the demand for accountability, privacy protection, and fair use of these tools will become more pronounced.

        As new threats emerge, from cyberattacks to the impacts of climate change, global cooperation and adaptive strategies will be essential for maintaining both public safety and human rights. The future of security and justice will be defined by the need to navigate these complex, interconnected issues, while ensuring that technological progress serves the greater good.

        Authors

        Vanshikha Bhat

        Senior Manager, Global Public sector / Industry platformÌý
        ” We at ÎÚÑ»´«Ã½ public sector help governments organizations across the globe in driving initiatives that address the diverse needs of vulnerable populations. Our involvement also aids in navigating complex processes, optimizing resource, and fostering innovation. We thrive towards enhances the impact and sustainability of government programs, positively affecting the lives of those in need.”

        Nick James

        Executive Vice President, Central Government and Public Security
        “To continue to be relevant, public security and safety agencies require better tools, data, and shared intelligence, available when and where they need them. Digitalization, cloud and real time communications are key enablers to achieving this, and are likely to be a key building block for future public security strategies.”

        Pierre-Adrien Hanania

        Global Offer Leader – Data & AI in Public Sector
        At the crossroads between citizenship, political action, and common values, artificial intelligence (AI) and data hold great treasures for the public sector if their full potential is realized for enhanced citizen services. By intelligently using data, public organizations will be able to augment their processes with automation and their decisions with insights, to the benefit of both public servants and citizens.

        Conrad Agagan

        CGS Account Executive for US Department of Homeland Security
        “As a retired career law enforcement officer who has dedicated 25 years of my life in helping secure the U.S. homeland, I feel very fortunate to now be in a position at ÎÚÑ»´«Ã½ that allows me the honor of continuing to work with the dedicated men and women of the Department in support of the mission.â€

        Anne Legrand

        Group Account Executive, National Security

          The post Trends in 2025 for Security and Justice appeared first on ÎÚÑ»´«Ã½ Norway.

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          857103
          Future of Engineering Biology: Trust matters /no-no/insights/expert-perspectives/future-of-engineering-biology-trust-matters/ Thu, 27 Mar 2025 07:38:54 +0000 /no-no/?p=856879&preview=true&preview_id=856879 How to develop Bio-Engineering solutions that demonstrate public value, while building public trust and understanding in the markets.

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          Future of Engineering Biology: Trust matters

          Kieran McBride
          Mar 27, 2025
          capgemini-invent

          There’s a crucial need to demystify bio-engineering solutions to demonstrate their value, gain public trust and understanding, and drive adoption and economic growth.

          The COVID-19 pandemic was a moment in history, when global governments, private institutions, academia, and society as a whole, coalesced around the need and to benefit global wellbeing. When a vaccine was produced and rapidly approved through extraordinary measures, the collective relief was palpable, but there was an unexpected problem.

          Public trust in science, representation, and a lack of bio-literacy around mRNA vaccine technology challenged the progress of the global vaccination program. Governments worldwide invested hundreds of millions in fighting disinformation about bio-engineering solutions.

          For a new innovative solution to be adopted, the people it serves must trust that it will work in their best interests, and trust that those developing solutions have their best interests at heart. In the case of such new and emerging deep-tech as bio-engineering solutions, with their implications to natural evolution, building trust and understanding is more important than ever.

          There are clear steps we can take to build this trust, to drive adoption of bio-engineering solutions and related socio-economic growth.

          Low levels of literacy and trust: policy makers, citizens, and markets must better understand bio-engineering solutions to unlock their potential

          Recent data from the ÎÚÑ»´«Ã½ Research Institute highlighted the growing divide between industry innovation and public understanding: 96% of companies reported active engagement with, or plans to develop, bio-solutions. And yet, research by the showed that 76% of UK respondents feel they lack sufficient information to make informed decisions about bio-based products. The same report found that 61% have never heard of engineering biology. Although these findings are UK-specific, similar trends are evident globally. For example, the CRI report also revealed that 65% of startups and corporations view low public bio-literacy as a significant barrier to adoption.

          The consequences of this gap are profound. One showed that decades-old public resistance to the distinct domain of genetically modified organisms (GMOs) shaped overly cautious regulatory frameworks that impede advancements in plant genome editing to this day. As a result, solutions to critical challenges, such as food security, continued to be delayed. More recently, hesitancy around mRNA vaccines has shown how a lack of communication can erode trust and create a vacuum for misinformation to grow. Without proactive public engagement, societally-valuable bio-based innovations such as bio-based plastics, precision farming, and sustainable fuels risk similar skepticism, potentially stalling their adoption. Governments and industries must act collaboratively, ensuring consumers feel like active participants in this journey, not passive spectators.

          Business and government: shared responsibility to unlock economic growth through engineering biology vision

          The challenge of bridging the gap between innovation in bio-engineering solutions and public understanding is a shared responsibility. It demands coordinated action from both businesses in industry and policymakers.

          Businesses must go beyond simply creating innovative bio-products – they should actively engage citizens and a broad stakeholder group throughout the development process by employing user-centered and co-design techniques. This not only builds trust and understanding with consumers but of course ensures stronger market fit and a greater chance of commercial success.

          To unlock the economic growth potential of bio-engineering solutions, policy makers also need to engage in similar user-centered and co-design techniques early in the policy making process through techniques like ‘Policy Labs.’ Policy Labs help rapidly map ‘policy whitespace’ where a new emergent theme or technology means there are few existing policy ideas or policies to build on. If policy teams become blocked due to a lack of understanding of complex new technologies, this prevents private markets from progressing due to a lack of support or legislation and regulation that unnecessarily blocks development. 

          The importance of Policy Labs

          Policy Labs are a proven technique to test early policy hypothesis, to ensure policy solutions will work for all stakeholders. For instance, if there’s to be a proposed investment by the Department of Environment Farming and Rural Affairs (DEFRA) into alternative proteins or GM crops, the farming industry should be engaged to test early policy hypothesis to see whether proposed solutions, services, regulations or legislations are likely to empower farmers with new economic means. Or disempower them by creating new competition in the markets or an even more complex post-Brexit funding landscape.

          A Policy Labs approach also ensures better cross-departmental working and inputs from the relevant government bodies that should be involved, across such a broad problem space. For example, in the UK, the Department of Business and Trade (DBT) or Department of Science Innovation and Technology (DSIT) might work with DEFRA to provide a better holistic approach and collaborative understanding to launching new solutions by data sharing agreements. Subject Matter Experts (SMEs) and private sector specialists in Engineering Biology could also be engaged through the policy labs process to ensure policy ideas are technologically viable, operationally feasible, and economically scalable in markets.

          Open and honest dialogue

          Governments should also be creating an ongoing dialogue with citizens to ask what assurances they need to feel confident about bio-engineering solutions (products and services) and how perceived risks can be addressed. Transparent, accessible communication mechanisms are key, alongside independent oversight to ensure safety and accountability. As the puts it: ‘Public engagement, improvement of public perception, and building trust are critical factors for the growth of the bio-economy and market.’

          Building a bio-economy strategy for the future

          Bringing together the preceding thoughts, to bridge the gap between innovation and public understanding, and to accelerate bio-economy solutions, businesses and policymakers must adopt a collaborative, forward-thinking strategy. The following four actions are essential for the creation of a bio-ready society:

          Build public understanding through collaboration

          Businesses should adopt a user-centered approach to product development, building user research into all phases of the development process to ensure bio-economy solutions meet people’s needs. In addition to continual product and market testing, an ongoing engagement strategy should be developed to create a dialogue between industry and the public to educate people on the value of these new innovations. This ensures they are not simply launched on the market without a prepared soft landing. Ensuring ongoing dialogue with bodies, such as trade unions (national farmers union for instance) or user testing communities, to continually test product development ideas and ensure they are viable. To create a true product to market fit.

          Policymakers must embrace a Policy Lab approach to ensure industry, citizens, SMEs, regulators, and other relevant departments are involved in collaboratively shaping the future of bio-innovation. By testing early policy assumptions with the people these policies impact, ensures that the resulting services, legislation, and regulation will work for all, while preventing bad policy (and resulting regulation) from restricting economic growth and costing the public purse.

          Clear and accessible communication and campaigns about safety measures, sustainability practices, and regulatory standards are critical to building public trust. Transparency must be paired with independent oversight to reassure citizens that risks are identified and mitigated responsibly. Policymakers and businesses should collaborate on creating streamlined regulatory pathways that eliminate unnecessary barriers while maintaining high safety and sustainability standards. Behavioral change and public awareness campaigns can also be developed in collaboration to inform or drive people to adopt new products and services.

          A call to action for bio-economy solutions

          Engineering biology has the potential to solve some of the world’s most pressing population scale challenges, from climate change to healthcare. But without better collaboration between governments, industry, and the markets they serve, progress on bio-engineering solutions will stall. By employing co-design both in the product development and policy making lifecycles, we will build better understanding and trust among stakeholders and citizens, while enabling policymakers and businesses to ensure bio-solutions not only innovate but also meet public needs.

          At ÎÚÑ»´«Ã½, we provide end-to-end support in Engineering Biology and AI for Science strategy, helping public and private sector clients accelerate bio-solutions while addressing the challenges outlined here. From fostering public bio-literacy and engaging citizens to building transparency into innovation and driving growth through regulatory collaboration, we leverage our insight drawn from practical laboratory bio-engineering, to advise our clients on delivering impactful, trusted, and scalable outcomes. Together, we can draw up a bio-economy strategy that maximizes public trust in the solutions that will shape the world for years to come.

          The time is now to act. Together, we can do more than just create bio-solutions; we can create a bio-ready society. Get in touch to explore how we can help accelerate your engineering biology journey.

          Engineering Biology

          Engineering biology is an emerging discipline of biotechnology with disruptive potential across all industries.

          Authors

          Richard Traherne

          World Economic Forum Bioeconomy Steering Group Member, ÎÚÑ»´«Ã½ Invent

          Dr. Cassandra Padbury

          Associate Director, Technology Strategy at Cambridge Consultants, part of ÎÚÑ»´«Ã½ Invent

          Kieran McBride

          Head of Public Sector & Policy Labs proposition, frog, part of ÎÚÑ»´«Ã½ Invent

          Bill Hodson

          Consulting Director at Cambridge Consultants, part of ÎÚÑ»´«Ã½ Invent

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          The post Future of Engineering Biology: Trust matters appeared first on ÎÚÑ»´«Ã½ Norway.

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          The FinOps evolution: Embracing on-demand technology for financial efficiency /no-no/insights/expert-perspectives/the-finops-evolution-embracing-on-demand-technology-for-financial-efficiency/ Tue, 25 Mar 2025 08:30:17 +0000 /no-no/?p=856733&preview=true&preview_id=856733 Cloud FinOps is evolving beyond cost management. Discover how the shift to On-Demand technology FinOps unlocks greater business value and empowers data-driven decisions.

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          The FinOps evolution
          Embracing on-demand technology for financial efficiency

          Jez Back new
          Jez Back
          Mar 25, 2025
          capgemini-invent

          The jump from cloud FinOps to on-demand consumption FinOps is underway, it’s time to embrace the evolution and unlock greater business value.

          Cloud FinOps was built around utilizing the public cloud to manage financial investments and has become a standardized part of today’s technology landscape. However, FinOps cloud cost management is no longer enough – it’s time to evolve. To do so, it’s worth considering what cloud FinOps is currently defined as to better highlight our solution.

          The FinOps Foundation, a project to advance the community who practices FinOps, defines cloud FinOps in their Framework as: “an operational framework and cultural practice which maximizes the business value of cloud, enables timely data-driven decision making, and creates financial accountability through collaboration between engineering, finance, and business teams.â€1

          However, with the growth of on-demand technologies like SaaS and Gen AI, FinOps needs to go further. And with this evolution in business and technology comes a new focus on operating expenses (OpEx), particularly in how these technologies affect cost. Companies need to evaluate the value of OpEx spend, while FinOps need to underline the benefits.

          To put it simply it’s time to ask: What’s the cost of a click?

          It is undeniable that cloud FinOps has proved its worth both quantitatively and qualitatively in terms of cutting the fat out of technology investments. It’s driven elevated business value and has become a staple discipline across organizations.

          Still, there are a number of limitations that contemporary FinOps practices have run into. These primarily circle around common themes such as isolation and lack of strategic input. And with companies seeking to manage their OpEx spend, ensuring that FinOps and on-demand technology integration continues can become complicated by a lack of visibility among FinOps teams.

          Let’s examine these challenges in more detail. 

          Isolated FinOps Teams 

          There’s no denying FinOps teams do a great job in their areas of influence, however, those areas are often too limited. This is especially true when executive sponsorship wanes and other priorities take precedence. The focus on cloud FinOps seems to return only when a negative cost incident occurs, and this only further isolates these teams. 

          Low demand management influence 

          Even the most successful cloud FinOps capabilities are limited in their influence in demand management. Specifically, in how FinOps teams influence acquiring new technologies and resources to support the growth of the discipline. Sure, they can highlight cost implications and even help shape cost-effective solutions, however, they can rarely influence demand management directly – especially as part of a wider, end-to-end system.  

          Limited strategic reach 

          FinOps teams usually report to a technology leader who isn’t likely sitting in the C-Suite. Naturally, this limits the influence on strategic decision-making that FinOps has. This is a compounding problem, as other areas of the business group beyond the IT organization are driven by C-Suite priorities and bridging that gap is hard – if not impossible – in a large enterprise.  

          Isolated from other initiatives 

          To add to this gap, initiatives from senior leadership most often do not happen in concert with FinOps teams. The consequence can impact how architectural principles are re-designed, or result in frugal investments made in architecture that should support FinOps. It translates into a lack of FinOps integration with the wider business. The result? Unplanned or unexpected complexity later.  

          Singularly focused on cloud and public cloud services 

          In the current market, there is an ever-increasing variety of consumption-based technologies in use. FinOps teams who are only using public cloud are limiting the potential value they can generate. This in turn results in the web challenges we’ve described. It’s precisely the place where traditional cloud FinOps stumbles, and where exactly is that leading us?

          While the FinOps Foundation is widening their scope to include SaaS and AI, within the overall FinOps community, these concepts are still in their infancy. This is true both in terms of implementation but also integrating them into current FinOps practices.

          However, we believe this is a vital moment to start the shift towards acquiring new technologies and re-defining goals. This is reinforced by the fact that there is an increasing amount of tooling available to not only identify, but also to introduced automated optimization of various FinOps processes. And with the continued adoption of machine learning and AI (which is already rolling out as AI for FinOps) – this will only accelerate.  

          Welcoming the new era of FinOps:

          Cloud Consumption On-Demand 

          From the very beginning, the rise of Cloud Consumption On-Demand in FinOps must consider all consumption-based technologies and business strategies. This includes everything from SaaS, Gen AI, AI infrastructure, and other cloud FinOps services. In turn, these technologies must be introduced to encompass the entire business value chain.

          To do this, FinOps teams need to be challenging traditional capital expenditure (CapEx) governance systems. In a world where a growing number of technologies are purchased under OpEx scenarios, this can be complex. In other words, FinOps teams must not only prove long-term financial benefits but provide a solid short-term business case that tangibly shows how Cloud Consumption On-Demand technologies create cost savings and better efficiency.

          It also means re-defining how FinOps is perceived. From their current periphery, FinOps teams must harness on-demand technologies to demonstrate their value with faster and automated solutions, reduced expenses, and elevated data-led decision making. This will help showcase the strategic value FinOps teams are creating.

          Additionally, FinOps teams need to underline that the challenges they face are business problems. It is not a question of managing IT systems or technologies. This is a matter of delivering better business value thanks to the benefits of on-demand technologies, such as accurate budgeting and elevated forecasting results.

          FinOps teams can also deepen collaboration across business units with on-demand technologies, For example, providing more flexible scalability that best fit the immediate business need. This can help keep various disciplines, like finance and engineering, connected and transparent in terms of financial accountability via integrated tools and platforms with accurate, real-time data sharing. 

          Transforming company culture

          While harnessing on-demand technologies and more modern FinOps practices are vital, it is education that will really make an impact. FinOps teams have the opportunity to transform a company’s culture by pushing for stronger education around on-demand technologies and their potential. Such a step is not only desirable but business critical. 

          These cultural practices that on-demand FinOps can create will help spark greater levels of integration across business lines. This translates into the reduction of silos across the organization. Ideally, the new FinOps will fully integrate with demand architecture, purchasing, on-demand technology, reporting, analysis, finance forecasting, and more.

          It’s time to evolve the FinOps framework 

          This new world of FinOps will play a far more active role. Powered by on-demand technologies, the discipline can escape the limitations that reliance on public cloud has introduced and in turn, generate greater value. Most importantly, it gives FinOps teams deserved recognition as fundamental players in modern business practices. 

          Do you know the cost of a click?

          With Cloud Consumption On-Demand, we can help diagnose the potential of FinOps has in store for companies, while supporting them with action plans to help them make it a reality.

          It’s time to welcome in the new era of cloud FinOps…

          … It’s time to know the cost of a click.

          Reference: 1.

          Cloud Consumption On-Demand

          Optimize costs and elevate the value of On-Demand technology across public cloud, Software as a Service (SaaS), and generative AI.

          Meet our expert

          Jez Back new

          Jez Back

          Cloud Economist & Global Offer Leader, ÎÚÑ»´«Ã½ Invent
          Jez is a subject-matter expert and global leader in Cloud Economics and FinOps with deep experience of cloud and digital transformations with over 15 years of industry experience. He has extensive knowledge of cloud computing strategies and business cases to form ecosystems that deliver innovation targeted at creating business value. Jez is a Certified FinOps Professional, who has regularly featured on TV, documentaries and podcasts as well as speaking events and conferences.

            Stay informed

            Subscribe to get notified about the latest articles and reports from our experts at ÎÚÑ»´«Ã½ Invent

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            856733
            Trends in 2025 for Public Administration /no-no/insights/expert-perspectives/trends-in-2025-for-public-administration/ Mon, 24 Mar 2025 05:30:42 +0000 /no-no/?p=856613&preview=true&preview_id=856613 The post Trends in 2025 for Public Administration appeared first on ÎÚÑ»´«Ã½ Norway.

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            Trends in 2025 for Public Administration

            ÎÚÑ»´«Ã½
            Ravi Arunachalam, Simone Botticini, Pierre-Adrien Hanania, Sandra Prinsen
            Mar 24, 2025

            The future of public administration lies in partnerships—not silos—with citizens, businesses, and civil society. In an era of rapid digital transformation, while the guiding principle of providing accessible, inclusive and high-quality public services remains fundamentally unchanged, the way public administrations are creating value for their citizens is undergoing a profound evolution.

            As technology evolves and societal challenges grow more complex and interconnected, traditional siloed structures are increasingly being replaced by dynamic ecosystems where value co-creation is critical to the success or failure of public interventions.

            In 2021, . Today, this approach has become a widespread foundational principle. Key technological enablers are driving this shift, empowering public administrations to move towards a collaborative approach of public service delivery that brings together governments, businesses and citizens to address challenges more effectively. From leveraging interoperability to dissolve boundaries and advance data-sharing ecosystems to the rise of GovTech, proactive service delivery and the transformative potential of government AI, these key trends are laying the groundwork for a smarter, more inclusive and efficient public governance designed to meet the demands of modern, interconnected societies.

            In today’s interconnected world, traditional boundaries in government at every level (local, state, national) are increasingly dissolving. This shift is driven by the urgent need for integrated, citizen-centric service delivery and the efficient utilization of resources. Governments are moving from siloed operations to a whole-of-government approach, where entities collaborate across jurisdictions to achieve shared objectives and provide responsive, efficient public services.

            At the heart of this transformation is interoperability. Governments are prioritizing interoperability principles to foster collaboration among agencies, sectors, and even across national borders. This requires the seamless exchange of data, systems, and processes, supported by a robust framework that addresses organizational, legal, semantic, and technical challenges.
            Around the world, interoperable services are reshaping public administration, showcasing the value of integrated public services:
            ¶Ù±ð²Ô³¾²¹°ù°ì—offers and consistently rank 1st in UN e-gov survey
            ´¡³Ü²õ³Ù°ù²¹±ô¾±²¹â€”delivers life-event-based services through
            ³§¾±²Ô²µ²¹±è´Ç°ù±ð—the integrates and provides a wide range of unified public services
            Many societal challenges today transcend national or jurisdictional boundaries. Issues like climate change, public health crises, rapid urbanization, cybersecurity threats, and migration & displacement require coordinated, cross-border interoperability efforts.Ìý To assist governments in their efforts, several interoperability frameworks are gaining traction:
            – : Established in 2017, the EIF provides guidance for EU member states to achieve cross-border public service integration. The Interoperable Europe Act (2024) promises to accelerate these efforts, mandating more rigorous interoperability initiatives (e.g. the Once Only Technical System).
            – Defined as interoperable and shared digital systems open for collaboration across public and private services, DPIs are gaining traction along their promise to enhance initiatives in the field of digital identity or wallets.
            – Currently in negotiation phase, DEFA emphasizes cross-border data flows, data protection, and cybersecurity. Once implemented, it is expected to transform digital collaboration within the ASEAN region.
            These efforts promise not only more efficient service delivery but also better preparedness for collaboratively tackling global societal challenges.Ìý ÎÚÑ»´«Ã½ is committed to helping our clients address the interoperability challenges to transform public services delivery within and across borders

            As EU President Ursula von der Leyen aptly stated, “Europe needs a data revolution,” highlighting the urgency for governments to harness data’s untapped potential. Governments worldwide are now reimagining how they share and leverage data, moving away from centralized data hubs toward decentralized, sovereign data-sharing ecosystems.
            Historically, centralized data hubs allowed limited collaboration due to agency concerns about losing control over their data. Today, data spaces, enabled by protocols and technologies that ensure sovereignty and security, are fostering new levels of trust and cooperation. These frameworks empower sector and cross-sector data sharing, facilitating innovation and improving public services.
            Supportive initiatives like the EU and open-source projects like act as catalysts, standardizing and enabling broader adoption of data spaces, both on the implementation and the governance perspective. Stakeholders such as the have been instrumental in formalizing these efforts, promoting the Data Spaces Protocol as a potential global standard for interoperability.
            The EU leads the way with its Common European Data Spaces initiative, creating sector-specific data ecosystems for health, agriculture, cultural heritage, and climate goals (Green Deal). These initiatives are already yielding results, such as the , which enhances cross-border healthcare and crisis response.
            Globally, interest in data spaces is growing.Ìý through its leading national data infrastructure research agency Australian Research Data Commons (ARDC), inspired by EU efforts.Ìý China, through its 2024-2028 National Data Administration Action Plan, aims to , driving an integrated national data market, while securely connecting with international partners.
            Data spaces are evolving from niche proofs-of-concept to broader ecosystems capable of addressing complex societal challenges. Still there are significant developments happening in the application of decentralized identity management, privacy-preserving technologies, and robust usage control mechanisms at protocol and technology components level.Ìý These developments will further enhance trust and accelerate wider adoption, while the existence of such privacy-enhancing techniques should skip the human part, along needed organizational change and stakeholder management. The rise of new roles such as the Chief Data Officer, the role of scoping phases, and a tailormade data collaboration approach along specific use cases and the culture of the organizations, remain key features of a successful journey towards sharing data.

            GovTech is no longer just a buzzword. It’s a revolution that’s transforming the way public administrations operate and deliver public services. What was once an afterthought relegated to IT departments, has now become a strategic priority of administrations worldwide. GovTech, defined as the public sector’s adoption and use of innovative technological solutions to improve public service delivery, is the key to achieving better social outcomes, digital inclusion, and improved public sector services.Ìý

            With government technology and become the largest software market by 2028, it´s clear that public administrations do not want to be merely passive buyers of innovation—they want to be innovative players themselves. Indeed, GovTech is not just about purchasing technology, it’s about co-creating value through partnerships. While legacy IT systems, siloed governance structures and traditional procurement processes that favor large vendors still pose challenges, public administrations are increasingly trying to overcome them by rethinking their engagement with the private sector, turning to public-private partnerships (PPPs) to tap into the creativity, agility, and expertise of startups and SMEs. These collaborations allow administrations to work with non-traditional players to co-create solutions, share risks, and scale innovations to improve service delivery. In this regard, a pivotal moment in the worldwide GovTech ecosystem came with the , a hub for collaboration and digital transformation.
            ÎÚÑ»´«Ã½ is proud to be a co-founder of this first-of-its-kind center, which brings together governments, startups, and private enterprises to accelerate the adoption of GovTech. GGTC promotes a systematic approach to GovTech, encouraging cross-sector collaboration and co-creation among global experts to tackle challenges like interoperability and siloed systems, ensuring that solutions can be shared across borders to benefit countries with fewer resources, helping bridge the digital divide.
            Looking ahead, and as exemplified by the GGTC, a strategic, systematic, and sustainable approach to GovTech will mark the new era of innovation for public administrations. As the GovTech ecosystem matures, public administrations will unlock new technological solutions, ensuring digital transformation is inclusive, scalable, and impactful across borders, all while being more agile, innovative, and responsive to digitally native societies.

            Digitally sophisticated citizens are demanding faster, seamless, and personalized digital services. Simply digitizing public services is no longer enough; public administrations must step up their game by adopting a human-centered approach, organized around citizens’ life events to proactively meet their needs.

            While digital public services have become more efficient and accessible, many remain . Challenges such as persist in many public administrations, along with the growing pressure to match the intuitive user experience and responsiveness of private-sector platforms. Moving public services online is insufficient; administrations must ensure that citizens can and will use them. Governments with lower service design maturity levels are only now moving beyond basic digitalization, while more advanced administrations are shifting from fragmented electronic services to proactive, fully integrated service delivery. This transformation requires systemic reforms and interagency collaboration to co-create Citizen Services that are human centered by design and informed by real-time user insights rather than outdated government silos. Meeting citizen expectations today means providing multi-service, omnichannel experiences that anticipate their needs, mirroring the seamless interactions they have with private-sector services.
            Some countries are already exploring proactive governance approaches, moving towards a truly , where services are seamlessly embedded into daily life. By leveraging data-driven insights, governments can determine eligibility and deliver services automatically, without requiring citizens to apply. For example, has been pioneering this transformation, offering bundled, proactive services that range from offering 18 housing services in just one platform to bundled services for hiring employees or saving families time and effort when a baby is born. This new reality extends public services’ reach to underserved populations, with the user-friendliness of private sector platforms. Citizens no longer need to apply or even be aware of service delivery, minimizing bureaucratic burdens while enhancing user satisfaction.
            Ultimately, the future of public service delivery is not just about making public services digital, it is about making them intelligent, integrated, and anticipatory. Achieving this vision requires breaking down silos and fostering strong partnerships across government agencies, private-sector innovators, and civil society to co-create data-driven services that proactively meet citizens’ needs.

            As citizen expectations rise, budget shrinks and workloads increase, AI has emerged as a powerful tool in the hands of public administrations to improve internal operations and deliver better public services. No longer a distant promise, and is now transitioning from experimentation to large-scale implementation, but challenges remain.
            Unlike with previous technological innovations, accessible, “democratic†tools like ChatGPT and GitHub Copilot have empowered civil servants to explore (Generative) AI’s potential from the outset. In countries like and the , trials of Microsoft 365 Copilot and RedBox Copilot have demonstrated significant time savings on tasks such as document summarization, information retrieval, and briefings creation. This allows civil servants to focus on strategic high-value work, improving their productivity and job satisfaction. This is in line with which show how GenAI could increase productivity by up to 45%, automating 84% of routine tasks across over 200 government services, ultimately driving a global by 2033.
            Beyond internal operations, AI is reshaping how administrations interact with citizens. Tools like chatbots and virtual assistants are improving transparency and fairness while creating more personalized, accessible, and inclusive public services. For example, the Generalidad de Catalunya in Spain partnered with ÎÚÑ»´«Ã½ to implement a GenAI chatbot for handling citizens’ queries in both Catalan and Spanish, reducing employees’ workloads and ensuring equitable access to services for all citizens. By incorporating human oversight to verify chatbot outputs, the AI-powered chatbot is driving efficiency and inclusion in public service delivery without compromising quality and trust.
            These early successes are just the tip of the iceberg for (Gen) AI applications in public administrations. Now, the challenge is no longer experimentation but scaling these innovations to embed them into everyday processes. Beyond automation, the true transformative potential of AI lies in applications such as AI-driven decision-support mechanisms and predictive governance, which will redefine how administrations function and serve citizens. This path is not without obstacles: data privacy, security and biases in AI outputs remain top concerns as administrations grapple with protecting citizens’ sensitive information while integrating AI into their systems. The solution lies in developing customized AI tools with built-in trust layers and guardrails that will prevent inaccuracies and biases. Here Catalonia’s approach, balancing automation with accountability, offers a model for building trust in (Gen)AI.

            Time for action in an increasingly interconnected world

            To fully harness the potential of these digital trends, public administration leaders must adopt an action-oriented approach. A combination of political commitment to digital transformation, inter-agency collaboration and leveraging robust PPPs to bridge resource gaps and accelerate innovation will be key. Together they will help to overcome budget constraints, siloed institutional frameworks, cultural resistance to change and complexities in measuring and reporting progress that still afflict public administrations worldwide. While strategically investing in cutting-edge technologies like AI, leaders must also champion a culture of continuous learning and upskilling among civil servants, ensuring they are equipped to leverage effectively these emerging tools. Ultimately, aligning digital strategies with citizens’ needs through human-centered service delivery will enable administrations to build trust, improve efficiency, and deliver meaningful public value in an increasingly interconnected world.

            Authors

            Pierre-Adrien Hanania

            Global Offer Leader – Data & AI in Public Sector
            At the crossroads between citizenship, political action, and common values, artificial intelligence (AI) and data hold great treasures for the public sector if their full potential is realized for enhanced citizen services. By intelligently using data, public organizations will be able to augment their processes with automation and their decisions with insights, to the benefit of both public servants and citizens.

            Ravi Shankar Arunachalam

            Public Administration & Smarter Territories SME – Global Public Sector
            “As a Public Sector strategist and technologist at ÎÚÑ»´«Ã½, I assist local, state, and federal governments worldwide in harnessing the full potential of a collaborative, Government-as-a-platform model to revolutionize citizen service delivery. With a deep understanding of industry challenges, citizen expectations, and the evolving technology landscape, I develop systemic transformation strategies and solutions that provide lasting value to both people and the planet”

            Simone Botticini

            Associate Consultant, ÎÚÑ»´«Ã½ Invent Belgium
            “Public administrations worldwide are undergoing a major transformation, driven by digitalization, evolving citizen expectations, and the move toward proactive, data-driven governance. By leveraging digital technologies, they can improve service delivery, streamline bureaucracy, and create more inclusive, citizen-centric administrations. ÎÚÑ»´«Ã½ is leading this transformation, helping public administrations harness the power of technology to enhance public services while ensuring trust, transparency, and security.â€
            Sandra Prinsen

            Sandra Prinsen

            Group Client Partner and Global Public Admin Segment Lead
            I work with our public clients to create a more sustainable, diverse and inclusive society, fueled by technology. The combination of this digital and sustainable transition offers governments the opportunity to navigate towards a society and a data-driven ecosystem that is ready for the future. That is why I am looking forward to think along in suitable solutions, to jointly make real impact in the lives of citizens.

              The post Trends in 2025 for Public Administration appeared first on ÎÚÑ»´«Ã½ Norway.

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              Mulder and Scully for fraud prevention: Teaming up AI capabilities /no-no/insights/expert-perspectives/mulder-and-scully-for-fraud-prevention-teaming-up-ai-capabilities/ Wed, 05 Mar 2025 07:12:23 +0000 /no-no/?p=852837&preview=true&preview_id=852837 The post Mulder and Scully for fraud prevention: Teaming up AI capabilities appeared first on ÎÚÑ»´«Ã½ Norway.

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              Mulder and Scully for fraud prevention:
              Teaming up AI capabilities

              Joakim Nilsson
              March 5, 2025

              While Mulder trusts his gut; Scully trusts the facts – in fraud detection, we need both. Hybrid AI blends the intuition of LLM with the structured knowledge of a knowledge graph, letting agents uncover hidden patterns in real time. The truth is out there—now we have the tools to find it.

              Fraud detection can be revolutionized with hybrid AI. Combining the “intuitive hunches†from LLMs with a fraud-focused knowledge graph, a multi-agent system can identify weak signals and evolving fraud patterns, moving from detection to prevention in real-time. The challenge? Rule sets need to be cast in iron, whereas the system itself must be like water: resilient and adaptive. Historically, this conflict has been unsolvable. But that is about to change.

              A multi-agent setup

              Large language models (LLMs) are often criticized for hallucinating: coming up with results that seem feasible but are plain wrong. In this case though, we embrace the LLM’s gut-feeling-based approach and exploit its capabilities to identify potential signs of fraud. These “hunches†are mapped onto a general ontology and thus made available to symbolic AI components that build on logic and rules. So, rather than constricting the LLM, we are relying on its language capabilities to spot subtle clues in text. Should we act directly on these hunches, we would run into a whole world of problems derived from the inherent unreliability of LLMs. However, this is the task of a highly specialized team of agents, and there are other agents standing by, ready to make sense of the data and establish reliable patterns.

              When we talk about agents, we refer to any entity that acts on behalf of another to accomplish high-level objectives using specialized capabilities. They may differ in degree of autonomy and authority to take actions that can impact their environment. Agents do not necessarily use AI: many non-AI systems are agents, too. (A traditional thermostat is a simple non-AI agent.) Similarly, not all AI systems are agents. In this context, the agents we focus on primarily handle data, following predefined instructions and using specific tools to achieve their tasks.

              We define a multi-agent system as being made up of multiple independent agents. Every agent runs on its own, processing its own data and making decisions, yet staying in sync with the others through constant communication. In a homogeneous system, all agents are the same and their complex behavior solves the problem (as in a swarm). Heterogeneous systems, though, deploy different agents with different capabilities. Systems that use agents (either single or multiple) are sometimes called “agentic†architectures or frameworks.

              For example, specialized agents can dive into a knowledge graph, dig up specific information, spot patterns, and update nodes or relationships based on new findings. The result? A more dynamic, contextually rich knowledge graph that evolves as the agents learn and adapt.

              The power is in the teaming. Think of the agents Mulder and Scully from The X-Files television show: Mulder represents intuitive, open-minded thinking, while Scully embodies rational analysis. In software, there always have been many Scullys but, with LLMs, we now have Mulders too. The challenge, as in The X-Files, is in making them work together effectively.

              The role of a universal ontology

              We employ a universal ontology to act as a shared language or, perhaps a better analogy, a translation exchange, ensuring that both intuitive and analytical agents communicate in terms that can be universally understood. This ontology primarily consists of “flags†–generic indicators associated with potential fraud risks. These flags are intentionally defined broadly, capturing a wide range of behaviors or activities that could hint at fraudulent actions without constraining the agents to specific cases.

              The key to this system lies not in isolating a single flag but in identifying meaningful combinations. A single instance of a flag may not signify fraud; however, when several flags emerge together, they provide a more compelling picture of potential risk.

              “This innovation shifts the approach from simple fraud detection to proactive prevention, allowing authorities to stay ahead of fraudsters with scalable systems that learn and evolve.â€

              Hybrid AI adaptability

              The adaptability of the system lies in the bridging between neural and symbolic AI as the LLM distills nuances in texts into hunches. They need to be structured and amplified for our analytical AI to be able to access them. As Igor Stravinsky wrote in his 1970 book Poetics of Music in the Form of Six Lessons, “Thus what concerns us here is not imagination itself, but rather creative imagination: the faculty that helps us pass from the level of conception to the level of realization.†For us, that faculty is the combination of a general ontology and vector-based similarity search. They allow us to connect hunches to flags based on semantic matching and thus address the data using general rules. Because we work in a graph context, we can also explore direct, indirect, and even implicit relations between the data.

              Now let’s explore how our team of agents picks up and amplifies weak signals, and how these signals, once interwoven in the graph, can lead the system to identify patterns spanning time and space, patterns it was not designed to identify.

              A scenario: Welfare agencies have observed a rise in fraudulent behavior, often uncovered only after individuals are exposed for other reasons like media reports. Identifying these fraud attempts earlier, ideally at the application stage, would be extremely important.

              Outcome: By combining intuitive and analytical insights, authorities uncover a well-coordinated fraud ring that would be hard to detect through traditional methods. The agents map amplified weak signals as well as explicit and implicit connections. Note also that the system was not trained on detecting this pattern; it emerged thanks to the weak signal amplification.

              One of the powers of hybrid AI lies in its ability to amplify weak signals and adapt in real time, uncovering hidden fraud patterns that traditional methods often miss. By blending the intuitive insights of LLMs with the analytical strength of knowledge graphs and multi-agent systems, we’re entering a new era of fraud detection and prevention – one that’s smarter, faster, and more effective. As Mulder might say, the truth is out there, and with the right team, we’re finally close to finding it.

              Start innovating now –

              Implement a universal ontology

              Create a shared ontology to bridge neural (intuitive) and symbolic (analytical) AI agents, transforming weak signals for deeper analysis by expert systems and graph-based connections.

              Form specialized multi-agent teams

              Build teams of neural (real-time detection) and symbolic (rule-based analysis) AI agents, each specialized with tools for their role.

              Leverage graph technology for cross-referencing

              Use graph databases to link signals over time and across data sources, uncovering patterns like fraud faster, earlier, and at a lower cost than current methods.

              Interesting read?

              ÎÚÑ»´«Ã½â€™s Innovation publication, Data-powered Innovation Review – Wave 9Ìýfeatures 15 captivating innovation articles with contributions from leading experts from ÎÚÑ»´«Ã½, with a special mention of our external contributors from, and .ÌýExplore the transformative potential of generative AI, data platforms, and sustainability-driven tech. Find all previous Waves here.

              Meet the authors

              Joakim Nilsson

              Knowledge Graph Lead, ÎÚÑ»´«Ã½ & Data, Client Partner Lead – Neo4j Europe, ÎÚÑ»´«Ã½Ìý
              Joakim is part of both the Swedish and European CTO office where he drives the expansion of Knowledge Graphs forward. He is also client partner lead for Neo4j in Europe and has experience running Knowledge Graph projects as a consultant both for ÎÚÑ»´«Ã½ and Neo4j, both in private and public sector – in Sweden and abroad.

              Johan Müllern-Aspegren

              Emerging Tech Lead, Applied Innovation Exchange Nordics, and Core Member of AI Futures Lab, ÎÚÑ»´«Ã½
              Johan Müllern-Aspegren is Emerging Tech Lead at the Applied Innovation Exchange (AIE) Nordics, where he explores, drives and applies innovation, helping organizations navigate emerging technologies and transform them into strategic opportunities. He is also part of ÎÚÑ»´«Ã½â€™s AI Futures Lab, a global centre for AI research and innovation, where he collaborates with industry and academic partners to push the boundaries of AI development and understanding.

                The post Mulder and Scully for fraud prevention: Teaming up AI capabilities appeared first on ÎÚÑ»´«Ã½ Norway.

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                Are data spaces the future? /no-no/insights/expert-perspectives/are-data-spaces-the-future/ Wed, 05 Mar 2025 06:59:40 +0000 /no-no/?p=852824&preview=true&preview_id=852824 The post Are data spaces the future? appeared first on ÎÚÑ»´«Ã½ Norway.

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                Are data spaces the future?

                ÎÚÑ»´«Ã½
                Peter Kraemer, Phil Fuerst, Debarati Ganguly
                Mar 5, 2025

                Europe is building a data-driven economy in a changing geopolitical context. As it strives for both innovation and sovereignty, decentralized ecosystems offer a way to create value with data, while safeguarding freedom of choice.

                Data has the potential to transform processes, businesses, economies, and society by unlocking new kinds of value creation. It’s also how we are going to make AI work as a crucial component of the future European data economy—but only if that data is built on strong foundations that ensure its quality and relevance.

                Of course, value creation depends on the data that’s available to you, and you might not have all the data you need. That’s why data needs to be shared and combined. In this article, we consider how data spaces meet this need, offering what the as the “ability to provide the essential foundations for secure and efficient data sharingâ€. While our focus in this article is on European data spaces, we recognize that this is becoming a relevant topic around the world.

                Why a decentralized data economy makes sense for Europe

                Data spaces are, in effect, decentralized ecosystems that have a powerful resonance in the world today. Indeed, recognizing their huge potential, the European Commission established a series of domain-specific/sectoral common European data spaces designed to help “unleash the enormous potential of data-driven innovationâ€.

                We see three main drivers for these common data spaces in Europe: geopolitics, commercials, and choice. In the first instance, in light of the unstable geopolitical landscape, data spaces give you assurance that all your (data) eggs aren’t in one basket.  You select the datasets you want to reside in what data space. Interoperability and portability can help avoid the dreaded lock-in effect where changing from one service provider to another might be prohibitively complicated. Commercially, data spaces address any exposure to potential monopolistic lock-in effects by individual companies cornering the market in data platforms. Then there’s the matter of choice. You choose who you interact with in a common data space, which puts you in control of who to share data with.

                Why we need data spaces

                Sharing data is key to data-driven growth. Indeed, it’s a vital aspect of the European strategy for data. But over-reliance on data platforms predominantly controlled by a limited number of international technology firms introduces potential vulnerabilities regarding data security, access, and strategic autonomy. We may also lose the ability to share data on our own terms, in accordance with our own values—freedom, privacy, control.

                An alternative future for Europe is to share data on a sovereign basis. And that implies across industries. That’s why we’re so excited to be working on the DSSC and on Simpl, the open source, smart and secure middleware platform that supports data access and interoperability among European data spaces.

                Beyond technology to value creation

                Let’s not forget that a data space is only an instrument. It’s what you do with it that matters. In a data space you will be able to aggregate, combine and correlate data that you can’t today because it is stored in different places. And that’s where we begin to create significant value from data, specifically in a number of areas, as follows.

                1. Global challenges: Data spaces will prove inordinately useful in tackling grand challenges that cut across sectors and geographies. Here we’re talking about achieving mission-oriented policy goals, such as reducing healthcare inequality and achieving net zero/carbon neutrality targets. For example, the (EHDS) will be an enabler of patient empowerment, with better access to and control over health data. Further, increased reuse of EHDS data for research and policy making will improve public health interventions. A 2025 from the World Economic Forum in collaboration with ÎÚÑ»´«Ã½ suggests the EHDS could generate €5.5 billion savings over ten years. We’ve already seen the huge value of data sharing in a global crisis when, in the Covid 19 pandemic, our governments needed data from many areas at once to form policy—healthcare systems, pharma, mobility, employment and economic data. There will be future pandemics.
                2. Innovation: Data spaces will undoubtedly contribute to data-driven innovation across the EU as it continues on its mission to build the Single Market for Data. The states, “Common European Data Spaces will enhance the development of new data-driven products and services in the EU, forming the core tissue of an interconnected and competitive European data economyâ€.  In this respect, the combination of data from different sources across sectors can produce fascinating new applications. Think, for example, of the traffic flow in a city, where the observation of vehicle movement and a subsequent adjustment of traffic lights can help avoid congestion, and the monitoring of parking lots eases the burden of finding a parking spot, possibly connected to a recommendation of a charging port for the car’s battery. The energy grid could then be supplied with better anticipation of demand peaks and control energy distribution accordingly.  The seamless integration of real-time public transport data can then be used to recommend the best option for getting from A to B.
                3. Efficiency: Data spaces will help in the more efficient use of resources and improve public services. A great example here is that of road surface observation. By correlating data from cars’ electrical sensors, it becomes possible to monitor, in real time, the deterioration of the road, and carry out preventative maintenance to optimize spend / return. And returning to the healthcare sector, access to comprehensive patient histories in a shared data ecosystem has the potential to lead to better and faster diagnosis and treatment.
                4. Science and research: Shared data can create new evidence bases for scientific and medical research. Let’s consider the following scenario—I drive to work in a convertible most days; the farmer of the field sprays an experimental fertilizer; later I develop  neurological issues but doctors are unsure how to treat them. In the future we might be able to correlate this illness with the exposure to the fertilizer by aggregating mobility data, air quality data, times that the farmer used the fertilizer, and the contents of that fertilizer.

                Questions at the edges of our data economy

                The potential for value is clear, but there are numerous challenges still to overcome—and they are not principally digital ones. One unknown factor is what it will cost to set up and run a common data space. At this point we don’t have an adequate way to price data, so this question remains unanswered. Other questions include: How can we quantify the value of new data-driven business models vs traditional business models? And how can we pinpoint the strengths and weaknesses of data ecosystems and technologies?

                The answer to all of these questions at present is that we are all on a journey with common data spaces. We improve every day and the answers will come. But it is hard to imagine that the massive contribution of sharing data to the common good will not outweigh the costs and barriers that need to be overcome.

                Above all, the decentralized model depends on participants’ willingness to share data. That means they must trust the other participants and the infrastructure. There is no other way to build trust except enabling people to say no. Letting people choose in itself invites trust.

                Europe can do data differently

                Data spaces are a way for Europe to reap the benefits of data for economic growth and positive societal outcomes, while affirming European values in the digital domain. They remain an integral part of the European strategy aiming to make the EU a leader in a data-driven society.

                Find out more

                Peter Kraemer will speak about the future of data sharing in Europe at the Data Spaces Symposium in Warsaw on 11-12 March. Register at

                Authors

                Peter Kraemer

                Director Data Sovereignty Solutions, ÎÚÑ»´«Ã½
                “A European data economy based on openness, fairness and transparency is possible, and we are determined to help make it a reality. In a flourishing data economy, all sectors will have new ways to generate value. Sovereignty means making independent and well-informed decisions about our digital interactions: where data is stored, how it is processed, and who can access it. Data spaces make these principles concrete, and we are committed to helping them grow.â€

                Dr. Philipp Fuerst

                VP Data-Driven Government & Offer Leader, Global Public Sector
                To unlock the value of their data, governments need to make organizational changes and meet new technology requirements. Yet, the many examples of public sector agencies that have already successfully embarked on the journey to become data-driven organizations show that these hurdles can be overcome. Their gains in decision making, operational efficiency and citizen experience are tangible and significant. Our clients believe the benefits they have reaped are well worth the effort.

                Debarati Ganguly

                Director, Data & AI – Global Public Sector
                Debarati is a seasoned expert in Data-Driven Government, specializing in data ecosystems, governance, and AI-driven analytics for the public sector worldwide. She collaborates with leaders and AI specialists to drive strategic initiatives, ensuring ethical, sovereign, and anonymized data solutions. Her expertise helps governments and citizens unlock the true value of data, enhancing decision-making, service delivery, and overall public benefit through AI and Generative AI innovations.

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