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.”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.

Forget chatbots. The age of the agent is here. Imagine a digital workforce that understands, empathizes, and anticipates customer needs as a trusted advisor – a network of AI agents collaborating to deliver truly human-centric experiences.

This isn’t science fiction; it’s the dawn of the Agentic AI era, and it’s poised to revolutionize customer interactions.  is projecting the global Agentic AI market will be valued at $196.6 billion by 2034, a dramatic leap from $5.2 billion last year. This exponential growth is not just exciting; it signals a fundamental shift. While the possibilities are vast, companies must move beyond simply creating “cool agents” to building robust, collaborative systems.

Agentic AI is rapidly evolving, and the conversation needs to shift towards building networks of interconnected AI agents. This next stage, focusing on multiagent systems, is where real value will be unlocked. 

Next-level hyper-personalization: The game changer 

The true power of multiagent systems lies in their ability to deliver hyper-personalized experiences. Imagine AI agents seamlessly orchestrating across different business areas, instantly accessing client information to tailor interactions in real-time. This level of hyper-personalization, incorporating individual preferences, creates a genuine sense of personal connection. 

Multiagent systems represent the next evolution in personalized interactions. We’ve moved beyond deterministic chatbots and automated processes to a realm where embedded generative AI enables faster, more personalized interactions that build loyalty and connection. The impact is already evident: according to the ��ѻ��ý Research Institute, 31 percent of organizations using generative AI see faster response times, and 58 percent anticipate further improvements. 

Efficiency and beyond: Connecting agents across departments 

Beyond enhancing customer experience, connecting agents across departments drives efficiency and productivity through automated, complex workflows. The ability for agents to communicate and operate seamlessly at faster speeds across departments unlocks significant potential. 

This also expands service capabilities. For example, overcoming language barriers in global call centers becomes possible with multilingual digital agents. Research indicates that 60 percent of consumers would pay more for premium customer service, highlighting the value of these enhanced capabilities. provides the AI technology and natural language processing (NLP) that can provide enhanced customer experiences.  

Connecting agents and data: Unlocking deeper insights 

Multiagent systems generate valuable data on information and conversations, which, when shared, provides a deeper understanding of customer behavior and trends. 

This data spans various departments – sales, order management, supply chain, ERP, and marketing – highlighting that inquiries rarely fit neatly into departmental silos. Agents need to be able to access data across these silos is crucial for providing cohesive responses to complex customer questions. 

This is why cross-department collaboration is crucial. Agents need seamless handoffs and access to different departments so that when a person engages with them, the conversation continues without waiting for the next agent to be updated. 

However, simply opening up data is not enough. Robust security protocols are necessary to ensure that not all information is accessible to every agent. Agents must pull information in a way that maintains visibility, requiring a deep understanding of systems for effective deployment. Data security and privacy are paramount. Accessing various data sources requires clear guidelines and governance to ensure compliance with existing data rules. 

Agentic change management: Blending the human workforce with the “digital workforce” 

Ideally, digital and human workforces will seamlessly blend, working in unison on daily tasks and customer interactions. Generative AI will continuously learn from feedback and algorithms, while large language models adapt. However, potential biases must be addressed to ensure fairness. 

Companies must also address the impact of multiagent systems on the human workforce. Clear communication early in the process can prevent resentment toward AI agents. Reassuring employees is a crucial part of change management. If employees fear job losses, they will be less inclined to engage with companies using AI agents. Multiagent systems offer exciting possibilities, but everyone must be part of the solution to maximize the benefits. 

Building a resilient agentic infrastructure 

Agentic AI does not mean creating a single, all-encompassing agent. Companies must prioritize resilience. Humans have bad days, and so can AI agents. If a single agent fails, the entire operation can grind to a halt. A multiagent system allows agents to focus on specific areas, ensuring that if one fails, others remain unaffected. 

The challenge for companies lies in the complexity of the infrastructure required for seamless agent communication. While technology is increasingly sophisticated, the talent to make it work is scarce. Companies need the right skills to build and effectively operate these agentic systems. 

is an orchestration platform that allows companies to deploy agents easily. The Google ecosystem integrates seamlessly with any system, ensuring smooth information flow, regardless of whether a company is using Google applications and infrastructure. 

Working with Google Cloud, ��ѻ��ý can support customer service transformation that creates seamless, quality interactions that deliver an exceptional level of service, support, and delight to all stakeholders. Advanced AI capabilities and scalable infrastructure means Google Cloud can build and deploy intelligent virtual agents, enhance agent productivity, and personalize customer experiences easily. We can leverage the power of Google’s Customer Experience Suite to innovate for growth and reinvent business models to unleash what is possible. 

Join us at Google Cloud Next to discover how we’re helping companies embrace the agentic era and benefit from the intersection of innovation and intelligence.

Call centers are seeing gains but reliability and consistency need to be a focus. Adopting a copilot approach is the best way to ensure real efficiencies and positive customer experience. 

It has been said that AI agents could be a . Intelligent software agents capable of learning to manage actions and tasks have the potential to transform almost everything. Work and life will be impacted by the drive for productivity and efficiency. But AI agents will also democratize access and help overcome barriers to empower more people and drive innovation.

The road to agentic AI is still being built and there will be many routes to explore but, today, one of the biggest pushes is coming from the telecommunications industry. Call centers have been early adopters for this kind of generative AI because it is a natural evolution from bots. Existing chat features were interesting but they do not always work well, or customers were so annoyed by the menu systems on phones that they were unhappy before they even spoke to someone.

Moving beyond bots

Generative AI brings a much better experience to the call center structure, and it enhances existing technology. For example, (CES) was built on its Contact Center AI and enhanced with generative AI technology. It has better engagement with customers in both the chat channel and live. With the emerging capabilities of large language models (LLMs) and the growth of companies like OpenAI, AI agents can take on expanded tasks.

Creating a multi-modal experience allows an AI agent like Google Gemini to intake text, visuals, and audio, and add a communications layer through actual voice and text-to-voice features that are extremely realistic.

Combining Gen AI, language features, the ability to understand a vast amount of context instantly, and better and more human communication with text-to-voice capabilities creates systems with huge potential.

Enhancing the AI agent

We have recently launched the concept of thinking models that are capable of handling much more complex tasks. This is achieved through reinforcement learning based on human feedback, which means these models can actually think, process, and approach problems from multiple angles and explore different paths to find the best solution. It is very reminiscent of how a human would work to solve a problem.

Agentic AI has the capability to not only understand what a customer needs but to communicate in our own language with the right nuances and even slang. Communication is there. The thought process is there. The ability of AI agents to think through problems at length is there. And they bring the ability to use tools during interactions.

For example, a customer calls in with multiple inquiries. The AI agent can quickly understand the intent of the call so there is no longer a need to sift through menus or listen to a bunch of options. Because the intent is read in the early stages of the call, the problem resolution process operates better, as the AI agent has the information to solve the problem and the tools to execute it.

The right AI team

After detecting the true intent of a customer call, a master AI agent can act as the interactive layer with a customer, while simultaneously accessing a team of subagents to delegate tasks. The subagents can specialize in different areas, like billing issues or new installations. There is no more waiting on hold to be transferred to a different department or a manager. The master agent can access a whole host of tools and know what it needs to take action.

For example, a customer may want to process a payment. The master agent can identify the request and decide how to proceed. It can give a credit, research a billing discrepancy, or initiate other searches to complete the request. It can call different APIs to get information, update the account, and process the bill. With access to tools, there is really no end to what an AI agent can do.

These reasoning capabilities and tools mean agents can do very similar things to humans. However, it is still early days in the process and there are concerns to be addressed. Reliability and consistency are two factors. The monitoring and evaluating are improving to help ensure the responses and decisions by AI agents are correct.

Improving the call center experience

We worked with one telco client to deliver better knowledge searching, to leverage LLMs to use new methods of data acquisition summarization. The goal was to make technical documentation more accessible so when someone calls to troubleshoot a modem, for example, the answer is readily available.

Call centers are also a common sales channel. Agents can provide additional information or offer specific deals. That requires the agent to understand the needs of the customer, align them with a product, make an offer, address objections, and close the deal. Now an AI sales agent can interview the customer to understand the needs and wants and match them with potential solutions. They can even address objections and concerns to help get to the sale.

Copilots: Finding the agentic balance

According to a recent ��ѻ��ý Research Institute report, being an agent is not an overly satisfying career choice, with only 16 percent of human agents surveyed report overall satisfaction with their roles. They face a number of pressures, from rising customer expectations to inefficient systems and a high attrition rate. There are efficiency gains to be made by employing AI agents that can help humans do a better job. In addition, AI agents can help resolve issues more quickly so the customer and employee have positive experiences.

This is why the copilot effect is a popular AI agent option. Google has Agent Assist to support live agents to resolve queries and issues more quickly. It is like having an expert in the room at all times with a call center agent. For example, the human agent can use it to help digest what is being said, with information automatically appearing on dashboards to assist with the call resolution. The copilot can also provide real-time assessments of the sentiment of the caller. Now the human agent has prompts with potential resolutions, rather than having to bounce between different systems for information or consult with a manager.

The human in the middle

So the concept of the human in the middle is very important. AI agents are a powerful tool meant to enhance experience, but sometimes a model can hallucinate or produce an error – and a company is responsible for an AI agent’s output. That means companies have to own the net result. So employing copilots with the human in the middle is happening even in new call centers. Once a system is proven, the role of AI agents can expand but, since call centers have a major impact on customer experience, there needs to be a high level of comfort with the system.

Call centers that use Google Customer Experience Suite (CES) engage customers with generative AI for many tasks, like determining what a client needs and other lower-level processes, to make calls more efficient and get to resolution quicker. AI agents can, for example, engage with back-office operations so humans can focus on more high-value tasks.

It takes time for companies to be comfortable with exploring generative AI solutions.  Companies need to focus on the business case and ensure innovation results in efficiency and savings.

Working with Google Cloud, ��ѻ��ý can help companies move into the agentic future. We can help companies build a competitive edge with agents to drive real customer service transformation. Google Cloud’s advanced AI capabilities enable businesses to build and deploy intelligent virtual agents easily. It is time to create, frictionless environment to scale agents where everything supports the needs of the organization and its customers.

Join us at Google Cloud Next to discover how we’re helping companies embrace the agentic era and benefit from the intersection of innovation and intelligence.

Our Global Data Science Challenge is shaping the future of learning. In an era when AI is reshaping industries, ��ѻ��ý’s 7^th Global Data Science Challenge (GDSC) tackled education.

By harnessing cutting-edge AI and advanced data analysis techniques, participants, from seasoned professionals to aspiring data scientists, are building tools to empower educators and policy makers worldwide to improve teaching and learning.

The rapidly evolving landscape of artificial intelligence presents a crucial question: how can we leverage its power to solve real life challenges? ��ѻ��ý’s Global Data Science Challenge (GDSC) has been answering this question for years and, in 2024, it took on its most significant mission yet – revolutionizing education through smarter decision making.

The need for innovation in education is undeniable. Understanding which learners are making progress, which are not, and why is critically important for education leaders and policy makers to prioritize the interventions and education policies effectively. According to UNESCO, a staggering 251 million children worldwide remain out of school. Among those who do attend, the average annual improvement in reading proficiency at the end of primary education is alarmingly slow—just 0.4 percentage points per year. This presents a sheer challenge in global foundational learning hampering efforts made to achieve the learning goal as set forth in the Sustainable Development Agenda.

The Grade-AI Generation: A collaborative effort

The GDSC 2024, aptly named “The Grade-AI Generation,” brought together a powerful consortium. ��ѻ��ý offered its data science expertise, UNESCO contributed its deep understanding of global educational challenges, and Amazon Web Services (AWS) provided access to cutting-edge AI technologies. This collaboration unlocks the hidden potential within vast learning assessment datasets, transforming raw data into actionable insights for decision making that could change the future of millions of children worldwide.

At the heart of this year’s challenge lies the PIRLS 2021 dataset – a comprehensive global survey encompassing over 30 million data points on 4^th grade children’s reading achievement. This dataset is particularly valuable because it provides a rich and standardized data that allows participants to identify patterns and trends across different regions and education systems. By analyzing factors like student performance, demographics, instructional approaches, curriculum, home environment, etc. the AI-powered education policy expert can offer insights that would take much longer time and resources to gain from traditional methods. Participants were tasked with creating an AI-powered education policy expert capable of analyzing this rich data and providing data-driven advice to policymakers, education leaders, teachers, but also parents, and students themselves.

Building the future: Agentic AI systems

The challenge leveraged state-of-the-art AI technologies, particularly focusing on agentic systems built with advanced Large Language Models (LLMs) such as Claude, Llama, and Mistral. These systems represent a significant leap forward in AI capabilities, enabling more nuanced understanding and analysis of complex educational data.

“Generative AI is the most revolutionary technology of our time,” says Mike Miller, Senior Principal Product Lead at AWS, “enabling us to leverage these massive amounts of complicated data to capture for analysis, and present knowledge in more advanced ways. It’s a game-changer and it will help make education more effective around the world and enable our global community to commit to more sustainable development.“

The transformative potential of AI in education

The potential impact of this challenge extends far beyond the competition itself. As Gwang-Chol Chang, Chief, Section of Education Policy at UNESCO, explains, “Such innovative technology is exactly what this hackathon has accomplished. Not just only do we see the hope for lifting the reading level of young children around the world, we also see a great potential for a breakthrough in education policy and practice.”

The GDSC has a proven track record of producing innovations with real-world impact. In the 2023 edition, “The Biodiversity Buzz,” participants developed a new state-of-the-art model for insect classification. Even more impressively, the winning model from the 2020 challenge, “Saving Sperm Whale Lives,” is now being used in the world’s largest public whale-watching site, happywhale.com, demonstrating the tangible outcomes these challenges can produce. 

Aligning with a global goal

This year’s challenge aligns perfectly with ��ѻ��ý’s belief that data and AI can be a force for good. It embodies the company’s mission to help clients “get the future you want” by applying cutting-edge technology to solve pressing global issues.

Beyond the competition: A catalyst for change

The GDSC 2024 is more than just a competition; it’s a global collaboration that brings together diverse talents to tackle one of the world’s most critical challenges. By bridging the gap between complex, costly collected learning assessment data and actionable insights, participants have the opportunity to make a lasting impact on global education.

A glimpse into the Future

The winning team ‘insAIghtED’ consists of Michal Milkowski, Serhii Zelenyi, Jakub Malenczuk, and Jan Siemieniec, based in Warsaw Poland. They developed an innovative solution aimed at enhancing actionable insights using advanced AI agents. Their model leverages the PIRLS 2021 dataset, which provides structured, sample-based data on reading abilities among 4^th graders globally. However, recognizing the limitations of relying solely on this dataset, the team expanded their model to incorporate additional data sources such as GDP, life expectancy, population statistics, and even YouTube content. This multi-agent AI system is designed to provide nuanced insights for educators and policymakers, offering short answers, data visualizations, yet elaborated explanations, and even a fun section to engage users.

The architecture of their solution involves a lead data analyst, data engineer, chart preparer, and data scientist, each contributing to different aspects of the model’s functionality. The system is capable of querying databases, aggregating data, performing internet searches, and preparing elaborated answers. By integrating various data sources and employing state-of-the-art AI technologies like Langchain and crewAI, the insAIghtED model delivers impactful, real-world, actionable insights that go beyond the numbers, helping to address complex educational challenges and trends.

Example:

Figure 1: Show an example of the winning model. The image has the model answering the following prompt “Visualize the number of students who participated in the PIRLS 2021 study per country”

As we stand on the brink of an AI-powered educational revolution, the Grade-AI Generation challenge serves as a beacon of innovation and hope. It showcases how the combination of data science, AI, and human creativity and passion can pave the way for a future where quality education is accessible to all, regardless of geographical or socioeconomic barriers.

Start innovating now –

Dive into AI for good
Explore how AI can be applied to solve societal challenges in your local community or industry.

Embrace agentic AI systems
Start experimenting with multi-agent AI systems to tackle complex, multi-faceted problems in your field.

Collaborate globally
Seek out international partnerships and datasets to bring diverse perspectives to your AI projects.

Interesting read? ��ѻ��ý’s Innovation publication, Data-powered Innovation Review – Wave 9 features 15 captivating innovation articles with contributions from leading experts from ��ѻ��ý, with a special mention of our external contributors from, and . Explore the transformative potential of generative AI, data platforms, and sustainability-driven tech. Find all previous Waves here.

Leading the charge: Puneeta on cybersecurity, inclusion, and building a future at ��ѻ��ý

In today’s ever-evolving digital landscape, cybersecurity is crucial for safeguarding information and building trust. At ��ѻ��ý, leaders like  are at the forefront of this mission, bringing a unique blend of expertise, passion, and vision. In this Q&A, she shares her journey, the value of inclusion in cyber leadership, and her advice for those looking to join the ��ѻ��ý Cybersecurity team.

1. What makes you proud to work at ��ѻ��ý?

The variety of projects, clients, and cultures at ��ѻ��ý keeps my work exciting and fulfilling, and knowing we are helping organizations grow and solve complex cyber challenges is incredibly rewarding. ��ѻ��ý fosters an environment where everyone feels relevant and respected. The appreciation of everyone’s personal situation and making a flexible working environment thrive with balance is distinctive to ��ѻ��ý’s DNA, making it a unique and supportive place to work.

2. How are you working towards the future you want?

I’m diligently working towards the future I want at ��ѻ��ý by sticking to my value system and finding the right chord to strike with ��ѻ��ý’s values. Whether it’s picking up uncharted territories to grow business, building meaningful connections, or staying laser-focused in accelerating cyber business across APAC, I’m taking small, consistent steps to stay on track. I’m also embracing opportunities that align with my virtues and passions, helping me move closer to where I want to be.

3. What value does inclusion bring to cyber leadership?

Inclusion in cyber leadership isn’t just about representation ­– it’s about building a team capable of thinking outside the box and adapting to unforeseen challenges. In a field where threats are constantly evolving, having leaders from different walks of life brings a variety of strategies, insights, and approaches. This inclusion fosters a culture of resilience and innovation, where challenges are seen as opportunities for growth. It also ensures that cybersecurity solutions are well-rounded, addressing the needs of diverse users and creating a stronger and more proactive defense system.

4. What advice would you give to someone joining ��ѻ��ý Cybersecurity?

Life at ��ѻ��ý Cybersecurity is like an exhilarating adventure: you feel a rush of excitement as you reach new heights, followed by a burst of adrenaline that keeps you energized. There’s that light, joyful feeling in your stomach as you navigate through stimulating challenges, and the thrill of new experiences keeps you engaged. It’s a dynamic mix of enthusiasm and learning, making every moment enjoyable and rewarding!

Empowerment and learning are at the heart of ��ѻ��ý Cybersecurity. You’ll find yourself in an environment that encourages you to embrace challenges and grow both personally and professionally. One of the standout initiatives is the Cyber Angels program, which mentors women seeking careers in cybersecurity, fostering a supportive and inclusive community.

You’ll also have the opportunity to work with CyberPeace, a Geneva-based NGO, supporting non-profits in enhancing their digital security posture and resilience, and making a positive impact on society. This collaboration not only enhances your technical skills but also allows you to contribute to meaningful causes.

My advice for someone joining ��ѻ��ý Cybersecurity is to embrace the challenges and build a community of trusted colleagues and clients. Be proactive – take ownership of your development and contribute your unique perspective to the team. Remember, the journey may be thrilling but it’s also incredibly rewarding and full of opportunities for growth and empowerment.

If you are looking for a role in cybersecurity at ��ѻ��ý, please visit our career page.

In the rapidly evolving realm of IT Mergers and Acquisitions (M&A), cloud computing emerges as a transformative force, enabling efficient, secure, and collaborative environments for post-merger integrations and separations.

This comprehensive article explores how tailored cloud strategies can transform traditional IT M&A and divestiture processes, ensuring strategic alignment and operational agility.

1. Cloud flexibility: unleashing the strategic and financial success of your M&A operation

As a merger or acquisition does not inherently mandate the integration of IT systems, it is important to remember that in cloud integration strategies following M&A transactions, companies tailor their approach to meet specific business objectives, which can extend beyond just IT synergies. Companies can leverage cloud computing flexibility to align with specific business goals, choosing from assimilation, conglomerate, or cooperation models based on their strategic needs. While assimilation involves the acquired entity aligning its operations with the acquirer’s standards, conglomerates maintain separate cloud infrastructures post-merger. Finally, the cooperation archetype is characterized by leveraging the best practices from each entity, resulting in a mutually beneficial cloud environment.

The strategic use of these characteristics offered by cloud flexibility is further exemplified by ‘Account Management,’ which deals with the separation of customer accounts. It serves as a catalyst for M&A operations by enabling the isolation and sale of specific accounts along with their associated assets. For instance, a client with multiple public cloud accounts attached to a master account can isolate one for individual sale. Thereby, it facilitates seamless carve-out processes without system shutdowns or extensive reconstruction efforts. This not only facilitates smooth transitions but also allows finance organizations to perform efficient valuations and due diligence by enabling precise cost identification and alignment with supported infrastructure components.

Moreover, cloud computing has broader strategic and financial implications for the value creation plan as it also revolutionizes business strategies and M&A readiness, offering scalable, customizable solutions through service models like Lift & Shift, PaaS, and SaaS. This facilitates the transition from traditional infrastructures to cloud-based systems, marking a significant financial transition from capital to operational expenditures. Furthermore, by enabling the consolidation of IT infrastructures, cloud computing increases negotiating leverage with vendors. Merged entities can secure more advantageous terms and pricing, leveraging the strategic benefits of a cohesive cloud approach. This consolidation not only enhances negotiating power but also optimizes IT resource utilization, strengthening integration merger processes and post-M&A integration strategies.

2. Cloud-driven agility: meeting the reactivity need associated with integration challenges

The agility provided by cloud computing is essential in post-merger integrations and carve-outs. It allows organizations to rapidly align IT infrastructures with their strategic objectives, leveraging hybrid and multi-cloud options to manage fragmented technology stacks.

In traditional on-premises or private cloud infrastructures, foundational components such as identity, backup, or monitoring are tightly coupled, which minimizes operational costs and reduces the attack surface. Conversely, public cloud environments enable the development of loosely coupled infrastructure and applications. These systems utilize reusable building blocks consumed through APIs, which can be easily redeployed in another tenant. This fosters greater agility and scalability in cloud-based architectures, crucial for dynamic M&A operations.

Such architectural flexibility is particularly beneficial in scenarios requiring the creation of IT infrastructures from scratch, such as carve-outs or new entity formations. In such cases, landing zones with transparent cost controls through predictable patterns of deployment play a crucial role, preparing technical layers for seamless integration merger strategies and significantly enhancing operational efficiency and scalability.

Moreover, the implementation of Infrastructure as Code (IaC) exemplifies cloud computing’s role in enabling the rapid setup of new environments, replication of existing configurations, and consistent deployment across merged entities. These predefined configurations allow organizations to quickly establish a secure, scalable cloud environment while awaiting the full implementation of technical layers.

Additionally, the use of containerization and microservices bolsters this flexibility by allowing applications to run consistently across various environments, further supporting CI/CD practices. This approach not only speeds up the Software Development Life Cycle (SDLC) but also reduces the Total Cost of Ownership (TCO) while ensuring operational continuity in post-M&A integration processes.

3. Cloud security management: protecting against deal vulnerabilities while driving seamless integration

Security, operational resilience, and compliance remain paramount, with cloud platforms offering advanced data protection and robust risk management essential in regulated industries.

Identity Access Management (IAM) solutions well illustrate the matter as they enhance security by ensuring only authorized users can access critical systems. With regards to compliance, ��ѻ��ý’s launch of BLEU, a “trusted cloud” platform, and other similar existing ones on the market cater to the needs for sectors with stringent regulations by providing enhanced data sovereignty and security. Overall, cloud-native security strategies integrate security measures from the ground up into the software development life cycle of applications, maintaining observability across cloud layers to contain threats unique to the ever-changing environment.

Moreover, the integration and exploitation of heterogeneous data – a common challenge in M&A operations – are significantly enhanced by AI and GenAI technologies. These technologies are intrinsically dependent on robust cloud infrastructures for their operation. AI and GenAI significantly enhance post-merger data integration by enabling advanced analytics, automation, and decision-making. These technologies leverage cloud computing to process and analyze vast datasets, uncovering synergies and operational efficiencies.

Similarly, in M&A due diligence, GenAI can automate contract analysis, rapidly identifying potential risks and compliance issues across thousands of legal documents in minutes—significantly reducing manual workload and legal costs.

Beyond integration, AI-driven predictive analytics can optimize supply chains, forecast market trends, and enhance customer experience strategies, ensuring that the newly merged entity maximizes value creation.

Additionally, cloud technology tools facilitate and foster collaboration among dispersed teams, crucial for the success of post-M&A integration processes. Cloud-based workplace solutions and IAM systems thereby provide seamless access to applications and services, streamlining the consolidation of IT resources and enhancing the needed organizational collaboration.

Conclusion – the pivotal role of cloud in navigating the future it M&A landscape

To conclude, in recent years, the M&A landscape has been increasingly shifting towards spin-offs and divestitures driven by their capacity to unlock shareholder value, optimize capital allocation, and elicit positive market responses leading to a re-rating of stock prices. We believe that cloud computing’s role is set to expand, helping organizations drive financial value creation and operational acceleration. The agility and scalability of cloud solutions are key to navigating these transitions, promising more innovative, secure, and efficient cloud-based strategies to support the strategic focus on core business strengths or newly separated activities.

For organizations looking to leverage cloud computing in their M&A strategies and stay ahead of evolving trends, engaging with experts can provide the insights needed to harness the transformative power of the cloud and accelerate the value creation plan in post-merger integrations.

Switzerland’s M&A landscape

Switzerland’s M&A landscape is characterized by its strong financial sector, multinational presence, and regulatory rigor, making cloud computing an indispensable tool for seamless integrations, data sovereignty, and operational agility. Swiss companies can leverage cloud-driven solutions to ensure compliance with strict data protection laws such as the Swiss Federal Act on Data Protection (FADP) while optimizing IT resources and maintaining business continuity.

Looking to unlock the full potential of cloud computing in your next M&A transaction? ��ѻ��ý’s Swiss-based cloud experts specialize in IT integration, regulatory compliance, and cost optimization tailored to the unique Swiss market. Contact us today to explore how cloud solutions can accelerate your M&A success in Switzerland.

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.”

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?

��ѻ��ý’s Innovation publication, Data-powered Innovation Review – Wave 9 features 15 captivating innovation articles with contributions from leading experts from ��ѻ��ý, with a special mention of our external contributors from, and . Explore the transformative potential of generative AI, data platforms, and sustainability-driven tech. Find all previous Waves here.

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 sharing”. 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 innovation”.

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 ��ѻ��ý 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 economy”.  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

As the global workforce continues to operate across hybrid workplaces, companies must find ways to cultivate a strong employee experience without the aid of shared spaces and physical touchpoints. Defining a future-oriented digital workplace strategy is the cornerstone of a company’s success; an effective strategy manifests how traditional ways of working will be reinvented not only structurally, but also in terms of culture, workplace, and technology-use.

Employees’ needs pertaining to their (digital) work environment have changed since the COVID-19 pandemic and keep evolving. Organizations must create adaptable, flexible, and hybrid workplaces capable of responding to the evolving needs and expectations of their employees. Leveraging new digital workplace solutions with associated organizational change is key to providing a connected and digital employee experience (DEX).

To understand the impact of the above-mentioned changes on the nature of work, we assess their relevance across three key workplace dimensions – People, Organization, and Technology. The following framework integrates current market trends and changing employee needs along these three dimensions.

People – How can organizations provide an environment in which employees can thrive and have meaningful human interactions?

• Culture of belonging and transparency: For organizations to attract new talent and retain their existing workforce, they must evolve; they must address the need for belonging and establish a culture that makes employees feel safe and supported in a digital environment.
• Reward and recognition: Rather than just appreciating praise for the result, employees desire a showing of appreciation for the effort invested and their unique and specific attributes contributing to an engagement’s overall success.
• Collaboration and communication: Increased distance between employees resulting from hybrid and remote working models made it more difficult for employees to collaborate effectively. This resulted in an increased focus on ensuring cross-functional teams are enabled to collaborate effectively and efficiently through new technologies in their workplace.
• Efficient knowledge management/ease of access to information: The shift to digital workplace solutions highlighted the challenge of maintaining an efficient knowledge exchange, as conversations and documents became increasingly scattered and siloed across systems. There is a need for more centralized and efficient knowledge management platforms, integrating systems and knowledge repositories, accessible to employees from anywhere.
• Learning and self-development: The degree to which companies provide comprehensive training and support resources to help employees enhance their skills and knowledge contributes to the organization’s overall success. Employers need to invest in their people and begin providing advanced features such as personalized learning and development plans, leveraging digital learning platforms to improve engagement and ensure the retention of their employees.
• Accessibility: Across all industries, employers need to consider the accessibility of their workplace for people with disabilities. With the increased remote work resulting from the pandemic, accessibility has expanded to include physical working spaces and inclusive and accessible technologies.
• User-friendly and user-centered approach: The level of exposure most people have to high-quality user experiences across varying technologies in their daily lives has a prominent spill-over effect; employees expect their workplace technologies to keep up with the pace of technologies they have at home. Digital workplace solutions must be user-centered and should help employees connect, engage, learn, deepen knowledge, and get work done in the most efficient way possible.
• Personalized employee experience: Employees expect their company to provide a digital employee experience tailored to their needs, providing access to the resources and tools relevant to their roles, responsibilities, and preferences.

Organization – How can organizations create an empowering place of work?

• Adapt policies supporting hybrid working: Although it is still too early to assess the long-term societal impact of hybrid working models, initial reports suggest that increased flexibility leads to increased worker happiness and higher productivity.
• Measure performance (improve people analytics): People analytics can improve how organizations identify, attract, develop, and retain talent, enabling them to better understand the drivers of engagement within and outside the organization. The pandemic played a role in accelerating the implementation of advanced people analytics. Leaders identified people analytics as an opportunity to make quick, informed, and strategic decisions concerning the workforce.
• Integrate the needs of employees continuously: Organizations must develop two-way communication channels, supported by relevant technology platforms, to gather and consolidate people data and to generate actionable measures to improve the (digital) employee experience continuously.

Technology – How can organizations implement relevant digital workplace solutions to propel themselves toward a Digital Workplace?

• System integration (shift from siloed to connected): Organizations aim to streamline the integration of various systems into one user interface, making it easier for employees to complete their tasks, share relevant information, and exchange seamlessly with colleagues from anywhere across any device.
• Flexible device policy (mobile support): Although not a new concept, the implementation of ‘Anywhere, Anytime, Any Device’ policies was more rapidly adopted by laggard organizations due to the pandemic. The basic concept is to empower the workforce by offering them a secure digital workplace accessible from anywhere with any device, including the employee’s personal devices.
• Security: With a more distributed workforce accessing corporate re-sources from remote locations, there is a strong need for robust security and end-point management solutions. Organizations that have not provided their employees with adequate collaboration tools and applications run the risk of their employees relying on unprotected personal devices and applications to facilitate the exchange with colleagues.
• Increase self-service capabilities: The advancement of digital workplace solutions has led to a shift from traditional support-service-provisioning within organizations to a model focused on employee autonomy and what is commonly referred to as employee self-service. Self-service empowers employees to perform functions such as updating their personal information, requesting time off, and accessing HR resources themselves.

Conclusion:

Organizations need to embrace new ways of working to remain competitive and adapt to the needs of their employees, providing a leading DEX. To improve the overall DEX leading organizations have begun to implement an entirely new working environment, integrating the technologies employees use (e-mail, instant messaging, enterprise social media tools, HR applications, and virtual meeting tools. This collection of previously siloed systems in one collective platform is known as the People Experience Platform (PXP). Managers will play a leading role in shaping the employee experience in the workplace of the future (see our “Future of Work” report that evaluates new hybrid working models made possible by digitalization). The workplace of the future will not only require greater attention to habits and processes that we once took for granted, but also a reimagining of what it means to collaborate with one another on a daily basis. The organizations that succeed in creating flexible, inclusive, and efficient workplaces will be those that approach the future of work holistically and systematically while developing a culture of collaboration in transparency to reap the rewards of new digital workplace solutions.