Last week, Campaign’s Media360 event in Brighton brought together a vibrant community of marketing and media professionals. The strong attendance and a packed agenda covering key trends, challenges, and opportunities clearly demonstrated the industry’s keen engagement. For Nick Waters, Global CEO Northern Europe at Making Science, the event offered a unique lens through which to view the evolving UK media landscape, delivering some surprising insights beyond the usual headlines.

Beyond the Usual Agenda: The Power of Resilience and Contrarian Thought

The event kicked off with a truly engaging interview with Jamie Laing, whose natural energy immediately set a dynamic tone. From his conversation, two powerful messages resonated deeply with Nick: the vital importance of resilience – the unwavering ability to push past countless “no’s” – and the courage of contrarian thinking, daring to make the illogical choice when intuition calls. This prompts Waters to question whether our industry has perhaps become too comfortable simply following the obvious path.

The Elephant in the Room: Where Was the AI Conversation?

Perhaps the most striking observation from Media360 was the unexpected underrepresentation of Artificial Intelligence. In an era where AI dominates global headlines and is undeniably central to the future of nearly every industry, its relatively limited presence in the content stream was notable. This raises crucial questions for Nick: Does the UK marketing community feel entirely on top of these rapid developments, is there an element of complacency, or has the truly transformative power of AI yet to be fully appreciated within this market? A direct consequence was also a noticeable lack of discussion around the agency ecosystem’s role in guiding advertisers through this monumental shift.

A Clear Spotlight: The “Media for Good” Movement

In stark contrast to the quietness around AI, the theme of “media for good” commanded significant attention. Discussions prominently featured topics such as diversity in media, pressing sustainability initiatives, decarbonization efforts, and the critical importance of supporting quality journalism. This strong emphasis clearly signals a significant and positive priority for the UK industry.

Navigating Channel Dynamics and Demographic Shifts

The event also provided fertile ground for debates on evolving media channels. The enduring relevance of Television, despite fragmentation and the rise of social media, was a key point. The notion that “social media killed the TV star” was confidently dismissed, underscoring TV’s continued powerful influence on national social culture. Discussions around the impact of Gen Z were also present, though the consensus suggested that their material influence on reshaping the industry is still an evolving story, perhaps predictably so given their nascent professional journey.

Conclusion: Re-evaluating Industry Priorities for the Future

Media360 offered a compelling snapshot of the UK media landscape’s current focus. With “media for good” so prominently featured and AI surprisingly relegated to the periphery, Nick Waters’ core question echoes: Has the UK industry truly found the right balance in its attention to what truly matters for tomorrow? At Making Science, we firmly believe that future success lies in a holistic approach, where societal responsibility goes hand-in-hand with an aggressive embrace of technological innovation, particularly AI. It’s about leveraging cutting-edge tools to enhance human impact and drive meaningful, sustainable growth for our clients.

At Making Science, we’ve always been committed to staying ahead of tech transformation. We’re excited to share that our UX/UI department is evolving into CX/AX Design – a change that represents our vision for the future of digital experiences.

This transition comes at a pivotal moment. After years of remarkable growth and innovation at Making Science, we’ve witnessed firsthand how rapidly the technology landscape is transforming. This evolution in our approach isn’t merely a rebranding – it’s a strategic shift that prepares our clients for the next wave of digital engagement.

Why we’re expanding our focus

For years, User Experience (UX) and User Interface (UI) have been the industry standard terms for describing digital product design. These concepts have served us well, focusing our attention on creating intuitive, beautiful interfaces for human users.

However, as noted UX pioneer Jakob Nielsen recently observed in his article “Hello AI Agents: Goodbye UI Design, RIP Accessibility,” we’re entering an era where direct human interaction with interfaces may no longer be the primary mode of digital engagement.

At Making Science, we’ve anticipated this shift. By expanding our focus to Customer Experience (CX) and Agent Experience (AX), we’re ensuring our work remains relevant and impactful in an increasingly AI-mediated world.

The dual future of design with CX and AX

Customer experience (CX)

CX takes a holistic view that extends beyond interface interactions:

• The entire customer journey across all touchpoints
• Emotional responses and satisfaction at each stage
• Brand perception before, during, and after engagement
• Seamless integration of online and offline experiences
• Service design and delivery

While UX focuses on how users interact with specific products, CX considers the entirety of a customer’s relationship with a brand or organization.

Agent experience (AX)

Here’s where we’re truly breaking new ground. Agent Experience (AX) recognizes the emerging reality that AI agents will increasingly mediate between humans and digital services:

• Designing for AI agents that navigate interfaces on behalf of users
• Creating structured data and content that both humans and AI can efficiently process
• Developing interaction patterns optimized for both direct human manipulation and agent-based operations
• Ensuring our digital products work seamlessly in an agent-first ecosystem

Transforming possibilities for our clients

This evolution significantly expands what we can deliver:

• Comprehensive ecosystem design: We now address not just how your product works, but how it fits into your customers’ lives and interacts with emerging technologies.
• Future-proofed digital assets: Your digital presence will be prepared for both traditional human users and the coming wave of AI agent interaction.
• Strategic advantage: Our approach bridges UX fundamentals with business strategy and technological innovation, positioning your brand ahead of competitors.
• Excellence across all touchpoints: While expanding our scope, we remain committed to creating beautiful, usable interfaces for direct human interaction.

Our unique AI methodology 

At Making Science, we’ve been developing our proprietary Synthetic Users Methodology to gain unprecedented insights into how both humans and AI agents interact with digital experiences.

This methodology has become our own “search for intelligent life” – uncovering behaviors and patterns that traditional methods simply can’t detect. The insights we’ve gained have been instrumental in our evolution toward CX/AX Design, allowing us to create experiences that not only meet current needs but anticipate future demands.

Honoring fundamentals while embracing the future

This strategic shift doesn’t abandon the principles that have guided exceptional design for decades. Rather, it expands our perspective to address new realities. The fundamentals of human psychology, visual design, and usability remain relevant – but they’re now applied in increasingly sophisticated contexts.

As Nielsen points out, AI agents don’t replace human users – they serve them. Our goal remains creating exceptional experiences that serve human needs, whether directly or through technological mediation.

Partner with Making Science

As we embrace this new approach, we invite forward-thinking organizations to join us in exploring the expanded possibilities of CX/AX Design. The tech landscape continues to evolve at a breathtaking pace, and together, we can create solutions that work brilliantly today while anticipating the needs of tomorrow.

Ready to transform your digital experience?

Our CX/AX Design team is ready to help elevate your digital presence. Whether you’re looking to enhance your customer journey, prepare for an AI-first future, or simply create more engaging digital experiences, we have the expertise and vision to help you succeed.

Are you tapping into the full potential of your digital data or just scratching the surface? Google Analytics 4 is here to change the rules of the game… but only if you know how to play.

📍 Introduction

Since its launch, Google Analytics 4 (GA4) has positioned itself as the new standard in digital analytics. But what makes it so special? And why are so many companies looking for Data Analyst profiles specialized in GA4?

In this post, we explore what GA4 is, why it has replaced Universal Analytics, what this change implies for your company, and how an analyst can turn data into real growth. If you manage digital channels, ecommerce, paid media or UX, then read on.

💡 Fun fact: according to Google, more than 50% of companies that have migrated to GA4 are still not properly exploiting their data due to a lack of analysts or advanced configuration.

🎯 What makes GA4 different?

1. It measures people, not just sessions

GA4 focuses on the user, not on visits. This allows you to analyze the real journey of your customers across channels, devices and platforms.

It no longer matters how many users visited your site, but who they are and what they do.

2. Personalized events: you decide what matters

No more measuring only page views. In GA4 you can track specific interactions: clicks, forms, scrolls, downloads etc. And the best part: everything is flexible, without depending on your IT team at every step.

3. Predictive models within everyone’s reach

GA4 gives you predictions such as conversion or abandonment probability, so you can prioritize actions based on data, not assumptions.

🧠 What if you knew which users are about to stop shopping… before they do?

4. Goodbye cookies, hello privacy

GA4 complies with regulations such as GDPR and adapts without cookies, which is key if your company wants to continue to scale without penalties.

👨‍💻 Why do you need a Data Analyst with GA4 experience?

It is simple: because having data is not the same as understanding it. Let alone acting on it.

✔️ An analyst turns data into decisions

• Configures relevant events and conversions
• Detects opportunities and problems before anyone else
• Relates data to business: sales, leads, profitability

✔️ Able to design useful dashboards for each team

A good analyst doesn’t just extract data, he translates it for Marketing, Product or C-Level. The result? Aligned teams and faster decisions.

✔️ Automate, predict and scale

With GA4 and BigQuery, your Data Analyst can build attribution models, automated reports, or identify hidden patterns that make (or lose) you money.

📌 Raw data is just noise. Advanced analytics turns it into music for your business.

🔚 Conclusion 

Google Analytics 4 has redefined how we measure and understand the digital experience.
However, to take advantage of its full potential, you need a Data Analyst who knows how to extract it, customize it and align it with your business strategy.

At Making Science, we help companies like yours turn analytics into impact. If you’re ready to transform your data into decisions, it’s time to take the next step.

🎯 Do you already have the person to turn your GA4 into a growth engine?

Did you find this article useful?
Share it, leave us a comment, or message us to show you how we work with advanced analytics at Making Science!

Because data doesn’t change your business. The decisions you make with it do.

In the rapidly evolving landscape of enterprise AI, the difference between good and exceptional implementation lies in the architecture beneath. Here’s the insider’s perspective on what truly powers today’s most successful AI agent deployments.

The New Reality of Enterprise AI

Imagine walking into a modern enterprise where AI agents orchestrate complex operations with apparent effortlessness—scheduling meetings across time zones, analyzing market trends in real-time, and handling thousands of customer interactions simultaneously. This visible excellence represents merely the tip of the actual system. The real innovation? It’s in the sophisticated engineering that lies beneath.

The journey to this point has been remarkable. From basic chatbots in 2023 to rudimentary AI assistants in 2024, we’ve now entered an era of truly autonomous AI agents. But this evolution isn’t just about better language models – it’s about the intricate architecture that makes enterprise-grade AI possible.

Beneath the Surfaces: Deconstructing the AI Agent Infrastructure

1. The Visible Layer: Where Human Meets Machine

The sleek interfaces of platforms represent the visible face of AI agents. But this polished exterior relies on sophisticated front-end engineering. Modern implementations leverage technologies not just for aesthetics, but to create intuitive interactions that adapt to human user needs. When a sales representative requests market analysis or a developer needs code review, this layer translates complex operations into natural conversations, leading to a redefinition of our concept of User Interface/User Experience.

2. The Intelligence Layer: Beyond Simple Processing

At the heart of enterprise AI lies an intricate network of complex computing systems. Memory management platforms ensure conversations maintain context across all the participants, during weeks or months. Dynamic model routing systems instantly determine whether a financial analysis requires different processing than a creative task. This orchestration happens in milliseconds, invisible to the end user but crucial for enterprise-grade performance.

3. The Control Framework: Enterprise-Grade Security and Governance

In today’s regulatory environment, control systems aren’t optional – they’re critical layers. Enterprise AI agents operate within a sophisticated framework that ensures:

• Advanced MFA protects sensitive operations
• Real-time monitoring provides unprecedented operational visibility
• Advanced orchestration enables complex multi-agent workflows

4. The Foundation: Scale, Speed, and Reliability

The true differentiator in enterprise AI isn’t the models – it’s the infrastructure. Cloud-native architectures powered by advanced containerization ensure global scalability. Next-generation databases enable instant information retrieval across petabytes of data. This foundation turns theoretical AI capabilities into practical business advantages.

The Engineering Reality: Why 90/10 Matters

The true value of enterprise AI agents lies not in individual AI models but in the engineering that enables their practical application. This 90% engineering, 10% LLM split reflects a crucial truth: successful AI implementation requires more robust infrastructure than cutting-edge algorithms.

Consider a global enterprise deploying an AI agent for customer support. While the LLM handles natural language understanding, the engineering infrastructure:

• Manages millions of concurrent conversations
• Ensures sub-second response times
• Maintains security compliance across jurisdictions
• Seamlessly integrates with existing business systems
• Scales resources based on demand

The Road Ahead: Next-Generation Enterprise AI

As we move forward, the architecture of enterprise AI continues to evolve. The future of enterprise AI lies not in incrementally better language models, but in more sophisticated infrastructure.

• Advanced orchestration systems enabling true multi-agent collaboration
• Predictive scaling systems that anticipate business demands
• Hybrid architectures blending cloud and edge computing
• Enhanced security frameworks for autonomous decision-making
• Sophisticated observability tools for AI governance

The Strategic Imperative

Understanding the full depth of AI agent architecture isn’t just technical knowledge – it’s a strategic advantage. Organizations that appreciate and invest in robust AI infrastructure position themselves to leverage the full potential of this transformative technology.

The visible “magic” of AI agents rests upon a foundation of sophisticated engineering. As you consider implementing AI agents in your enterprise, remember that success lies not just in choosing the right AI models, but in building and maintaining the robust infrastructure that powers them.

Ready to explore how AI agents can transform your business operations? Contact our team to discuss your AI strategy and learn how we can help build the infrastructure for your success.

• Over 800 users from Making Science, a pioneer in business AI applications, are already participating in the progressive adoption of the technology
• Making Science validating real-world use cases, developing adoption methodologies, and building technical accelerators to share their experience with clients
• This implementation strengthens Making Sciences’ position as a Google Cloud Premier Partner with six specializations, including Generative AI

Chicago, April 9th, 2025 – Making Science, a technology and marketing consultancy specializing in digital business acceleration and a pioneer in business-applied artificial intelligence, has deployed Google Agentspace, Google Cloud’s innovative platform based on generative AI and intelligent agents.

More than 800 Making Science users are already participating in the progressive adoption of Agentspace, which enables conversational interaction from a single interface with corporate applications such as Salesforce, SAP, Jira, BigQuery, and Atlassian, among many others, facilitating decision-making, data access, and execution of complex tasks. Upcoming milestones include the automation of key processes, such as sending SAP invoices directly from the platform.

Making Science has deployed this system as part of a broader approach to AI adoption that includes various gen AI solutions, with a structured department-by-department training model, an internal “AI Champions” program to support adoption and the integration of impact and return metrics.

“Choosing Google Agentspace reaffirms our commitment to technological innovation and our ability to lead real digital transformation from within. This initiative allows us not only to gain efficiency but also to learn from experience to better support our clients in their own generative artificial intelligence adoption processes,” explains José Antonio Martínez Aguilar, Founder and CEO of Making Science.

This implementation reinforces Making Science’s leadership as a pioneering company in the adoption of disruptive technologies and is part of its strategic plan, “AI Transformation Quarter”. This is a global program that promotes the integration of generative AI tools, process automation, and continuous training of the company’s teams.

Prepared to Lead Implementation for Clients

In addition to making its own processes more efficient, the adoption of Agentspace throughout its structure, from technical teams to business areas, positions Making Science as a strategic ally for companies interested in integrating this technology into their operations. Leveraging this hands-on experience, the company is validating use cases, developing adoption methodologies, and creating technical accelerators and integration templates. In parallel, knowledge and expertise are being generated to connect Agentspace with complex business environments and measure its impact with clear metrics of return and business value.

“With Agentspace, we advance in our collaboration with Google Cloud to enhance artificial intelligence in business environments. This technology represents the future of interaction with corporate data and applications. By implementing Agentspace at Making Science, we can transfer real learnings to our clients, accelerating adoption and maximizing impact,” comments Álvaro Verdeja, COO – Cloud, AI, Cyber and Software at Making Science.

As a Google Cloud Premier Partner, Making Science strengthens its ability to accompany organizations in the implementation of solutions based on generative artificial intelligence, with an approach that combines technical knowledge, operational experience, and business vision. This implementation aligns with Google Cloud’s strategy to drive AI technology adoption through its network of certified partners.

As artificial intelligence continues to reshape business landscapes, Making Science and Google Cloud recently brought together industry leaders at Google’s Madrid headquarters to explore the transformative power of Enterprise AI Agents. This event highlighted how companies are moving beyond basic automation to adopt intelligent, autonomous systems that redefine customer engagement and operational efficiency.

The Strategic Evolution of Enterprise AI: From Tools to Autonomous Entities

The evolution of AI in enterprise settings has been remarkably rapid. Businesses have progressed from experimental AI in 2023 to real-world implementations in 2024. Now, in 2025, the emergence of AI agents is driving the next stage of transformation. These intelligent systems are no longer just tools but autonomous decision-makers capable of optimizing operations, automating complex workflows, and accelerating business growth.

Key Themes & Insights:

• From White Box to Black Box AI: Embracing Agility over Algorithmic Detail

Enterprises are moving away from the need to understand every algorithmic detail (white box AI) toward testing AI-driven outcomes (black box AI). As Large Language Models (LLMs) become more advanced, the focus is shifting to “trusting” the LLM while applying the required prompting, grounding and testing; leveraging AI’s agility, rather than dissecting every aspect of each neural network supporting each LLM in an AI Agent.. This approach allows businesses to prioritize adaptability, performance, and user experience over algorithmic transparency.

• AI Assistants vs. AI Agents: Understanding the Difference

AI Assistants support customer interactions and internal processes but still require human supervision. AI Agents function independently, making decisions and taking actions without direct human input. However, effective governance is essential to ensure reliability and trust in fully autonomous systems.

• AI Adoption: Bridging Innovation and Practical Implementation

Despite the rapid advancements in generative AI, widespread adoption in business remains in its early stages. Organizations seeking to maximize AI’s potential must focus on:

• Seamless integration with business processes and APIs
• High-quality, structured data to improve AI-driven insights
• Interconnected platforms to enhance automation and decision-making

A case study presented at the event illustrated how Conversational AI and Retrieval-Augmented Generation (RAG) are transforming real estate searches. With over 30,000 successful AI-driven interactions with improved conversion rates, this solution has significantly improved customer engagement while reducing operational costs. AI-powered chatbots in real estate now maintain conversation context, understand user needs, and provide personalized recommendations. One of the key propositions of the AI Agent was the continuous incorporation of new real estate inventory and the enrichment of the data applying points of interest such as schools, hospitals, metro stops, police stations, airports, etc from Google Maps.

• Expanding AI Applications Across Industries

Predictive Analytics – AI detects fraud and prevents network abuse by analyzing suspicious activity patterns.

Legacy System Migration – Generative AI evaluates risks, costs, and timelines for transitioning outdated IT systems.

Data Integration – AI enhances cross-platform data consistency, improving analytics and business intelligence.

• Security and Compliance in AI: Ensuring Responsible Deployment

As AI adoption scales, data security, privacy, and compliance remain critical priorities. Discussions at the event underscored the importance of:

• Regulatory alignment to meet evolving data privacy laws
• Ethical AI governance to ensure responsible decision-making
• Robust security frameworks to prevent misuse and protect sensitive information

A well-structured approach to AI governance will be essential to maintain trust and unlock AI’s full potential.

Google Cloud’s Innovations for Enterprise AI Agents

Google Cloud continues to lead enterprise AI innovation, equipping businesses with powerful tools to integrate AI Agents effectively into their operations.

Customer Engagement Suite (CES): Gemini 2.0 and the Future of Multimodal AI

One of the key announcements at the event was the introduction of Customer Engagement Suite’s (CES) integration of Gemini 2.0, a multimodal AI solution integrated into the. This advancement enables businesses to facilitate seamless interactions across voice, text, and visual inputs, ensuring a fluid and personalized customer experience.

For example, a customer support system powered by Gemini 2.0 can interpret a mix of text-based queries, voice commands, and image uploads, all while maintaining contextual understanding. This level of interaction enhances customer satisfaction and streamlines service efficiency.

The adoption of multimodal AI represents a major leap forward in enterprise AI, providing organizations with an intuitive and adaptable engagement platform.

Revolutionizing Workplace Efficiency with AgentSpace

The introduction of Google’s AgentSpace platform addresses a critical challenge in modern enterprises: the fragmentation of information and tools. By integrating advanced search capabilities with AI-powered assistance, AgentSpace transforms how employees interact with enterprise systems. This integration reduces the time spent switching between applications while enhancing decision-making through improved access to organizational knowledge. 

Employees are no longer bound by siloed data or the need to navigate multiple interfaces. AgentSpace intelligently connects relevant information, allowing for faster, more informed decisions and a more productive work environment.

NotebookLM: Advancing Enterprise Intelligence

A significant advancement in enterprise tools, NotebookLM Enterprise demonstrates the power of AI in research and information synthesis. The platform’s ability to process diverse content types – from documents to video content – while maintaining context and generating actionable insights represents a new paradigm in enterprise information management. Imagine being able to analyze a complex research paper, a lengthy video presentation, and a series of internal reports, all within a single interface. 

NotebookLM not only processes these diverse formats but also identifies key themes, synthesizes information, and generates actionable insights, effectively turning raw data into strategic knowledge. This tool empowers professionals across various sectors to accelerate research, enhance analysis, and ultimately, make more informed decisions.

Looking Ahead: The Future of Enterprise AI

Enterprise AI is advancing at an unprecedented pace. The integration of intelligent AI agents marks not just an evolution in technology but a fundamental shift in how businesses operate, engage with customers, and drive innovation.

As a strategic Google Cloud partner, Making Science continues to play a pivotal role in guiding organizations through AI adoption, implementation, and optimization. For businesses looking to leverage AI-driven transformation, our team of experts is ready to support their journey toward greater efficiency, agility, and intelligence.

The future of enterprise AI is here, and organizations that embrace it today will define the market leaders of tomorrow.

Slow IT is dead. In a world where speed defines winners, sticking to old ways means falling behind. Businesses that fail to innovate at speed while ensuring security and scalability risk being left behind. Traditional IT models can no longer handle the relentless demand for rapid software releases, resilient cloud infrastructures, and frictionless user experiences. This is where DevOps steps in, not as a mere technical fix but as a strategic weapon for survival and growth.

Companies that successfully integrate DevOps see faster time-to-market, reduced costs, and improved system reliability. Research shows that high-performing DevOps teams:

• Deploy software 30 times more frequently than traditional IT teams
• Experience 75% fewer failed deployments
• Recover from failures 24 times faster

As businesses accelerate their cloud adoption, Google Cloud’s DevOps Specialization serves as a benchmark for expertise in delivering these benefits. Making Science recently achieved this specialization, reinforcing our ability to help companies automate operations, enhance security, and scale cloud environments efficiently.

How DevOps Delivers Measurable Business Value

1. Accelerating Software Delivery with Automation

Companies that rely on manual development and deployment processes often struggle with long release cycles and inconsistent software quality. DevOps introduces continuous integration and continuous deployment (CI/CD) pipelines, enabling organizations to:

• Deploy new features faster and more frequently
• Reduce human error by automating repetitive tasks
• Improve collaboration between development and operations teams

Use Case: An e-commerce company struggling with frequent website crashes during high-traffic sales events implemented DevOps automation to streamline deployments. By automating testing and release management, they reduced deployment time from weeks to hours, ensuring a seamless shopping experience for customers.

2. Enhancing Security Without Slowing Innovation

Security is often treated as a separate process, leading to vulnerabilities being detected too late in the development cycle. DevSecOps, a key component of DevOps, integrates security checks from the start, reducing risks while maintaining agility. Businesses benefit from:

• Automated security scans and compliance checks
• Faster identification and resolution of vulnerabilities
• Reduced downtime due to security incidents

Use Case: A financial services company dealing with strict regulatory requirements adopted DevSecOps to automate compliance reporting and security audits. This cut their compliance costs while ensuring that every software update met industry standards.

3. Scaling Cloud Infrastructure Efficiently

Many organizations face challenges in scaling their applications without over-provisioning resources and inflating costs. DevOps, combined with Google Cloud’s scalable infrastructure, enables:

• Auto-scaling to handle traffic spikes efficiently
• Cost optimization by aligning cloud resources with demand
• Improved system resilience with automated failover mechanisms

Use Case: A telecom provider expanded its services to new markets but needed to ensure consistent application performance across different regions. By adopting DevOps practices, they optimized their cloud infrastructure, reducing server downtime and lowering cloud costs. 

Why DevOps is Now a Competitive Advantage

Businesses that embrace DevOps gain operational efficiency, stronger security, and cost savings, but more importantly, they stay ahead of the competition. DevOps is no longer just for tech companies—retailers, banks, healthcare providers, and manufacturers are all leveraging it to:

• Reduce time-to-market for new products and services
• Optimize IT and development costs with automation
• Strengthen security while maintaining agility
• Enhance customer experiences with more reliable applications

With digital transformation accelerating across industries, companies that fail to adopt DevOps risk falling behind. A modern business strategy must include automated, secure, and scalable cloud operations—which is exactly what DevOps enables.

How to Get Started with DevOps for Your Business

Implementing DevOps requires more than just tools; it involves a cultural and operational shift. Here are key steps businesses can take:

1. Assess current processes – Identify bottlenecks in development, deployment, and security workflows.
2. Automate where possible – Introduce CI/CD pipelines, infrastructure as code (IaC), and automated security testing.
3. Adopt cloud-native practices – Use scalable cloud infrastructure, serverless computing, and containerization.
4. Ensure security is built-in – Implement DevSecOps to maintain compliance without slowing innovation.
5. Partner with specialists – Work with certified experts to accelerate adoption and maximize impact.

Future-Proofing Your Business with DevOps

DevOps isn’t just about improving IT efficiency—it’s about enabling business growth. Companies that integrate DevOps into their cloud strategy, security model, and automation efforts gain a significant edge.

Google Cloud’s DevOps Specialization provides reliability for businesses, ensuring they wor with proven experts in cloud transformation. Making Science, with its six Google Cloud specializations, offers end-to-end support for companies looking to:

• Scale cloud operations efficiently
• Strengthen security while accelerating innovation
• Reduce costs through automation and optimized cloud usage
• Improve software reliability and customer satisfaction

Staying ahead in the digital race requires more than just technology… It demands a shift in mindset and strategy.

Want to explore how DevOps can transform your business? Get in touch with our team to discuss a tailored strategy that aligns with your goals.

In a world where data is the new gold, its protection has become an absolute priority for companies of all sizes. The year 2024 has taught us important lessons about digital security, with cases that have exposed critical vulnerabilities in personal data management. City councils, hospitals, newspapers, the DGT, and major companies like Telefónica, Deloitte, and Banco Santander have been just some examples of victims affected by exposed sensitive data issues. 2025 will be no different, as cybercriminals, increasingly prepared and innovative, are ready to improve their attack vectors.

Is your organization prepared for the data protection challenges of 2025? Discover the most significant data protection errors of the past year and how to avoid them in your company during this new year.

1. AI Misuse: The Double-Edged Sword

Artificial Intelligence continues to transform everything in its path. The proliferation of models and their integration with various tools have significantly expanded access to information, often without considering how this data is used in model training processes.

For example, lawsuits against OpenAI for privacy violations marked a turning point in how we consider data use in AI model training. The AI tools available in today’s market are endless – models, platforms, extensions, code assistants – all are very useful and make our work more efficient, but users often have no idea what happens with the information they use.

How to protect your company in 2025?

• Implement privacy impact assessments before adopting any AI tool
• Implement sandboxing for AI applications (usually possible with paid instances)
• Develop clear policies on what information can be shared with AI assistants
• Focus on education about responsible AI use rather than excessive restrictions

2.Unencrypted Communications: An Open Door for Cybercriminals

Unencrypted communications have become one of the main access routes for cybercriminals, who exploit this vulnerability to intercept sensitive data in transit. It’s like sending all letters in transparent envelopes.

Unencrypted communications are particularly dangerous in today’s hybrid business ecosystem, where data constantly travels between offices, homes, and the cloud. When this data travels “in plain text,” any malicious actor with network access can intercept, read, and manipulate the information without leaving a trace. It’s like shouting confidential information in a public square hoping no one else is listening.

The problem is magnified in multi-cloud environments, where data passes through multiple network points before reaching its destination. In 2024, 40% of data breaches occurred precisely because of this vulnerability, with an average cost exceeding 5 million euros per incident. More worryingly, these breaches took an average of 283 days to detect, during which attackers had free access to sensitive information.

Your shield for 2025:

• Implement end-to-end encryption in all communications
• Adopt robust key management solutions
• Establish regular encryption system audit schedules

3. Poor Access Control: The Weakest Link

Imagine a corporate building where all doors are open, with no record of who enters or leaves, and where any employee can access the safe. This is the perfect analogy to describe how many companies managed their digital access in 2024.

The complexity and workload involved in properly managing and monitoring access means many organizations fail in critical aspects: former employees maintain active credentials months after their departure, elevated privileges granted for specific projects remain indefinitely, and when attackers manage to compromise a single access point, they find an almost clear path to move laterally throughout the corporate network.

The 2024 Snowflake case taught us a clear lesson: more than 165 companies suffered security breaches for not activating multi-factor authentication. A basic error with devastating consequences.

Your winning strategy for 2025:

• Rigorously implement the principle of least privilege
• Invest in next-generation IAM systems
• Use mandatory MFA systems
• Schedule quarterly access permission reviews

4. Insufficient Monitoring: The Price of Neglect

The math is simple: according to IBM, organizations with reduced security teams paid $1.76 million more in breach costs. Investment in cybersecurity continues to be underestimated; it’s a matter of priorities – SMEs don’t consider cybersecurity one of them, and this can lead to data leaks, loss of money, customers, or reputation.

Your action plan for 2025:

• Implement 24/7 monitoring systems with behavior analysis
• Adopt AI-powered threat detection tools
• Partner with specialists for implementing monitoring measures and tracking hybrid multi-cloud environments

5. Insufficient Investment in Training and Systems: The False Economy

Companies that invested in AI and security automation saved $2.2 million in breach costs during 2024. Training and awareness play a fundamental role in this area – if your employees know the risks and problems, they’ll be much more alert in their daily work. At Making Science, around 2,500 phishing cases were detected, which is why we raise awareness among our employees with internal tests updated with the latest attack methodologies, keeping us trained and in shape.

Your smart investment for 2025:

• Allocate specific budget for cybersecurity training
• Modernize your infrastructure with advanced security technologies
• Implement regular security drill programs such as phishing campaigns

Conclusion: Data Protection as a Competitive Advantage

In 2025, data protection won’t just be a legal obligation but a crucial competitive advantage. Companies that learn from past mistakes and implement proactive solutions will not only protect their sensitive information but will also gain the trust of their customers and partners.

Are you ready to turn data protection into your strategic ally? The time to act is now. Investment in data security isn’t an expense; it’s an investment in your company’s future.

Need help implementing these measures in your organization? Contact us for a free assessment of your data security.

As we approach 2025, technology leaders face a pivotal moment. The convergence of AI autonomy, quantum advancements, and human-machine collaboration isn’t just reshaping technology – it’s fundamentally transforming how businesses create and capture value. Drawing from extensive Gartner and IDC research, we explore how forward-thinking CIOs and technology leaders will navigate this new landscape.

The stakes have never been higher. With 50% of G1000 organizations facing divergent regulatory changes by 2025 and 85% implementing formal AI governance policies, technology leaders must balance innovation with responsibility. This isn’t just about adopting new technologies – it’s about architecting the future of business itself.

The New AI Paradigm

The landscape of AI is rapidly evolving, with Generative AI emerging as a powerful force. This transformative technology is not only automating tasks but also creating entirely new products, services, and business models. As Conversational AI continues to advance, organizations are increasingly leveraging AI to drive innovation, optimize operations, and enhance customer experiences.

According to recent research from Google Cloud, early adopters are seeing significant benefits, including:

• Productivity Boost: A 45% increase in productivity.
• Enhanced User Experience: An 85% improvement in user engagement.
• Strengthened Security: A 56% fortification of security posture.
• Revenue Growth: An 86% increase in revenue.

To fully realize the potential of AI, organizations must adopt a strategic approach. This includes:

• Ethical AI Governance: Establishing guidelines to ensure responsible AI development and deployment.
• Quantum Readiness: Preparing for the quantum computing era by implementing quantum-resistant cryptographic algorithms.
• Human-AI Collaboration: Leveraging AI to augment human capabilities and drive innovation.

Generative AI represents a fundamental shift in how organizations approach automation and decision-making. Autonomous agents capable of planning, adapting, and executing complex strategies independently will handle 15% of daily business decisions by 2028, necessitating both vision and careful governance.

The research reveals that organizations must formalize policies and oversight to address AI risks, including ethical considerations, brand protection, and personal information security. By 2025, 85% of organizations will align AI governance with strategic business objectives, making it a cornerstone of digital transformation.

By embracing these principles and recognizing the transformative potential of Generative AI, organizations can navigate the complexities of the AI landscape and emerge as leaders in the digital age.

Key Security Trends

Two critical security trends are emerging:

• Quantum-Ready Infrastructure: By 2027, only 50% of organizations will effectively leverage AI for incident detection and resolution. Organizations must begin transitioning to quantum-resistant algorithms now, following a structured approach from evaluation to implementation.
• Defense Against Disinformation: By 2028, 50% of companies will invest in specialized security capabilities to combat disinformation, up from just 5% in 2024. This investment reflects the growing recognition that reputation and trust are as crucial as data protection in an AI-driven world.

The Evolution of Computing

The future of computing is neither purely cloud-based nor exclusively on-premises—it is intelligent hybrid computing. This new paradigm combines:

• Energy-Efficient Computing: As AI workloads intensify, sustainability becomes a board-level priority. New computing architectures promise substantial efficiency gains while meeting environmental responsibilities.

• Ambient Invisible Intelligence: Through 2028, the proliferation of low-cost sensors and IoT devices will focus on practical applications: reducing costs, improving efficiency, and enabling real-time decision-making.

• Hybrid Architectures: Organizations must balance security with innovation, combining on-premises control with cloud scalability to optimize performance and compliance.

Human-Machine Collaboration

Perhaps most transformative is the evolution of how humans and machines work together. By 2030, 80% of humans will regularly interact with smart robots, while spatial computing creates new paradigms for collaboration and creativity.

Research predicts that by 2028, 80% of G1000 CIOs will be recruited externally, highlighting the need for leaders who can orchestrate this human-machine synergy effectively. Success will require:

• Expertise in digital innovation
• Strategic leadership capabilities
• Industry versatility
• Change management proficiency

Strategic Implications

For technology leaders, these trends converge to create both opportunities and imperatives. Success in 2025 and beyond will require:

• A balanced approach to AI autonomy and governance
• Proactive preparation for quantum security threats
• Strategic investment in sustainable computing infrastructure
• Thoughtful orchestration of human-machine collaboration

Another report predicts that by 2028, 50% of organizations will adopt cutting-edge tools to address the digital and AI skills gap, reducing dependence on specialized talent while enhancing workforce capabilities for innovation.

Conclusion

The technology landscape of 2025 demands more than technical expertise: it requires strategic vision and deep business insight. Leaders who can interpret these converging trends and focus on value creation will define the next era of digital transformation.

The future is arriving faster than we think. Is your organization ready for these transformative changes? At Making Science, we understand the implications of these trends and know how to help organizations manage change effectively. Get in touch with our experts to explore how these trends align with your business strategy and prepare for the changes ahead.

As businesses strive to gain a competitive edge, the ability to transform raw data into actionable insights has become paramount. However, the path to leveraging data effectively has often been an uphill climb plagued by technical hurdles and siloed information. At our recent event “GenAI + BI: Accelerating Time-to-Insight with Looker” in collaboration with Google Cloud, we witnessed how generative AI is revolutionizing this paradigm, ushering in a new era of conversational analytics.

The Evolution of Business Intelligence

For decades, extracting insights from data meant wrestling with complex queries, static dashboards, and clunky reporting processes accessible only to the technically skilled. While tools like Looker provided powerful data exploration and visualization capabilities, ensuring data democratization remained an uphill battle. What set Looker apart was its robust data governance foundation – a centralized model that promoted trust and accuracy while unlocking self-service analytics across an organization.  However, even with these advancements, a gap persisted between businesses and the true democratization of data they sought.

This disconnect has been a driving force for our team at Making Science. As I shared during the event, “For too long, data democratization and getting insights from data has been an uphill climb.” But the integration of Looker and generative AI (GenAI) is profoundly changing the game.

Fostering True Data Literacy

At the core of this transformation lies the power of natural language interaction. Through Looker’s GenAI capabilities, users across an organization can simply ask questions and receive accurate, real-time insights without grappling with SQL, data modeling, or visualizations. This intuitive approach empowers teams to become active participants in data-driven decision-making, fostering a culture of true data literacy.

Fabrizio Russo, Cloud BI Specialist at Google Cloud, encapsulated this shift perfectly: “With Looker and GenAI, you can ask complex questions and receive answers instantly, without the need for technical expertise or data manipulation. This is data democratization in action.”

From Data to Profit: AI-Powered Monetization

But the potential of Looker’s GenAI integration extends far beyond interactive Q&A. Through live product demonstrations, we witnessed firsthand how these capabilities enable users to automatically generate visualizations, build custom dashboards, rapidly develop data-driven applications, and much more – all via simple natural language prompts.

This seamless translation from insights to action unlocks new frontiers in monetizing data. Tools like Looker Actions and Embedded Analytics empower businesses to package and monetize their analytics offerings, create tiered pricing models for differentiated data access, embed insights into customer-facing platforms, and drive sustainable revenue growth.

We had the privilege of hearing from Sergio Pescador, Corp Dev. & BI Director at Playtomic, the leading padel reservation app. Sergio detailed their inspiring journey of leveraging Looker to unify organizational data, enabling data-backed decision-making across departments while maintaining their competitive edge.

Looking Ahead: Embracing the Future of BI

As this groundbreaking event made clear, the fusion of Looker and GenAI is merely the beginning of a seismic shift in how businesses will interact with and extract value from their data. Generative AI will continue to push the boundaries of what’s possible, propelling us toward an era of hyper-personalized data experiences and unprecedented efficiencies.

At Making Science, we remain committed to staying at the forefront of these transformative AI capabilities in the BI space. Our team of seasoned experts stands ready to guide organizations in navigating this new frontier and unlocking the full potential of a conversational, AI-driven approach to data analytics.

The path from insights to impact has never been more clear or achievable. The dawn of conversational analytics is here, and those who embrace it will gain a decisive competitive advantage. I encourage you to explore Making Science’s comprehensive offerings and engage our team to begin your data transformation journey.