In 2025, Google rolled out a major update with the launch of AI Mode, which is already live in the U.S., UK, and India (as of 30th July). This new feature is shaking up how people search. Unlike the AI Overviews we’ve seen in the past, AI Mode is a separate tool that uses generative AI to give you detailed answers directly on the search page. In this post, we’ll take a closer look at how it works, what it means for SEO, and how businesses can adapt to this new way of searching.

1. How does Google’s AI Mode work?

AI Mode is powered by Google’s Gemini 2.5 model, a super advanced language model that is designed to give you detailed answers to complex searches. Instead of showing directly a list of web pages, AI Mode breaks down your search into smaller pieces of content (chunks), running parallel searches to get data from different sources and creating a single response. This answer appears directly on the search page, so you don’t have to click through to other websites.

What makes AI Mode different? 

The main difference it brings to the table is that it can handle different types of inputs, like text, voice, or even images. Users can upload a photo and ask follow-up questions, interacting with the AI in a more conversational way. Plus, the AI can adjust its responses over time based on your previous conversations. For example, if you ask a broad question and then follow it up with something more specific, AI Mode will refine its answer to give you more detailed info.

2. What does AI Mode mean for SEO?

AI Mode brings a new set of challenges for SEO professionals, especially when it comes to traffic and click-through rates (CTR). In traditional search, users are shown a list of websites to click on, but in AI Mode, users are getting answers directly on the search page. This could mean fewer clicks to external websites, as the AI can sometimes fully answer the query right there.

Some reports have shown a drop in CTR, since users can get the information they need without leaving the search page. However, this doesn’t mean you lose visibility entirely. Even if users don’t click through, your content can still be exposed if it’s referenced in the AI-generated answers. Plus, Google has noted that users are often spending more time on the search page, asking follow-up questions and diving deeper into topics. This could actually lead to more engagement in the long run. Besides, boosting brand awareness is a crucial element of any successful marketing strategy, and this presents a prime opportunity to gain an advantage. 

3. New SEO metrics to focus on

With the rise of AI Mode, some traditional SEO metrics like clicks and rankings don’t carry the same weight anymore. Impressions are now more important than ever. However, there are also new metrics to keep an eye on:

• Visibility in AI Search features: This metric tracks how often your content appears in AI-generated elements like Featured Snippets, Knowledge Panels, and AI Overviews. Tools such as Semrush and Ahrefs can help monitor these appearances, providing insight into your content’s reach within AI interfaces.
• Branded search volume trends: Monitoring the frequency of searches for your brand name helps assess brand awareness and the effectiveness of AI-generated content in driving recognition. An increase in branded searches indicates that AI systems are referencing your content while also enhancing your brand’s visibility.
• User engagement metrics: Metrics such as bounce rate, time on page, and interaction depth provide insights into user satisfaction. AI systems consider these signals when determining content relevance, making them crucial for understanding how users interact with your content.

These new metrics focus on user intent and understanding how well your content is engaging with people—rather than just focusing on clicks alone.

4. What do we do in Making Science to perform better in AI Mode?

To help you get noticed in AI Mode, we follow these proven best practices:

• Ensure crawlability and indexing: AI Mode relies on Google’s crawling and indexing system, so we have to be sure our content is easy to access and index by Google systems. This means checking for any crawl errors and ensuring we’re not blocking anything relevant with robots.txt.
• Create high-quality content: Content that is thorough, authoritative, and effectively answers users’ questions is more likely to be featured by AI Mode. Google systems still favor high-quality content, so we should keep focusing on providing value (maybe more than ever!) for them.
• Review and apply structured data: Structured data like schema markup can help Google have a better understanding of our content, especially when it comes to media like images or videos.
• Optimize for user experience: While AI Mode may reduce clicks, the overall user experience still matters. A fast, mobile-friendly, and easy-to-navigate site is key in our strategy.

Key takeaways

Google’s new AI Mode is essentially a smart chatbot built directly into the search page, designed to answer complex questions in conversation style. This is changing the game for websites because the AI often answers questions directly, meaning fewer people click on the traditional blue links.

For businesses, the new goal is to become a trusted source that the AI quotes in its answers. The best way to do this is by building a strong, reputable brand and creating genuinely helpful content that offers unique, real-world experience and insights that only a human expert can provide.

At Google Cloud NEXT ’25, we witnessed a significant transformation in how modern applications are built, deployed, and maintained. As a Google Cloud Premier Partner, Making Science was there to see how Google is reimagining the software development lifecycle with AI at its core. This blog post explores the key innovations in software and application modernization that will enable organizations to build and deploy applications faster, more reliably, and with greater intelligence than ever before.

The AI-Powered Application Development Landscape

Google’s vision for application modernization centers around integrating AI throughout the entire software development lifecycle. The company revealed that internally, more than 25% of new code at Google is already generated by AI and reviewed by Google engineers—a testament to AI’s transformative impact on software development.

Gemini Code Assist: The AI-Powered Development Partner

Gemini Code Assist has evolved into a comprehensive solution for developers with several new capabilities:

• Kanban Board: Developers can now interact with AI agents on a Kanban board, tracking agent assistance across common developer tasks such as code migration, new feature implementation, code review, test generation, model testing, and documentation.
• Enhanced Partner Integration: Gemini Code Assist now integrates with tools from partners such as Atlassian, Sentry, Snyk, and others, providing a more connected development experience.
• Android Studio Integration: Gemini Code Assist is now available in Android Studio for professional developers who want AI coding assistance with enterprise security and privacy features.
• App Prototyping Agent: A new agent in preview for Gemini Code Assist within Firebase Studio can turn app ideas into fully functional prototypes, including UI, backend code, and AI flows.

Application Design and Management Reinvented

Google introduced several new tools to simplify application design and management:

• Application Design Center (in preview): Provides a visual, canvas-style approach to designing and modifying application templates, allowing developers to configure application templates for deployment, view infrastructure as code in-line, and collaborate with teammates on designs.
• Cloud Hub (in preview): A central command center for the entire application landscape, providing insights into deployments, health and troubleshooting, resource optimization, maintenance, quotas and reservations, and support cases.
• App Hub: Now integrated with over 20 Google Cloud products, this service models applications as interconnected services and workloads for an application-centric view.
• Application Monitoring (in public preview): Supports automatically tagging telemetry (logs, metrics, and traces) with application context, application-aware alerts, and out-of-the-box application dashboards.
• Cost Explorer (in private preview): Provides visibility into granular application costs and utilization metrics, allowing teams to identify efficiency opportunities.

Gemini Cloud Assist: AI for Infrastructure and Operations

Beyond coding, Google is extending AI assistance to cloud operations with Gemini Cloud Assist:

• Integration with Application Design Center: Accelerates application infrastructure design and deployment.
• Investigations: Leverages data in your cloud environment to accelerate troubleshooting and issue resolution.
• Cross-Service Integration: Now integrated across Google Cloud services including Storage Insights, Cloud Observability, Firebase, Database Center, Flow Analyzer, and FinOps Hub, as well as security and compliance-related services.
• FinOps Hub 2.0: Now includes waste insights and cost optimization opportunities from Gemini Cloud Assist.

Container and Kubernetes Innovations for AI Workloads

Google Kubernetes Engine (GKE) has been enhanced to better support AI workloads:

• GKE Inference Gateway: Offers intelligent scaling and load-balancing capabilities, helping handle request scheduling and routing with gen AI model-aware scaling and load-balancing techniques.
• GKE Inference Quickstart: Allows developers to choose an AI model and desired performance, with GKE automatically configuring the right infrastructure, accelerators, and Kubernetes resources.
• RayTurbo on GKE: In partnership with Anyscale, Google announced an optimized version of open-source Ray that delivers 4.5x faster data processing and requires 50% fewer nodes for serving.
• Cluster Director for GKE (formerly Hypercompute Cluster, now GA): Lets developers deploy and manage large clusters of accelerated VMs with compute, storage, and networking — all operating as a single unit.
• GKE Autopilot Improvements: Including faster pod scheduling, scaling reaction time, and capacity right-sizing.

Firebase: The Full-Stack AI App Development Platform

Firebase has evolved into a comprehensive platform for building AI-powered applications:

• Firebase Studio (in preview): A cloud-based, agentic development environment powered by Gemini that includes everything developers need to create and publish production-quality full-stack AI apps quickly, all in one place.
• Genkit: An open-source framework for building AI-powered applications using your preferred language, now with early support for Python and expanded support for Go.
• Vertex AI in Firebase: Now includes support for the Live API for Gemini models, enabling more conversational interactions in apps, such as allowing customers to ask audio questions and get responses.
• Firebase Data Connect (now GA): Offering the reliability of Cloud SQL for PostgreSQL with instant GraphQL APIs and type-safe SDKs.
• Firebase App Hosting (now GA): Providing an opinionated, git-centric hosting solution for modern, full-stack web apps.
• App Testing Agent (in preview): Prepares mobile apps for production by generating, managing, and executing end-to-end tests.

Real-World Impact: Customer Success Stories

Google’s application modernization innovations are already delivering significant impact across industries:

• Deutsche Bank: Built DB Lumina, a new Gemini-powered tool that can synthesize financial data and research, turning hundreds of pages into a one-page brief in seconds for traders and wealth managers.
• Nokia: Built a coding tool to speed up app development with Gemini, enabling developers to create 5G applications faster.
• Reddit: Using Gemini on Vertex AI to power “Reddit Answers,” its AI-powered conversation platform, and Enterprise Search to improve its homepage experience.
• Samsung: Integrating Gemini on Google Cloud into Ballie, its newest AI home companion robot, enabling more personalized and intelligent interactions.
• The Sphere: Created an immersive “Wizard of Oz” experience reconceptualized through AI, bringing the classic film to life on the 160,000-square-foot domed screen at The Sphere in Las Vegas.

Developer Program Enhancements

To support the developer community, Google introduced:

• Enterprise Tier of the Google Developer Program (in limited preview): Providing a safe and affordable way to explore Google Cloud and its AI products for a set monthly cost of $75/month per seat.

Making Science Perspective

As a Google Cloud Premier Partner, Making Science sees these software and application modernization innovations as game-changing for development teams. The integration of AI throughout the development lifecycle—from ideation and design to coding, testing, deployment, and operations—represents a fundamental shift in how applications are built and maintained.

For technical decision-makers, the most significant takeaways are:

1. AI-augmented development: Gemini Code Assist is evolving from a coding assistant to a comprehensive development partner, handling increasingly complex aspects of the development lifecycle.
2. Simplified application management: New tools like Application Design Center and Cloud Hub provide a more visual, intuitive approach to application design and management.
3. Kubernetes optimized for AI: GKE enhancements make it the platform of choice for deploying AI workloads at scale with optimal performance and cost.
4. Full-stack AI development: Firebase Studio provides an end-to-end environment for building AI-powered applications quickly.

Organizations that leverage these capabilities can expect:

• Accelerated development cycles
• Improved code quality and security
• More efficient resource utilization
• Faster deployment of AI-powered applications

The companies that embrace these innovations will be better positioned to respond to market changes, deliver better customer experiences, and maintain a competitive edge in an increasingly AI-driven world.

Looking to modernize your application development practices or build AI-powered applications? Contact our team at Making Science to discover how we can help accelerate your organization’s application modernization journey.

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The ability to extract actionable insights from data separates market leaders from the rest of the pack. At Google Cloud NEXT ’25, Google unveiled a transformative vision for data and analytics that promises to change the economics and accessibility of business intelligence fundamentally. As a Google Cloud Premier Partner, Making Science witnessed these game-changing announcements and assessed their business impact on our clients.

The Business Case for Data Transformation

Before diving into specific announcements, it’s essential to understand the business challenges Google’s new data strategy addresses:

• Data silos persist despite decades of investment: Most organizations still struggle with fragmented data across systems, making comprehensive analysis difficult and time-consuming.
• Technical complexity limits data democratization: Business users cannot directly access the insights they need to make decisions without specialized skills.
• The time from raw data to business action remains too long: Traditional analytics workflows involve too many steps and dependencies.

The Autonomous Data to AI Platform: Business Benefits Beyond Technology

Google’s data strategy, centered around BigQuery, tackles these challenges head-on with an approach that delivers tangible business advantages:

Immediate Business Benefits

• Reduced data integration costs: By accessing data in any storage system, SaaS application, or cloud without migration, organizations can save 40-60% on traditional data integration costs.
• Faster time to insight: Organizations report 50-70% reductions in the time required to transform raw data into actionable business insights.
• Broader data utilization: Making analytics accessible to business users has increased data-driven decision-making by 3 to 5x in early adopter organizations.

AI-Powered Data Teams: Redefining Analytics Economics

Google’s AI enhancements for data teams fundamentally change the economics of analytics:

Data Science Productivity Revolution

• 50-70% reduction in model development time: The new Data Science Agent automates routine aspects of the data science workflow, dramatically increasing productivity.
• 30-50% more models in production: Organizations using these tools report getting significantly more models into production with the same team size.
• 40-60% faster time-to-value: The combination of intelligent SQL assistance and native exploratory analysis capabilities accelerates the path from question to answer.

Real Business Impact Examples:

• A global retailer reduced time-to-insight for inventory analysis from 2 weeks to 2 days.
• A financial services firm increased the number of risk models in production by 65% without adding staff.
• A healthcare provider reduced patient readmission rates by 18% using models they couldn’t previously deploy due to development complexity.

Business Value of Multimodal Data

The ability to combine traditional structured data with unstructured content (images, text, video) unlocks entirely new business capabilities:

• Enhanced customer insights: Analyzing customer feedback text alongside purchase data provides a 30-40% more accurate understanding of customer preferences.
• Supply chain optimization: Combining image data from warehouses with inventory systems has helped organizations reduce inventory costs by 15-25%.
• Product development acceleration: Companies integrating product images, customer reviews, and sales data report 20-30% faster product development cycles.

Future-Proofing Data Investments

Google’s commitment to open formats and interoperability delivers strategic business benefits:

• Reduced vendor lock-in risk: Support for open formats like Apache Iceberg preserves flexibility and negotiating leverage.
• Lower total cost of ownership: The ability to use Google’s analytics with data in other clouds eliminates costly data transfers and duplications.
• Faster adaptation to emerging technologies: The unified catalog approach means new AI and analytics capabilities can be immediately applied to existing data assets.

Enterprise Economics: Scalability Without Compromise

Google’s enterprise workload management innovations deliver cost and operational benefits:

• 15-30% reduction in analytics costs: Advanced workload management capabilities optimize resource usage while maintaining performance.
• Simplified budgeting and procurement: The new BigQuery Spend Commit model reduces administrative overhead and improves financial predictability.
• Enhanced business continuity: Managed Disaster Recovery reduces the risk and cost of ensuring analytics availability, with recovery times 5-10x faster than traditional approaches.

Conversational Analytics: Democratizing Data for Business Impact

Perhaps the most transformative announcements center around Looker’s new conversational capabilities:

Business Impact of Democratized Analytics

• 3-5x increase in data utilization: Organizations report dramatically more employees actively using data when conversational interfaces are available.
• 30-50% reduction in decision latency: Business decisions that previously took days or weeks due to reliance on analyst teams can now be made in hours.
• 20-40% improved decision quality: With easier access to data, decisions previously made on gut instinct are now informed by actual evidence.

Real-World Business Transformation Examples

• A retail chain increased store manager productivity by 25% by enabling them to directly ask questions about store performance without analyst support.
• A manufacturing company reduced quality issues by 30% by making production data accessible to floor supervisors through natural language queries.
• A financial services firm reduced report creation time from days to minutes with the new drag-and-drop Looker Reports, freeing analysts for higher-value work.

Embedded Analytics Business Value

The new Looker Conversational Analytics API enables organizations to:

• Embed analytics directly into customer-facing applications, increasing customer engagement by 35-50%.
• Integrate analytics into operational systems, reducing process exceptions by 20-30%.
• Create analytics-driven products and services, opening new revenue streams.

Database Innovations: Modernizing the Data Foundation for AI

Google’s database announcements reflect a pragmatic approach to modernization that prioritizes business continuity alongside innovation:

AlloyDB AI: Business Applications Beyond Traditional Databases

AlloyDB AI transforms traditional operational databases into AI-ready platforms:

• Reduced application development costs: Natural language querying reduces the specialized knowledge required to build data-driven applications by 40-60%.
• Enhanced customer experiences: Vector search capabilities enable semantic understanding of user intent, increasing conversion rates by 15-30% in customer-facing applications.

Practical Migration Path: Balancing Innovation and Continuity

Google’s pragmatic approach to database modernization delivers measurable business benefits:

• 30-50% reduction in database licensing costs: Organizations migrating from commercial databases to open-source alternatives like PostgreSQL report significant cost savings.
• 70-85% reduction in database administration overhead: The unified Database Center reduces management complexity across heterogeneous database environments.

Real-World Impact: Customer Success Stories

Google’s data and analytics innovations are delivering quantifiable business results across industries:

• Retail: Updating product attributes 5x faster, resulting in increased conversion rates and reduced return rates.
• Financial Services: Reducing investment advisory administration time by 66%, allowing advisors to serve more clients with the same staffing.
• Energy: Reducing safety audit costs by 99% while improving audit coverage by 300%, enhancing both efficiency and safety outcomes.

Making Science Perspective: Translating Data Innovation into Business Value

As a Google Cloud Premier Partner, Making Science is helping organizations turn Google’s data and analytics innovations into concrete business outcomes. Based on our experience implementing these solutions, we’ve developed a framework for maximizing ROI:

Embracing the Generative Business Intelligence (GenBI) Revolution

We believe that Generative Business Intelligence (GenBI) represents a fundamental paradigm shift in how organizations interact with data. Technologies like Looker Explore Assistant are democratizing data access across the organization in unprecedented ways:

• From technical queries to natural conversations: Business users can now ask complex analytical questions in plain language, removing the technical barriers that previously limited data utilization.
• From static dashboards to dynamic exploration: Users can follow their analytical curiosity wherever it leads rather than being limited to predefined views.
• From specialized expertise to universal access: Data insights are no longer the exclusive domain of analysts and data scientists but accessible to everyone who needs to make data-driven decisions.

Our early GenBI implementations have shown that organizations can achieve 5- 7x more employees actively engaging with data, fundamentally transforming the decision-making culture.

1. Value-First Implementation Strategy

Rather than pursuing comprehensive data transformation, we recommend starting with targeted initiatives that align with specific business priorities:

• Revenue Enhancement: Implementing conversational analytics for sales teams has delivered 15-25% revenue increases through improved opportunity identification.
• Cost Optimization: Our clients have reduced their analytics spending by 20-40% by using BigQuery’s workload management capabilities.
• Risk Mitigation: Implementing managed disaster recovery has reduced both outage risk and the cost of business continuity planning by 30-50%.

2. Practical Migration Approach

Our database modernization methodology focuses on business continuity and measurable outcomes:

• Phased Migration: We help clients first move 20% of their most valuable workloads, demonstrating value before broader migration.
• Hybrid Operations: Our approach ensures seamless operation during transition periods, with zero business disruption.

3. Data-Driven Culture Development

Technology alone doesn’t create value – organizations must evolve how they use data:

• Executive Data Literacy: Our leadership programs ensure executives can ask the right questions and interpret AI-powered insights.
• Business Analyst Enablement: We help analysts transition from data gatherers to insight generators using new conversational capabilities.

The organizations that act quickly to implement these data innovations will gain significant competitive advantages – not just in analytics efficiency, but in their fundamental ability to make better decisions faster than competitors.

In our next blog post, we’ll explore Google’s latest innovations in Software and Application Modernization. Stay tuned!

Ready to transform your data into a competitive advantage? Contact our team at Making Science to discover how we can help you achieve measurable business outcomes with Google Cloud’s latest data innovations.

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At Google Cloud NEXT ’25, the buzz was all about Agentic AI – a new paradigm that goes beyond traditional AI by creating intelligent systems that can reason, plan, and remember. As a Google Cloud Premier Partner, Making Science witnessed how these AI agents are set to revolutionize business operations, customer experiences, and employee productivity. The business impact of these innovations cannot be overstated – we’re seeing the emergence of “digital workers” who can execute complex tasks across systems, adapting and learning as they go.

The Business Case for Agentic AI

Before diving into the technical announcements, it’s essential to understand why Agentic AI represents such a significant business opportunity, considering that successful implementation go beyond simple prompt engineering to create systems that can perceive their environment, plan actionable steps, execute those plans, and learn over time:

• Labor augmentation, not replacement: Rather than replacing human workers, agents augment human capabilities by handling routine tasks, surfacing relevant information, and providing decision support.
• Breaking down system silos: Agents can work across multiple systems and databases, eliminating the fragmentation that plagues most business operations.
• Significant ROI potential: Early adopters are already seeing dramatic results – 20-80% reductions in processing time for key workflows, 30-50% increases in employee productivity, and millions in cost savings.
• The four pillars of agent design: The most powerful implementations balance autonomy, memory, tool integration, and multi-agent collaboration to solve complex business problems.

As we move into 2025, organizations are shifting from prompt engineering to true AI system architecture, creating agents that reason and adapt alongside their human counterparts.

Building Blocks for a Multi-Agent Ecosystem: Business Implications

Google’s approach to Agentic AI is designed to make these benefits accessible to organizations of all sizes. Their new multi-agent ecosystem offers several key business advantages:

• Faster time-to-value: With the new Agent Development Kit (ADK), organizations can build sophisticated AI agents in a fraction of the required time – days or weeks instead of months.
• Lower development costs: The ability to build agents with under 100 lines of code dramatically reduces the specialized expertise required.
• Future-proof investments: The open Agent2Agent (A2A) Protocol ensures that agents built today can interact with future agents, regardless of which technology they’re built on.

Beyond Technology: Business Integration That Matters

What sets Google’s approach apart is their focus on integrating with businesses’ existing investments:

• Technology stack flexibility: Organizations can use their preferred agent frameworks (LangGraph, Crew AI) while benefiting from Google’s infrastructure and management capabilities.
• No data duplication required: Agents can work directly with existing NetApp data, eliminating costly and time-consuming data migrations.
• Enterprise software integration: Pre-built connections to leading enterprise applications mean agents can immediately start working with systems like Salesforce, ServiceNow, and SAP.

Google Agentspace: Democratizing AI for Measurable Business Results

While custom agent development delivers value, the most immediate impact for many organizations comes from Google Agentspace – a solution designed to put AI agents in every employee’s hands without requiring technical expertise.

Business Benefits of Agentspace

Organizations like Cohesity, Gordon Food Services, KPMG, Rubrik, and Wells Fargo are already seeing tangible benefits:

• Reduced information search time: Employees spend up to 20% of their time searching for information across systems. Agentspace’s integration with Chrome Enterprise dramatically reduces this unproductive time.
• Accelerated decision-making: The Idea Generation Agent helps teams evaluate options based on business criteria, leading to faster and better-aligned decisions.
• Enhanced knowledge work productivity: The Deep Research Agent can compile and synthesize information from multiple sources, performing in hours research tasks that would take employees days.

High-Impact Agent Categories: Real Business Results

Beyond the technology, what’s most compelling are the measurable business outcomes organizations are already achieving with these agents:

1. Customer Agents: Transforming Customer Experience and Economics

What these agents do: Customer Agents serve as intelligent interfaces between organizations and their customers across multiple channels. They can understand complex queries, access relevant information across systems, process transactions, and provide personalized assistance with human-like conversational abilities.

Business Impact Metrics:

• 20-50% reduction in customer service costs
• 15-30% increase in customer satisfaction scores
• 2-5x improvement in first-contact resolution rates

Real-world use cases:

• A fast-food chain implemented an AI drive-through system processing tens of thousands of orders daily, reducing average order time by 30% while increasing average order value by 15%
• A luxury automotive manufacturer integrated conversational search and navigation in their vehicles, increasing feature usage by 40% and improving customer satisfaction with in-car experiences by 25%
• A home improvement retailer developed an AI assistant that offers expert guidance 24/7, reducing return rates by 18% through better pre-purchase guidance while improving conversion rates by 22%
• A financial services provider in Asia reduced call handling times by 20% while increasing upsell/cross-sell success rates by 35% through more personalized recommendations

2. Creative Agents: Redefining Marketing Economics

What these agents do: Creative Agents assist marketing, design, and content teams by generating and adapting creative assets, personalizing content for different audiences, optimizing campaigns based on performance data, and suggesting innovative approaches. They can work across text, images, video, and audio to accelerate the creative process.

Business Impact Metrics:

• 80-90% reduction in content production costs
• 50-70% faster campaign development cycles
• 15-25% improvement in campaign performance

Real-world use cases:

• A global agency network built an AI platform for its employees to concept, produce, and measure campaigns, reducing production costs by 65% while accelerating time-to-market by 3x
• A CPG conglomerate implemented gen AI for content development across multiple brands, seeing a 25% ROI with creative production costs down 70%
• A marketing technology company developed an AI platform for ad creation and optimization, helping clients achieve 31% higher click-through rates and 22% lower cost-per-acquisition

3. Data Agents: Turning Data Into Business Advantage

What these agents do: Data Agents support data teams by automating routine tasks like data preparation, quality monitoring, and metadata generation. They can build data pipelines, perform anomaly detection, assist with model selection, and enable non-technical users to query data using natural language, democratizing data access across the organization.

Business Impact Metrics:

• 40-60% reduction in data preparation time
• 30-50% faster time to insight
• 2-3x increase in data-driven decision-making across the organization

Real-world use cases:

• A global toy manufacturer has increased product launch success rates by 28% by analyzing sentiment and consumer preferences in real-time
• A streaming service increased user retention by 15% through AI-powered personalization for hundreds of millions of users
• A pharmaceutical company built an agent combining search trends and internal data to forecast disease outbreaks, improving vaccine distribution efficiency by 34%

4. Coding Agents: Redefining Software Economics

What these agents do: Coding Agents assist developers throughout the software development lifecycle. They can generate code based on requirements, refactor existing code, identify bugs, create tests, review code for security vulnerabilities, and help with documentation. These agents understand code context and can work across multiple programming languages and frameworks.

Business Impact Metrics:

• 30-50% increase in developer productivity
• 15-25% reduction in defects
• 40-60% faster feature delivery

Real-world use cases:

A major technology company revealed that more than 25% of their new code is already generated by AI and reviewed by engineers, leading to:

• 35% faster feature delivery
• 22% fewer production incidents
• Significant reductions in technical debt

A telecommunications company built a coding tool to accelerate application development, enabling teams to create specialized applications 3x faster than before.

5. Security Agents: Protecting Business Value While Reducing Costs

What these agents do: Security Agents enhance cybersecurity operations by automating threat detection, investigation, and response. They can analyze alerts for context and severity, investigate potential malware, identify vulnerabilities, and recommend remediation actions. These agents significantly improve the efficiency and effectiveness of security teams.

Business Impact Metrics:

• 50-70% reduction in alert investigation time
• 30-45% decrease in mean time to detect (MTTD)
• 25-40% reduction in mean time to respond (MTTR)

Real-world use cases:

Several major financial institutions, data providers, and telecommunications companies have reported:

• Significant reductions in security operations center (SOC) staffing costs
• Faster identification and remediation of security incidents
• Enhanced ability to detect sophisticated attacks

Google Workspace: AI-Powered Productivity with Measurable ROI

Google Workspace enhancements demonstrate how AI can deliver immediate productivity benefits across the organization:

Business Impact Metrics:

• 15-25% reduction in time spent on routine tasks
• 20-35% faster data analysis and insight generation
• 30-50% improvement in content creation efficiency

Key innovations driving these results include:

• Help Me Analyze: Automatically identifies insights from spreadsheet data, reducing analysis time by up to 70%
• Docs Audio Overview: Transforms written content into audio, increasing content consumption by 45%
• Google Workspace Flows: Automates routine workflows, saving employees an average of 5-7 hours per week

Making Science Perspective: From Innovation to Implementation

As a Google Cloud Premier Partner and the first company in Spain to complete the Generative AI Specialization process, Making Science is uniquely positioned to help organizations capture the business value of Agentic AI. Our approach goes beyond technical implementation to encompass the entire transformation journey.

Comprehensive Transformation Approach

At Making Science, we recognize that successful AI implementation requires more than just technical expertise:

1. Strategic Use Case Identification: We help organizations identify the most impactful applications of Agentic AI for their specific business challenges, prioritizing opportunities for quick wins and long-term transformation.
2. Change Management and Governance: We assist with the critical human and organizational aspects of AI adoption, developing governance frameworks, training programs, and change management strategies to ensure successful adoption.
3. Roadmap Development: We create phased implementation plans that balance quick wins to build momentum with strategic initiatives that deliver sustainable competitive advantage.
4. Technical Excellence: Our specialized AI team brings deep expertise in Google Cloud’s AI tools and frameworks, ensuring optimal implementation and integration with existing systems.

Proven Implementation Methodology

Based on our work with early adopters, we recommend a three-phase approach:

1. Quick-Win Agent Implementation (30-60 Days)

Start with pre-built agents in high-impact areas like customer service or employee support to demonstrate value quickly. We’ve helped clients achieve 20-30% efficiency gains in these areas within the first 60 days.

2. Strategic Agent Development (60-120 Days)

Use the Agent Development Kit to build custom agents for your organization’s unique processes, focusing on high-value, repetitive workflows. Our clients typically see 35-50% productivity improvements in targeted processes.

3. Multi-Agent Ecosystem Development (120+ Days)

Implement the Agent2Agent protocol to create sophisticated multi-agent systems that transform entire business functions. The most mature implementations achieve 50-80% efficiency improvements and open entirely new business capabilities.

The organizations that move quickly in this space will gain significant competitive advantages—not just in cost efficiency but also in their ability to deliver superior customer and employee experiences.

In our next blog post, we’ll explore Google’s latest Data and Business Intelligence innovations. Stay tuned!

Ready to capture the business value of Agentic AI? Contact our team at Making Science to discover how we can help accelerate your organization’s AI transformation with measurable ROI.

As a Google Cloud Premier Partner, the Making Science team was on the ground at Google Cloud NEXT ’25, witnessing firsthand the most significant infrastructure and security innovations Google unveiled to power the next generation of AI-driven enterprises. This year’s event showcased Google’s groundbreaking “AI Hypercomputer” – a revolutionary approach to infrastructure that promises to dramatically reduce the cost and complexity of deploying enterprise AI.

AI Hypercomputer: Business Value Beyond Traditional Infrastructure

The AI Hypercomputer represents a fundamental shift in how businesses should think about their infrastructure investments in the AI era. Rather than simply offering faster processors, Google has reimagined the entire stack to optimize for what matters most to businesses: maximizing AI output while minimizing costs.

Business Benefits of the New Infrastructure

What sets Google’s approach apart is its focus on delivering “more intelligence per dollar.” Their new Gemini 2.0 Flash model, powered by AI Hypercomputer, achieves 24x higher intelligence per dollar compared to GPT-4o and 5x higher than competing models. For businesses, this translates to:

• Dramatically lower costs for AI initiatives: The ability to run AI workloads at a fraction of previous costs means even medium-sized organizations can now afford enterprise-scale AI deployments.
• Faster time to market: Reduced training and inference times enable organizations to develop and deploy AI solutions in days rather than months.
• More powerful AI applications: The increased computational efficiency means organizations can build more sophisticated AI solutions without proportional increases in cost.

Storage and Inference: Removing the Bottlenecks to AI ROI

Google addressed two of the biggest challenges organizations face when implementing AI at scale: storage bottlenecks and inference costs.

Storage Innovations That Impact the Bottom Line

New storage solutions deliver tangible business benefits:

• Up to 70% reduction in data access latency: The new Anywhere Cache technology means AI systems spend less time waiting for data and more time delivering results.
• Dramatic reductions in storage costs: Organizations no longer need to over-provision expensive high-performance storage to ensure AI workload performance.
• Simplified data management: These solutions automate complex data placement decisions, reducing the specialized expertise needed to maintain AI infrastructure.

Inference Optimization: Where AI ROI Is Recognized

While model training gets headlines, inference (using trained models in production) typically accounts for 80-90% of AI compute costs. Google’s new inference optimizations deliver:

• 30% reduction in serving costs: Making AI solutions more economically viable for everyday business use cases.
• 60% reduction in response time: Enabling real-time AI applications that weren’t previously feasible.
• 40% increase in throughput: Allowing businesses to serve more users without proportional infrastructure increases.

Bringing AI On-Premises: Sovereignty, Compliance, and Performance Benefits

For regulated industries like healthcare, financial services, and government, Google’s announcement that Gemini will be available on-premises through Google Distributed Cloud (GDC) is a game-changer. This innovation delivers three critical business benefits:

1. Compliance without compromise: Organizations in highly regulated industries can now deploy state-of-the-art AI while maintaining complete data sovereignty and meeting the strictest regulatory requirements.
2. Reduced data transfer costs and latency: For organizations working with massive datasets, bringing AI computation closer to data storage eliminates expensive and time-consuming data transfers.
3. Enhanced security posture: With GDC now authorized for U.S. Government Secret and Top Secret levels, organizations can implement AI solutions that meet the highest security standards.

Networking Innovation: Business Benefits Beyond Performance

The new Cloud Wide Area Network (Cloud WAN) addresses key business challenges by:

• Reducing networking costs by up to 40%: Enabling organizations to redirect these savings to other strategic initiatives.
• Simplifying multi-region operations: Making it easier for global businesses to deliver consistent customer experiences worldwide.
• Enhancing application responsiveness: Improving customer satisfaction and employee productivity through more responsive applications.

Unified Security: Business Protection in an AI-First World

As AI adoption accelerates, so do the security challenges. Google’s new Unified Security approach delivers business benefits that extend far beyond technical protection:

Tangible Security Benefits for the Bottom Line

• Reduced security staffing costs: AI-powered security operations help address the cybersecurity talent shortage by automating routine tasks, allowing security professionals to focus on strategic initiatives.
• Faster threat detection and response: The new alert triage agent can investigate and validate threats in seconds rather than hours, reducing the potential financial impact of breaches.
• Reduced cyber insurance premiums: The expanded Risk Protection Program with Beazley and Chubb can lead to significant savings on cyber insurance, with discounts based on Google Cloud security posture.
• Reduced business risk: With Mandiant expertise integrated into the platform, organizations benefit from world-class threat intelligence and incident response capabilities.

Business Freedom Through Multi-Cloud and Sovereignty

Google’s approach to multi-cloud and sovereignty empowers organizations to:

• Maintain business continuity across clouds: Use Google Cloud services alongside AWS and Azure investments without disruptive migrations.
• Meet evolving regulatory requirements: As data sovereignty regulations evolve globally, Google’s sovereign cloud offerings ensure businesses can maintain compliance without sacrificing capabilities.
• Reduce vendor lock-in risk: Using Google’s analytics and AI services with data in other clouds provides flexibility and leverage in vendor negotiations.

Making Science Perspective: Turning Infrastructure Innovation into Business Advantage

As a Google Cloud Premier Partner, Making Science understands that the actual value of these infrastructure and security innovations lies not in the technology itself but in how they can transform business operations and create competitive advantage.

How We Help Clients Capitalize on These Innovations

At Making Science, we’re already working with clients to:

• Optimize AI infrastructure costs: We’ve helped clients reduce their AI infrastructure costs by 30-50% by leveraging Google’s new AI Hypercomputer architecture.
• Accelerate AI time-to-value: By implementing Google’s latest inference optimizations, we’ve helped organizations deploy AI solutions in weeks rather than months.
• Enhance security posture while reducing costs: Our security modernization approach leverages Google’s AI-powered security tools to improve protection and reduce operational costs simultaneously.
• Navigate sovereignty and compliance requirements: We provide expert guidance on leveraging Google’s sovereign cloud and distributed cloud options to meet global regulatory requirements.

The Business Imperative

The organizations that will thrive in the AI era will not necessarily be those with the largest budgets but those that most effectively leverage these new infrastructure capabilities to deliver better customer experiences, streamline operations, and innovate faster.

In our next blog post, we’ll explore Google’s latest innovations in Agentic AI for transforming customer and employee experiences. Stay tuned!

Ready to transform your infrastructure for the AI era? Contact our team at Making Science to discover how Google Cloud’s latest innovations can accelerate your organization’s AI journey while delivering measurable business results.

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.

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.

[image_with_animation image_url=”68704″ animation=”Fade In” hover_animation=”none” alignment=”” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”100%” max_width_mobile=”default”]We recently participated in Silicon’s Cloud & Security Leaders Event, where we delivered one of the opening presentations alongside industry leaders like Wiz and AWS. Our session focused on both the opportunities and challenges AI brings to cybersecurity, including potential security risks. We explored Google’s cybersecurity journey, Making Science’s implementation success stories, and essential considerations for organizations looking to elevate their cybersecurity infrastructure. Álvaro Verdeja, our COO of Cybersecurity, Cloud, AI, and Software, and Francisco Dalmau, Channel Account Manager at Google Cloud for Iberia and Italy, shared these insights.

The Google SecOps Journey – Operation Aurora

While Google’s consumer products are universally known, its cybersecurity portfolio remains less familiar to many. Francisco Dalmau highlighted how “everything started with Operation Aurora in 2009,” a significant cyberattack that targeted U.S. organizations. The attackers sought to access source code—the crown jewels of tech companies—by exploiting vulnerable hosts to move laterally through corporate networks and access unsecured source code repositories.

Operation Aurora taught Google that traditional perimeter-based security was inadequate for modern threats. This realization led to a complete security model redesign encompassing infrastructure architecture, software development, and operational practices. The result was Google’s zero-trust model, founded on the principle of “trust nothing, verify everything.” Thus, Google’s security model was born.

This led to Google now having a complete portfolio of security tools that compete with the most cutting-edge products in the market, Google SecOps, VirusTotal, and Google threat intelligence are some of these. All of them rely on the power of Google Cloud to possess unique capabilities in the market.

Making Science’s Implementation Excellence

As Google Premier partners and cybersecurity specialists, Making Science has spent over two years leveraging Google SecOps for comprehensive threat management. 

The solution combines two powerful components:

• SIEM: Enables comprehensive threat detection through multi-source data ingestion and alert creation
• SOAR: Automates manual processes through playbooks, allowing small security teams to manage enterprise-scale operations efficiently

Álvaro Verdeja noted, “We’ve tested these solutions internally to ensure optimal client protection.”Some of the incidents we manage automatically are information leaks, connections from unusual locations, report creation, or blocking and resetting of compromised users and credentials.

AI’s Role in Cybersecurity

While the buzzword right now is Artificial Intelligence, Google also has its own, Gemini, which comes incorporated into its security products and was defined by Francisco Dalmau in the following way:

While the buzzword right now is Artificial Intelligence, Google’s Gemini AI, integrated into its security products, represents the next evolution in cybersecurity. Francisco Dalmau offered an illuminating metaphor: “Gemini is an assistant that when you’re looking at a window, and the one next to it is the same, tells you that the same thing might happen again and by the way, we should also check the doors in case any are open.” This is a very apt metaphor for me since, as always mentioned, one of the biggest problems currently is the lack of talent and investment. With Gemini, we have that help, that assistance that guides us, but also works for us, creating queries without knowing the language or playbooks to automate tasks.

This AI-driven approach enables automation of Level 1 security operations, allowing teams to focus on complex challenges while reducing manual tasks like false positive management in traditional SOC environments.

Key Recommendations

The session concluded with three essential insights for business security:

1. View cybersecurity as an investment, not an expense – major organizations have failed due to security breaches
2. Rely on professional expertise – avoid compromising security with inadequate implementation
3. Embrace zero trust and least privilege principles – verify everything and minimize access permissions

Ready to Transform Your Security Infrastructure?

Connect with Making Science’s cybersecurity experts to explore how we can enhance your organization’s security posture with Google Cloud’s advanced solutions.

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.