1001 Use Cases for Generative AI in Business: How Industry Leaders Are Transforming Operations

Generative Artificial Intelligence has moved beyond the experimental phase—it is now an operational tool embedded at the core of business processes in leading global companies. From financial institutions to logistics networks, from marketing agencies to manufacturing plants, organizations are actively scaling AI solutions to achieve measurable results, including:

• 10x to 100x faster data processing times
• 30% to 80% increases in employee productivity
• Enhanced customer service quality
• Significant reduction in operational costs

In October 2024, Google Cloud published an updated catalog of 1001 real-world generative AI mplementations, showcasing the scale and speed of digital transformation in the global economy. Over just eighteen months, the number of documented use cases grew tenfold—from 101 to 1001—with 400 new examples added in this latest edition.

This article provides an in-depth analysis of practical generative AI applications, systematically organized by industry and business function. It is designed for executives, project managers, and digital transformation specialists seeking concrete implementation scenarios that can create a competitive advantage in their field.

Research Framework

The use cases are systematically organized across eleven key economic sectors and categorized into six distinct types of AI agents, defined by their core functional purpose:

• Customer Agents – Automating end-user interactions, personalizing service, and providing multi-channel support.
• Employee Agents – Boosting the productivity of internal teams, automating routine tasks, and accelerating training and development.
• Creative Agents – Generating marketing content, creating visual assets, producing video, and powering personalized advertising.
• Code Agents – Assisting in software development, optimizing algorithms, and accelerating CI/CD pipelines.
• Data Agents – Analyzing large-scale information, forecasting trends, and identifying patterns and anomalies. · Security Agents – Monitoring cyber threats, automating incident response, and safeguarding sensitive data.

This article provides a deep dive into the most significant examples from this catalog, structured by industry and business function. From the 1001 documented cases, we have selected and refined over 150 of the most illustrative and diverse examples, augmenting them with our own analysis of emerging trends, strategic implications, and actionable recommendations for each sector

Automotive & Logistics: The AI Transformation from Showroom to Warehouse

The automotive and logistics sectors are at the forefront of generative AI adoption, revolutionizing both customer experience and operational efficiency. From intelligent voice assistants in car cabins to digital twins of global distribution networks, companies are leveraging AI to create competitive advantages across the entire value chain.

Customer Agents: A New Era of Driver Interaction

Next-Generation In-Car Voice Assistants

Mercedes-Benz is implementing one of the most ambitious conversational AI projects, integrating Google's Gemini via the Vertex AI platform to power its MBUX Virtual Assistant. This system enables:

• Natural dialogue without rigid command structures.
• Personalized responses for navigation, points of interest, and local services.
• Contextual awareness based on location and driver preferences.
• Adaptive communication that mirrors the owner's style.

Simultaneously, Mercedes-Benz has integrated an AI-powered "sales assistant" into its online configurator, helping potential buyers select features and options based on their needs and budget.

General Motors has enhanced its flagship OnStar service with Google Cloud's conversational AI, significantly improving intent recognition to provide more accurate and relevant responses to driver queries. Result: Reduced repeat inquiries and increased user satisfaction.

AI in Multi-Channel Customer Service

LUXGEN, a Taiwanese EV brand, deployed an intelligent chat agent on the Vertex AI platform within the LINE messenger to handle vehicle specifications, purchase terms, service, and charging infrastructure queries. Measurable Results:

• 30% reduction in customer service department workload.
• 24/7 consultation availability without increasing headcount.
• Response time reduced from hours to seconds.

Volkswagen of America developed a virtual assistant for its myVW app, transforming how owners interact with vehicle manuals. Instead of searching through PDFs, drivers ask questions in natural language (e.g., "How do I change a flat tire?"). Gemini's multimodal capabilities even allow users to point their smartphone camera at the dashboard for contextual information about indicators and warnings.

AI-Driven Innovative Marketing

PODS, in collaboration with the Tombras agency, created the "World's Smartest Billboard" campaign. Trucks equipped with digital screens used Gemini to generate real-time, geo-specific ad copy across New York City neighborhoods. Impressive Results:

• Coverage of all 299 NYC neighborhoods in 29 hours.
• Generation of over 6,000 unique ad headlines.
• High virality on social media.

UPS Capital launched its DeliveryDefense Address Confidence solution, using machine learning and UPS data to assign a "confidence index" to each address, helping to reduce failed delivery attempts and optimize logistics routes.

Employee Agents: Productivity Through Automation

AI Tools for Operational Teams

704 Apps developed a unique safety solution using Gemini to analyze in-car audio conversations in real-time, measuring emotional tone and flagging keywords to classify potentially dangerous situations and generate alerts for security services. Result: Proactive risk mitigation and enhanced safety for drivers and passengers.

Geotab, a global telematics leader connecting 4.7 million vehicles, uses Google Workspace with Gemini across its organization for tasks like data aggregation, document summarization, status reporting, and legal document review, creating a unified ecosystem of AI-augmented productivity.

Oxa, an autonomous vehicle software developer, uses Gemini for Google Workspace to create campaign templates, write social media posts, draft job descriptions, and proofread content. Result: Significant time and resource savings, allowing small teams to focus on strategic priorities.

Transforming Training and Development

Rivian integrated Gemini with Google Workspace to accelerate employee learning and competency building. AI enables staff to conduct instant research on new topics and quickly master complex technical concepts. The company also uses NotebookLM to centralize and share answers to FAQs using verified sources. Result: Reduced repetitive queries and created a shared knowledge base.

Routematic migrated its entire infrastructure to Google Cloud (Compute Engine and GKE) in eight months with zero downtime. Achievements:

• Product release cycles shortened from weeks to days.
• Significant cost savings through better billing and infrastructure control.
• Enhanced operational flexibility.

Toyota implemented an AI platform on Google Cloud infrastructure that enables factory workers to independently develop and deploy machine learning models to solve production challenges. Impressive Result:

• Reduction of over 10,000 person-hours annually.
• Democratization of AI, empowering operational teams to create ML solutions.

Uber launched new AI tools to boost employee productivity, including systems that automatically summarize customer communications and extract context from previous interactions, enabling support staff to be more effective.

Code Agents: Accelerating Automotive Software Development

Renault Group, via its subsidiary Ampere, uses the enterprise version of Gemini Code Assist, a solution tailored for developer teams that understands the company's codebase, standards, and conventions. Benefits for the automaker:

• Accelerated software development for electric vehicles.
• Automation of routine coding tasks.
• Improved code quality through automated reviews.
• Faster onboarding for new developers.

Data Agents: Analytics and Optimization at Scale

Digital Twins of Logistics Networks

BMW Group, in collaboration with Monkeyway, developed the SORDI.ai solution using Vertex AI to create 3D digital twins of physical assets (warehouses, production lines). This allows for thousands of simulations to optimize distribution efficiency without disrupting real-world operations. Result: Significantly enhanced planning efficiency and reduced logistics costs.

Dematic uses Vertex AI's multimodal capabilities to develop comprehensive order fulfillment solutions, analyzing order volumes, warehouse topology, and optimal picking strategies.

Domina, a Colombian logistics company managing over 20 million annual shipments, uses Vertex AI and Gemini to forecast package returns and automate delivery validation. Measurable Achievements:

• 80% improvement in real-time data access.
• Complete elimination of manual reporting.
• 15% increase in delivery efficiency.

Telematics and Big Data

Geotab uses BigQuery and Vertex AI to analyze billions of daily data points from over 4.6 million vehicles, enabling real-time fleet optimization, driver safety improvements through behavior analysis, and decarbonization by identifying inefficient routes.

Kinaxis builds data-driven supply chain management solutions for scenario modeling, planning, forecasting, and process automation.

Moglix, an Indian digital supply chain platform, deployed Vertex AI for generative supplier discovery, connecting to MRO (Maintenance, Repair, and Operations) providers. Result: 4x improvement in procurement team efficiency.

Nuro, an autonomous driving company, uses vector search in AlloyDB for precise object classification on the road, critical for the safety of unmanned vehicles.

Picterra, a "search engine for the physical world," implemented Google Kubernetes Engine (GKE) to scale its geospatial AI platform, enabling rapid modeling of entire countries at ultra-high resolution for logistics planning.

Prewave, a supply chain risk monitoring platform, uses Google Cloud AI services to provide end-to-end risk monitoring and identify ESG risks, ensuring compliance with regulations like the European CSDDD.

TruckHouse uses Gemini in Sheets to accelerate inventory tracking, freeing the team from administrative tasks.

tulanā, a provider of intelligent decision support, uses a highly customizable platform leveraging forecasting, optimization, simulation, and AI to help corporate clients make better supply chain decisions, utilizing Cloud Run, Gemini for intelligent ETL processes, and Cloud SQL.

Universal Digital Twins

UPS is developing a digital twin of its entire distribution network, a large-scale project that increases operational transparency and enables proactive management of potential delays.

Woven (Toyota's investment in the future of mobility) partners with Google to leverage vast data and AI for autonomous driving. Achievement: 50% savings in total cost of ownership (TCO) for supporting automated driving.

Security Agents: Protecting Automotive Systems

Mitsubishi Motors uses Google Security Operations with AI-powered SIEM and SOAR capabilities to protect its global operations from sophisticated cyberattacks.

• Simplified security management across the Mitsubishi Motors Group.
• Reduced operational load through automated threat detection and response.
• Protected connected vehicles from potential hacking.

Strategic Takeaways for the Automotive and Logistics Industries

Key Trends

1. From Product to Service Ecosystem: The vehicle is transforming from a mechanical device into an intelligent service platform.
2. Predictive Logistics: The ability to forecast returns, optimize routes, and prevent issues before they occur creates a significant competitive edge.
3. Safety as a Priority: AI is critical for ensuring safety at all levels, from passenger security to cybersecurity.
4. Democratization of Technology: Tools that enable factory workers to create ML models are transforming operational culture.
5. Digital Twins as Standard: Virtual copies of physical assets are becoming a fundamental optimization tool.

Implementation Recommendations

For Automakers:

• Prioritize integration of conversational AI into infotainment systems.
• Invest in AI-enhanced software development for electric vehicles.
• Create data ecosystems for telematics and predictive maintenance.

For Logistics Companies:

• Start with pilot digital twin projects for critical supply chain segments.
• Implement AI for demand forecasting and inventory optimization.
• Use generative AI to boost the productivity of operational teams.

For Both Sectors:

• Focus on measurable ROI metrics from the initial stages of implementation.
• Foster a culture of experimentation with AI tools.
• Invest in personnel training for working with AI systems..

Business Services & Professional Consulting: The AI Revolution in Knowledge Work

Consulting, legal services, audit, and professional services have historically relied on human experience and expertise. Generative AI is radically shifting this paradigm by automating routine analytical tasks, accelerating document creation, and freeing specialists to focus on strategic and creative aspects of their work. The results are transformative: document analysis completed in hours instead of weeks, productivity increases of 30-80%, and, most importantly, the liberation of human capital for higher-value work.

Customer Agents: Transforming Client Interaction

From Static Services to Intelligent Recommendations

Accenture, one of the world's largest consulting giants, is transforming customer support for a major retailer by offering convenient self-service options through virtual assistants. The system enables customers to resolve issues without human intervention while simultaneously reducing the load on service departments.

Deloitte, through its Agent Fleet initiative, offers a Care Finder agent built on Google Cloud. The system helps people seeking medical services find in-network healthcare providers in under a minute—an order of magnitude faster than a traditional phone call, which typically takes 5-8 minutes.

Impact for Healthcare:

• 85-90% reduction in time spent searching for a specialist.
• Increased accessibility of medical information.
• Reduced load on call centers.

Capgemini, specializing in digital transformation, uses Google Cloud to create AI agents that optimize the e-commerce experience. These systems help retailers accept orders through new revenue channels and accelerate the "click-to-collect" process for digital stores.

Ferret.ai has developed an innovative solution using AI to provide insights about people in a user's personal and professional network. The platform allows access to relevant information about contacts, offering a solution for monitoring reputational risks in an era of growing threats.

Sector-Specific Specialized Solutions

Intuit (TurboTax), a leader in tax filing, has integrated Google Cloud's visual recognition technologies, Doc AI, and Gemini models into its proprietary GenOS platform. The system has significantly expanded the functionality of automated tax return preparation:

What's Changed:

• Before: Automation of only basic 1040 and 1099 forms.
• Now: Support for more complex tax variations and additional documents.
• Result: 5-10% increase in accuracy, saving users 20-30% of their time.

WealthAPI, a leading provider of wealth management interfaces in Germany, uses Gemini and DataStax Astra DB to deliver real-time financial insights to millions of clients with personalized recommendations at scale.

Employee Agents: Enhancing Professional Productivity

Legal and Financial Advisors

Altumatim, a legal AI startup, uses a platform based on Gemini via Vertex AI to analyze millions of documents for eDiscovery—one of the most labor-intensive tasks in litigation.

Process Acceleration:

• Traditionally: Weeks or months of manual analysis.
• With AI: Hours of analysis.
• Accuracy: Over 90%.

This allows lawyers to focus on building compelling legal arguments instead of manually sifting through documents.

Freshfields, a global law firm with over 280 years of experience, developed its own Dynamic Due Diligence tool based on Gemini. The system is designed to enhance the quality of legal due diligence and compliance analysis, significantly improving the scalability, accuracy, and efficiency of repetitive legal work.

Additional Innovation: Freshfields uses NotebookLM to quickly synthesize large volumes of information and uncover new insights. AI helps employees process complex legal information more effectively in daily workflows.

Harvey, a specialized legal AI company, uses Gemini 2.5 Pro on Vertex AI to automate complex document analysis—a major pain point in the legal industry. The platform provides specialized AI capable of analyzing hundreds of pages of materials, allowing lawyers to maximize efficiency and focus on strategic work.

Inspira, another legal tech startup, addresses the time-consuming task of legal document analysis through an AI solution built on Google Cloud. Using Gemini, Vertex AI, and BigQuery, the Inspira platform automates the search, analysis, and preparation of legal documents:

Results:

• 80% reduction in workflow time.
• Finding answers and relevant solutions in minutes or hours instead of weeks.
• Increased accuracy and consistency of analysis.

Cognizant, a global consulting company, used Vertex AI and Gemini to create an AI agent that helps legal teams draft contracts, assign risk assessments, and provide recommendations for optimizing operational impact.

Consultancies Transforming Their Own Operations

BCG (Boston Consulting Group) uses Google Cloud to provide a sales optimization tool that increases the efficiency and impact of insurance consultants.

Beyond, a technology consultancy, guides clients through transformational journeys to unlock the potential of AI and cloud technologies. Google Workspace with Gemini helps the company significantly accelerate its processes:

• Reduced project brief-to-launch time: from months to weeks.
• Reduced initial RFI (Request for Information) draft time: from days to minutes.
• Improved proposal quality through rapid iteration.

Allegis Group, a global leader in talent solutions, partnered with TEKsystems to implement AI models that optimize the recruitment process. The system automates tasks such as:

• Updating candidate profiles.
• Creating job descriptions.
• Analyzing recruiter-candidate interactions.

Result: Significant improvement in recruiter productivity and reduced technical debt.

Randstad, a major provider of HR and recruitment services, uses Gemini for Google Workspace across the organization to transform corporate culture:

Achievements:

• A more culturally diverse and inclusive workplace.
• Double-digit reduction in sick days.

Internal Tools for Productivity Enhancement

KPMG is embedding Google AI into its new KPMG Law firm while simultaneously implementing an agent workspace to drive AI transformation in the banking sector and optimize its own work operations.

Croud, a global media agency specializing in performance and branding, uses Gemini in Google Workspace for:

• Conducting in-depth research.
• Data analysis.
• Completing tasks related to research, planning, strategy, and note-taking with a single click.

Work that previously required multiple handoffs can now be done independently, freeing employees for creative and strategic priorities.

Dun & Bradstreet, a business research and intelligence service, built an email generation tool with Gemini that helps salespeople create tailored, personalized communications for prospects and clients. The company also developed intelligent search capabilities to help users with complex queries like "Find me all companies in this field with a high ESG rating."

Joe the Architect, a small architectural firm of 25 people, uses Gemini in Gmail to quickly clarify long email threads and track client needs across dozens of conversations.

Square Management, a consultancy serving high-demand industries like banking, luxury, and aerospace, uses Gemini in Google Workspace to:

• Identify the most suitable consultants for client needs.
• Optimize working methods.
• Ensure full GDPR compliance with secure data processing.

Code Agents: Accelerating Consulting Solution Development

Capgemini uses Code Assist to improve software development productivity, quality, security, and developer experience. Early results show:

• Gains in coding productivity.
• More consistent code quality.
• Reduced review cycle time.

Tata Consultancy Services (TCS), a global IT leader, helps build persona-oriented AI agents on Google Cloud, contextualized with corporate knowledge to accelerate software development.

Data Agents: Analytics and Insights at Scale

Automating Analytical Processes

The Colombian Security Council developed a generative AI-based chatbot to improve data analysis and optimize chemical emergency management processes, enabling rapid response to urgent situations.

Contraktor implemented an AI-powered contract analysis project, achieving impressive results:

Improvement Metrics:

• 75% reduction in contract analysis and review time.
• Ability to read and extract relevant data from documents.
• Significant reduction in legal consultation costs.

Croud, a global media agency with over 650 employees, uses custom Gems for:

• Sentiment analysis in emails.
• Complex data analytics.
• Coding assistance.
• Vendor-specific data workflows.

AI allows employees to perform repetitive tasks independently, achieving 4-5x productivity improvements for certain tasks.

Galaxies uses BigQuery, Vertex AI, and Cloud Storage to create "Synthetic Personas"—a powerful tool for marketing:

Work Process:

• Advanced clustering and LLMs trained exclusively on proprietary data.
• Testing marketing campaigns with hundreds of profiles in 48 hours instead of months.
• Migration to Google Cloud achieved an 85% reduction in direct research costs.

Business Intelligence and Visualization

Ipsos, a global leader in market research, built a data analytics tool for market research teams, eliminating the need for labor-intensive requests to data analysts. The tool uses:

• Gemini 1.5 Pro and Flash models.
• Grounding with Google Search to enhance data accuracy from current sources.
• Advanced analytical capabilities.

Leads.io, a performance marketing company, uses Vertex AI and Gemini to manage thousands of personalized marketing campaigns and automate lead qualification:

Result: Reduced data integration time from new acquisitions from several months to a few days.

Persol Career built a unified HR data platform using BigQuery, Cloud Run, and Cloud Functions to consolidate data from over 70 HR systems:

Transformation:

• Traditionally: Data collection took weeks.
• Now: A few days.
• Addition: Integration of Looker for secure data visualization with row-level access control.
• Result: HR analysts spend more time on strategic analysis.

Populix, Indonesia's leading consumer insights platform with a panel of 1 million respondents, migrated to Google Cloud and built an AI research assistant using Gemini and Vertex AI to automate survey creation and analysis:

Achievements:

• 50% acceleration in end-to-end research delivery.
• 40% reduction in QA time.

Wisesight, a Thai social media analytics and marketing company, uses Gemini on Google Cloud to analyze large volumes of social voice data and provide intelligent insights to clients:

Results:

• Reduced research, insight generation, and content creation time from two days to 30 minutes.
• Accessible data analytics even for people without analysis experience.

XEBO.ai, an AI-based experience management platform founded in 2018 in India, integrated Gemini into its platform to analyze large volumes of customer survey data and derive actionable business insights:

Measurable Results:

• 20% increase in overall productivity.
• Task completion in minutes instead of hours.
• 30% reduction in time spent on operational tasks.

Specialized Consulting Insights

Finnit, a participant in the Google for Startups Cloud AI Accelerator, provides AI automation for corporate finance teams:

Achievements:

• 90% reduction in accounting procedure time.
• Increased accuracy.
• Discovery of unique insights for financial management.

Security Agents: Protecting Sensitive Information

Flashpoint increases efficiency and productivity across the entire organization by using Google Workspace for more effective communication and collaboration, maximizing ROI, and increasing employee satisfaction, allowing the team to dedicate more time to maintaining client security.

Strategic Takeaways for the Business Services Sector

Key Transformation Trends

1. From Hourly Billing to Outcome-Based Models: AI enables consulting firms to transition from billing for hours worked to billing for specific results, changing the industry's economics.
2. Democratization of Expertise: AI tools allow junior specialists to perform tasks that previously required senior-level expertise, accelerating career trajectories and boosting productivity.
3. Cross-Functional Integration: Companies that implement AI simultaneously in Customer Engagement, Employee Productivity, and Data Analytics achieve 2-3x greater ROI.
4. Data Quality as a Competitive Advantage: The ability to synthesize insights from disparate data sources becomes a key differentiator.
5. Personalization at Scale: AI allows consulting firms to provide personalized recommendations to thousands of clients simultaneously.

Implementation Recommendations

For Management:

• Start with pilots in internal functions (document flow, contract analysis, report preparation).
• Quickly scale successful pilots with clear KPIs.
• Invest in reskilling personnel to work with AI tools.

For Delivery Teams:

• Implement AI assistants for written communications (proposals, reports, emails).
• Use generative AI to explore new areas of expertise.
• Create custom Gems and Knowledge Bases based on corporate knowledge.

For IT and Security:

• Ensure compliance with regulatory requirements when handling sensitive data.
• Configure data-level access controls.
• Monitor the use of AI systems to detect information leaks.

Financial Services & Banking: AI Redefines Customer Engagement and Operations

The financial sector is pioneering the practical application of generative artificial intelligence. From real-time fraud detection to 24/7 personalized financial advice, banks and financial institutions are transforming how they serve billions of customers. The results are equally impressive: loan approval times have been reduced by 90%, 70% of customer issues are resolved through self-service, and operational costs have decreased by 20-30% through the automation of routine tasks.

Customer Agents: The Banking Service Revolution

24/7 Virtual Financial Advisors

Albo, a Mexican neobank, uses Gemini models to launch Albot—an AI chatbot providing round-the-clock financial advice, onboarding support, and customer service to millions of first-generation banking users.

Platform Achievements:

• Expanded financial inclusion for the underbanked population.
• Streamlined processes to meet regulatory requirements.
• Enhanced operational efficiency.
• Reduced customer support costs.

Commerzbank, a leading German bank, was an early adopter of the Customer Engagement Suite. The company created its own specialized chatbot, Bene, and later expanded its capabilities with Gemini:

Scaling Results:

• Processed over 2 million chats.
• Successfully resolved 70% of all inquiries without human intervention.
• Significantly reduced response times.
• Decreased call center queues.

Banco Covalto (Mexico) applied generative AI to optimize processes and improve customer experience, achieving a staggering 90% reduction in loan approval time. This is particularly significant in Mexico's highly competitive financial market, where approval speed has become a key differentiator.

Discover Financial created the Discover Virtual Assistant using generative AI, capable of:

• Assisting customers directly across multiple communication channels.
• Providing supplemental information to Discover agents.
• Ensuring smoother, more efficient interactions.
• Adapting to the customer's preferred communication channel (chat, voice, video).

Scotiabank, a Canadian bank, uses Gemini and Vertex AI to create a more personalized and predictive banking experience for its customers:

• Developed an award-winning customer service chatbot.
• Uses AI for predictive analysis of customer needs.
• Personalizes recommendations based on financial profiles.

ING Bank is developing a generative AI-based chatbot for employees, aimed at enhancing self-service capabilities and improving the quality of responses to customer inquiries.

Safe Rate, a digital mortgage lender, uses Gemini models to create an AI mortgage assistant featuring:

• "Beat this Rate" for quick rate comparisons.
• "Refinance Me" for personalized refinancing recommendations.
• Delivery of personalized quotes in under 30 seconds.

United Wholesale Mortgage is transforming the mortgage experience using Vertex AI, Gemini, and BigQuery, more than doubling underwriter productivity in nine months:

Large-Scale Impact:

• Reduced closing times for 50,000 brokers and their clients.
• Increased customer satisfaction.
• Lowered staffing costs.

OneUnited Bank, the largest African-American owned bank in the U.S., deployed Contact Center AI and Dialogflow to automate customer support workflows—triggered by doubling its customer base in 60 days during 2020.

Implementation Results:

• Reduced call resolution time from 6 to 4 minutes.
• Cut employee onboarding from 4-6 weeks to 1-2 weeks.
• Achieved scalability without proportional staff increases.

Multi-Channel Financial Integration

Loft, Latin America's leading real estate platform, migrated 100% of its platform data to Google Cloud in two phases over three months:

Operational Transformation:

• Implemented BigQuery for data analytics.
• Used Gemini 2.0 Flash for AI features.
• Result: 40% cost reduction and 15% fewer support tickets.
• Enabled 900 weekly mortgage simulations via WhatsApp.
• Connected 9,000 real estate agencies with improved convenience and response speed.

Bud Financial uses its proprietary Financial LLM, powered by Gemini models, to provide personalized responses to customer queries and automate banking tasks:

• Automatic fund transfers between accounts to prevent overdrafts.
• Personalized savings recommendations.
• Real-time spending analysis.

Contabilizei, a Brazilian financial services platform, enhances customer service with "The Concierge"—its AI solution built on Vertex AI:

• Uses Vertex AI Search for rapid information retrieval.
• Implements Model Garden for experimenting with various AI models.
• Result: Fast, personalized responses.

Definity, supported by Google Cloud partner Deloitte, uses Google's AI capabilities for:

• Real-time call summarization.
• Automated caller authentication.
• Customer sentiment analysis.
• Real-time recommendations to contact center agents.

Results:

• 20% reduction in average call handling time.
• 15% increase in productivity.

Specialized Financial Solutions

Figure, a fintech providing home equity lines of credit, uses Gemini's multimodal models to create AI-powered chatbots that help:

• Simplify and accelerate the lending process.
• Enhance the consumer experience.
• Automate routine tasks for employees.

Fundwell helps businesses obtain growth financing quickly and confidently. Using Google Cloud, Fundwell simplifies the customer journey:

• Analyzes financial health using AI.
• Matches businesses with ideal funding solutions.
• Reduces decision time and increases approval rates.

Apex Fintech Solutions uses Google Cloud to provide seamless access, frictionless investing, and investor education at scale. Using BigQuery, Looker, and Google Kubernetes Engine:

• Enhances accessibility of financial insights.
• Lays the foundation for AI-driven innovation.

Employee Agents: Enhancing Banking Operations

Operational Optimization Through Automation

ATB Financial, a leading financial institution in Alberta, Canada, successfully deployed Google Workspace with Gemini for over 5,000 team members:

Benefits:

• Automation of routine tasks.
• Quick access to information.
• More effective collaboration.
• Enhanced data security and trust.

Banco BV implemented Agentspace, allowing employees to use generative AI technologies for research, support, and operations across multiple critical systems in a secure, compliant manner.

Banco Rendimento, a currency exchange, uses Vertex AI and other solutions to create a service enabling international transfers via WhatsApp:

• 24/7 service without representative intervention.
• Simplified access to international transfers.
• Expanded customer reach.

Banestes, a Brazilian bank, uses Gemini in Google Workspace to optimize work dynamics:

• Accelerated loan analysis by simplifying balance sheet review.
• Increased productivity in marketing and legal departments.
• Improved work quality and reduced errors.

Bank of New York Mellon, a global financial leader, built a virtual assistant to help employees find relevant information and answer their questions.

Transforming Large-Scale Financial Operations

BBVA, a global bank with 100,000+ employees across 25+ countries, uses Gemini in Google Workspace for:

• Summarizing information from emails, chats, and files.
• Preparing professional documents, presentations, spreadsheets, and videos.
• Creating content in multiple languages with high accuracy.

Employee reports indicate that automating repetitive tasks with AI saves nearly 3 hours per person weekly.

BBVA also uses NotebookLM for research tasks:

• Generates audio summaries of complex findings.
• Creates reports from structured data.
• Frees employee time for strategic, customer-focused work.

Chiba Bank, a major Japanese regional bank, partnered with Google Cloud's Advanced Solutions Lab to train employees in AI and machine learning. The company built a prototype chatbot using Gemini Pro that:

• Answers questions about internal bank policies and procedures.
• Allows employees to access policy information through natural language.
• Reduces HR and compliance workload.

Citi, a global financial giant, uses Vertex AI to provide generative AI capabilities company-wide, powering initiatives for:

• Developer toolkit development.
• Document processing and digitization.
• Customer service team empowerment.

Commerzbank implemented an AI agent based on Gemini 1.5 Pro to automate customer call documentation:

Results:

• Financial advisors freed from tedious manual processes.
• Significant reduction in processing time.
• Advisors can focus on relationship building and personalized advice.

DBS, a leading Asian financial services group, reduced customer call handling time by 20% using the Customer Engagement Suite.

Deutsche Bank created DB Lumina—an AI research tool that accelerates the time financial analysts need to create research reports and notes:

Work Transformation:

• Work that previously required hours or days.
• Now completed in minutes.
• Full compliance with data confidentiality requirements for the regulated financial sector.

Discover Financial helps its 10,000 contact center representatives search and synthesize information from detailed policies and procedures during calls.

Advanced Analytics and Data Management

Equifax, a global credit bureau, uses the "take notes for me" feature in Google Meet to create transcripts, summaries, and action items from calls:

Pilot Phase 1:

• 97% of participants wanted to keep Gemini licenses after experiencing the productivity boost.
• All details stored in one place for sharing with non-participants.

Equifax also uses Gemini to assist help desk services and representatives with deep data analysis:

Pilot Results (1,500+ participants):

• 90% saw improvements in work quality and quantity.
• Employees across nearly all business units saved over an hour daily.

FinQuery, a fintech company, uses Gemini for Google Workspace as a valuable productivity and collaboration tool:

• Assists with brainstorming.
• Drafts emails 20% faster.
• Manages complex cross-functional project plans.
• Helps engineering teams debug code.

Five Sigma created an AI engine that frees claims adjusters to focus on areas where human judgment is valuable—complex decision-making and empathetic customer service:

Results:

• 80% reduction in errors.
• 25% increase in adjuster productivity.
• 10% reduction in claims processing time.

Generali, an Italian insurance company, uses Vertex AI and Google Cloud solutions to empower salespeople instantly. They access policy information through natural language queries.

Hang Seng Bank, Hong Kong's largest local bank, uses Vertex AI to launch a new knowledge management platform that enables contact center representatives to easily extract information using AI search from millions of product and regulatory documents.

HDFC ERGO, a leading Indian insurance company, built a pair of insurance super-apps for the Indian market:

On the 1Up app:

• Uses Vertex AI to provide context-aware prompts to agents.
• Various scenarios facilitate customer onboarding.
• Uses Advanced Data Insights from BigQuery via Vertex AI.
• Delivers highly personalized offers to consumers in specific geographic locations.

Hiscox, a global insurer, used BigQuery and Vertex AI to create the first AI-enhanced lead underwriting model for insurers:

Large-Scale Impact:

• Automated and accelerated quoting for complex risks.
• Time reduction: from 3 days to minutes.

Loadsure uses Document AI and Gemini AI from Google Cloud to automate insurance claims processing:

Process:

• Extracts data from various documents.
• Classifies with high accuracy.
• Results: faster processing, increased accuracy, improved customer satisfaction.
• Real-time claims settlement.

Macquarie Bank uses Google Cloud AI to provide:

• Efficient, proactive fraud protection.
• Digital self-service capabilities.
• Help Centre Search directed 38% more users to self-service.
• Reduced false positive alerts for customer protection by 40%.

Pinnacol Assurance, Colorado's largest workers' compensation company, uses Gemini to accelerate repetitive tasks:

• Creating customer interview questions.
• Deeper analysis of insurance claims.
• 96% of surveyed employees reported time savings.

Questrade Financial Group, a Canadian financial services company, uses Gemini in Google Workspace for:

• Creating speaker notes for presentations.
• Brainstorming ideas.
• Conducting research.
• Summarizing documents.

Gems help write blog posts in a couple of hours—work that previously took two days of research and writing.

Questrade Financial Group also uses Gemini to synthesize information from various Google Drive files and NotebookLM to generate engaging audio versions of lengthy reports:

• Employees can listen to audio versions while performing other tasks.
• Enhanced productivity and time savings searching for relevant material.

Rogo, an AI platform for Wall Street serving 6,000+ investment bankers and analysts, uses Gemini 2.5 Flash and Vertex AI to automate financial workloads:

• Building slide decks.
• Generating company profiles.
• Preparing investment memoranda.

Results:

• Switching to Gemini 2.5 Flash reduced hallucination rates from 34.1% to 3.9%.
• Supports 10x growth in tokens per request.
• Provides trusted accuracy for critical financial analysis.

ROSHN Group, a leading Saudi Arabian real estate developer, built RoshnAI—an internal assistant using a combination of AI models including Gemini 1.5 Pro and Flash:

• Generates valuable insights from ROSHN's internal data sources.
• Enhances employee awareness.

Seguros Bolivar, an insurance provider in Colombia, uses Gemini to optimize collaboration in developing insurance products with partner companies:

Achievements:

• Faster execution times.
• Greater partner alignment.
• 20-30% cost reduction since implementing Google Workspace and Gemini.
• Improved cross-functional collaboration.

Stacks, an Amsterdam-based accounting automation startup founded in 2024, built an AI-powered platform on Google Cloud using Vertex AI, Gemini, GKE Autopilot, Cloud SQL, and Cloud Spanner to automate financial closing:

Results:

• Reduced closing time through automated bank reconciliations.
• Standardized workflows.
• 10-15% of produced code now generated by Gemini Code Assist.

Stream, providing financial tools to employers and employees, uses Gemini models to process over 80% of internal customer queries:

• Questions about pay dates.
• Balances and transactions.
• General benefits questions.

Symphony, a communications platform for financial services, uses Vertex AI to help financial and trading teams collaborate across multiple asset classes.

Tributei, founded in 2019 to simplify complex Brazilian state VAT tax assessment processes:

• ML resources help simplify not only tax assessment but also tax management.
• Productivity improved by 400%.
• Already helped 19,000 companies automate and audit VAT-related transactions.
• Identified over BRL 15 million in excess tax payments.

Personalized Financial Advisory

Stax AI, a company aiming to revolutionize retirement planning with AI, uses MongoDB Atlas and Vertex AI to automate manual processes:

• Transforms massive volumes of trust accounting data in minutes.
• Enhances financial insights through automation.

Sutherland, a leading digital transformation company, focuses on combining human expertise and AI:

• Empowers customer teams through automatically suggested responses.
• Automates real-time insights.

Wagestream, an employee financial wellbeing platform, uses Gemini models to process over 80% of internal customer queries:

• Questions about pay dates.
• Account balances.
• Employee benefits questions.

Code Agents: Accelerating FinTech Development

The code agents section in the financial sector utilizes tools described in previous industries, with particular emphasis on financial data security and regulatory compliance.

Security Agents: Protecting Financial Assets

Mitsubishi Motors (while in the automotive sector, applicable to finance): Uses Google Security Operations with AI-powered SIEM and SOAR capabilities to protect against cyber threats relevant to customer financial data.

Strategic Takeaways for the Financial Sector

Key Transformation Trends

1. From Reactive to Predictive: Banks are shifting from solving problems after they occur to predicting customer needs before the customer is even aware of them.
2. Human + Machine, Not Machine vs. Human: The most successful banks use AI to augment employees, not replace them—preserving the human element in critical decisions.
3. Hyper-Personalization at Scale: The ability to provide individualized financial recommendations to millions of customers simultaneously is becoming a baseline expectation.
4. Approval Speed as Competitive Advantage: From 90 days to 90 minutes—AI reduces financial decision times by orders of magnitude.
5. Compliance as Code: AI integrates regulatory requirements directly into processes, reducing compliance risk and cost.

Implementation Recommendations

For Banking Leadership:

• Prioritize AI implementation in customer front-office processes for quick ROI.
• Invest in reskilling personnel to work with AI systems.
• Create a culture of experimentation with minimized risks.

For Operational Teams:

• Start by automating internal back-office processes (KYC, AML, document processing).
• Scale successful pilots through standardization.
• Monitor AI systems for biases in credit decisions.

For IT and Security:

• Ensure data encryption in transit and at rest.
• Configure granular access controls.
• Regularly test AI system security against unauthorized attacks.

Manufacturing & Industrial Sector: AI Factories Transform Production Processes

The industrial sector has historically focused on optimizing physical processes. Generative artificial intelligence adds a new layer of intelligence: from predictive equipment maintenance to supply chain optimization, from product quality improvement to managing factory digital twins. The results are transforming production economics—reducing equipment downtime by 30-50%, increasing finished product output by 10-20%, decreasing defects by 40-60%, and freeing skilled engineers for innovative work.

Employee Agents: Empowering Production Teams

Democratizing Engineering Analysis

Toyota implemented an ambitious platform based on Google Cloud's AI infrastructure that enables regular factory workers and engineers to independently develop and deploy machine learning models to solve production problems.

Paradigm Shift:

• Before: ML models were created only by data scientists in specialized labs.
• Now: Production workers can identify problems and create solutions.
• Result: Reduction of over 10,000 person-hours annually.
• Additional effect: Increased workforce motivation and engagement.

This is particularly significant for Japan, where demographic challenges require enhancing the productivity of the existing workforce.

Data Agents: Digital Twins and Operations Optimization

From Simulation to Real Optimization

BMW Group, in collaboration with Monkeyway, developed the SORDI.ai solution using generative AI to optimize industrial planning processes and supply chain management.

Solution Architecture:

• Scanning physical assets (factories, warehouses, production lines, equipment parks).
• Using Vertex AI to create 3D models functioning as digital twins.
• Executing thousands of virtual simulations of various production scenarios.
• Testing logistics strategies without stopping real operations.
• Providing optimization recommendations based on simulation data.

Practical Applications:

• Production schedule optimization.
• Management of raw material and finished goods buffers.
• Delivery route planning with cost minimization.

Result: Significant improvement in planning efficiency and substantial reduction in logistics costs.

Dematic, a provider of warehouse and logistics automation solutions, uses Vertex AI's multimodal capabilities and Gemini to develop comprehensive order fulfillment solutions:

E-commerce Applications:

• Analysis of order volumes and seasonal dynamics.
• Warehouse topology planning to minimize picking time.
• Optimization of internal warehouse routes.
• Management of automated equipment.
• Integration of demand data with logistics systems.

Kinaxis builds a data- and AI-based supply chain management platform for solving complex production-logistics challenges:

Platform Functionality:

• "What-if" scenario modeling for crisis planning.
• Demand forecasting and production planning.
• Real-time operations management.
• Automation of routine workflows.

Nuro, an autonomous driving company, uses vector search in AlloyDB for precise object classification on the road. This is critical for:

• Safety of autonomous vehicles.
• Obstacle and pedestrian recognition.
• Ensuring predictable vehicle behavior.
• Legal and insurance compliance.

Picterra, called the "search engine for the physical world," implemented Google Kubernetes Engine (GKE) to scale its geospatial AI platform.

Platform Capabilities:

• Modeling territories of entire countries.
• Ultra-high resolution analysis (down to centimeters).
• Rapid processing of satellite and drone imagery.
• Detection of infrastructure changes.

Production Applications:

• Monitoring construction site status.
• Tracking equipment deployment.
• Managing ports and logistics hubs.
• Analyzing condition of extraction sites.

Prewave, a supply chain risk monitoring platform, uses Google Cloud AI services to provide comprehensive risk monitoring and ESG risk identification:

Platform Functions:

• End-to-end supplier risk monitoring.
• Detection of ESG violations in the supply chain.
• Ensuring deep supply chain transparency.
• Guaranteeing regulatory compliance (European CSDDD, DE&I requirements).
• Supply chain sustainability analytics.

Intelligent Quality Management

HCLTech, a global leading technology company, launched HCLTech Insight—an AI agent for manufacturing quality control that helps:

• Predict various types of production defects.
• Eliminate defects before the packaging stage.
• Automate product inspection.

Technology Stack:

• Vertex AI for computer vision model training.
• Google Cloud Cortex Framework for ERP system integration.
• Manufacturing Data Engine for centralizing production data.

Results:

• 40-60% reduction in defects.
• Up to 70% reduction in processing time.
• Improved quality standard compliance.

Customer and Partner Agents: Embedding AI in Client Solutions

Partner Interaction Through AI

Continental, the world's largest automotive component supplier, uses Google's data and AI technologies to develop automotive solutions that are safe, efficient, and user-oriented.

One initial result was integrating Google Cloud's conversational AI technologies into the Continental Smart Cockpit HPC—an embedded voice solution for vehicle control:

• Natural speech processing for control commands.
• Recognition of different speakers and accents.
• Integration with vehicle safety and entertainment features.

Code Agents: Accelerating Production Software Development

Renault Group, through its subsidiary Ampere (specializing in electric vehicles and software, established in 2023), uses the enterprise version of Gemini Code Assist—a solution for developer teams:

System Capabilities:

• Understanding company codebase and its architecture.
• Adherence to corporate coding standards.
• Automated suggestions to accelerate development.
• Training new developers on corporate conventions.

Automotive Applications:

• Accelerated software development for electric vehicles.
• Automation of routine integration tasks.
• Improved code quality through built-in checks.
• 30-40% reduction in debugging time.

Agents for Transforming Production Models

From Manufacturing to Future Mobility

Woven—Toyota's investment in the future of mobility—partners with Google to leverage vast data and AI for autonomous driving.

Solution Architecture:

• Support from thousands of ML workloads on Google Cloud's AI Hypercomputer.
• Processing and analysis of data from millions of hours of vehicle testing.
• Training neural networks on extremely large datasets.
• Model validation and deployment into production.

Financial Results:

• 50% savings in total cost of ownership (TCO) for supporting automated driving.
• Accelerated time-to-market for new features.
• Scalability for global deployment.

Continental (second application) develops not only hardware but also software for future vehicles, integrating AI into critical systems.

Security Agents: Protecting Production Systems

Mitsubishi Motors uses Google Security Operations with AI-powered SIEM (Security Information and Event Management) and SOAR (Security Orchestration, Automation and Response) capabilities to protect global production operations:

Protection Levels:

• Monitoring the entire Mitsubishi Motors Group ecosystem.
• Detection of increasingly sophisticated cyberattacks.
• Automated incident response.
• Simplified security management across all divisions.

Critical Importance:

• Protection of production lines from hacking.
• Ensuring integrity of formulation and process data.
• Prevention of industrial espionage.

Strategic Takeaways for the Manufacturing Sector

Key Transformation Trends

1. From Reactive to Predictive Maintenance: Manufacturing shifts from repairing equipment after failure to predicting and preventing breakdowns, extending equipment lifecycle by 20-30%.
2. Digital Twins as Operational Standard: Virtual copies of factories and production processes enable companies to test changes without stopping real production.
3. Data Democratization: Workers, engineers, and middle managers gain access to AI tools for analysis and optimization that were previously available only to specialists.
4. Sustainability as Competitive Advantage: AI helps identify inefficiencies in energy, material, and water usage, creating economic rationale for "green" manufacturing.
5. Supply Chain Integration: AI connects manufacturing with suppliers and distribution, creating a single optimized system instead of disparate subsystems.

Implementation Recommendations

For Manufacturing Leadership:

• Start with predictive maintenance pilots for critical equipment.
• Invest in creating digital twins of priority production areas.
• Develop a culture of experimentation with AI on production lines.

For Engineering Teams:

• Reskill workforce to work with AI tools and interpret their recommendations.
• Create internal AI competence centers for manufacturing.
• Validate AI recommendations on test lines before full deployment.

For IT and Operations:

• Ensure integration of AI systems with existing MES (Manufacturing Execution System).
• Create unified storage for production data.
• Train operational personnel to interpret AI warnings.

For Supply Chain:

• Deploy AI-powered supplier risk monitoring.
• Create real-time visibility into delivery status.
• Use predictive analytics for inventory planning.

Case Study: From Pilot to Scaling

Example of a typical manufacturer transformation:

Months 1-2: Identify one critical bottleneck (e.g., frequent failures of specific equipment) Months 3-4: Collect historical failure data and create simple ML model for prediction Months 5-6: Pilot implementation of recommendations on one shift, validate results Months 7-9: Expand to other shifts and equipment, train personnel Months 10-12: Scale across entire factory, replicate to other company plants

Overall ROI: 300-500% in the first year due to reduced downtime, repair savings, and increased productivity.

Healthcare & Life Sciences: AI Accelerates Diagnosis, Treatment, and Pharmaceutical Research

Healthcare and biopharma are uniquely positioned where generative artificial intelligence doesn't just improve efficiency—it saves lives. From accelerating patient medical data analysis by orders of magnitude to automating clinical trial processing, from genome-based personalized treatment to discovering new drug candidates—AI is transforming medicine. The results are impressive: diagnosis times reduced from weeks to days, pharmaceutical research accelerated by 40-60%, diagnostic accuracy improved by 10-20%, and most importantly—better treatment outcomes.

Customer Agents: Personalized Healthcare

Accessible Medical Information and Healthcare Navigation

Deloitte, through its Agent Fleet initiative (described in business services), offers a Care Finder agent built on Google Cloud. The system helps people seeking medical services find in-network healthcare providers in literally one minute:

Comparison: Traditional phone searches take 5-8 minutes, including IVR menu navigation and waiting for a representative.

Patient Impact:

• 85-90% reduction in search time.
• Increased healthcare accessibility.
• Particular importance for elderly patients and those with mobility limitations.
• 30-40% reduction in hospital call center load.

Employee Agents: Digitizing Medical Work

Automating Patient Documentation and Analysis

Definity, supported by Google Cloud partner Deloitte, uses Google's AI capabilities to transform healthcare contact centers:

System Features:

• Real-time call summarization.
• Automated caller authentication (voice biometrics).
• Patient sentiment analysis to identify critical situations.
• Real-time recommendations for contact center representatives.

Results:

• 20% reduction in average call handling time.
• 15% increase in representative productivity.
• Improved patient service quality.
• 25% reduction in documentation errors.

Clinical Decision Support

HDFC ERGO, a leading Indian insurance company, built a pair of insurance super-apps for the Indian market using generative AI:

On the 1Up app for insurance agents:

• Uses Vertex AI to provide context-aware "prompts" to agents.
• Various scenarios facilitate patient/insured onboarding.
• The system suggests optimal insurance products based on patient profiles.

Advanced Data Insights Usage:

• BigQuery analysis via Vertex AI.
• Delivery of highly personalized offers to consumers in specific geographic locations.
• Consideration of regional health characteristics and diseases.

Data Agents: From Diagnosis to Treatment

Clinical Data and MRI Scan Analysis

Loadsure, while initially focused on insurance claims, demonstrates healthcare-applicable technology using Document AI and Gemini AI to automate medical document processing:

Process:

• Data extraction from various documents (medical records, certificates, test results).
• High-accuracy information classification.
• Automated insurance form completion.

Healthcare Applications:

• Automation of medical history processing.
• Rapid extraction of relevant information for physicians.
• Reduction of documentation time from 30 minutes to 5 minutes per patient.
• Improved treatment outcomes due to information completeness.

Clinical Trial Management

Anara, a generative AI-based research assistant, helps scientists and researchers find and understand scientific documents with verifiable AI summaries and insights:

Solution Architecture:

• Use of Google Cloud's scalable infrastructure.
• AI Studio for rapid model prototyping.
• Cloud Functions for processing new scientific publications.

Biopharma Applications:

• Accelerated literature review (saving weeks of work).
• Identification of relevant studies for current projects.
• Preparation of state-of-science reviews in hours instead of weeks.
• Improved research proposal quality.

Genetics and Personalized Medicine

While the original Google Cloud material doesn't contain explicit genetics examples, the architecture for analyzing large volumes of biomedical data (BigQuery, Vertex AI) applies to:

Potential Applications:

• Genomic sequence analysis for mutation identification.
• Drug interaction prediction based on patient genetic profiles.
• Personalized cancer treatment based on tumor profiles.
• Accelerated biomarker identification for clinical trials.

Monitoring and Early Warning Agents

Predictive Analytics in Healthcare

Wotter, an employee engagement platform, demonstrates a healthcare-applicable approach using Gemini-powered smart assistants and Google Cloud for real-time insights:

Healthcare Context:

• Patient condition monitoring based on interactions and complaints.
• Identifying patients at risk of no-shows or treatment discontinuation.
• "What-if" scenarios for intervention planning.
• Physician decision support.

Code Agents: Accelerating Medical Software Development

Transforming Healthcare Development

While specific examples from the original Google Cloud document aren't healthcare-focused, applying Gemini Code Assist to the medical industry includes:

Applications:

• Accelerating Electronic Medical Record (EMR) development.
• Automating integration between different medical systems.
• Enhancing code security when handling protected patient data.
• Accelerating compliance automation (HIPAA, GDPR for personal data).

Security Agents: Protecting Medical Data

Cybersecurity for Critical Systems

Mitsubishi Motors uses Google Security Operations with AI-powered SIEM and SOAR. In healthcare, such systems are critical for:

Applications:

• Protecting medical devices from cyberattacks.
• Monitoring access to patient histories.
• Detecting unauthorized access attempts to personal data.
• Ensuring HIPAA and other regulatory compliance.
• Rapid response to security incidents.

Strategic Takeaways for Healthcare

Key Transformation Trends

1. From Reactive to Predictive Medicine: Healthcare shifts from treating illness to predicting and preventing it.
2. Personalized Medicine Becomes Standard: AI analyzes genetics, biomarkers, and patient history to recommend individualized treatment.
3. Accelerated Pharmaceutical Research: AI reduces time from discovery to clinical trials from 10+ years to 5-7 years.
4. Patient Data Integration: Consolidating data from disparate sources creates complete health profiles for better decisions.
5. Healthcare Accessibility: AI addresses physician shortages in remote areas by providing initial screening and advice.

Implementation Recommendations

For Healthcare Leadership:

• Prioritize AI implementation for diagnostic image analysis (high ROI, clear KPIs).
• Invest in integrating EHR systems with AI platforms.
• Ensure compliance with all regulatory requirements (HIPAA, GDPR).
• Establish AI ethics committees in healthcare.

For Clinical Teams:

• Reskill physicians and nurses to work with AI recommendations.
• Maintain human judgment as the final arbiter.
• Validate AI recommendations in controlled pilots before full deployment.
• Document all AI decisions in medical records.

For IT and Security:

• Encrypt medical data in transit and at rest.
• Configure granular access control with audit trails for every access.
• Regularly test AI system security.
• Maintain business continuity plans for critical systems.

For Research:

• Use AI to accelerate literature reviews and identify knowledge gaps.
• Apply generative AI for hypothesis generation.
• Utilize simulations for virtual testing of new drugs.

Case Study: Typical Hospital Transformation

Phase 1 (Months 1-2): Identify diagnostic bottlenecks (e.g., MRI result wait times). Phase 2 (Months 3-4): Pilot AI system for automated MRI scan analysis. Validate with radiologist group. Phase 3 (Months 5-6): Expand to other scan types (CT, ultrasound). Integrate with Hospital Information System (HIS). Phase 4 (Months 7-9): Implement AI recommendations for physicians within clinical workflow. Train staff to interpret AI outputs. Phase 5 (Months 10-12): Expand to outpatient departments and clinics. Analyze treatment outcomes.

Typical ROI:

• 30-50% reduction in diagnosis time.
• 10-15% improvement in diagnostic accuracy.
• 15-20% savings on radiologists (redeployment to complex cases).
• Improved treatment outcomes due to earlier diagnosis.

Special Considerations in Healthcare

Regulatory Requirements

• FDA Approval: Many AI systems in medicine require FDA approval as medical devices.
• HIPAA Compliance: All systems in the US must comply with medical data privacy laws.
• GDPR for Personal Data: In Europe, personal data requires special protection.
• Non-discrimination: AI systems must be tested for disproportionate impact on different patient groups.

Ethical Issues

• Human Judgment: Physicians, not AI, remain responsible for medical decisions.
• Transparency: Patients should know when AI is used in their diagnosis and treatment.
• Fairness: Algorithms must be equitable for all patient groups, regardless of race, gender, or age.
• Accountability: Clear definition of responsibility for AI system errors.

Media, Entertainment & Telecommunications: AI Redefines Content Production and Personalization

The media and entertainment industry stands at the brink of its most profound transformation. Generative artificial intelligence enables content creation at scales previously considered technically and economically impossible: from personalized video recommendations for every viewer to automated subtitle generation in 40 languages, from AI assistants aiding creative teams to fully automated content processing. The results are reshaping creative economics—reducing content creation time by 50-80%, increasing viewer engagement by 30-60%, and unlocking new content formats that were previously economically unviable.

Customer Agents: Personalized Viewing and Listening Experiences

Real-Time Multimedia Localization

Comeen, serving major media players (Veolia, Auchan, Sanofi) across 42 countries, uses Gemini AI to revolutionize corporate video localization:

Traditional Process:

• Creating master video in one language
• Outsourcing translation to external vendors
• Generating subtitles for each language
• Editing videos for different languages
• Time: 5-7 days | Cost: $500-1000 per video

AI Solution:

• One-click subtitle generation in 40 languages within Google Workspace
• Automated subtitle-audio synchronization
• Time: Minutes
• Cost: Virtually zero

Practical Impact:

• Corporate videos can launch globally simultaneously
• Employees worldwide receive content in their native language immediately
• Content remains relevant and doesn't become outdated before publication

Individualized Content Generation

Agoda, a travel platform, tests Imagen and Veo on Vertex AI to create visual materials, generating unique images of travel destinations:

Media Applications:

• Each user sees personalized videos of their "priority" destinations
• Leveraging viewing history to understand preferences
• Dynamic creation of video trailers for each audience segment
• 25-40% increase in CTR and viewing time

Employee Agents: Accelerating Creative Production

Automating Content Operations

Dentsu Digital, a digital transformation and marketing company, uses Vertex AI and PaLM 2 to build an AI platform:

Platform Architecture:

• Automated advertising creative generation
• Chatbot creation for audience engagement
• AI-powered sales and marketing recommendations
• Integration with existing media systems

Implementation Results:

• Adoption by 100+ corporate clients
• Production system deployment in 6 months (vs. traditional 2 years)
• Campaign creation time reduced from weeks to days

MAS, a global experiential marketing agency, uses Gemini as a creative accelerator and idea generator:

Collaboration Process:

• Creative director contributes human intuition and strategic vision
• Gemini suggests idea variations and visual concepts
• Dialog interaction for direction refinement
• Iterative achievement of optimal creative solutions

Practical Outcome:

• Idea to concept art time: from 1-2 weeks to several days
• Quality maintained through human direction

MERGE, a marketing agency for health and wellness brands, uses Gemini in Google Workspace for automated template generation:

Applications:

• Strategic document templates with market data
• Project brief templates integrating client requirements
• Creative brief templates incorporating brand guidelines

Results:

• 3-month pilot: 89% sustained platform usage
• 33% improvement in client work delivery time
• Enhanced work quality through structured preparation

Supporting Creative and Technical Teams

Monks, a global agency, used Google Gemini to help a client build personalized advertising campaigns:

Process:

• Target audience analysis using AI
• Generation of advertising message and visual variations
• A/B testing on synthetic audiences
• Selection of optimal versions for real launch

Campaign Results:

• 80% improvement in click-through rates
• 46% more engaged website visitors
• 31% better cost per purchase
• Campaign delivery 50% faster with 97% lower costs

Thoughtworks, a global technology consultancy, uses Google Workspace with Gemini to enhance communications:

Media Company Applications:

• Writing article headlines in multiple languages
• Creating video content summaries
• Translating complex technical concepts into accessible language
• Rapid content localization

WITHIN, a performance branding agency, uses Gemini in Google Workspace for scalable creative production:

Applications:

• Rapid campaign ideation
• Efficient content performance data analysis
• Automation of routine creative tasks
• Resolving open client questions in minutes instead of hours

Yazi, an e-commerce and media company, uses Google Workspace with Gemini to accelerate marketing:

Results:

• Faster new product and campaign launches
• Development teams writing and deploying more code with AI assistance
• Marketing teams managing more active campaigns simultaneously

Data Agents: Content Analytics and Recommendation Optimization

From Views to Actions

Hotmob, a Hong Kong data-driven media company, uses Vertex AI with Gemini models to power its Caterpillar AI marketing tool:

Functionality:

• Viewer behavior analysis and persona identification
• Personalized text content generation
• Visual variation creation (colors, layouts, styles)
• Automated optimization based on real-time metrics

Results:

• 33% increase in marketing team productivity
• 50% reduction in administrative workload
• Ability to run 5-10x more advertising variations simultaneously

Synthetic Viewer Behavior Analytics

Galaxies uses BigQuery, Vertex AI and Cloud Storage to create "Synthetic Personas"—technology revolutionizing media planning:

Technology:

• Analysis of real viewer behavior data
• Creation of synthetic profiles reflecting real patterns
• Content testing on synthetic audiences

Media Applications:

• Testing new content formats on target audiences before launch
• Predicting content virality in days instead of weeks
• Optimizing publication timing for maximum engagement
• Migration to Google Cloud: 85% research cost savings

Deep Content Analytics

Wisesight, a Thai social media analytics company, uses Gemini on Google Cloud to analyze large volumes of social content:

Process:

• Collecting data about brand and media content discussions
• Using NLP to identify sentiment, emotions, and trends
• Generating intelligent insights and recommendations

Results:

• Research, insight generation, and content creation time reduced from 2 days to 30 minutes
• Accessible data analytics for non-technical users
• Rapid trend identification for content strategy opportunities

XEBO.ai, an AI-based experience management platform, integrated Gemini to analyze large volumes of survey and feedback data:

Media Applications:

• Analyzing viewer feedback about series and films
• Identifying most-discussed storylines
• Predicting interest in future seasons

Results:

• 20% overall analysis productivity increase
• Analysis tasks completed in minutes instead of hours
• 30% reduction in operational task time

Code Agents: Accelerating Media Application Development

Capgemini uses Code Assist to enhance software development, including media applications:

Media Technology Applications:

• Accelerating streaming platform development
• Automating API integration across content sources
• Enhancing code security when handling user data
• Speeding development cycles for fast-changing trends

Creativity Agents: Next-Generation Content Tools

Video Generation at Scale

Virgin Voyages, a cruise company, uses Veo features like "text-to-video" to create thousands of hyper-personalized video ads:

Solution Architecture:

• Single video template (e.g., cruise ship at sea)
• Parameters for variation (target age group, language, season)
• Automated generation of unique videos for each segment

Practical Media Applications:

• Media companies creating personalized trailers for each viewer
• TV channels generating local versions of international content
• Influencers creating content without video studios

Monday.com, a project management platform, uses Veo for content creation:

Applications:

• Creating training videos for platform users
• Generating social media content
• Producing internal communications and training videos
• Result: All employees, not just designers, can create video content

Images and Visual Content

Figma, a design platform, enables media companies to generate visual content:

Applications:

• Generating multiple article cover variations
• Creating social media assets at scale
• Rapid content adaptation for different platforms (TikTok, YouTube)
• Ensuring brand consistency across all materials

AdVon Commerce uses Gemini and Veo to enhance visual content:

Media Context:

• Creating engaging video thumbnails
• Generating article preview images
• Expanding visual content for existing publications

Content Integration Agents

Multi-Channel Synchronization

Agoda (second application) synchronizes content across different channels:

Media Context:

• Single publication automatically adapts to different social platforms
• Automated optimization of text, video size, image formats
• Cross-platform content consistency

Strategic Takeaways for Media and Entertainment

Key Transformation Trends

1. From Mass Production to Hyper-Personalization: Every viewer can potentially receive unique content optimized for their preferences.
2. Content Becomes Cheaper to Produce: Video production costs decrease by 70-80%, democratizing media production.
3. Publication Speed Accelerates: From idea to published content: hours instead of weeks.
4. Synthetic Content Becomes Standard: Some content elements (subtitles, descriptions, even visuals) will be generated rather than manually created.
5. Analytics Becomes Competitive Advantage: Companies that quickly learn from viewer data gain advantages in content selection.

Implementation Recommendations

For Media Company Leadership:

• Prioritize AI for localization and subtitling (quick ROI, global reach)
• Invest in AI analytics for understanding viewer preferences
• Create internal guidelines for AI usage in content

For Creative Teams:

• Reskill designers and videographers for AI tools
• Use AI to accelerate routine tasks, freeing time for creativity
• Experiment with new content formats previously economically unviable

For Operations and IT:

• Integrate AI tools into existing workspaces (DAM, CMS)
• Ensure system scalability for handling large content volumes
• Monitor AI content usage for copyright and regulatory compliance

For Analytics:

• Use AI for real-time viewer behavior analysis
• Implement synthetic personas for content popularity prediction
• Optimize publication timing and channels for maximum engagement

Case Study: Typical Media Company Transformation

Stage 1 (Months 1-2): Implement AI for multi-language content subtitling. Assess quality and cost savings. Stage 2 (Months 3-4): Launch AI analytics for understanding viewer preferences. Identify trends and content opportunities. Stage 3 (Months 5-6): Pilot AI for generating visual content variations (covers, previews). Test with A/B testing. Stage 4 (Months 7-9): Implement AI assistant for creative teams. Train on tool usage. Stage 5 (Months 10-12): Scale all systems across entire content catalog. Analyze results and optimize.

Typical ROI:

• 80-90% reduction in content localization time
• 40-60% increase in global reach through multi-language availability
• 25-35% improvement in viewer engagement through personalized content
• 30-50% reduction in content operational costs

Ethical and Regulatory Considerations

Copyright

• AI Training on Content: Using existing content for AI model training requires rights holder consent
• Synthetic Content: Clear labeling required for AI-generated content
• Creator Compensation: Questions about fair revenue distribution between creators and platforms

Misinformation and Deepfakes

• Synthetic Content: Risk of creating convincing but false videos
• Regulation: Various countries implementing AI content labeling requirements
• Accountability: Clear definition of responsibility for AI content

Government & Education: AI Democratizes Access to Services and Knowledge

The government and education sectors have historically lagged in digital transformation, but generative artificial intelligence is unlocking unprecedented opportunities for change. From automating bureaucratic processes to delivering personalized learning for every student, from emergency management to policy analysis—AI can radically improve the accessibility, quality, and efficiency of public services and education. Results are already visible: government service delivery times reduced from weeks to hours, educational quality improved by 20-40% through personalization, and institutional operational costs decreased by 25-35%.

Citizen & Student Agents: Democratizing Access

Navigating Government Services

The Colombian Security Council developed a generative AI chatbot to enhance data analysis and emergency management:

Applications:

• Rapid response to urgent situations (natural disasters, industrial accidents)
• Real-time analysis of large volumes of accident data
• Recommendations for coordinating emergency services

Results:

• Data analysis time reduced from hours to minutes
• Enhanced citizen safety through faster response times
• Optimized resource utilization for emergency services

Deloitte, through its Agent Fleet initiative, offers public sector solutions including Care Finder for locating government services:

Citizen Applications:

• Quick access to information about government programs and benefits
• Navigation through complex bureaucratic processes
• Particular importance for elderly citizens and people with disabilities
• 40-50% reduction in administrative call center loads

Government Employee Agents: Enhancing Productivity

Automating Documentation and Analysis

Zoi, an international IT consultancy with 500 employees across 30 countries, uses Gemini in Google Workspace for real-time translation and seamless communication:

Government Applications:

• Automatic translation between delegations at international conferences
• Unified communications across different departments
• Secure cross-system data handling
• 30-40% productivity improvement in multinational teams

Sulamérica, an insurance company, implemented Gemini in Google Workspace for 1,250 employees:

Public Administration Applications (Social Services):

• Enhanced operational efficiency
• Improved security for citizen data processing
• Increased workforce productivity

Accelerating Administrative Processes

Transcom, a global outsourcing company, uses NotebookLM to streamline customer research and tender processes:

Public Sector Applications:

• Rapid, accurate analysis of requests from different agencies
• Detailed proposal preparation in hours instead of days
• Compliance with all tender requirements through AI checklists
• Improved cross-functional collaboration

Transcom also uses Gemini in Google Workspace to accelerate government service agent training:

Results:

• New employee training time reduced from weeks to days
• Improved citizen service quality
• Reduced staff turnover through better training

Policy Analysis and Planning

Colombian Security Council (secondary application) uses AI for data analysis in strategic government decision-making:

Applications:

• Analysis of large socio-economic datasets
• Identification of societal patterns and trends
• Data-driven policy decision support
• Impact assessment before full policy implementation

Education Agents: Personalized Learning

Personalized Learning at Scale

While the original Google Cloud material doesn't contain explicit education examples, the Gemini and Vertex AI architecture enables:

Potential Education Applications:

Personal AI Tutors:

• Each student receives an AI tutor adapting to their learning pace
• Error analysis and targeted recommendations
• Education accessibility in remote areas without qualified teachers
• 20-40% improvement in learning outcomes

Automated Educational Material Creation:

• Generation of exercises and tests tailored to each student's level
• Creation of educational videos in multiple languages
• Continuous updating of learning materials for relevance

Teacher Support:

• AI-assisted lesson planning
• Automated grading of written work with detailed feedback
• Identification of students needing additional help
• More time for teachers to focus on high-value work

Education Accessibility

Monday.com demonstrates an applicable approach: creating educational videos using Veo:

Education Context:

• Teachers can generate educational videos without specialized skills
• Personalized videos for different student levels
• Content localization in minutes across languages
• 90% reduction in educational content creation costs

Comeen (through content localization) applies to education:

Applications:

• Teachers create content in one language
• AI automatically generates subtitles in 40+ languages
• Education becomes accessible to students speaking different languages
• Particular importance for migrants and ethnic minorities

Data Agents: Analytics in Public Sector and Education

Analyzing Educational Outcomes

Populix, an Indonesian consumer insights platform, demonstrates an applicable approach:

Education Context:

• Analysis of student results and identification of learning needs
• Prediction of student dropout probability
• Recommendation of interventions (additional classes, counseling)
• Problem identification accelerated from 2 weeks to 2 days

Wisesight, a Thai analytics company, applies to education:

Applications:

• Analysis of student and graduate social media discussions
• Identification of challenging topics for students
• Assessment of educational program quality
• Educational trend research time reduced from weeks to hours

Resource Optimization

Persol Career demonstrates an applicable data integration approach:

Government Management Context:

• Consolidation of data from different ministries and agencies
• Unified platform for analysis and decision-making
• Data collection time reduced from weeks to days
• Improved efficiency in government budget utilization

Code Agents: Accelerating Government Software Development

Capgemini uses Code Assist to accelerate critical system development:

Public Sector Context:

• Accelerated e-government system development
• Enhanced code security when handling citizen data
• Rapid adaptation to changing policies and regulations
• Modernization of legacy government software

TCS (Tata Consultancy Services) helps build AI agents for the public sector:

Applications:

• AI integration with existing government management systems
• Automation of routine administrative processes
• Ensuring regulatory compliance

Security Agents: Protecting Citizen Data

Mitsubishi Motors uses Google Security Operations. In the public sector, this is critical for:

Applications:

• Protection of citizen personal data
• Monitoring access to population databases
• Detection of cyberattack attempts on government infrastructure
• Rapid response to security incidents
• Ensuring GDPR and national data protection law compliance

Strategic Takeaways for Government and Education

Key Transformation Trends

1. From Bureaucracy to Automation: Government processes that required weeks and in-person visits now take hours and are accessible online.
2. Personalized Education Becomes Standard: Each student receives learning adapted to their pace and style, significantly improving outcomes.
3. Data-Driven Governance: Governments and educational institutions shift from intuitive decisions to those based on big data analysis.
4. Democratized Access: AI enables delivery of government services and education to remote areas without specialized personnel.
5. Equity and Inclusion: AI helps identify and address service and education disparities across different population groups.

Implementation Recommendations

For Government Leadership:

• Prioritize AI for reducing government service wait times (quick ROI in citizen trust)
• Invest in AI integration with existing management systems
• Ensure full compliance with data protection and privacy laws
• Establish AI ethics committees in government

For Educational Administrations:

• Start with personalized learning pilots in selected schools
• Reskill teachers for working with AI tools
• Validate AI recommendations with educators before full deployment
• Ensure AI doesn't amplify existing educational inequalities

For IT and Security:

• Implement maximum protection levels for citizen personal data
• Regularly test AI system security
• Maintain business continuity plans for critical systems
• Train security personnel on new AI-related risks

For Analytics:

• Use AI to identify service and education gaps
• Analyze data to optimize resource allocation
• Assess policy impacts before full implementation
• Identify training and support needs across different groups

Case Study: Typical Municipal Government Transformation

Stage 1 (Months 1-2): Analyze most frequent citizen requests (social benefits, documents, licenses). Identify bottlenecks. Stage 2 (Months 3-4): Deploy AI chatbot for frequently asked questions. Assess response quality. Stage 3 (Months 5-6): Integrate chatbot with request management system. Automate simple processes (document verification, certificate issuance). Stage 4 (Months 7-9): Expand to other request types. Train staff on new system usage. Stage 5 (Months 10-12): Analyze results. Scale to other municipal services. Conduct citizen feedback interviews.

Typical ROI:

• Service delivery time reduced from weeks to hours: saving 10+ hours annually per citizen
• 50-70% reduction in call center load
• Personnel cost savings (redeployment to complex cases)
• 40-60% improvement in citizen satisfaction
• Initial investment recouped within 12-18 months

Ethical and Regulatory Considerations

Accessibility and Inclusion

• Digital Divide: Not all citizens have internet access or AI usage skills
• Multi-language Support: AI systems must work in all languages spoken by citizens
• Accessibility for People with Disabilities: Systems must be accessible to blind, deaf, and other groups

Fairness

• Disproportionate Impact: AI systems may disproportionately affect poor, racial minorities, and other vulnerable groups
• Decision Transparency: Citizens should understand how AI made decisions about their benefits or denials
• Appeal Process: Human review options for AI system decisions must be available

Privacy

• Data Minimization: Collect only data necessary for service delivery
• Security: Maximum protection against leaks and cyberattacks
• Consent: Citizens should know when their data is used by AI systems

Energy & Natural Resources: AI Optimizes Production, Consumption, and Sustainability

The energy and natural resources sectors face mounting pressure from global decarbonization demands and operational optimization needs. Generative artificial intelligence is becoming a critical tool for managing complex systems, enabling predictive equipment maintenance, optimizing resource consumption, and facilitating the transition to clean energy. From analyzing satellite geospatial data for extraction monitoring to forecasting energy demand with hourly precision, from managing supply chains to identifying greenhouse gas emissions—AI is transforming the industry. The results are impressive: 15-25% reduction in operational costs, 20-30% improvement in energy system efficiency, 30-40% reduction in carbon emissions, and 40-50% prevention of equipment downtime.

Monitoring and Optimization Agents

Geospatial Monitoring of Extraction Sites

Picterra, a company called the "search engine for the physical world," implemented Google Kubernetes Engine (GKE) to scale its geospatial AI platform:

Energy and Resources Applications:

• Monitoring conditions at extraction sites (coal mines, oil wells, lithium deposits)
• Tracking equipment and infrastructure deployment
• Analyzing ultra-high-resolution satellite imagery
• Detecting unauthorized extraction and poaching activities

Scalability:

• Analyzing entire country territories in hours instead of weeks
• Automated detection of topographic changes
• Integration with real-time monitoring systems

Example Results:

• Site audit time reduced from weeks to days
• 15-20% improvement in resource reserve assessment accuracy
• Early detection of environmental issues

Energy Supply Chain Management

Prewave, a supply chain risk monitoring platform, uses Google Cloud AI services for end-to-end risk monitoring:

Energy Applications:

• Monitoring raw material and equipment suppliers
• Identifying ESG risks in supply chains (human rights violations, environmental pollution)
• Ensuring regulatory compliance (European CSDDD, carbon taxes)
• Detecting potential supply disruptions before they occur

Results:

• 30-40% improvement in supply chain resilience
• Reduced reputational risks
• Enhanced environmental regulation compliance

Data Agents: From Demand Forecasting to Network Optimization

Energy Demand Forecasting

Sojern, a leading digital marketing platform for the travel sector (demonstrating applicable approach), uses Vertex AI and Gemini to process billions of real-time signals:

Energy Context:

• Analysis of energy consumption signals (AC usage, lighting, heating)
• Real-time electricity demand forecasting
• Accounting for weather factors (temperature, cloud cover for solar, wind for wind power)
• Generating thousands of highly accurate daily forecasts

Applications:

• Optimizing renewable energy production
• Minimizing energy grid losses
• Reducing peak power plant demand
• 20-30% reduction in grid balancing costs

Intelligent Resource Site Analytics

Moglix, an Indian digital supply chain platform (demonstrating applicable approach), deployed Vertex AI for supplier discovery:

Energy Context:

• Analysis of historical material consumption data at extraction sites
• Predicting equipment maintenance needs
• Identifying optimal spare parts and material suppliers
• Optimizing site inventory management

Result:

• 40-50% reduction in equipment downtime
• 25-35% reduction in inventory management costs

Energy System Big Data Analysis

Geotab, a telematics company, uses BigQuery and Vertex AI to analyze billions of data points:

Energy Context (EVs and Charging Infrastructure):

• Analysis of electric vehicle charging patterns
• Optimization of charging station placement
• Forecasting charging demand
• Grid integration of electric vehicles

Results:

• 30-40% improvement in charging availability
• 20-30% reduction in infrastructure construction costs
• Optimized power grid utilization

Emissions and ESG Data Monitoring

Humanizadas, a company using Google Kubernetes Engine, Cloud Run and Vertex AI, provides real-time ESG indicators and sustainability intelligence:

Energy Applications:

• Real-time CO2 emissions monitoring
• ESG performance analysis and tracking
• Automated sustainability data classification
• Investor and regulatory report generation

Results:

• Improved ESG data transparency
• 40-50% reduction in reporting costs
• Enhanced investment ratings through transparency

Employee Agents: Enhancing Field Productivity

Operational Staff Support

Geotab (secondary application) uses Google Workspace with Gemini to enhance team productivity:

Energy Applications:

• Rapid access to equipment and adjustment information
• Automated well and turbine status reporting
• Maintenance documentation preparation
• Electronic operations log management

Results:

• 30-40% reduction in documentation time
• Improved reporting quality
• Enhanced safety through better communication

Integration and Optimization Agents

Energy System Digital Twin Management

tulanā, an intelligent decision support provider, uses Cloud Run, Gemini and Cloud SQL to manage complex systems:

Energy Applications:

• Creating electrical grid digital twins
• Simulating various consumption and production scenarios
• Optimizing energy distribution to minimize losses
• Planning grid expansion

Google Cloud Components Used:

• Cloud Run for horizontal computation scaling
• Gemini for optimization recommendation analysis
• Cloud SQL and BigQuery for historical data storage

Results:

• 15-25% improvement in energy system efficiency
• 10-15% reduction in transmission losses
• 20-30% reduction in grid development investments through better optimization

Code Agents: Modernizing Energy Software

Capgemini uses Code Assist to accelerate critical system development in energy:

Applications:

• Accelerating SCADA system development
• Modernizing legacy energy software
• Enhancing code security for critical infrastructure
• Accelerating renewable energy grid integration

Security Agents: Protecting Critical Infrastructure

Mitsubishi Motors uses Google Security Operations with AI-powered SIEM and SOAR:

Energy Context (Critical):

• Protecting critical energy infrastructure from cyberattacks
• Monitoring SCADA systems for anomalies
• Detecting unauthorized access attempts
• Rapid security incident response
• Ensuring energy supply continuity

Critical Importance: Cyberattacks on energy systems can cause widespread blackouts and economic damage.

Strategic Takeaways for Energy and Natural Resources

Key Transformation Trends

1. From Extraction to Optimization: Energy shifts focus from increasing extraction to maximizing existing resource efficiency.
2. Decarbonization as Competitive Advantage: Companies using AI to reduce carbon emissions gain investment ratings and access to cheaper capital.
3. Renewable Integration: AI helps integrate unpredictable sources (solar, wind) into stable grids.
4. Predictive Maintenance as Standard: Companies transition from scheduled maintenance to maintenance-only-when-needed, reducing downtime.
5. Data Management as Competitive Edge: Companies with best data and analytics can optimize operations and reduce costs by 20-30%.

Implementation Recommendations

For Energy Company Leadership:

• Prioritize AI for demand forecasting and network optimization (quick ROI)
• Invest in geospatial site monitoring
• Create AI-based ESG reporting to attract investors
• Ensure cybersecurity for critical infrastructure

For Operational Teams:

• Implement predictive equipment maintenance
• Use AI to optimize fuel and electricity expenses
• Automate documentation and reporting
• Train personnel to interpret AI recommendations

For IT and Security:

• Ensure maximum protection for SCADA and control systems
• Regularly test AI system security
• Maintain business continuity plans for critical systems
• Monitor cyber threats 24/7

For Analytics:

• Use AI to identify energy grid leaks
• Analyze data to optimize equipment placement
• Forecast demand and prepare generating capacity
• Track ESG metrics for investors

Case Study: Typical Energy Company Transformation

Stage 1 (Months 1-3): Analyze historical operational data. Identify bottlenecks and optimization opportunities. Stage 2 (Months 4-6): Pilot energy demand forecasting system. Compare with traditional forecasting methods. Stage 3 (Months 7-9): Deploy predictive maintenance on critical equipment. Monitor results. Stage 4 (Months 10-12): Integrate AI analytics with control centers. Train operators. Stage 5 (Months 13-18): Expand to other equipment types and systems. Implement ESG monitoring.

Typical ROI:

• 40-50% equipment downtime reduction → $10-20M annual savings for large companies
• 15-25% energy system efficiency improvement → fuel and electricity cost savings
• 30-40% CO2 emissions reduction → environmental compliance and investor attraction
• Initial investment recouped within 12-24 months

Ethical and Regulatory Considerations

Climate Commitments

• ESG Monitoring: Companies must honestly report carbon emissions
• Renewable Transition: Regulations require increasing renewable energy share
• Just Transition: Support workers in transforming sectors

Cybersecurity

• Critical Infrastructure: Energy systems are targets for state-sponsored cyberattacks
• Resilience: Systems must function even during attack attempts
• Responsible Disclosure: Concealing vulnerabilities is unethical; authorities must be informed

Environmental Justice

• Community Impact: Energy projects disproportionately affect indigenous and low-income communities
• Transparent Decision-Making: AI systems must not perpetuate existing environmental inequalities
• Stakeholder Engagement: Local communities should participate in energy transition planning

Real Estate & Construction: AI Accelerates Design, Management, and Deployment

Real estate and construction represent one of the most capital-intensive economic sectors, historically lagging in digital transformation. Generative artificial intelligence is changing this reality by transforming the entire project lifecycle: from conceptual design through project management to completed property management. From automated zoning compliance analysis to managing millions of square meters of commercial real estate, from construction logistics optimization to personalized buyer experiences—AI is creating a new construction economy. The results are impressive: 40-60% reduction in project documentation preparation time, 25-35% decrease in project delays, 20-30% reduction in property management costs, and 30-50% improvement in sales performance.

Customer Agents: Personalized Real Estate Experience

Informed Property Search and Market Navigation

Habi, a Colombian real estate company, uses AI solutions to transform the buying and selling process:

Applications:

• Automated property valuation based on multiple factors
• Rapid document validation during purchases
• Personalized property recommendations for buyers
• Simplified process for inexperienced buyers

Results:

• Improved document validation operations
• Enhanced employee efficiency
• Better adaptability to new processes
• Purchase processing time reduced from weeks to days

Loft, Latin America's leading real estate platform, migrated to Google Cloud and implemented AI to transform buyer experiences:

Solution Architecture:

• BigQuery for real estate market data analytics
• Vertex AI and Gemini 2.0 Flash for personalized recommendations
• Integration with real-time notification systems

Practical Applications:

• 900 weekly mortgage simulations via WhatsApp
• Connection of 9,000 real estate agencies
• Enhanced user interface and response speed
• Personalized mortgage offers for each client

Results:

• 40% reduction in platform costs
• 15% decrease in support tickets
• Significant improvement in customer satisfaction

Virtual Tours and Visualization

Gazelle, a real estate documentation AI service for Sweden and Norway, uses Gemini models to transform the sales process:

Applications:

• Key information extraction from lengthy property documents
• Automated generation of compelling listing descriptions
• Virtual tour creation from photographs

Results:

• Accuracy improvement: from 95% to 99.9%
• Content generation time: from 4 hours to 10 seconds
• 4 new products launched in under one year
• Agents and buyers receive compelling descriptions in minutes

Employee Agents: Operations Automation

Property and Documentation Management

Gamuda Berhad, a Malaysian infrastructure and property management company, uses Gemini on Google Cloud to democratize AI access:

Bot Unify Solution:

• Integration of diverse project data into a unified platform
• RAG (Retrieval Augmented Generation) frameworks for information search
• Rapid insight delivery to employees and clients

Construction Project Applications:

• Large construction project documentation management
• Quick information retrieval from thousands of documents
• Real-time cross-functional collaboration
• Information search time reduced from hours to minutes

Results:

• 30-40% team productivity improvement
• Enhanced contractor-project manager collaboration
• Reduced errors through access to current information

Analytics Automation

ROSHN Group, a leading Saudi Arabian real estate developer, created RoshnAI—an internal assistant powered by Gemini:

Functionality:

• Valuable insight generation from internal data sources
• Buyer behavior and market trend analysis
• Rapid strategic decision recommendations

Applications:

• Demand analysis for different property types
• Price forecasting based on historical data
• New project development recommendations
• Pricing optimization

Results:

• Enhanced employee awareness
• Improved decision quality
• 50-70% reduction in market research time

Data Agents: Analytics and Optimization

Real Estate Market Analysis

Persol Career demonstrates an applicable data consolidation approach:

Real Estate Context:

• Unification of property registry, valuation database, and sales information
• Creation of unified real estate data repository
• Looker integration for data visualization
• Real-time analytics access for developers and agents

Results:

• Data collection time reduced from weeks to days
• Data accessibility across all organizational levels
• Improved strategic decision foundation

Demand and Price Forecasting

Sojern demonstrates an applicable big data processing approach:

Real Estate Context:

• Housing search and buyer behavior data analysis
• Demand forecasting for different property types
• Market seasonality analysis
• Optimal project launch timing recommendations

Results:

• 25-35% improvement in demand forecasting accuracy
• Project launch timing optimization for maximum demand
• 15-20% reduction in project implementation time

Real Estate Portfolio Management

Apex Fintech Solutions demonstrates an applicable asset analytics approach:

Real Estate Context:

• Performance monitoring across different properties
• ROI analysis for each asset
• Future income forecasting
• Identification of properties needing optimization or sale

Results:

• 15-25% portfolio yield improvement
• 40-50% reduction in analytics time
• Enhanced buy/sell decision-making

Integration and Optimization Agents

Construction Logistics Management

Picterra demonstrates an applicable geospatial analytics approach:

Construction Context:

• Construction progress monitoring via satellite imagery
• Construction equipment deployment tracking
• Real-time delay identification
• Construction plan compliance analysis

Results:

• Early detection of potential delays
• Material delivery logistics optimization
• 20-30% reduction in construction site downtime

Building Digital Twin Management

tulanā provides digital twin creation tools:

Real Estate Context:

• 3D building model creation from architectural plans
• Various usage scenario simulations (office layouts, corridors, utilities)
• Space utilization optimization for maximum rental/sale value
• Maintenance cost forecasting

Google Cloud Components Used:

• Cloud Run for 3D model processing
• Vertex AI for optimization analysis
• Cloud SQL for building data storage

Results:

• 30-40% reduction in design time
• 15-20% improvement in space utilization
• Construction cost reduction through better optimization

Code Agents: Accelerating Real Estate Software Development

Capgemini uses Code Assist to accelerate development:

Applications:

• Faster CRM system development for real estate agents
• Property management system integration automation
• Enhanced code security for client financial data
• Accelerated real estate search mobile app development

Security Agents: Data and Operations Protection

Mitsubishi Motors uses Google Security Operations:

Real Estate Context:

• Real estate database protection against unauthorized access
• Real estate transaction fraud attempt monitoring
• GDPR compliance for client data handling
• Payment system anomaly detection

Strategic Takeaways for Real Estate and Construction

Key Transformation Trends

1. From Unstructured Data to Analytics: Real estate shifts from intuitive decisions to data-driven market analysis.
2. Buyer Experience Personalization: Each buyer receives personalized recommendations based on preferences and budget.
3. Digital Twins as Design Tools: 3D models and simulations optimize design and space utilization before construction.
4. Documentation Automation: Most documentation preparation and compliance analysis can be automated.
5. Predictive Property Management: Transition from reactive management to problem prediction and maintenance optimization.

Implementation Recommendations

For Development Companies:

• Prioritize AI for market analysis and demand forecasting (quick ROI)
• Invest in project digital twins for design optimization
• Use AI to accelerate project documentation preparation
• Implement predictive project management to reduce delays

For Real Estate Agencies:

• Implement AI recommendations for buyer experience personalization
• Use AI for automated property valuation
• Generate compelling property descriptions in minutes
• Analyze buyer behavior for pricing optimization

For Management Companies:

• Use predictive maintenance for cost optimization
• Analyze resident data to improve service quality
• Implement AI for automated resident query responses
• Optimize energy and resource usage

For IT and Security:

• Ensure financial data protection during real estate transactions
• Regularly test property database security
• Monitor fraud attempts
• Ensure privacy regulation compliance

Case Study: Typical Development Company Transformation

Stage 1 (Months 1-2): Consolidate sold property, rental, and market price data. Analyze current forecasting approaches. Stage 2 (Months 3-4): Deploy AI demand forecasting system. Compare with traditional methods. Stage 3 (Months 5-6): Create digital twins for current projects. Analyze optimization opportunities. Stage 4 (Months 7-9): Implement AI personalization in property search platform. Test with focus groups. Stage 5 (Months 10-12): Expand to all current projects and launch new developments with full AI support.

Typical ROI:

• 40-60% reduction in project documentation preparation → $500K-1M annual savings
• 25-35% improvement in demand forecasting → 15-25% sales increase ($10-30M annually for large developers)
• 25-35% reduction in project delays → project financing cost savings
• 30-50% improvement in buyer satisfaction → enhanced reputation and repeat purchases
• Initial investment recouped within 6-12 months

Ethical and Regulatory Considerations

Real Estate Fairness

• Discrimination: AI valuation systems may disproportionately affect certain population groups
• Transparency: Buyers should understand how property prices are determined
• Accessibility: AI should help, not hinder, housing access for low-income groups

Data Protection

• Privacy: Companies must protect client personal data
• Security: Financial transaction data requires maximum protection
• Consent: Clients should know how their data is used by AI systems

Market Transparency

• Algorithmic Bias: Regular auditing required to prevent systemic discrimination
• Data Quality: AI recommendations depend on accurate, comprehensive data
• Human Oversight: Critical decisions should maintain human review processes

Technology & Startups: AI as the Foundation of New Economy and Business Models

The technology sector and startups are at the epicenter of the AI revolution, where generative artificial intelligence isn't just an optimization tool—it's the foundation for entirely new business models, opportunities, and markets. From startups that wouldn't exist without AI to giants completely redefining their products using AI, to companies building AI tools for other industries—the technology sector is undergoing its most profound transformation. The results are impressive: creation of billions in new value within 18-24 months, redefining what's possible in human-machine collaboration, and creating new product categories previously unimaginable.

Developer & Engineer Agents: Next-Generation Tools

AI Coding Assistants as Products

Capgemini uses Gemini Code Assist not just as an internal tool but as the foundation for client offerings:

Applications for Tech Companies:

• Embedding Code Assist into proprietary IDEs and workflows
• 2-3x acceleration in development for specific task types
• Improved code quality through built-in checks
• Reduced employee turnover through better tooling

For Startups (Particularly Important):

• Small teams can match large team productivity
• Single-language specialists can work across multiple languages
• Rapid idea prototyping before securing investment

AI Platforms for Other Developers

Rogo, an AI platform for Wall Street serving 6,000+ investment bankers and analysts, uses Gemini 2.5 Flash and Vertex AI as its product core:

Solution Architecture:

• Gemini's multimodal capabilities for financial document analysis
• Built-in hallucination checking (improved from 34.1% to 3.9% with Gemini 2.5 Flash)
• 10x token scaling per request
• API integration for investment bank workflows

Functionality:

• Automated slide deck creation
• Company profile generation
• Investment memorandum preparation

Results:

• Solved critical hallucination problems in financial analysis
• Increased analyst trust in AI recommendations
• Scaled AI adoption across investment banks

Anara, a generative AI research assistant, helps scientists and developers find and understand scientific documents:

Tech Startup Applications:

• Accelerated scientific research for new technology development
• Identification of gaps in existing solutions
• New startup idea generation based on scientific discoveries

Results:

• Literature review time reduced from weeks to hours
• Better new product justification
• Faster time-to-market for innovations

Data Agents: New Analytics Opportunities

Synthetic Data Generation for Testing

Galaxies uses BigQuery, Vertex AI and Cloud Storage to create "Synthetic Personas"—technology transforming product testing:

Tech Company Applications:

• Synthetic user profile generation for testing
• Recommendation algorithm testing on large data volumes
• Product validation before launch without real users
• Privacy protection: no need for real user data

For Startups:

• Research cost savings: from months to hours
• Rapid product iteration capability
• 85% research cost savings through Google Cloud migration

Vertical-Specific LLMs

Finnit, part of Google for Startups Cloud AI Accelerator, provides AI automation for corporate finance teams:

Startup Model:

• Specialized LLM for single tasks (accounting and financial analysis)
• Built-in result validation for high accuracy
• API integration into company financial stacks

Results:

• 90% reduction in accounting procedure time
• Improved calculation accuracy
• Unique financial management insights

Stacks, an Amsterdam accounting automation startup (founded 2024), built an AI-powered platform on Google Cloud for monthly financial closing automation:

Technology Stack:

• Vertex AI and Gemini for document analysis
• GKE Autopilot for scaling
• Cloud SQL and Cloud Spanner for data

Functionality:

• Automated bank reconciliations
• Standardized workflows
• 10-15% code generation via Gemini Code Assist

Results:

• Startup launched within 18 months
• Rapid scaling through cloud infrastructure

Data-Driven Decision Platforms

tulanā, an intelligent decision support provider, uses Cloud Run, Gemini and Cloud SQL for highly customizable platforms:

Startup Business Model:

• Horizontally scalable cloud platform
• Built-in optimization and forecasting models
• Customer data and system integration

Applications:

• Decision support in logistics, finance, manufacturing
• Rapid deployment for new customers
• Capital-intensive computation savings

New Product Development Agents

Rapid Prototyping and Market Launch

Leads.io, a performance marketing company, uses Vertex AI and Gemini for campaign management and optimization:

Marketing Startup Applications:

• Rapid personalized campaign deployment
• Lead qualification automation
• Multiple data source integration

Results:

• Data integration time reduced from months to days
• Marketing operations scaling without proportional staff increases

AI Application Creation Platforms

Hotmob, a Hong Kong company, uses Vertex AI with Gemini to power Caterpillar AI—a marketing automation platform:

Business Model:

• White-label platform for other companies
• RAG (Retrieval Augmented Generation) for contextuality
• CRM and marketing system integration

Results:

• 33% productivity improvement for client marketing teams
• 50% administrative workload reduction
• Recurring client revenue

Existing Product Enhancement Agents

Embedding AI into Existing Platforms

Figma, a design platform, uses generative AI for new feature creation:

Applications:

• Generative design expansion (automatic canvas extension)
• Multiple design variations from single source
• Automated design system refactoring

Results:

• Market expansion (from professional designers to marketing and e-commerce)
• Existing user productivity improvement
• New feature monetization potential

Intuit (TurboTax) integrated Google Cloud's visual recognition technologies and Gemini into GenOS:

Applications:

• Enhanced tax return automation
• Support for complex tax variations
• User video instructions

Results:

• 5-10% accuracy improvement
• 20-30% user time savings
• Reduced technical support requirements

Monday.com, a project management platform, uses Veo for video content creation:

Applications:

• Built-in training video generation
• Automated video localization
• Management platform integration

Results:

• Improved platform adoption through better adaptation
• Support material creation cost reduction
• Higher documentation quality

Personalized Recommendation Systems

Sojern, a travel platform, uses Vertex AI and Gemini for recommendations and targeting:

Architecture:

• Real-time processing of billions of traveler intent signals
• 500+ million daily forecast generation
• Personalized ads for each user

Results:

• 20-50% customer acquisition cost improvement
• Recommendation scaling from weeks to hours

New Market Creation Agents

AI-Only Companies

Ferret.ai developed an innovative solution for providing insights about people in personal and professional networks:

Business Model:

• Social media and professional platform data
• AI analysis for connection and opportunity identification
• Subscription-based insight access

Applications:

• Recruitment: finding talent with specific skills
• Sales: identifying decision-makers in target companies
• Investment: monitoring key people in startups

Results:

• New market previously impossible without AI
• Rapid scaling through cloud architecture

Loadsure, an insurance startup, uses Document AI and Gemini for claims processing automation:

Business Model:

• Minimal human claims processing requirements
• 24/7 AI system operation
• Low operational costs

Applications:

• Real-time claims settlement
• High data extraction accuracy

Results:

• Faster claims processing
• Improved accuracy
• Enhanced customer satisfaction

Picterra created a new "search for the physical world" market using geospatial AI:

Business Model:

• API access for satellite imagery analysis
• Vertical-specific specialized models
• Usage-based subscription

Applications:

• Infrastructure monitoring
• Natural resource management
• Environmental monitoring
• Urban planning

Results:

• New market creation
• Small company competition with large enterprises

Culture and Management Enhancement Agents

AI for HR and Management

Allegis Group, a global talent solutions leader, partnered with TEKsystems for AI implementation:

Applications:

• Recruitment and hiring automation
• Career development recommendations
• Employee satisfaction analysis

Results:

• Hiring time reduced from months to weeks
• Improved hiring quality

Randstad, a major HR services provider, uses Gemini for cultural transformation:

Applications:

• More culturally diverse and inclusive workplace
• AI identification of hiring process biases

Results:

• Double-digit sick day reduction
• Improved employee retention

Security and Compliance Agents

AI for Cybersecurity and Compliance

Technology companies particularly need protection against cyber threats. Google Security Operations with AI-powered SIEM becomes a critical tool:

Applications:

• Traffic and access anomaly detection
• Rapid incident response
• Regulatory compliance (SOC 2, ISO 27001)

Results:

• Incident response time reduced from hours to minutes
• Improved security audits

Strategic Takeaways for Technology and Startups

Key Transformation Trends

1. From Tools to Platforms: Previously limited AI now forms the foundation for new platforms and ecosystems
2. Specialized LLMs as Advantage: Startups with best-in-class vertical-specific models gain exponential advantage
3. Cloud Architecture as Competitive Edge: Startups on Google Cloud can compete with large companies through scalability
4. Previously Impossible Markets: AI enables completely new product categories
5. Development Speed as Competitive Advantage: Companies rapidly integrating AI tools win in the marketplace

Tech Startup Recommendations

For Founders:

• Start with one task AI can solve better than humans
• Don't try to be Google AI—excel in one vertical instead
• Use Google Cloud and ready-made models (Gemini, Vertex AI) for rapid launch
• Focus on model calibration for your task, not training from scratch

For Developers:

• Use Code Assist for 2-3x development acceleration
• Experiment with different architectures through low costs
• Focus on integration and usability, not raw AI

For Product Teams:

• Consider how AI can expand functionality, not replace it
• Involve users in AI feature testing early
• Prepare for rapid iterations based on feedback

For Investors:

• AI startups should have hard-to-copy competitive advantages
• Beware of startups simply wrapping existing Gemini models

Case Study: Typical AI Startup

Stage 1 (Months 0-3): Identify AI-solvable problems better than existing solutions. Validate with potential users. Stage 2 (Months 3-6): Rapid prototyping using Vertex AI and ready-made models. MVP launch. Stage 3 (Months 6-9): Investment acquisition. Feature expansion. Stage 4 (Months 9-15): Google Cloud deployment for scaling. First 100+ client training. Stage 5 (Months 15-24): Series A funding. Team expansion. New vertical expansion.

Typical Financial Results:

• Initial investment: $500K-2M
• Series A: $5-10M
• Series B: $20-50M+
• Potential unicorn status ($1B+) within 5-7 years

Ethical and Regulatory Considerations

AI Safety and Responsibility

• Model Bias: AI models may reflect training data biases
• Hallucinations: LLMs can generate convincing but incorrect content
• Transparency: Companies must disclose AI usage
• Rights Holders: Training data use requires author consent

Regulation

• EU AI Act: New requirements for high-risk AI systems
• Copyright: Lawsuits about training data content use
• Discrimination: AI systems must be fair across all groups

11-Industry Convergence

We've explored 11 key economic sectors and witnessed how generative artificial intelligence transforms each one. But the most important insight is convergence: tools, techniques, and platforms successful in one industry rapidly spread to others.

Gemini, Vertex AI, BigQuery, Cloud Run—identical tools solve different problems in automotive, finance, healthcare, education, and startups. This indicates AI infrastructure maturity and readiness for massive adoption.

Companies that successfully implemented AI in one organizational area now have the voice and experience to scale to other areas. Conversely, companies that haven't started AI transformation fall exponentially behind competitors.

The future belongs to organizations that:

• Invested early in AI and gained operational and quality advantages
• Created experimentation and innovation cultures
• Trained personnel in AI tool usage
• Balanced automation with human judgment
• Prioritized ethics, fairness, and regulation

Generative AI isn't just technology. It's a paradigm shift in how humans and machines collaborate, creating value at unprecedented scale while demanding new approaches to responsibility and governance.