Google Cloud NEXT ’25: The Rise of Agentic AI for Transformative Customer and Employee Experiences

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

The Business Case for Agentic AI

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

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

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

Building Blocks for a Multi-Agent Ecosystem: Business Implications

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

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

Beyond Technology: Business Integration That Matters

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

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

Google Agentspace: Democratizing AI for Measurable Business Results

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

Business Benefits of Agentspace

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

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

High-Impact Agent Categories: Real Business Results

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

1. Customer Agents: Transforming Customer Experience and Economics

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

Business Impact Metrics:

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

Real-world use cases:

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

2. Creative Agents: Redefining Marketing Economics

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

Business Impact Metrics:

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

Real-world use cases:

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

3. Data Agents: Turning Data Into Business Advantage

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

Business Impact Metrics:

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

Real-world use cases:

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

4. Coding Agents: Redefining Software Economics

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

Business Impact Metrics:

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

Real-world use cases:

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

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

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

5. Security Agents: Protecting Business Value While Reducing Costs

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

Business Impact Metrics:

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

Real-world use cases:

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

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

Google Workspace: AI-Powered Productivity with Measurable ROI

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

Business Impact Metrics:

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

Key innovations driving these results include:

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

Making Science Perspective: From Innovation to Implementation

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

Comprehensive Transformation Approach

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

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

Proven Implementation Methodology

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

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

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

2. Strategic Agent Development (60-120 Days)

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

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

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

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

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

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