Real-World GenAI Use Cases | Google Cloud Report Analysis

Is Generative AI just hype, or is it driving real business value?

Google Cloud recently published a definitive report, “1,001 Real-World Generative AI Use Cases from Industry Leaders”, and the answer is clear: the most forward-thinking companies are already in production.

At Alps Agility, we have analysed the report to extract the key themes that matter most to modern enterprises. Here is what we found, and how you can replicate their success.

1. The Rise of the “Customer Agent”

The most immediate impact of GenAI is in customer experience. It is no longer about simple chatbots that get stuck in loops; it is about intelligent agents that resolve complex queries.

Albo’s “Albot”: This Mexican neobank uses Gemini to power a chatbot that handles financial advice and onboarding for millions of users, driving financial inclusion 24/7.
Commerzbank: By leveraging advanced retrieval-augmented generation (RAG), their assistant “Bene” now handles over 2 million chats, successfully resolving 70% of all inquiries without human intervention.

The Takeaway: If your customer support is still purely human-driven, you are missing an opportunity to scale.

2. Supercharging Employee Productivity

Internal tools are the unsung heroes of efficiency. The report highlights how companies are using “Employee Agents” to banish drudgery.

Trellix: This cybersecurity platform embedded Gemini directly into their internal workflows, allowing security analysts to conduct industry research instantly without leaving their docs.
Skyvern: Taking automation further, Skyvern uses GenAI to navigate websites and automate browser-based workflows, like filling forms, procuring materials, and downloading invoices automatically.

The Takeaway: Your internal knowledge base shouldn’t be a static wiki. It should be an active agent that answers questions and performs tasks.

3. Acceleration via Code Agents

For engineering teams, GenAI is the ultimate force multiplier.

accessiBe: By streamlining their development process with Google Cloud AI, they reduced their time-to-deploy by 5x.
Cognizant: Integrating Gemini into their software development lifecycle has visibly improved code quality and developer velocity, allowing teams to ship features faster.

The Foundation: It All Starts with Data

Reading through these 1,001 use cases, one common thread emerges: successful AI requires a robust data platform.

You cannot build a “Customer Agent” if your customer data is locked in silos. You cannot build a “Code Agent” if your documentation is fragmented. The companies succeeding with GenAI are the ones who have invested in their Data Engineering foundations.