Build smarter business processes with agentic AI on Google Cloud

AI has already changed how work gets done, but the chatbots, copilots and workflow automation that most employees have experienced so far are just the opening act.

A bigger shift is underway as agentic AI ushers in the next stage of enterprise intelligence. These tools don’t wait for prompts. They set goals, break work down into steps, coordinate with systems, make decisions and act. They learn from outcomes and improve over time.

For CTOs and CIOs, this presents a once-in-a-decade opportunity to redesign how their organizations function at their core — and platforms like Google Cloud make that possible.

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Why business processes must be redesigned, not just automated

Most enterprise workflows were designed around human constraints such as inbox queues, approval chains, batch processing and departmental silos. Latency and handoffs were inevitable, and data was stored and managed in multiple locations.

Then we layered software on top — and now we’re adding AI on top of that. But if you apply AI to a broken process, you don’t fix it. You just amplify the inefficiencies.

Agentic AI changes the equation because it goes beyond automation to also redesign the workflow itself. This is why leading organizations are seeing these agents — large numbers of tireless “digital employees” working in concert — as more than incremental automation. They’re laying the foundation for entirely new operating models.

Gartner® predicts that “at least 15% of day-to-day work decisions will be made autonomously through agentic AI by 2028, up from 0% in 2024. In addition, 33% of enterprise software applications will include agentic AI by 2028, up from less than 1% in 2024.”*

That’s structural change in action, prompting CTOs and CIOs to reinvent and redesign workflows around intelligence-first principles:

• Eliminate unnecessary handoffs.
• Collapse sequential steps into parallel execution.
• Replace reactive monitoring with ongoing optimization.
• Augment the workforce to make faster decisions, not just complete tasks.

Agentic AI in practice across industries

Consider the following scenarios that illustrate the gains in speed, efficiency and customer experience when workflows formerly dependent on humans are carried out by AI agents instead.

Enabling these scenarios requires a cloud platform designed for orchestration, scale and governance.

Supply chains that reroute themselves

As a shipment departs for Southeast Asia, a sudden storm leads to severe port congestion. Traditionally, a human would receive an alert, leading to a series of emails and meetings over the coming hours and days.

In an agentic model, supply chain agents ingest a constant stream of weather data, shipping telemetry, port capacity metrics and contractual obligations. Analyzing simulated scenarios, cost and service impact, they find ways of rerouting the shipment with minimal disruption.

These multiagent models can collaborate across functions and even organizations to solve complex problems and make workflows more resilient.

Customer service with fast and accurate resolution

In a legacy environment, when a customer contacts support about a billing discrepancy, the ticket is categorized, routed and escalated.

An agentic ecosystem sees the broader picture. It reviews transaction history, cross-checks against policy changes, evaluates risk signals and updates customer records. It can even coordinate with fraud-detection and customer relationship management systems in real time.

The issue is resolved quickly instead of languishing in queues.

By deploying service agents, some IT organizations have reduced ticket volumes by 60%–70%, according to NTT DATA’s own observations.

Financial operations that continuously manage risk

In financial services, AI agents are already cross-checking transactions against millions of historical records and detecting anomalies in timing and location data before approving or rejecting activity on the spot. Some institutions have cut the number of fraudulent transactions in half.

Now imagine extending that logic across credit exposure, liquidity risk and regulatory reporting, where risk needs to be managed continuously.

IT that self-diagnoses and remediates

Just like manufacturing agents can monitor sensor data to detect signs of impending equipment failure and alert teams and even schedule repairs, enterprise IT agents can monitor logs, performance metrics and configuration drift to isolate root causes of problems, apply remediation scripts and document the changes.

Human IT support agents now oversee and refine policy rather than spend their days firefighting.

Why Google Cloud is an enabler of agentic AI

To operate at this level, agents need more than models. They also need a platform, and this is where Google Cloud becomes critical.

• Agents orchestrated across systems: Agentic AI thrives on connectivity. Agents must access data, invoke application programming interfaces, update systems and collaborate with other agents.
  
  Google’s ecosystem offers deep integration capabilities and a rich set of connectors that simplify building and scaling low-code agents across software-as-a-service platforms.
  
  Technologies such as Google’s Agent2Agent Protocol and NTT DATA’s agent factory frameworks — which bring together scalable computing, governed data pipelines and repeatable deployment processes — push this even further to enable structured multiagent communication and orchestration.
• Real-time decision-making on unified data: Fragmented architectures undermine agent performance. Unified data pipelines and high-performance infrastructure are essential to industrialize AI.
  
  Here, too, what NTT DATA refers to as an “AI factory” helps to turn raw enterprise data into actionable intelligence at scale. On Google Cloud, this is enabled through an end-to-end AI platform that brings together BigQuery, Vertex AI (including Agent Builder and Reasoning Engine), Gemini models and integrated data pipelines to provide a secure foundation for agents to operate within enterprise data and cloud environments, in line with governance standards.
• Continuous learning and optimization: Unlike traditional software, agents improve through experience. That requires monitoring, governance and lifecycle management.
  
  Google Cloud supports this with built-in observability, model monitoring, version control and policy enforcement capabilities that allow you to track agent behavior, evaluate performance against defined key performance indicators and safely retrain or refine agents within governed environments.
  
  This also prevents the proliferation of “shadow agents” that operate without oversight.
• Secure, scalable infrastructure: Operationalizing agentic AI demands high-performance infrastructure and strong security.
  
  Google Cloud delivers this through purpose-built AI infrastructure, including Tensor Processing Units (TPUs) for large-scale model training and inference, as well as Google Distributed Cloud for deploying AI workloads in hybrid and edge environments. This is reinforced by Google’s end-to-end security fabric that enables you to scale agentic AI securely while maintaining compliance and governance.
  
  According to NTT DATA’s 2026 Global AI Report: A Playbook for AI Leaders, organizations classified as AI leaders (based on their AI strategy, maturity and AI-related profitability) are more likely than other organizations to prioritize scalable, secure technology stacks like Google Cloud as they seek to prevent bottlenecks and enable compliant scaling.

Let’s design an intelligent enterprise

Every CIO and CTO should be asking: “If I were building my organization from scratch, with agentic AI native to the core on Google Cloud, what would I design differently?”

It’s likely that you would eliminate handoffs, unify data and embed governance into the architecture as you align workflows around outcomes, not tasks. And you can now do exactly that, before your competitors catch up, because the technological foundation is ready.

The partnership between NTT DATA and Google Cloud helps you redesign your business processes as you begin implementing autonomous, goal-oriented AI agents.

Google Cloud provides the scalable infrastructure, data unification, model deployment capabilities, orchestration frameworks and governance controls needed to operationalize agentic AI responsibly at scale.

At the same time, NTT DATA develops customized intelligent agents for specific industry challenges, while our Agentic AI Factory for Hyperscaler Technologies helps you scale AI responsibly by blending strong data foundations, robust governance and Google Cloud’s AI.

Let us work with you to rebuild the future of your organization around AI.

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* Gartner. Gartner predicts over 40% of agentic AI projects will be canceled by end of 2027. 25 June 2025.

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