Presented by Google Cloud

Overlooking the potential of agentic AI, especially its requirement for updated data infrastructure, poses a similar existential threat to that faced by retailers who neglected the internet. The real question is not about whether to invest, but rather how to ensure these investments yield tangible, real-world benefits. Yet, assessing the concrete return on agentic AI investments can often seem elusive. How can you position yourself for a future dominated by agentic AI while also securing measurable achievements along the journey?

Clarify Your Objectives

This is a pivotal time for businesses to progress beyond the experimental phase of AI. The period of experimenting merely for the sake of it is over. Today’s AI models are incredibly potent, but their true value hinges on the clarity of the objectives they aim to achieve. Without a precise understanding of business goals, even the most advanced AI capabilities risk becoming expensive scientific endeavors. It's essential to define what success looks like and work deliberately towards it.

For example, agents now handle governance, orchestrate pipelines, expedite onboarding, and enhance customer engagement. Some advantages are easily quantifiable, like a 15% increase in marketing conversion rates or a 40% reduction in onboarding time. Others are more structural, such as optimized resource usage and the removal of redundant tools. When beginning, it's crucial to identify which use cases will have the most significant impact in the shortest time and build from there.

Governance: The Foundation of ROI

How can you incorporate more specific ROI goals into your AI strategy?

It all begins with governance. This is not solely about compliance; governance agents actively enforce policies, detect schema drift dynamically, and identify lineage gaps in real time. This creates trustworthy feedback loops for both developers and executives assessing outcomes.

Successful organizations do not fixate on a single major AI use case. They integrate agents across the entire stack, from customer-facing applications to internal systems for governance, data quality monitoring, and workload optimization. Without a comprehensive understanding of your data, grasping what these agents achieve and, more importantly, measuring their ROI, becomes impossible.

As investor and author Robert Kiyosaki remarked, “The rich don’t work for money; they make money work for them.” A similar principle applies to your data. When your data is agile, clean, and actively working for you — enhancing decisions, training sophisticated systems, and empowering autonomous agents — ROI from AI becomes not just theoretical, but tangible.

The most successful early adopters established governance intentionally. They invested in metadata systems, automation, and domain-based organization. This creates efficiencies, from eliminating redundant data pipelines to expediting delivery. The payoff may not be immediate, but it is foundational. Robust governance transforms raw data into a reliable, usable product, enabling agents to deliver consistent, repeatable value.

Evaluating ROI Across the Stack

ROI can manifest in various areas, and not all of them appear the same.

On the business front, agentic AI is already making a significant impact. Marketing teams utilize generative agents for highly personalized campaigns, while sales and support teams deploy copilots that drastically enhance response times and customer satisfaction. These are direct accelerators for revenue and key performance indicators. For instance, I recently conversed with a financial services firm that used generative agents to personalize onboarding sequences, reducing customer setup time from two weeks to three days, while boosting conversion by 20%.

On the supply side, AI agents are optimizing infrastructure, greatly reducing manual labor, and mitigating risks. This includes automating complex governance, improving observability, and intelligently tuning workloads to lower costs. These efficiency gains often materialize more quickly than customer-facing improvements.

A common pitfall is fragmented platforms. When teams adopt overlapping tools, hidden costs accumulate. Whether you operate a unified platform or a mixed environment, significant ROI is achieved by reducing duplication and consolidating workloads. Interoperability is crucial. When agents function across systems and governance is consistent, both compute and operational costs decrease. The most agile and successful enterprises relentlessly streamline their core platforms.

Think of AI ROI as a continuum. Some investments provide immediate returns, while others build long-term value. The key is understanding where you are and what to measure.

From Guesswork to Guidance

Don’t view AI merely as a cost-cutting tool. Its deeper opportunity is horizontal: enabling teams to move faster, innovate more, and focus on higher-value work. However, this benefit only materializes if your data is prepared, and that readiness begins with governance.

By making ROI visible and trackable, governance inherently breaks down organizational silos that fragment efforts and dilute results. It establishes a shared framework that directly aligns data investments with company-wide OKRs. In this era of agentic AI, ROI isn’t a static number on a dashboard; it’s a distributed force waiting to be harnessed across your enterprise.

Discover how Google Cloud offers the integrated platform to turn these challenges into your competitive advantage.

Gus Kimble is GM and Head of North America Data Analytics Customer Engineering at Google Cloud.