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Ignoring agentic AI’s potential, particularly its demand for modernized data infrastructure, carries the same existential risk that faced retailers who ignored the internet. The question isn’t whether to invest, but how to ensure those investments translate into measurable, real-world payoff. But measuring tangible return on agentic AI investment can feel elusive. So how should you position yourself for the agentic AI future, while also ensuring measurable successes along the way?
Get clearer about what you’re aiming for
This is a crucial moment for enterprises to move beyond the tinkering phase of AI. The era of experimenting for experimentation’s sake is over. Today’s models are powerful, but their value depends on the clarity of the outcomes they’re meant to achieve. Without a sharp understanding of business objectives, even the most advanced AI capabilities risk becoming expensive science projects. It’s time to get precise about what success looks like, and build towards it deliberately.
For instance, agents now manage governance, orchestrate pipelines, accelerate onboarding, and enhance customer engagement. Some benefits are easily quantified, like a 15% lift in marketing conversion or a 40% drop in onboarding time. Others are more structural, such as optimized resource utilization and the elimination of redundant tools. When starting out, determine what use cases make the most impact in the least amount of time, and build from there.
Governance: Where ROI takes root
So how do you model more specific ROI goals into your AI strategy?
It starts with governance. This isn’t just about compliance; governance agents actively enforce policies, dynamically detect schema drift, and pinpoint lineage gaps in real time. That creates trustable feedback loops for both developers and executives evaluating outcomes.
Successful organizations aren’t fixated on a single big AI use case. They’re embedding agents across the stack, from customer-facing applications to internal systems for governance, data quality monitoring, and workload optimization. Without a strong command of your data, however, understanding what these agents achieve and, more importantly, measuring their ROI, becomes impossible.
As the investor and author Robert Kiyosaki noted, “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 — improving decisions, training sophisticated systems, and powering autonomous agents — ROI from AI becomes not just theoretical, but real.
The most successful early adopters built governance deliberately. They invested in metadata systems, automation, and domain-based organization. This creates efficiencies, from eliminating redundant data pipelines to speeding delivery. The payoff isn’t always immediate, but it is foundational. Robust governance transforms raw data into a reliable, usable product that enables agents to deliver consistent, repeatable value.
Measuring ROI across the stack
ROI can emerge in many places, and not all of them look the same.
On the business side, agentic AI is already delivering impact. Marketing teams use generative agents for hyper-personalized campaigns, while sales and support teams deploy copilots that dramatically improve response times and customer satisfaction. These are direct accelerators for revenue and key performance indicators. For example, I recently spoke with a financial services firm that used generative agents to personalize onboarding sequences, cutting customer setup time from two weeks to three days, while improving conversion by 20%.
On the supply side, AI agents are optimizing infrastructure, significantly reducing manual work, and mitigating risk. This includes automating complex governance, improving observability, and intelligently tuning workloads to reduce spend. These efficiency gains often materialize more quickly than customer-facing improvements.
A common antipattern is fragmented platforms. When teams adopt overlapping tools, hidden costs pile up. Whether you run a unified platform or a mixed environment, significant ROI is gained by reducing duplication and consolidating workloads. Interoperability matters. When agents operate across systems and governance is consistent, both compute and operational costs fall. The most agile and successful enterprises relentlessly streamline their core platforms.
Think of AI ROI as a continuum. Some investments yield immediate returns. Others build long-term value. The key is knowing where you are and what to measure.
From guesswork to guidance
Don’t treat AI as a mere cost-cutting tool. Its deeper opportunity is horizontal: helping teams to move faster, innovate more, and focus on higher-value work. This benefit, however, only materializes if your data is ready, and that readiness begins with governance.
By making ROI visible and trackable, governance inherently breaks down the organizational silos that fragment efforts and dilute results. It establishes a shared framework that directly aligns data investments with company-wide OKRs. In this age of agentic AI, ROI isn’t a static number on a dashboard; it’s a distributed force waiting to be captured across your enterprise.
Learn how Google Cloud provides 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.
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