Measuring AI’s Real Business Impact

Artificial Intelligence (AI) has long moved past the experimental stage. From banking to retail, it’s being woven deep into business strategy. Yet, for most enterprises, the question isn’t whether to adopt AI—but how to measure its true impact.

According to Mayank Baid, Regional Vice President, India & South Asia at Cloudera, the path to AI ROI is rarely linear. “AI is a long-term investment,” he points out, “and its return isn’t captured through a single metric.” Unlike traditional IT spends, AI’s value lies in a multi-dimensional matrix of outcomes—from improved decision-making to personalized customer experiences.

Rethinking ROI: It’s Not Just About Cost

The obsession with AI cost savings is common, but it’s also narrow. As Baid explains, the real business case for AI includes qualitative advantages: better decisions, stronger compliance, agile operations, and enriched customer interactions.

Take Axis Bank, for example. By deploying Cloudera’s hybrid data platform, the bank didn’t just cut costs—it transformed into a data-driven enterprise. The results speak for themselves:

• 45% of term deposits and
• 70% of instant loans
  were secured through digital channels.

This transformation wasn’t just about convenience. It marked a cultural shift—embedding data as the backbone of customer engagement, personalization, and operational excellence.

Trust the Data, Trust the AI

Every AI model is only as good as the data it’s trained on. Poor data? Poor outcomes.

That’s why Baid emphasizes data quality and trust as foundational. Cloudera’s hybrid data platform addresses this by providing seamless integration, governance, and consistency across cloud, on-premises, and hybrid environments.

But there’s more. Enterprises are often overwhelmed by the scale of data and the complexity of managing it. To bridge that gap, Cloudera is introducing tools that make AI accessible beyond the data science elite. Case in point: Cloudera RAG Studio.

This no-code platform brings Retrieval-Augmented Generation (RAG) to life by enabling real-time enterprise data to power GenAI applications. The result?

• Fewer hallucinations.
• Faster chatbot deployment.
• Better alignment between IT and business teams.

And it’s not just about accessibility. With Octopai’s acquisition, Cloudera is strengthening its data lineage and cataloging abilities. Enterprises can now track, audit, and migrate data more efficiently—ensuring that their AI models operate on high-quality, reliable, and discoverable data.

From Pilots to Platforms: Scaling AI with Confidence

Let’s face it—AI pilots are easy. Scaling them is not. Many organizations get stuck in proof-of-concept mode, unsure of how to move forward. The reasons?

• Siloed data.
• Infrastructure bottlenecks.
• Lack of governance.

Cloudera’s response is clear: bring models to the data—not the other way around. This model-centric approach reduces latency, enhances security, and boosts AI performance. It also avoids the risks of moving sensitive data across platforms unnecessarily.

The scalability factor came into play for PhonePe, one of India’s leading fintech firms. Through Cloudera’s flexible workload migration and hybrid capabilities, PhonePe streamlined its infrastructure, optimized costs, and modernized its data strategy—all while preparing its systems for large-scale AI deployment.

AI is a Strategy, Not Just a Tool

AI should not be boxed in as just another IT solution. Baid sees it as a strategic pillar, one that sits at the heart of modern enterprises. From mitigating risk to enhancing regulatory compliance, the applications are vast—but only if the foundations are strong.

And those foundations start with:

• High-quality, unbiased data
• Scalable platforms that bridge IT and business
• End-to-end governance and transparency

This is where Cloudera’s vision stands out: a hybrid-first, open data architecture that supports real-time insights, secure AI deployments, and broad organizational access to AI innovation.

Measuring AI ROI is no longer about tallying cost reductions. It’s about capturing value—across compliance, engagement, agility, and foresight.

As enterprises look to scale AI responsibly, Cloudera’s approach—rooted in hybrid data platforms, trusted data pipelines, and tools like RAG Studio—is offering a clear roadmap.

AI’s future doesn’t lie in experimental models. It lies in trustworthy, scalable, data-driven ecosystems—and that’s a future businesses must start building now.