AI isn’t a mystery. It’s leverage. And according to Ramprakash Ramamoorthy, Director of AI Research at Zoho Corp, the businesses that see real value from AI are the ones that are digitally disciplined—not just digitally active.

At Zoholics USA, Zoho’s annual user conference, Ramprakash laid out a forward-looking, practical, and highly technical view of how businesses can prepare to extract value from AI.

The core message? Digital maturity is the first step to AI maturity. Below is a deep dive into his key points, how your business can apply them, and why playing the long game with AI will set you up for sustainable success.

1. Streamline Your Processes Before You Scale AI

Before diving into the promise of machine learning, neural nets, or generative AI, your house needs to be in order. Ramprakash emphasized the foundational step: process maturity.

• Automate rules-based tasks first. These can serve as the backbone for more intelligent automation down the road. Think approval workflows, notifications, or repeatable backend tasks.
• Capture the employee and customer lifecycle thoroughly. If your systems don’t know your customer journey or employee touchpoints, AI has nothing to optimize.
• AI needs a stable launchpad. Don’t expect intelligent systems to fix broken or unstructured workflows.

Tip: Before introducing an AI project, audit your business for manual, repeatable tasks and start with rule-based automation.

2. Your CRM Should Never Be an Afterthought

A business’s customer relationship management system (CRM) isn’t just for tracking leads—it’s the nervous system of customer intelligence.

• AI thrives on complete, consistent, and contextual data. A well-structured CRM becomes the fuel that AI needs to make recommendations, identify patterns, and suggest next best actions.
• Siloed tools fail to deliver cumulative intelligence. Integrating your marketing, sales, and service platforms into your CRM ensures AI has a full view of customer behavior.

Reminder: A CRM is not a place to dump data. It’s a place to build institutional memory—which is exactly what AI learns from.

3. Data Creation and Transparency Go Hand in Hand

“Data creation should not be an afterthought,” said Ramprakash. That means not just collecting data—but collecting it intentionally and transparently.

• Structured and unstructured data both matter. Whether it’s form submissions, documents, call recordings, or chat logs, AI can use all of it—if it’s captured thoughtfully.
• Privacy controls and permission structures are critical. Companies must enable data visibility with boundaries. For example, an AI agent trained on sales calls should not have access to HR records.
• Transparency breeds trust—and trust is essential when AI systems begin making decisions or recommendations.

Action Step: Evaluate your permission structures. Make sure only the right people—and systems—have access to the right data.

4. Break Down the Silos: AI Hates Fragmented Data

A repeated theme throughout Ramprakash’s talk was the cost of data silos.

• Data in isolation leads to poor predictions and false positives.
• AI systems perform best when they can correlate signals across departments—for example, marketing and support data may together signal churn risk.
• Over time, your AI should help build a unified data layer across your enterprise. This becomes a powerful knowledge base.

Pro Tip: Don’t just integrate your tools—unify your data with platforms that support shared intelligence.

5. From a System of Record to a System of Intelligence

The evolution of enterprise systems is moving from static databases to dynamic intelligence platforms.

• Records are no longer enough. AI can extract patterns, insights, and actions from those records.
• Ramprakash emphasized the idea of transitioning to platforms that provide “long-term know-how”—AI that grows smarter with time.
• Combine that with right-sized AI models (not overbuilt) and a customizable platform, and you unlock what he called “insane value” for customers.

Insight: Don’t chase flashy AI features. Invest in systems that evolve with your business.

6. The Long Game: Don’t Fear the FOMO

Perhaps the most grounded advice of all: play the long game.

• AI is not a race—it’s a transformation.
• Businesses that succeed don’t panic or follow the crowd. Instead, they take deliberate steps: digitize, clean data, create good governance, and then introduce AI where it’s useful.
• AI is not plug-and-play. It requires investment in systems, people, and process discipline.

Mindset Shift: You’re not behind—you’re building resilience. That’s the win.

Final Takeaway: Digital Discipline Drives AI Success

Ramprakash Ramamoorthy’s presentation wasn’t filled with AI hype. It was a blueprint for how businesses should prepare for a world where AI is embedded in every workflow, customer journey, and decision loop.

If your company isn’t seeing value from AI yet, it’s not because AI doesn’t work. It’s likely because you haven’t matured the digital foundations it requires. Streamline your systems. Invest in clean, structured data. Make transparency a priority. Then—layer in intelligence that compounds over time.

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