This interview is with Ranjith Raghunath, CEO, CX Data Labs.
For Featured readers, how would you introduce your role as a CEO in the computer hardware sector focused on data and AI?
My core skills are defining technical vision and driving organizational efforts. In other words, I help businesses clearly understand how technology can improve their operations, and I lead implementation of those changes throughout the organization.
Looking back, how did you move from hands-on data architecture and ETL (Informatica, DataStage, Hadoop, SQL) to leading AI-driven strategy as a CEO?
AI tools became increasingly essential to the work I was already doing. Traditional data architecture still has its place, but AI enables smaller businesses and teams to achieve enterprise-level outcomes with the right strategy and implementation.
It required plenty of trial and error, especially around keeping data secure and avoiding hallucinations. I was learning and growing as the technology matured.
When you enter a hardware or hardware-adjacent organization with fragmented data, how do you establish a pragmatic data foundation in the first 90 days?
Before we can implement genuinely useful AI solutions, we need a data ecosystem that is unified, consistent, and secure.
In the first 90 days, our typical approach is:
- Conduct a data audit to identify inputs, storage, and access points.
- Work with the client to clarify data goals—what are they trying to achieve?
- Build on existing systems, implement new inputs where needed, and establish a firm foundation for AI.
Across DB2, Oracle, Teradata, SQL Server, and modern stacks like Hive and Pig, how do you decide which systems to integrate first to unlock near-term value without creating long-term technical debt?
SQL is the easiest entry point, especially for smaller startups, and it’s fairly cross-compatible with other platforms—much more so than Oracle. We generally start with SQL Server and build out from there, confident that we will be able to integrate that core with other systems.
Once core pipelines are flowing, what is your minimum viable analytics pipeline—from ingestion to a deployed predictive model—that you stand up before scaling?
The four essential elements we need are collection, storage, transformation, and visualization.
- Collection
- Storage
- Transformation
- Visualization
In very early stages, this means setting up autotracking on KPIs with Freshpaint, storing the input in spreadsheets and using SQL to structure it. From there, clients can use the visualization tool of their choice.
After dealing with file-format mismatches and sensitive document transfers in past transformations, what guardrails do you now put in place up front to keep AI outputs reliable and secure?
Response validation is the starting point here. We’ll carefully monitor this in the early days and regularly audit it to ensure factual outputs are true.
We combine this with PII protection whenever we’re working with personal or proprietary financial data.
When implementing AI on the shop floor or in customer operations, what single change-management tactic has most reliably earned frontline trust?
Feedback from frontline workers and customers isn’t optional. You can’t just deploy a new AI system and then start asking for feedback. If they will directly interact with the AI platform, they should have input on it from day one.
This helps optimize the customer experience without alienating people and ensures that AI actually produces efficiency gains instead of creating more work for production teams.
What single metric have you found most trustworthy for proving early ROI on AI/data integration projects?
Operational cost savings are ultimately what AI implementation is about. At its core, AI can’t do things that people couldn’t already achieve with traditional tools, but it can do those existing tasks much more quickly and reliably when implemented correctly.
Measuring this correctly takes time, of course. Implementing AI comes with plenty of up-front technical and human costs, and it’s important to separate those start-up costs from ongoing ones.
As a hiring leader and mentor, what practical exercise do you use to identify candidates who can bridge domain expertise with AI analytics and ship reliable results?
Im looking for real-world results wherever possible. People who have already implemented AI tools to achieve measurable results are the ones who wont be afraid to experiment with these tools and understand their limitations.
I combine this with hypotheticals: I ask candidates how they would achieve a given result for a client based on our past work.