Vaibhav Jha Is Designing the Blueprint for Scalable GenAI in the Enterprise

While most organizations remain stuck in generative AI pilot purgatory, struggling to move experimental chatbots into production-grade systems, a different approach is emerging. At IBM Consulting, Product Data Scientist and Full-Stack AI Engineer, Vaibhav Jha, isn’t just deploying GenAI; he’s engineering scalable frameworks that transform prototypes into enterprise-ready solutions.

From fraud detection to autonomous agent orchestration, Jha has consistently delivered measurable business impact, including an 80x acceleration in root-cause analysis workflows, uncovering over $79 million in potential billing discrepancies through fuzzy matching and clustering of install-disconnect records, and AI systems that now handle most employee HR queries autonomously.

Jha’s passion for artificial intelligence and data science stems from early experiences during his career, where he witnessed the tangible impacts of turning raw data into actionable insights. After earning his B.Tech in Computer Science at VIT University, he joined PayPal in 2017, where he modernized fraud-management APIs and built sentiment-analysis pipelines that flagged high-risk transactions.

“I witnessed how quickly fraud could compromise user trust and financial integrity,” Jha shares. This experience at PayPal, where he implemented a 2-way SMS validation strategy that saved $115,000 per month in fraud losses, established his foundation for building systems that prevent problems rather than just detect them.

In 2022, he transitioned to IBM Consulting in San Francisco, where he worked as part of a Data Science initiative for a major telecom client. There, he architected scheduled pipelines to connect, install, and disconnect records, which uncovered millions in revenue discrepancies. This also led to the development of a survival analysis churn reduction model, which cut customer attrition by 5.3%, resulting in a $10.2 million annual retention impact.

Jha won IBM’s Watsonx GenAI Hackathon in 2023, emerging victorious from over 160,000 global registrations by designing a novel RAG-based agent workflow that outperformed other submissions. His winning solution demonstrated how retrieval-augmented generation could be orchestrated with autonomous agents to solve complex business problems.

This Hackathon win brought notable industry recognition to Jha, who became a guest speaker at IBM’s Silicon Valley Labs in 2024, where he presented on “Agentic AI for Real-World Troubleshooting” to a cross-disciplinary audience of engineers and researchers.

Most organizations struggle to move GenAI beyond experimental pilots, facing challenges with latency, hallucinations, and integration complexity. Jha’s systematic approach bridges this gap between cutting-edge research and its adoption. He achieves this primarily through guest lectures, where he demonstrates how Retrieval-Augmented Generation (RAG) can be applied beyond academic theory and guides students through engineering, retrieval design, and tool integration processes.

However, as qualified as Jha may be, stakeholders are often wary about adopting generative AI for critical workflows, such as those involving HR support. Jha has developed a trust-building approach grounded in measurable outcomes.

“We built a focused Q&A PoC using a small set of curated internal documents and demonstrated 85% accuracy within a week,” Jha shares. “That tangible proof converted skepticism into buy-in and led directly to full AskHR and Researcher Agent projects.”

Today, the AskHR system autonomously resolves 85% of employee HR queries, a metric that represents the difference between GenAI experimentation and GenAI transformation.

The lessons Jha has learned throughout his journey have shaped the way he approaches both technical architecture and stakeholder management.

“Whether you’re pitching a GenAI PoC or negotiating project scope, remember that stakeholders are people first,” Jha says. “Listen more than you speak, ask about their pain points, celebrate small wins with them, and acknowledge their concerns. When clients see you value their perspective, they become collaborators instead of skeptics.”

His technical philosophy emphasizes modularity, observability, and context-aware fallback mechanisms. Rather than building monolithic AI systems, he designs solutions with lightweight governance and clearly defined failure modes. He has also learned that admitting knowledge gaps often earns more respect from others than projecting false certainty.

“Treat every unknown as a puzzle to solve, and you’ll inspire teams to rally around you rather than turn away,” Jha says.

Jha envisions agentic AI as the next layer of enterprise decision-making, where orchestrated systems enable multiple agents to collaborate and solve complex business problems. His work represents a replicable blueprint: start with focused use cases, build trust through demonstrable results, and build for scale from day one.

In an industry where most GenAI initiatives remain trapped in proof-of-concept limbo, Vaibhav Jha’s systematic approach breaks the norm. By treating enterprise GenAI as an engineering discipline rather than a research experiment, he’s building the foundation for AI-driven business change. With a technical foundation based on observability and a human-centered strategy, his work establishes a new benchmark for what enterprise GenAI should look like: engineered, explainable, and long-lasting.