Parth Krishna

Engineered a Global Leadership AI Agent Control Tower in Microsoft PowerApps Suite, governing 20+ deployed AI agents and reducing agent oversight effort by 40% while improving leadership response to escalations by 60%.

Delivered an AI-powered Finance Insights platform, automating end-to-end workflows and saving an estimated 200 FTE hours/month, reducing invoice resolution TAT by 65% in the AR function.

Orchestrated requirement-gathering workshops with 8+ global stakeholders, authoring BRDs and functional specifications for agentic solutions and accelerating solution delivery timelines by 30%.

Conducted technical design reviews, identifying an average of 5–7 functional gaps per release pre-development, and supported UAT for 3 major deployments with zero critical post-go-live defects.

Drove operations analytics initiatives for NAM and LATAM clients, using process automation to improve data integrity, reduce human error and increase operational efficiency.

Boosted process efficiency by 98% through a global bi-lingual MS Power Platform solution, integrating approval workflows and KPI dashboards for enhanced decision-making.

Implemented a Microsoft PowerApps solution integrating triggered workflows and AI Builder Sentiment Analysis, generating more than $40,000 in business impact through improved BI capabilities.

Automated reporting and data workflows using Python, improving TAT by 96% and streamlining data ingestion and datasource refresh across multiple sources.

Developed versatile Python-based automation solutions using Lean Six Sigma principles, enabling timely datasource refresh and failure notifications while helping regional leaders identify impacted dashboards and address aging issues.

Developed Predictive Analytics and NLP-based ML models to forecast resource utilization and future trends, including a Python web application with interactive dashboards to support decision-making and prospect analysis.

Supported EMEA Manufacturing Operations across inventory, order management and Procure-to-Pay workflows, improving On-Time Delivery by 22% through process and workflow optimization.

Engineered ServiceNow workflows, KPI frameworks and forecasting models by analyzing inventory and operational data using Python.

Designed and implemented a Python-based Data Quality framework tailored to client requirements, incorporating forecasting and Retrieval-Augmented Generation (RAG) capabilities.

Automated data validation and forecast analysis, enabling more efficient planning and natural-language access to operational insights.