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Endpoint Automation in 2025: 7 Upgrades That Will Shock IT Leaders
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In this episode of TCAST, host Alexander McCaig welcomes Shirish Nimgaonkar, founder of EBLISS AI, to unpack how endpoint automation is redefining enterprise IT. With devices multiplying across industries, traditional support models—manual, reactive, and costly—no longer scale. EBLISS AI addresses this gap through an AI-powered platform that dynamically learns, predicts, and resolves endpoint issues with precision.
Shirish explains how the platform integrates classic machine learning and proprietary small language models to build agentic AI—intelligent agents capable of real-time remediation and proactive diagnostics. They delve into the importance of synthetic data, human-in-the-loop safety mechanisms, and why productivity and risk management must co-evolve. The conversation also touches on the ethical implications of automation and the need for vision-led tech adoption that enhances quality of life, not just profits.
Whether you’re a data strategist, IT leader, or AI ethicist, this episode delivers actionable insights on how to future-proof your digital infrastructure.
Timestamps[00:03] – Introduction: Shirish Nimgaonkar joins to talk AI-powered endpoint automation.
[00:47] – Device Explosion: Why legacy IT support can’t scale with modern device fleets.
[03:11] – Enter EBLISS AI: A platform for prediction, remediation, and self-healing.
[04:28] – Personalized Intelligence: How EBLISS tailors responses based on personas and use cases.
[06:05] – Hybrid AI Models: Mixing ML, LLMs, and synthetic data for scalable solutions.
[10:22] – Risk vs. Efficacy: Managing synthetic data and human oversight.
[12:18] – Scaling Insight: Monitoring drift across millions of endpoints.
[17:00] – Ethics of Adoption: Why vision, governance, and user empowerment matter.
“You’re not just shifting the risk—you’re minimizing it.” – Shirish Nimgaonkar
“Autonomy must be in service of human quality of life.” – Shirish Nimgaonkar
“The ratio of productivity to risk defines solution effectiveness.” – Shirish Nimgaonkar
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