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Only 35% of executives say AI consistently delivers the business outcomes they expect, and that single stat explains why “AI everywhere” suddenly feels risky. We sit down with Sushant Tripathi Vice President & North America Lead – AI & Digital Transformation from TCS to talk about what changes when enterprises move beyond experimentation and put autonomous agents into real business operations, where the cost of a bad decision is measured in customer harm, regulatory exposure, and millions in downtime.
We dig into why classic IT reliability metrics like uptime and latency are no longer enough. AI reliability now includes service-level experience, trust, and the ability to prove what happened and why it happened. Sushant breaks down how enterprises should think about trustworthiness as an engineering responsibility, not just a compliance checkbox, and why “more model capacity” does not automatically create dependable outcomes without strong data, integrations, evaluation, and observability.
We also get practical about controls: why many companies can detect AI failures but far fewer can stop them, what “kill switch” thinking looks like, and how an AI maturity journey changes the right level of autonomy. Finally, we talk governance and ROI: who inside the organization must own the policies that let agents act, and how to turn individual productivity gains into measurable enterprise KPIs like revenue, faster resolution, and better customer experience.
If you’re building enterprise AI, agentic AI, or AI governance programs, this is the reliability playbook you’ll want. Subscribe, share this with a leader who owns risk or operations, and leave a review with your take: what is the first guardrail every autonomous agent should have?
Learn more about Microsoft's ISOC announcement: https://fandf.co/4iQ9b5n
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