Aishwarya Reganti, an AI product practitioner, discusses research from UC Berkeley and Databricks regarding enterprise hesitation in deploying autonomous customer-facing AI products.
“It said about 74 or 75% of the enterprises that they had spoken to their biggest problem was reliability, and that's also why they weren't comfortable deploying products to their end users and building customer-facing products”
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More from Aishwarya Reganti (Ash)
Opinion
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“I probably will go as far to say that if someone's selling you one click agents, it's pure marketing.
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Insight
Reganti: Enterprise AI Workflows Require 4 to 6 Months for Real ROI
“To replace any critical workflow or to build something that can give you significant ROI easily takes four to six months of work, even if you have the best data layer and infrastructure layer.”
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Insight
Reganti: LLM judges fail in complex AI cases due to emerging patterns
“When you go to complex use cases, it's incredibly hard to build LLM judges because you see a lot of emerging patterns. If you build a judge that would you know, test for verbosity or something like that, it turns out that you're seeing newer patterns that your…”
Aishwarya Reganti (Ash)Jan 11, 2026▶ 40:07Why most AI products fail: Lessons from 50+ AI deployments at OpenAI, Google & Amazon
PredictionNot checkable as stated
Reganti: Prompt injection will become a major crisis as AI goes mainstream
“I think that will be a huge problem once systems go mainstream. We're still so busy building AI products that we're not worried about security, but it will be such a huge problem to kind of especially with this non-deterministic API again, right? So you're kin…”
Aishwarya Reganti (Ash)Jan 11, 2026▶ 23:03Why most AI products fail: Lessons from 50+ AI deployments at OpenAI, Google & Amazon
Insight
Reganti: Enterprise AI transformation cannot succeed as a bottom-up initiative
“It's almost always impossible for it to be bottom-up. You can't have a bunch of engineers go and get buy-in from the leader if they just don't trust in the technology, or if they have misaligned expectations about the technology, right?”
Aishwarya Reganti (Ash)Jan 11, 2026▶ 27:22Why most AI products fail: Lessons from 50+ AI deployments at OpenAI, Google & Amazon
Insight
Reganti: Workflow Automation Always Requires Combining ML Models and Deterministic Code
“Whenever you're trying to automate some part of a workflow, it's never the case that you could use an AI agent and that will kind of solve your problems, right?
It's always, you probably have a machine learning model that's going to do some part of the job.
Yo…”
Aishwarya Reganti (Ash)Jan 11, 2026▶ 29:36Why most AI products fail: Lessons from 50+ AI deployments at OpenAI, Google & Amazon
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