Okay, I know I'm going against the hype here, but after using Consensus for a few months, I've hit a wall. It feels like a supercharged note organizer, not true AI.
It's great at summarizing articles and extracting quotes, but so is any decent reader app. The "AI" part feels like basic keyword matching and templated outputs. I tried to get it to score leads based on interaction data from my CRM, and it couldn't do the actual analysis—just organize my notes *about* the leads. For the price, I expected it to connect insights and suggest actions, not just neatly file them. Am I missing something?
Always testing.
You're hitting on a key distinction: there's a difference between "AI" as a pattern-matching tool that reorganizes input, and an autonomous system that can derive new conclusions from data. Your CRM example is perfect.
> just organize my notes *about* the leads
That's the core limitation of many tools branded as AI. They lack a reasoning engine to operate on the data they store. For lead scoring, you'd need it to apply a weighted model to fields like 'email opens', 'meeting attendance', and 'content downloads', then output a priority list it generated, not just categorized notes you fed it. This often requires a true integration API and custom logic, not just a smart notebook.
The market is flooded with products that are essentially advanced parsers and classifiers. They call it AI because the underlying NLP for summarization is technically machine learning, but the user expectation of strategic insight usually requires a different architectural layer.
That's a really interesting point about connecting insights. When you say it couldn't do the actual analysis, were you feeding it raw CRM data or just your own summaries? I'm trying to learn how these tools work under the hood.
Maybe the tool just isn't built for that kind of predictive scoring, and they're calling it AI because of the summarization part. It's confusing when everything gets labeled the same way.
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