When we talk about AI in sales, people often mix up two important concepts - AI models and AI applications. But they're actually quite different!
Think of it like your CRM platform: the analytics engine is powerful, but you need the whole system to actually manage your pipeline.
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Can you guess which part AI models are like?
An AI model is the "brain" or "engine" - it's the mathematical core that's been trained on data to recognize patterns or generate outputs.
Models like GPT-4 or Claude are just algorithms with billions of learned parameters. They're pure intelligence without a way to interact with salespeople.
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What would a sales analytics engine be without a user dashboard?
An AI application (or system) is the complete package - it takes that model and wraps it with everything needed to make it useful for sales teams.
This includes the user interface, CRM integrations, lead databases, and all the code that lets you actually use AI in your sales process.
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Starting to see why sales platforms are more than just AI models?
Salesforce Einstein is the application - it provides dashboards, integrates with your pipeline, handles thousands of users, and manages all the technical complexity.
Inside, it uses AI models as its "engine" to score leads and predict outcomes. But without Einstein's application layer, those models would just be code sitting on a server!
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What other parts might a sales AI application need?
Sales AI applications typically include prospect databases, pipeline integration (connecting to your CRM), business logic (rules for lead scoring), and reporting dashboards.
Think of conversation intelligence tools like Gong - the speech recognition is the "model," but the app includes call recordings, coaching insights, deal analysis, and much more!
