AI时代: 好售前让客户想签合同 渣售前让客户想报警
Topic Keywords: #AI Presales #Presales Training #Solutions Engineer Training
AI technology is evolving at breakneck speed. These days, every company is an AI company. Every product is AI-driven. And every PowerPoint deck uses the word "empower" at least 30 times. So, it's no surprise that traditional presales professionals are gradually upgrading into AI presales specialists.
When customers open a vendor's technical proposal, they're bombarded with "large language models," "AI agents," and "digital transformation" – so dense they look like supermarket promotional tags.
Is the AI (product or service) good? Yes. Should they buy it? Absolutely.
But here's the thing – not all AI presales are created equal.
With the same product, one presales rep can have the customer ready to sign, while another might make them want to call security.
What's the difference?
Here's how most AI presales typically operate:
Talks specs | Talks features | Talks concepts | Talks business |
Let's look at how AI presales at different levels "pitch" different types of AI products:
General-Purpose LLMs
Novice says★
Our model has 1000B parameters and a 200K context window.
It leads on 10 authoritative benchmarks.
MMLU score of 89.7, crushing GPT-4.
We're far ahead.
Average says★★
Multimodal understanding, function calling, multilingual support, and a plugin ecosystem.
Everything is handled with a single API.
It also supports private deployment, so your data never leaves your domain.
Senior says★★★
This isn't just an LLM anymore.
This is a world model that understands cause and effect in the physical world.
It's already showing emergent abilities.
We're one step away from AGI.
What we're building is the infrastructure for the Fourth Industrial Revolution.
Expert says★★★★
Let's set the parameter count aside for a moment. First, let me ask you: What's the most repetitive task your team is struggling with right now?
Document summarization? Meeting minutes? Internal knowledge retrieval? These use cases have high error tolerance, so they work even with imperfect data.
I suggest starting with these scenarios. Once your team gets used to working with AI, you can gradually expand into core business processes.
As you use it, you'll naturally discover which data is valuable and what needs to be cleaned up.
AI isn't a player that waits for perfect data before entering the game. It learns to walk in the mud.
Enterprise Knowledge Base / RAG
Novice says
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