AI in CRM: Cutting Through the Hype

I'm going to be honest about something that most CRM vendors won't be: there is no such thing as proprietary AI. Not in the way they market it.

When Salesforce talks about Einstein, when HubSpot talks about their AI features, when any CRM vendor puts "AI-powered" on their homepage — they're all using the same underlying technology. Large language models from a handful of providers. The same foundations that power ChatGPT, Claude, Gemini, and everything else. They've added their own layer on top, sure. But the AI itself isn't magic they invented.

I'm telling you this because we use the same technology. We use the best industry models — Claude, specifically — configured with custom prompts and your business data. We don't claim to have built proprietary AI. We didn't. Nobody in the CRM space did, no matter what their marketing says.

What matters isn't the model. It's how it's configured, what data it has access to, and how it's integrated into your actual workflows.

AI Slop vs Real AI

Let me give you two examples of what AI looks like in a CRM.

Example 1 — AI Slop: A support ticket comes in. The AI reads the ticket and suggests a generic response based on keyword matching. "It sounds like you're having trouble with [product]. Here are some common solutions..." The customer writes back saying that's not what they meant. The AI suggests another generic response. The agent ignores the AI after the second bad suggestion and writes the response themselves. Everyone pretends the AI is helpful because the vendor charges extra for it.

Example 2 — Real AI: A support ticket comes in. The AI knows this customer bought a specific product six months ago, had an issue three months ago that was resolved by replacing a component, has a renewal coming up in 60 days, and has been a high-value customer for three years. It also knows the current issue matches a pattern that the engineering team flagged last week affecting a batch of products shipped in a specific period. The AI drafts a response that acknowledges the history, identifies the likely issue, proposes a specific solution, and flags the ticket for the account manager because this customer's renewal is coming up and you don't want a support issue to become a churn event.

Same underlying AI technology. Completely different result. The difference is context.

Why Most Vendors Can't Do This

The reason most CRM vendors deliver AI slop instead of real AI is structural. Their architecture doesn't support it.

If your CRM handles sales but not support, the AI can't connect support issues to deal context. If your marketing automation is in a different platform, the AI can't see how a customer's journey from lead to buyer affects their support expectations. If your billing is in another system, the AI can't flag that a customer with a billing issue also has an open support ticket and a deal in the pipeline.

The AI is only as good as the data it can see. And in a world where your business runs on five disconnected tools, each tool's AI is working with 20% of the picture.

That's why SCM is holistic. Not because we think it sounds good in marketing. Because the AI literally needs all the data to do its job properly. Support data, sales data, marketing data, communication history, customer lifecycle data — all in one place, all accessible to the AI, all connected.

What Honest AI Looks Like

Here's our approach, and I'm being fully transparent because I think the CRM industry needs more honesty and less marketing.

We use the best available AI models. Right now, that means Claude from Anthropic for most of our language tasks. If something better comes along, we'll switch. We're not locked into our own proprietary model that we have to defend.

We configure these models with custom prompts for each business. Not generic prompts. Prompts that understand your specific products, your terminology, your customer segments, your escalation procedures, your brand voice. The AI sounds like your company, not like a generic chatbot.

We give the AI access to the full context. When it drafts a support response, it can see the customer's entire history. When it scores a lead, it can see how similar leads converted. When it suggests a workflow optimization, it's based on your actual data patterns, not industry benchmarks.

And critically, your data stays yours. The AI configured for your business doesn't train our other customers' AI. There's no cross-pollination. Your competitive intelligence, your customer patterns, your operational data — it's used exclusively for you.

Is this less impressive-sounding than "proprietary AI"? Maybe. Is it more honest? Definitely. Does it produce better results for the actual business using it? Every single time. Because real AI isn't about the model. It's about the context, the configuration, and the data. And that's where we put all our effort.