The first question I ask myself when our users bring us product requests is “How would an AI-Native CRM solve this problem”?
When new users start with Day.ai, one of their first questions is inevitably "How do I import my deals from our current CRM?" It's a perfectly reasonable request. They've spent months or years building up their pipeline data, and naturally, they want to bring it with them.
But there’s a big difference between building exactly what users ask for and solving the problems they bring you. Sometimes those things align perfectly. But other times, you need to dig deeper to understand what users are really trying to achieve – and that might lead you to a completely different solution than what they initially asked for.
This is one of those times.
We're building an AI-native CRM, and while it’s usually hired to serve the same core needs as traditional CRMs, it works fundamentally differently under the hood. Traditional CRMs are built around properties and fields – they’re essentially spreadsheets with extra features. AI-native systems are built on context and reasoning.
This critical difference means that sometimes we need to explain to our users why certain things that worked one way in traditional systems work totally differently in an AI-native world. Not because we're being difficult, but because these changes unlock capabilities that simply weren't possible before.
When users ask for deal import, what they're really asking for is something like: "help me maintain continuity in my sales process as I switch systems." That's a valid need. But traditional deal import – copying records from one database to another – comes with serious problems:
- Most CRM pipelines are full of zombie opportunities that should have been closed months ago
- Many real opportunities never made it into the system because someone got too busy to create them
- The "source of truth" in a traditional CRM is whatever someone manually entered – or forgot to enter
In other words, importing deals often means importing a mix of outdated, incomplete, and incorrect data. Starting fresh with bad data undermines the very reason teams switch to Day.ai in the first place.
Instead of starting with traditional deal import, we built something we call Org Relationships. It's a simple way to tell Day.ai which companies are your prospects, customers, partners, and vendors. Then we let our AI analyze your complete communication history with these organizations to understand what's really happening.
This is the fundamental difference between traditional and AI-native systems. Traditional CRMs can only know what you explicitly tell them through (manual) data entry. Day.ai looks at your actual conversations – emails, meetings, messages – to understand the true state of your relationships and opportunities.When you tag an organization as a sales prospect in Day.ai, you're not just adding a label. You're telling our AI "this is an organization we're interested in selling to." The system then analyzes all your interactions with that organization to understand where you really stand with them. Are there active conversations happening? Did they explicitly say no three months ago? Did someone forget to follow up on their interest?
This approach delivers better outcomes for our users:
- Your pipeline reflects reality, not just what someone remembered to type in
- Every opportunity is validated by actual communications, not just data in a spreadsheet
- The system keeps learning and updating as new communications happen
- You get clear reasoning for every decision, so you can easily add missing context
- You can instantly see lists of all your customers, prospects, or any other relationship type
Yes, this approach requires users to think differently. But that's exactly the point. AI-native systems aren't just traditional CRMs with some AI features bolted on – they represent a fundamental shift in how we manage and understand business relationships.
When you embrace this new paradigm, you get something much more valuable than imported deals – you get a system that actually understands your business relationships and helps you manage them more effectively. You can think of it like giving your CRM a blueprint for all of your B2B relationships, which allows for more accurate record creation, but also sends ripples of context throughout all of your interactions. Your meeting notes get written in the perfect format automatically, your action items have better context around your goal state - everything improves downstream from accurate context.
Sometimes the best way to solve a user's problem isn't to build exactly what they ask for. Sometimes it's to show them a better way.
Here's a few examples of how the Org Relationships and Reasoning features work:
