Ball disagrees with that thought process though, saying: "I have spoken to big companies that are fine with the general models." The problem, Ball insists, is finding the right language, and maths, to dispel these concerns with: "It is a nuanced discussion, the math gets super nuanced", he said.
The key is customer choice, "they can opt in," Ball says. "You can build an org-specific model only using the data in that organisation, then there is a customer who would opt-in to a global or generalised data pool where they are still getting org-specific results. You are always getting an org-specific model, the difference is training only on your data or training on anonymised and sampled data."
Ball calls Einstein "AI for CRM" and because Salesforce has eight separate cloud products they started to go out and acquire AI startups that were operating within very specific domains to help, be it marketing, sales or customer service.
"We started looking at our eight clouds where the use case is very different, so that's when we identified some great companies breaking new ground in very specific areas," he said.
Salesforce acquired Implisit in May, which was focused on AI for sales reps. "They built a lot of models to figure out how to use natural language processing to extract signal from email that are relevant for a sales process, like detecting a competitor was mentioned," Ball explained.
Then there is the deep learning specialists MetaMind "which has incredible applicability in certain domains but doesn't solve all problems, you need a lot of data." MetaMind helped Salesforce build out its image recognition and classification capabilities, which are now baked into the Marketing, Services and App Clouds.
Then there is the analytics specialist BeyondCore, which helped bring predictive capabilities into the Analytics Cloud and wave apps.
On top of all the domain-specific acquisitions there is the likes of PredictionIO, which is an open source machine learning model management tool. Useful when you have "millions of models to manage," as Ball said.
It is becoming a cliche in itself that the best AI is the AI you don't see (think Amazon product recommendations) and many Einstein features aren't there yet. The predictive features are suggestions, and as many sales reps and marketers will know, professionals aren't necessarily the best at being told how to do things differently.
Salesforce has managed to neatly package up its AI capabilities under a single brand, and a cuddly avatar. Now it needs to see engagement figures to match. Einstein will need to prove itself effective to earn the trust of its users. The first step was baking it into the platform, the proof will be in the eating.
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