Why so much unused greatness?
Most Companies running Salesforce know they're missing much of the power that the suite of applications offers, and the other half have no idea. Salesforce admins are invariably hardworking, smart, curious and eager to innovate, but their immediate managers are often too busy and stretched too thin to stop and listen and understand what's possible.
The folks at Salesforce know this, and they have to be OK with it. Your AE will call at regular intervals and will ask how it's going. They have smart tools to tell them what you've implemented, what licenses are fully used vs sadly underused.
As our macro economic forces continue to squeeze more productivity per employee per company, managers are asked to look at ways to save money. Sometimes they're smart enough to look at where to increase revenues instead of cutting costs. Sometimes they have to do both.
Enter AI. We've all listened to the doom and glom narrative about the end of our jobs, the extinction event, etc. We do realize that 'edge AI', or a modest investment in artificial intelligence at the border between our people and our processes, is capable of increasing productivity immensely. Law firm employees can draft entire contracts in minutes instead of days, or summarize a huge tome of documents in minutes without reading it.
Why, then, are so many Salesforce orgs happily chugging along with no help from Einstein?
Why are we leaving so much free help on the table, unused?
Let me offer an answer. "Einstein doesn't work." So many folks still believe the offerings demonstrated at Dreamforce and promoted in marketing campaigns are not yet workable, therefore not worth pursuing.
"Good grief!" Said Charlie Brown.
It's not true. Einstein came out in 2016 and is on a journey of constant learning and improvement. Einstein does work, but it needs good quality data to begin with, and it needs to be nurtured with good training, before its results can be relied upon as trustworthy and high quality.
We must think of Einstein - as all artificial intelligence - as guessing, then checking. It makes a guess based on what it understands from historical data and whatever training it has absorbed so far, and it then checks its guesses after the event, and learns from its hits and misses.
Let's imagine Einstein Opportunity Scoring has assigned a score of 45 to a deal with Beenz Coffee. Based on a lack of completes in the opp fields, a lower frequency of updates to the record, and a low level of activity in Einstein Activity Capture. Then the opp updates as Closed-Won, a week before the expected close date. What were we missing? Well, it turns out the account exec had that one as an unexpected windfall, and wasn't sure the customer was serious, but they did receive a call saying this was a directive from the CEO and that they were buying for sure. Einstein didn't know this, but it did register an incoming call from the contact, and a smiley added by the AE.
Einstein just in creased its understanding of incoming phone calls followed by positive emoticons.
Next time something like this happens, Einstein will increase the opp score.
It's not magic. It's constant learning by your smart assistant.
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