Once upon a time, artificial intelligence was the stuff of science fiction.
But it isn’t quite so farfetched now. In fact, we're already glimpsing early signs of it in the workplace.
The reality of true artificial intelligence — software that comes close to human intelligence — could still be decades away. However, 2015 saw major advancements in machine learning for business use. And that is bringing us closer than ever before to an AI-infused future.
To understand artificial intelligence, you need to recognize that the technology learns from the data collected and adapts accordingly. The algorithms and analytics are designed with the ability to predict behavior and suggest the next action.
Ultimately, machines may be able to make better and faster decisions than humans, and already, they are helping humans be more productive.
As time goes by, the more data there is to collect and analyze, the better the machine becomes at making predictions. We’re becoming comfortable with this and even expect it — such as when we shop on Amazon. For example, when you select an item, it suggests other things you may need or like, based on your previous searches and purchases, as well as what other people bought. The more you visit this site, the better it knows you.
AI Can Improve Sales, Profits
That same machine learning technology is being applied to the workplace, where the potential is huge, particularly with sales and marketing teams.
How the machine-learning algorithm works in a simple sales environment:
- Gathers data about a potential customer in the CRM such as their specific industry, their position within the company, and where they are on the buyer’s journey
- Contextual usage elements of the seller such as patterns, frequency, validation and ratings
- Industry emphasis such as vertical, product, geography and more
For instance, if a few pieces of content have consistently helped move a deal from the “discovery phase” to “proposal phase,” the application will recommend that content for other similar prospects.
An important note is that with this type of machine learning, the content recommendations aren’t based solely on the item’s popularity, but rather on its effectiveness.
The content and training most frequently referenced that is instrumental in sales advancing through stages of pipeline and/or won, will recommend higher than ones that don’t. The learning continues the longer the application is in use. As more data is captured, it literally becomes smarter and more on target with its recommendations
Tapping Marketing Know-How
Analytics and information like this is something that marketing has understood for a long time. When marketing automation platforms became mainstream several years ago, marketers began tracking their campaigns carefully. Basically, they knew that if you can track and analyze your marketing leads, you could start to predict where you would find more customers and more revenue.
On the sales side, having a great sales team used to be about persistence, determination and focus. While those qualities are still important, new factors have come into play such as having efficient and repeatable processes, capturing information about engagement, and using new techniques to improve sales pipeline velocity. We know so much more about prospects and how they behave, which now goes beyond the simple demographic data we’ve usually stored in the CRM.
Advancements like these in the workplace allow us to shift our attention from tedious tasks and routine transactions to more creative and meaningful work. Sales managers may be able to shift from monitoring their sales reps to developing more insights into the sales process and working with marketing on creating more engaging content.
Studies have shown that buyers are hungry for more relevant content to make their buying decision. Marketing can learn specifically what data is needed, and what the sales team values the most, to then create compelling pieces.
AI = Actionable Insights
Machine learning and artificial intelligence show the most potential in not only increasing data driven decisions, but also giving the sales team actionable insights. It is possible that if you could identify which attributes will push larger deal sizes or greater loyalty. This could influence how you’d assign territories, prioritize prospects and increase satisfaction of existing clients.
While some of us may have concerns for our job security, we can assure ourselves that there are limitations to machines. They can learn, but they inherently lack intuition and people skills.
When it is all said and done, we are still humans selling to other humans. We have emotions and motivations that go beyond complex algorithms. Computers can compliment our behaviors and intelligence, but it is highly unlikely that humans can be replaced anytime soon.
With the constant pressure of higher productivity and sales goals, we predict adoption rates will continue an upward trend. Rather than resisting the coming technologies, employees and management will embrace the applications as they realize that mundane tasks are taken care of — making them more productive and successful overall.