When thinking about artificial intelligence and its effect on any job role, the best way to tackle the problem is by breaking down that role into a long list of tasks that are performed. Those tasks, together, build the outcome that the job role performs. Once a job role is broken down into tasks, each one can be looked at through the lens of AI and machine learning to see if it can be replaced, improved, changed, or enhanced.
This break-down of a role into tasks then allows roles to be created again, and it can significantly change the definition of the role. In the same way that Lyft and Uber completely changed the ride industry by breaking down the components of the taxi service and rebuilding them in a new way, job roles can be broken down and re-built into completely new forms.
The Job of Sales
Through this lens, the job of sales can be broken down into a very rough set of tasks, each of which can be changed through automation, artificial intelligence, and new data sets. At a high level, the job of sales is to:
- Target: select the right companies to sell into
- Discover: find the likely buyers within those organizations in the right roles
- Access: begin a conversation with each buyer by finding a way to start a dialog
- Shift: present new information or new ways of looking at things and shift the buyer’s point of view on a problem
- Build: create and grow consensus and agreement amongst all key stakeholders at an account
- Close: finalize the transaction to the benefit of both buyer and seller
Each of these tasks can be challenged by artificial intelligence, and, when rebuilt, the job of sales begins to look very different from how it looks today.
Rebuild Each Task
As we rebuild the individual tasks, a new picture of sales starts to take shape. The picture is one in which intuition is replaced by data and analytics in many cases, but the quintessential “human-ness” of people is emphasized.
Looking at each task in turn:
- Target: AI can use machine learning and similarity to find organizations to target based on far more than rough indicators of size, industry, and geography
- Discover: knowledge of the levels and roles within an organization and an understanding of past successful deals allows AI-based sales systems a way to understand who you need relationships with and who you do not yet have
- Access: using AI for sales intelligence allows a stream of insights that allow a conversation to start based on a unique perspective on the buyer’s current business challenges
- Shift: in working to shift the buyer’s perspective over time, it is key to stay “top of mind” and continue to inject new and interesting views that are relevant to current situation. Keeping track of hundreds or thousands of buyers over long periods of time is a task best left to machines, while the creation and communication of a unique perspective is a very human task
- Build: each relationship with each stakeholder is unique, and can only be developed by a human, but the analytics of where deals are at risk based on what relationships are missing or weaker than they need to be is best done by machine.
- Close: details of the transaction, legal terms, and pricing, are all being facilitated by AI, but the trust to make the final agreement is something that depends, solely, on human relationships
In today’s AI-driven world, the pieces of the stack are familiar, but they have each changed. The challenge remains of how to reconstruct the role into something new.