Editor’s Note: This post is republished with permission from Trust Insights, a company that helps marketers solve/achieve issues with collecting data and measuring their digital marketing efforts.
A little while back we introduced another segment of the market, the machines, You can read the full post here.
Now we need to explore your current skill set as a manager and leader. With more automation and machine learning, you might feel like you need to start over. Surprise! You don’t. Believe it or not, your management skills translate over from human to machine. Sounds crazy? Not really—let’s unpack.
We often say, there will be two types of jobs in the future, those who manage the machines, and those that will be managed by the machines. Think about the soft skills you’ve developed while running a team, how you’ve learned to negotiate difficult conversations, to inspire and motivate, and to help each team member grow and evolve. Managing machines is very similar.
Let’s start with the word “machine”. What are we really talking about? We’re talking about software, hardware, algorithms, databases, and math. Does that mean you have to be a technologist or data scientist to manage these things? Sort of. You don’t have to have a PhD in Data Science, but understanding how these systems work, how they collect data, and how they interact with the public on your behalf is critical to your own success.
I recently sat on a panel for General Assembly and was the only non-data scientist. The majority of the audience were not data scientists either, but the conversation continually reiterated that you should at least have a working knowledge of what’s happening in order to manage these skills and processes.
Watch the video here.
When you’re managing your team on a day to day, what are some of the tactics you employ and do they translate to managing the machines?
You listen more than you talk
This is incredibly important to understand what’s going on with your team. If you do all of the talking people will be less likely to open up to you because they won’t feel like they are being heard. You want to actively listen, not just wait for your turn to talk. When applying this to machines, you’re stepping back and actively paying attention to what the systems are telling you.
Decision making/problem solving
As a manager, you’re often tasked with being a tiebreaker and a decision maker. As you’re resolving conflicts, you start with collecting all of the information available and asking more questions—you know people don’t like to give you the whole story, especially when it’s bad news. Once you’ve gathered all the information you can about a situation, you need to make a decision and/or unstick a roadblock.
Ethics and unbiased judgment
Speaking of decision making, it’s your job as a manager to be fair and impartial. That goes with collecting as much information and making decisions based on the data, even if it’s an unpopular decision.
This is perhaps one of the most critical skills when managing the machines. Machines learn from humans, and they learn to make decisions based on how we train them. If we let our bias influence our judgment, then our decisions will be flawed. This is not a perfect science, but to the decision making and problem-solving skill the goal will be to collect as much data from as many sources as possible in order to make a fair, ethical, and unbiased decision.
Confidence and leading by example
Making fair decisions often leaves your team feeling angry or butthurt (yes, that’s a technical term). You, as the manager, need to be confident with your actions and decisions. You’re teaching your team how to lead with logic, not emotion. The same is true for managing the machines. It all ties together, collecting the data, making sure you understand the situation and then standing behind your decisions. Ultimately, that’s what you would want the machines to be doing as well.
You represent the team’s best interest, not your own individually
Related to good decision making and being fair and unbiased, you need to think about the situation and who will be affected by the decision. When you’re training the machines and models, you need to account for those types of decisions—is it self serving, or does it benefit the greater good?
As a manager, one of your greatest strengths will be your ability to be flexible. Having a repeatable process is great and it’s predictable, but humans are not machines and they are not always predictable.
As we’re teaching machines to be more human, we have to teach them about conditional logic, adaptability. There will be exceptions to every scenario and we need to think about all those possibilities. Very rarely is there a “one size fits all” solution. To that, you could have a repeatable process that works 90% of the time – but as technology changes, priorities change, and customer behavior changes, you need to adapt your process and solutions to evolve with the situation.
In the next post in this series, we’ll talk about some of the things that machines will struggle to do, such as interpret tone, emotion, and sarcasm. As a manager, being able to read people and non-verbals is half the battle. Can machines do the same?
Katie Robbert is CEO and cofounder at Trust Insights.