Episode Details

Melissa Mohr spent her early career at Amazon and built some amazing products before making the move to SVP of product at Vivint. Now she’s here to tell us how to maneuver office politics and survive the onslaught of AI

Mohr studied animal science in college. She expected to spend her career working with horses. Instead, she ended up helping solve some of the strange problems that come with trying to change how millions of people shop and live.

On Keep Going, Mohr talked about what she learned along the way. Much of it came down to a simple idea: careers are built as much from the things that go wrong as the things that go right.

You Don’t Need to Know Everything Before You Start

When Mohr joined Amazon’s Alexa smart home team, she had no professional background in smart home technology.

In fact, she didn’t have any smart home devices in her own house.

“I knew nothing about smart home,” she said.

She joined anyway.

She would spend roughly nine years working on Alexa, moving from the early days of asking Alexa to turn lights on and off toward much deeper home automation and personalization.

Her career is a useful answer to one of the questions that stops people from making a move: Am I qualified enough?

Sometimes you aren’t.

The better question may be whether you can become qualified.

Mohr credits part of her success at Amazon to the company’s willingness to put smart people into unfamiliar problems and allow them to figure things out. But that freedom came with responsibility. She had to understand the business, understand its numbers, understand the customer, and learn enough about each new field to make good decisions.

The lesson isn’t to fake expertise. It’s almost the opposite.

Know what you don’t know, then start learning.

Progress Can Be Very Small

Amazon Grocery presented a completely different problem.

Selling books and toys online was one thing. Selling groceries meant dealing with expiration dates, shipping costs, inventory rotation, customer habits, and products that could become worthless before Amazon managed to sell them.

There wasn’t one great idea that fixed everything.

Mohr’s team tested somewhere around 20 or 30 user experience designs. Some worked. Plenty didn’t. Instead of waiting for a single major breakthrough, the team learned to pay attention to small signs that something was getting better.

“Small progress” became something worth building on.

That’s a useful way to think about almost any difficult project.

We tend to want a clear win because wins are easy to understand. You launch the company. You get the job. You sell the product. You hit the number.

Most meaningful work doesn’t happen that cleanly.

You try something. It works a little better. You try again.

Then you keep going.

AI Is Changing Jobs, But That Doesn’t Mean You Stop Learning

Mohr also has a useful view of the current fear around AI and employment.

She doesn’t think AI simply replaces an entire employee in most cases. Instead, she sees it removing parts of jobs.

Maybe 30 percent of someone’s work consists of tasks that aren’t particularly strategic. AI can take some of that work. That lets employees move faster, but it can also allow a company to accomplish the same amount of work with fewer people.

That part shouldn’t be ignored.

Mohr’s response, however, isn’t to resist the technology. It’s to learn how to use it.

Her product teams can now turn an idea into an early app prototype in hours. She looks for job candidates who have experimented with AI and figured out how it can improve their work.

The danger of refusing to experiment with AI, in her view, goes beyond AI itself.

If someone refuses to try a new tool because they don’t like it or don’t understand it, w