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“Creating communities where women can explore and learn is vital”, With Tyler Gallagher & Katie Robbert

There are a lot of great organizations that are putting women first. For example, the Women In Analytics organization and conference is dedicated to highlighting the accomplishments of women in data science, AI, and analytics space. Creating communities where women can explore and learn is vital. These communities don’t need to be exclusively for women, […]

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There are a lot of great organizations that are putting women first. For example, the Women In Analytics organization and conference is dedicated to highlighting the accomplishments of women in data science, AI, and analytics space. Creating communities where women can explore and learn is vital. These communities don’t need to be exclusively for women, but they should be inclusive of women. I have been fortunate to know men and women who are incredibly supportive and judge people by skill and competence, not by gender.


As part of my series about the women leading the Artificial Intelligence industry, I had the pleasure of interviewing Katie Robbert Co-Founder and CEO of Trust Insights

Katie Robbert is an authority on compliance, governance, change management, agile methodologies, and dealing with high-stakes, “no mistakes” data. As CEO of Trust Insights, she oversees the growth of the company, manages operations and product commercialization, and sets overall strategy. Her expertise includes strategic planning, marketing operations management, organizational behavior, and market research and analysis.

Prior to co-founding Trust Insights, she built and grew multi-million dollar lines of business in the marketing technology, pharmaceutical, and healthcare industries. Ms. Robbert led teams of Microsoft Partner Software Engineers to build industry-leading research software to address and mitigate pharmaceutical abuse.

Ms. Robbert is a Google Analytics Certified Professional, a Google AdWords Certified Professional, a Google Digital Sales Certified Professional, and holds a Master of Science degree in Marketing and Technological Innovation. She is a published researcher in the Pharmacoepidemiology and Drug Safety Journal.

Ms. Robbert is the co-host of In-Ear Insights, a weekly podcast produced by Trust Insights and Punch Out with Katie and Kerry, a seasonal podcast focused on the things your favorite marketers do in their downtime.


Thank you so much for doing this with us! Can you share with us the ‘backstory” of how you decided to pursue this career path?

I’ve always been interested in computers, science, and problem-solving. As a kid I would use all available Lego sets to build out massive computer stations, pretending that I was creating an environment that would crack the code on big, complex issues.

I see data as a puzzle and artificial intelligence as a solution. At the end of the day, AI is math and it will eventually add up. I get really excited when presented with a complicated issue that I can pick apart, re-organize, and piece back together in a way that makes sense. AI is a nice compliment data analysis as you can use the technology to help make sense of otherwise unanalyzable data. Just about every job that I’ve had involved creative problem solving, establishing process, and data analysis.

I realized this was the right path a few years ago when I was overseeing a marketing technology team. It was the perfect blend of traditional and digital marketing while using AI and machine learning to expedite projects and campaigns. It was an opportunity to create processes, define the rules, and establish outcomes.

What lessons can others learn from your story?

It’s a very human job to oversee AI and my goal is to teach others how to understand how AI works and where they can get started with it. What I’ve told people who are working on finding their direction is to focus less on a job title and think more about the skills. Write a list of the things you enjoy doing and don’t enjoy doing. You don’t have to be a data scientist to take part in moving AI forward. I’m not a data scientist but I do lead my company in making AI approachable and understandable to larger audiences. Again, it’s that puzzle that I’m trying to solve — people are intimidated by AI, so how do I turn it into something that a non-technical person feels confident about? That’s where my passion lies. The lesson that someone should take away is that your path may not be a straight line and that your strengths can lead you to interesting places if you stay open.

I’ve worked in retail, food service, television casting, background investigations, clinical trials, pharmaceutical research, product development, and marketing — to name a few. What I realized was that in all of those roles I was trying to problem solve and empower others to reach their goals. When I started Trust Insights, I carved out my role to be able to do just that — all day every day. I now lead the charge for my own company, my own team, and my own projects.

Can you tell our readers about the most interesting projects you are working on now?

I work primarily in the digital marketing space and a lot of my clients want to know about trends — what is going to happen, what can I expect, where should I spend my money? My company, Trust Insights, is working on developing new and different ways to apply a predictive forecast. Predictive forecasts are my favorite kind of AI that we do because it’s the most scalable, flexible, and affordable to our clients. Predictive can be used across all digital channels and through the full customer experience. AI has allowed us to streamline the analysis so that we can create multiple reports in a short amount of time and pass along the cost savings to our customers. One of the things that I most enjoy is being able to introduce AI in a practical way — so that is probably one of the more interesting things that I am personally working on.

None of us are able to achieve success without some help along the way. Is there a particular person who you are grateful towards who helped get you to where you are? Can you share a story about that?

One of my first managers was the only female on a team of otherwise older men. She hired me because she liked my “take no prisoners” attitude, knew that I could keep up with the demands, and push my way through the obstacles. When things felt tough she would say to me, “If I waited around for someone to pat me on the back every time I accomplished something I’d be waiting forever.” Her point being, don’t wait around for other people to celebrate you — celebrate yourself. She was all about tough love and it was exactly what I needed when I was young. She pushed me harder than anyone else on her team, taught me that it was OK to fail, and helped me find my confidence as a leader. I have always tried to emulate her even-keeled approach and understated confidence. Even now, years later, I often think about how she would handle a tricky situation.

What are the 5 things that most excite you about the AI industry? Why?

1. Al is unlocking new career paths. There is a need for data scientists and engineers to develop AI, but there is also a need for project managers, subject matter experts, lawyers, human resources, UI/UX designers and analysts to make AI possible.

2. AI is helping us get to answers, faster. Because of the analytics capabilities of technology like AI, you can analyze large sets of data much more efficiently than before.

3. AI is helping people in new and innovative ways. People now have access to personal assistants to remember appointments or take medication. AI is assisting people who have disabilities become more capable. AI can put together pieces of history to fill in the gaps and make a complete story. AI can compose music and write stories. AI can translate language almost instantaneously.

4. AI is advancing the healthcare space. Chatbots are a type of AI being used to help gather intake data, allowing physicians to spend more time with their patients and less time filling out questionnaires. AI is helping to make connections between symptoms that might have otherwise gone undetected and can simulate surgeries for training purposes. There is still a way to go but the potential exists.

5. AI is integrating seamlessly into our daily lives. Most of us use AI in some way on a regular basis, probably without realizing or thinking much of it. The fears of “AI will get too smart” have been replaced with “Alexa, tell me a joke” and “Google, add eggs to my shopping list”

What are the 5 things that concern you about the AI industry? Why?

1. AI is integrating seamlessly into our daily lives. With the convenience that AI brings, comes privacy and security issues. While regulations are being created, there is no one set of rules that governs how data can be collected and used. Many consumers still breeze past the terms and conditions and/or aren’t aware that many of the devices that they have in their homes have privacy settings that can be altered to allow for less data collection. There is more general education needed about how AI works and how to protect yourself.

2. AI still has a lot of bias. AI is only as good as the data you put into it, and a lot of our data is incomplete and biased toward our personal preferences. A lot of times, we are not aware of the subconscious bias that we introduce into our data. When we use historical data as a training set for AI we need to be aware of how it was collected because old bias will be introduced into new analysis.

3. AI can easily create content. One of the dangers of AI is called “deep fakes” — you may have seen them when someone superimposes a face over someone else in a popular video. This technology is used to create incorrect new stories, videos and data. AI created content versus content created by a person is very difficult to differentiate.

4. AI is not “set it and forget it”. There needs to be human oversight with developing AI and accepting the outputs. As mentioned, AI is only as good as the data it is given and often times the first run of a program is problematic. Teams need to spend time planning, executing, analyzing, learning and revising AI versus rushing to the development phase.

5. AI jobs feel out of reach. While you don’t have to be a data scientist or engineer to work in the AI field, you do have to have an understanding of how the technical side works. This might mean additional schooling, training, or hands-on practice. I’ve gotten into the weeds with code enough times that I understand how it works and what the outcomes should look like. This is a vital part of the process for anyone who wants a part in this industry. Not everyone will have access to those kinds of resources or know how to easily find them.

As you know, there is an ongoing debate between prominent scientists, (personified as a debate between Elon Musk and Mark Zuckerberg,) about whether advanced AI has the future potential to pose a danger to humanity. What is your position about this?

Any technology can be dangerous if it’s misused. AI is no different. People leading the charge with AI, myself included, should always be concerned about bias, misuse, data falsification, and overall impact. These are real concerns and real issues that AI alone won’t solve but will, in fact, only amplify. Will there be people who try to exploit AI for their own gain? Absolutely. But the majority of teams that are working on developing the technologies are trying to enhance everyday life and advance businesses in more productive ways.

What can be done to prevent such concerns from materializing? And what can be done to assure the public that there is nothing to be concerned about?

Keep talking, keep asking questions. There will continue to be issues with AI if people aren’t questioning and challenging the methodology. That’s where the non-data science career paths will be essential. Human Resources, Ethics, and Risk Management jobs are just as important as the jobs of those who create the code.

How have you used your success to bring goodness to the world? Can you share a story?

Giving your time to others can be difficult to juggle but it is so vital to keep opening doors to more people who are interested in your world. One of my favorite things to do is teach and mentor. Seeing someone else succeed brings me so much joy. I recently had the opportunity to speak at a university about the topic of AI and the future of the workforce. It was great to share my experience with the students and let them know what is new and exciting with AI and what those jobs will look like for them.

As you know, there are not that many women in your industry. Can you share 3 things that you would advise other women in the AI space to thrive?

It can feel really daunting to be the only woman in a room at times. I cannot tell you how often I’m mistaken as my business partner’s assistant rather than the CEO of the company.

1. The first thing to do is surround yourself with people who support you and believe in you, even if it’s just one person.

2. The second thing is to know that it’s hard, and not every day will be a good one. It’s OK if not every day is a success.

3. The third thing is to remember that small, incremental movements forward count. You don’t have to change the world in one fell swoop. Just by showing up for yourself on a daily basis means you’re winning.

Can you advise what is needed to engage more women into the AI industry?

There are a lot of great organizations that are putting women first. For example, the Women In Analytics organization and conference is dedicated to highlighting the accomplishments of women in data science, AI, and analytics space. Creating communities where women can explore and learn is vital. These communities don’t need to be exclusively for women, but they should be inclusive of women. I have been fortunate to know men and women who are incredibly supportive and judge people by skill and competence, not by gender.

What is your favorite “Life Lesson Quote”? Can you share a story of how that had relevance to your own life?

My favorite “life lesson quote” is “solutions only” — this came about from an old boss of mine. She was constantly bombarded with people’s issues and was trying to help us all figure out how to problem-solve on our own. She kept saying, “if you bring me a problem you also need to bring me some potential solutions.” Her aim wasn’t to have us get it right every time, but rather to start thinking for ourselves and at least trying to fix our own issues. She finally just put up a sign in her office that said “solutions only” that hung behind her desk so as soon as you saw her, you also saw the sign. When someone came to her with a problem and no potential solutions she would just point behind her head. I’ve carried that with me almost a decade later and have found that it applies to both my personal and professional life. I don’t always have the right answer but this mantra helps me walkthrough, “what have I not tried” so that when I do reach out to someone for help I can list where I’m at. I have passed on this mantra to other teams that I’ve managed, encouraging people to try to problem-solve for themselves, and collect as much information as they can before escalating it.

You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people, what would that be? You never know what your idea can trigger. 🙂

Kindness is timeless. Especially right now when there is so much pain and anger in the world and in our communities, kindness can go a long way. We were talking recently on our podcast about how someone who wants to help can pitch in. Be kind, be open. Don’t jump to conclusions about someone based on appearance. This is advice that I would give someone in life, and in their professional career. The golden rule of “treat someone how you want to be treated” is golden for a reason. If you have the opportunity to help another woman make headway into the AI space, do it. We’re not competing with each other — there is enough space for all of us,

How can our readers follow you on social media?

I’m @katierobbert on LinkedIn and Twitter.

This was very inspiring. Thank you so much for joining us!

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