General Assembly Blog

It takes a community.

In celebration of our 10-year anniversary, we’re highlighting some of our best people, partners, and instructors. Over the next few weeks, you’ll have the opportunity to be inspired by some incredible stories that have driven the success of our enterprise business.

Keep reading to meet Kimberly Graham, who works as an AI transformation leader at Sage — one of our incredible clients. Over the past six months, Kimberly has partnered with us to upskill engineers on data and AI principles for transformation.

GA: Where are you located, and what is your role at Sage?

Kimberly Graham: I’m originally from Southern California, but I’m based out of Hayden, Idaho, about 100 miles from the Canadian border. I’m Director of AI Transformation for the Sage AI Labs team. 

Sage is a leading accounting and business management software company with worldwide customers. Sage AI Labs is chartered with helping Sage become an AI-enabled organization. Our customers depend on Sage to manage their businesses. By enabling AI, we’ll help transform the customer experience and provide efficiency and insight gains.

My role as the director of AI transformation helps facilitate that process and develop a path forward with AI and machine learning. Upskilling our internal engineering resources is a big part of that. 

GA: Why did you need a training partner? What was your goal?

Kimberly Graham: Sage AI Labs is a team of individuals with AI, machine learning, and infrastructure experience. We’re working with General Assembly to bring that skillset into the business outside of the Sage AI Labs team — within our product groups. By upskilling a subgroup of our engineers who don’t currently have AI and machine learning training, we can learn from GA and then work in concert with our more tenured AI and machine learning resources. The work that we’re doing with you leverages very current and effective GA training processes to upskill our engineers rather than tackling the upskilling externally.

GA: What made you decide to partner with General Assembly, and what has the experience been like?

Kimberly Graham: I was very picky when selecting a vendor and did a lot of due diligence since it’s such an important decision. When I found General Assembly, it was a natural fit. Working with the GA team feels like a partnership — and I sensed that immediately. We wanted to find a long-term partner to build out a customized training process; GA not only had a strong program but an outstanding reputation and excellent reviews. So, we wanted to start small with a pilot.

The initial pilot included a small group of participants so that we could work closely to understand the experience and content as they worked through the 10-week pilot program. It was very clear that General Assembly understood our mission, what we were looking for, and delivered on that. 

Working with GA has been a true partnership. Communication has been very candid, very thorough, and regular — we initially met weekly to set expectations, hear best practices from GA, and provide feedback. What has impressed me the most is feeling like GA is an extension of our team rather than a vendor. 

GA: You have been running your program throughout COVID-19. Can you tell me about your experience with live remote training? 

Kimberly Graham: When COVID-19 hit, the entire Sage organization went remote. Luckily, the Sage AI Labs team is close to 100% remote anyway. Our team is dispersed worldwide. Therefore, while there were many impacts of the remote working situation due to the pandemic, the impact on our team was somewhat minimized.

One thing that has been wonderful with our first full cohort post-pilot is that we chose participants from Western North America and India. We included participants from Canada, Washington, Oregon, California, and India. It’s brought together engineers from different Sage product lines and engineering teams that wouldn’t have had the opportunity to work together otherwise. The feedback from our participants has been outstanding.

We wouldn’t have done an in-person course anyway due to our distributed team. So, the fact that General Assembly offered remote upskilling was beneficial, seamless, and very easy to roll out. 

GA: Your first cohort is just about to end — congratulations! Have you seen any immediate impact or results through this training? 

Kimberly Graham: One of the things that struck me the most during our initial pilot was midway through, just five weeks into the program when Sage hosted an engineering team hackathon. Two of our pilot participants teamed up and participated in recommending an AI feature for a Sage product based on what they had learned from General Assembly in the data science pilot. They won a prize in our hackathon, and five weeks later, they co-delivered the idea as their capstone project for the GA course.

Post-program, they went on to present it in various engineering meetings, an AI brown bag, and more. We’re currently in the process of building and implementing that feature into our product. These two developers were able to address a long-standing product challenge through AI and machine learning. This problem couldn’t have been solved with traditional programming — and that was just five weeks into our pilot program!

The participants from the full cohort will be delivering their capstone projects this evening, so our first cohort of 30 engineers is close to completion. I’m looking forward to seeing what they deliver and am really excited to see what they come up with!  I’m also looking forward to continuing our partnership with the GA team to review the program and modify as needed, moving forward, as we expand with additional cohorts.

GA: How does Sage think about the post-program experience for participants?

Kimberly Graham: The sense of community that is created during the course, such as collaborating, sharing resources, projects, and tips in the Slack channel, is extremely important. I monitor those closely. It’s very collaborative.

I’ve picked up on the need for an ongoing community post-General Assembly upskilling since our participants have valued the collaboration and learning process so much. I’m in the process of working on creating an internal community so that our team members can continue to interact and learn from one another. We already have things such as a Yammer group that I’ve created for AI collaboration, but I want to take that a step further — that’s going to be key. It’s not just a matter of being educated, and then it’s done. It’s very important that our engineers continue to interact, experiment, and collaborate.

GA: When it comes to transformation, many organizations think about technology before people. It sounds like Sage has a different approach. Can you tell me about that? 

Kimberly Graham: I moved over to the Sage AI Labs team a year and a half ago and came from a customer success and marketing background. Our team’s executive leader identified the need to have somebody from the business that didn’t have a technical background come in and understand what it takes to transform the business.

This is my first role as part of an engineering team, and I’m far from technical. Regardless of technology, we’re people who need to adopt technology to make our business work and be at the forefront of our customers’ needs. A big part of the transformation process is to look at things from our customers’ perspectives rather than just technology. One of the biggest elements of our transformation process is factoring in how we can use technology for the betterment of employees and our customers rather than just technology for technology’s sake.

Stay tuned for more incredible stories from our team and partners in the coming weeks. Want to learn more about how GA can make a difference in your business today? Get in touch. 

Everything — including the way marketers used data — came to a halt when the pandemic hit. Given that all data builds upon the past, many companies found that what worked in 2019 suddenly became irrelevant in 2020 — a quick and jarring pivot.  

The effectiveness of marketing is measured by the consumer behavior it’s designed to predict. What can marketers do to meet the challenge of consumer behaviors that have changed due to COVID-19 and wider shifts in cultural psychology?

In an hour-long, intensely detailed, and conversational webinar, Matt Tumbleson, brand director for P&G Ventures and GA marketing instructor, sat down to talk post-COVID marketing strategies with Ben Harrell, CMO for Priceline; Philip Markmann, CMO for L’Oréal; Salim Holder, Co-founder + CEO of 4th Avenue Market and GA instructor; Rebekah Rose, director, P&G Ventures; and Rory Sutherland, vice chairman at Ogilvy UK. 

Three Things We Learned

Changes in consumer behavior necessitate changes in how we think about marketing strategy. This expert-driven webinar encouraged marketers to rethink their approach in three key ways: 

1. Data doesn’t drive everything.

Marketing strategists have increasingly found themselves taking a back seat to big data. This is not to say that data-driven marketing strategies have no value, but COVID-19 caused many businesses to flounder as the data they relied on was no longer as informative and impactful as it had once been. 

All told, what became expected consumer behavior morphed in a way that was not effectively predicted by marketing data; there was no precedent in the data most businesses were leveraging to address the psychological and emotional impact that a global pandemic would have on how consumers spend.

2. Marketing must be future-focused, not past-focused.

An ever-present consequence of data is that it’s reactive and not proactive. Ben Harrell sums this succinctly: “What worked yesterday won’t work today. What worked today won’t necessarily work tomorrow.” Never has that been more true in a post-COVID-19 era, when many consumers have altered not just how they shop, but why they shop and where they shop. Many are questioning whether they need a product at all and opting instead to purchase products or services that deliver deeper meaning or enhance their connections to other people. 

Sutherland importantly notes that “People don’t buy what something is; they buy what it means”.

3. Engage relationally, not transactionally.

Marketers must keep pace with changing customer profiles. Consumers saturate the internet, but COVID-19 accelerated already prolific online activity. As Sutherland explained in an intriguing keynote speech, “Understanding consumers’ wants, needs, motivations, and fears has become much more important following COVID-19.”

This is hardly more visual than with Gen Z, which is distinctly digital-native and cause-driven. Marketing challenges change with every generation, and companies must adapt to how upcoming generations effectively respond to marketing, but Gen Z has proven to upend many cultural marketing expectations. It’s a generation that values an emotional, authentic approach and is quick to flee from companies too heavily focused on the traditional transactional method. If Gen Z is an indication of a longer-lasting trend, marketers really must be quick to adapt.

On to the next. And next…

As Salim Holder of 4th Avenue Marketing pointedly asks in the webinar, “Do we know our consumers as well as we think we do?” This question helps define big ideas you’ll experience in this engaging webinar filled with actionable insights from a panel of marketing experts. Watch the entire webinar here.

Want to learn more about how General Assembly can help you unlock marketing skills for a post-COVID era? Get in touch.

AI is a powerful tool that can help businesses achieve monumental success. For the last few years, AI has been inaccurately depicted as a tool that will steal jobs from humans. The media, movies, and more have consistently created a false image of AI, what the technology is capable of, and what it means for businesses and society. Truthfully, it’s the people behind AI that determine the outcomes of its use  — not the technology itself.

Gonzague Dromard, our head of France, sat down with the co-founder of Siri and Renault chief scientific officer, Luc Julia, for a webinar discussion on demystifying AI and the big data that makes AI function. Here are the top three things we learned from that discussion.

Essentials To Know 

AI is more than just a buzzword and less than the harbinger of the next tech-driven apocalypse. Instead, AI is a tool with the enormous potential to uplift industries and workers worldwide. Yes, this is daunting — but, no, this does not make AI dangerous.

1. AI’s Framework: It’s Older Than You Might Think 

Although AI may seem to be a modern invention or trend, the groundwork has existed for decades. Artificial intelligence and big data are built around maximizing the efficiency of repetitive processes. Leaning into his new role at Renault, Julia highlighted how the car industry has been using automation for decades, starting in the 1960s with the introduction of industrial robots capable of simple tasks like spot welding. Lan Turing’s landmark paper “Computing Machinery and Intelligence” was published in 1950, giving us decades to properly define, create, and deploy early concepts of AI.

Although not as “smart” as some might expect, AI, at its fundamental level, is a repeatable set of rules that a machine follows — an instructional map, but certainly not the driver.

2. AI Does Not Destroy Jobs — It Accelerates Them in New Areas

Among the biggest questions that stymie wider acceptance of AI is whether the technology will replace human workers, destroying the livelihoods of millions of people. Dromard and Julia spent a significant portion of the webinar dismantling this misconception by underlining these AI truths:

1. There are multiple levels to artificial intelligence. If it becomes possible, we are far from a future where AI will completely replace human workers in most industries since AI relies on repetition — and the world at large is dynamic, not static.
2. Organizations must be proactive and introspective by identifying where efficiencies could exist and how AI can be used. 
3. Companies thrive and innovate best when existing teams — with diverse backgrounds and experiences — are trained to use big data and AI.

When applied properly, AI creates entirely new industries and provides existing workers the chance to reskill while working alongside AI systems that make less enjoyable, routine tasks easier to accomplish. Since these tasks don’t change, the big data backing them can perform those tasks more quickly and efficiently than human workers. 

3. AI Programmers Must Counter Personal Biases 

News-making examples of bias within AI systems, such as facial recognition technology and hiring algorithms, have raised legitimate questions about whether AI will always be applied ethically. Dromard and Julia also took on this challenging question with strategies for how to ensure that the big data working in the background mitigates the risk of bias in AI systems:

As AI integrates more into daily life, data ethics has become imperative to ensure AI does not harm societal equity.

Take the Next Progressive Step With Big Data & AI

As Julia noted in our webinar, “AI is a tool. We have the hammer.” In other words, if you give that hammer to the right person, innovation will happen. Not all workers will know how to use the tool right away or in the right way, but dedicated and ongoing training can create lasting relevancy for businesses and build loyalty among workers.

Bottom line, people are only innovative if they’re trained with intention. When your teams bring their existing skills to the table — and merge that with newly acquired knowledge — real innovation happens. Watch the full webinar here. 

Want to learn more about how GA can help you build a data-driven team? Get in touch.

It takes a community.

In celebration of our 10-year anniversary, we are highlighting some of our best people, partners, and instructors. Over the next few weeks, you’ll have the opportunity to be inspired by some incredible stories that have driven the success of our enterprise business.

Keep reading to meet Steven Longstreet, one of our instructors, who teaches data to enterprise businesses to help them upskill and reskill their employees for the future of work and is a member of our AI and Data Science standards board.

GA: Where are you located, and what is your role at GA? 

Steven Longstreet: I work out of the DC campus and live nearby in northern Virginia. I work as an instructor, and I am also a member of the AI and Data Science standards board.

I first discovered GA through a team member, an instructor for GA, who took the 10-week part-time data science course. She wasn’t confident with the content, so I decided to take it with her to support her. I had a blast, and then the GA team asked me to become a data science instructor. 

It was fun to take this class and see how different people were learning and how the program broke down the barriers to data science. 

GA: What is the instructor’s role in learning?

Steven Longstreet: People who sign up for our classes truly want to learn and better themselves. That’s actually one of the coolest things to be a part of. However, the reality is some of the things that we teach are complex, with barriers to learning. My role in learning is making these topics approachable and removing those barriers for each student’s personal journey.

When learning something new, you’re making yourself vulnerable. The instructor has to create an environment where it’s okay to be wrong. It’s my job to understand the student’s barriers and make sure they are okay to be vulnerable and ask questions. 

GA: Why did you get involved in our AI and Data Science standards board?

Steven Longstreet: The whole purpose of the (AI and) Data Science standards board is that the term “data scientists” means absolute dribble. To some people, a data scientist needs to have a Ph.D., and there are other groups who just give out the title to anyone. 

I’ve always struggled with this concept. Part of joining the board was coming together with a group of like-minded people and saying, “What is this term for us? What does it mean? What are the aspects of the new data scientists? What does a career look like? How can we best empower and leverage these people?”

The concept of a scientist is someone who pushes human understanding, and it doesn’t have to be something incredibly dramatic. It can be one microscopic new thing that you uncover that allows human advancement. A data scientist within the organization pushes the understanding of data in a more usable way. We set a clear definition that worked across the range of companies participating in the board.

The other jobs of the AI and Data Science standards board are explaining what a career in this field looks like and making it clear what fields someone can specialize in. We need to be clear about the roles and career path and delineate what these terms mean.

GA: What do you think the most critical data skills are — right now?

Steven Longstreet: Let’s break it into two parts: data engineering and data literacy. 

I’ll start with data engineering because it’s part of your workflow. No matter what. Data needs to be built in the context of the problem you’re trying to solve. These skill sets are both rare and powerful, as a data engineer looks at the needs and resources across the organization to build core data assets with the most powerful and broad applicability. We’ve been talking about big data pretty heavily for about seven years, which can be summarized as more data than you have the resources, skills, or knowledge to wrangle. Maybe 5% of people feel comfortable working with big data, and those people jump into that challenge head-on.

Data literacy is understanding basic data concepts. When I talk about data literacy, I want people to articulate the problem they are trying to solve. Are they trying to forecast something or classify something? It’s about understanding data isn’t perfect and has biases. We build data for a particular context, so you can take the same raw materials and build a pick-up truck or a car, depending on what you’re trying to do with it. In other words, you have to understand why you built your data in the first place.

It’s about knowing what’s possible with data in your field, asking the right questions, and effectively communicating with data people to get the answers.

GA: What advice do you have for leaders who are trying to prepare for the future of work? 

Steven Longstreet: You have to listen to your team, but you also have to recognize that there’s a problem you’re trying to solve. I’m in an industry where people are serving people. Most of my employees are in front of people, but for the last year, we’ve all been working from home. 

The future of work at my company, Hilton, isn’t changing from the face-to-face experience, but we’re listening to our team and realizing many people want flexibility. We are thinking of new ways to bring people together because there are aspects of work that you can’t replicate virtually. 

I think the future of work doesn’t change the fact that human interaction is incredibly important to advance any problem. If one person could work entirely in a vacuum, we wouldn’t talk about Tesla and SpaceX; we’d only talk about Elon Musk. The reality is that a lot of people make work happen; one person rarely solves all the problems by themselves.

Stay tuned for more incredible stories from our team and partners in the coming weeks. Want to learn more about how GA can make a difference in your business today? Get in touch. 

The Final Step: Understand What Good Looks Like

As companies across industries race to digitize, maintaining the pace of change required to get across the finish line can be overwhelming — especially for those leading the digital transformation efforts.

We’re here to break it down. 

Through our deep experience across many types of organizations, we’ve seen leaders’ transformation challenges boil down to four key goals:

1. Create digital mindsets across the company. This includes understanding digital trends, growing digital mastery, and building a product-driven organization.
2. Upgrade capabilities to reflect cutting-edge technical skills across marketing, technology, and data functions.
3. Accelerate technical hiring by upskilling and reskilling current employees and new hires. 
4. Understand what good looks like — a skill necessary in achieving every goal.

This series, Jobs To Be Done, unpacks each of these four goals, providing actionable recommendations that organizations can put into practice to help set their businesses on the path to sustainable digitization and success.

Finally, we’ve come to our final overarching concept: understanding what good looks like. Whether you’re evaluating a digital culture, growing capabilities, or filling talent gaps, it’s hard to know how you’re doing without clear benchmarks. We have some tips.

Defining Success in Uncharted Waters

Congratulations: you’ve bravely inspired your organization to try new things, creating a company with a more agile, empowered workforce, building on the latest cutting-edge technologies and best practices… But what are the details of those practices?  Once you’re deep in uncharted territory, how do you know when you’ve truly succeeded?

It can be difficult to specify what good looks like — especially when the fast-paced nature of digitization often means the goal line keeps moving as you work toward it. A lot of business leaders are largely unfamiliar with the technicalities of digital fields, especially when it comes to the details of the many roles they oversee. This makes it challenging to do simple tasks like mapping out leveling frameworks or evaluating talent.

When it comes to knowing what good looks like, getting an outside perspective can help you chart your way. One of the most helpful, multipurpose tools you can bring into your process is a well-built, quantitative assessment.

The Gold Standard: Map Success Against Data-Forward Assessments

Data-forward assessments that measure adeptness in key skills are tools you can use throughout your digital transformation. They give precise, repeatable data to track over time and leverage in decision-making. 

There are several touchpoints across your workforce pipeline where an assessment can be a big help in defining good — from analyzing candidates in interviews to tracking progress in your reskilling efforts. 

We recommend assessments to help companies improve many areas, such as:

1. Identify unique skills gaps in existing teams to develop your learning goals. Once you understand the specific areas your team needs to grow, the data will point you in the right direction for your transformation. You can even leverage them to prescribe personalized learning paths that target the specific strengths and areas for growth of each team member, which can be more tailored and effective than a one-size-fits-all approach. 
2. Benchmark the skills your teams have compared to the industry. With a broadly-used assessment, you not only understand your team’s skills but also how their scores relate to others in your field, allowing you to align your skilling investments with your strategic goals. 
3. Track improvement of skills over time. Multiple data touchpoints help you learn where you’re making progress and where a new approach may be needed — a key element of an agile workforce. You can also use this data to inform performance reviews, as your top performers set tangible goals and achieve them. 
4. Guide hiring and staffing decisions. Adding quantitative assessments to your interview processes help remove bias from candidate evaluations and allows you to compare internal and external candidates on a level playing field. In addition, they can identify surprising aptitudes or highlight areas where more evaluation is necessary.

Assessments in the Real World: L’Oréal

One great success story of implementing an assessment into a talent pipeline is L’Oréal. L’Oréal launched an assessment-led program in partnership with us to vet new candidates and encourage continuous learning among its global marketing workforce. 

Leveraging the Certified Marketing 1 (CM1) Assessment, built in partnership with our Marketing Standards Board, L’Oréal defined a company-wide standard for evaluating marketers, which is now fully baked into their talent system. The assessment helps them onboard new hires who meet their 70% benchmark, upskill 85,000 employees worldwide, and identify high-growth candidates for deep-dive training. This approach has driven results in their digital transformation, growing eCommerce sales to 25% of total sales. 

So, what’s next?

Over the last few months, we’ve talked about the top challenges we encounter from leaders of digital transformation efforts. Whether you are beginning to build the broad digital fluency that is a foundation of digital culture — investing in the technical skills upgrade of your teams and expanding top talent, we hope you found valuable tips within these series to help guide your way.

If you’d like to explore the range of courses that GA provides (and get a sense of how your teams stack up to the skills we teach), you can explore our catalog here, which covers roles from IC to strategic leader across digital fluency, marketing, data, and technology. 

Want to get specific about how GA could help your organization? Get in touch. 

This concludes our Jobs To Be Done series. We wish you luck on your digital transformation journey!

It takes a community.

In celebration of our 10-year anniversary, we are highlighting some of our best people, partners, and instructors. Over the next few weeks, you’ll have the opportunity to be inspired by some incredible stories that have driven the success of our enterprise business.

Keep reading to meet Tara Fosbre, who works as a technology leader at Guardian Life Insurance — one of our incredible clients. Over the past two years, Tara has partnered with us to deliver Code for Good at her organization to diversify technical teams and prepare more talent for the future of work. 

GA: Where are you located, and what is your role at Guardian?

Tara Fosbre: I’m currently based in our New Jersey office, but I am still working from home because of COVID-19.

I have been a technology leader for over 25 years, building customer-facing solutions, and have always had a passion for early career talent and championing women in technology. I’ve been at Guardian for almost eight years, and for the past year, I’ve had the opportunity to pivot and focus solely on building our tech talent pipeline.

My job is identifying opportunities and building out robust development programs. This is my sweet spot, and I think my leadership background gives me an edge to understand what is needed to ensure that we’re shaping our pipeline for the future.

I’m really excited to be doing this work because it’s super important, especially as we move into a real digital age, making sure that our workforce is ready to go there as well.

GA: How do you define the talent pipeline of the future? 

Tara Fosbre: I think digital is critical so having a digital mindset, having digital dexterity, and thinking about things in a “let’s work smarter, not harder” manner is important.

Not being afraid of technology and thinking about how to leverage technology for a solution first. Really shifting cultures and getting people to think differently about the work that they’re doing — whether that comes in the form of automation or optimization, it doesn’t matter. We have to stop doing manual work because it impacts our customers. 

Then we have to shift to a culture that’s open to innovating, collaborating, and being disruptive; a culture that’s unafraid to try things because that’s how we’re going to start to leapfrog. 

GA: Mindset and culture play a huge part in digital transformation, but those can be incredibly hard to shift. How are you approaching these changes at Guardian?

Tara Fosbre: It’s ongoing. I’m currently building out a proposal for shaping our talent in a digital-dexterous way. When we think about digital dexterity, it’s human beliefs, mindsets, and behaviors that accelerate business transformation; it’s the employees’ ability to adapt and adopt existing emerging technologies to produce better results. 

This is just the beginning. I don’t have all the answers, but Code for Good fits squarely within my plans because it’s not going away. We need developers and technologists, and I think it’s important that we pull them from wherever we can get them because there’s enormous value in diversity of thought. 

GA: You’ve been a part of Code for Good for two years. Why did you decide to pursue Code for Good, and why did you choose GA as a partner?

Tara Fosbre: General Assembly has been a partner of Guardian for some time, and this program sort of fell in my lap. I latched on because it fits into my passions for championing technology and building a diverse talent pipeline.

What really intrigued me about Code for Good was that the cohorts are blended with other companies, which adds invaluable perspective for the students. It’s not just learning the day-to-day; they’re also building a network, collaborating with folks they don’t know, and they’re getting through something together.

GA: Can you tell us about some of the participants that you’ve put into this program?

Tara Fosbre: For both cohorts, we pulled from our customer service operations teams. These are people who are front-lining with customers and have very strong business and system knowledge but zero tech knowledge. 

I am interested in possibly opening it up at the enterprise level because we’ve had amazing people interested in the program. I think the fact that we’ve offered it twice now — resulting in close to 100 people applying — which speaks volumes about our organization’s willingness and desire to learn.

As part of the application process, I instituted something totally new at Guardian, where the application review was blinded. We removed all the biases from the process, which leveled the playing field for everyone involved.

GA: What impact has Code for Good had on your business?

Tara Fosbre: One really amazing thing about Code for Good — and tapping into the operations team and bringing them onto the technology team — has been the partnership. Most tech teams are lower on business acumen but high on tech acumen, so these students coming in high on business and lower on tech have a real advantage.

What we’re seeing is the teams are learning from each other. The tech teams are getting strong on business knowledge while the operations folks — who went through the program — are getting stronger on tech. There are a lot of “aha!” moments that we’ve seen coming out of this. 

GA: What has been your experience working with GA?

Tara Fosbre: We’re in our second Code for Good class, and my experience has been great. I’m really impressed with the teaching support and monitoring of students. GA and I meet weekly, and I meet with the students weekly. 

It’s really important to coach these folks along with teaching them because it’s a scary thing to start a whole new career. They’ve essentially stopped their old jobs, and a lot of them have been out of the school mindset for a long time, and now they’re being thrown into it. I’ve been impressed with how the instructors are on top of the program and the individual needs of the student. I think that’s critical, and I think that’s why it’s been successful. 

Working with GA has been amazing. I’ve tried other programs where it’s a little bit of self-study with different blends of modalities, but without having very knowledgeable and in-tune instructors, it just won’t work.

GA: What excites you about the future of work?

Tara Fosbre: Technology is ever-changing and gives us opportunities to continuously learn something new, continue innovating, and finding ways to work smarter, not harder. These are exciting times. 

I know people are daunted by the words automation and optimization, etc., and I think they have it all wrong. This is about figuring out how to give a machine the boring things that you’re doing and sink your teeth into the cool new things that are coming. I think that’s part of that whole digital mindset shift that we have to go through. 

Stay tuned for more incredible stories from our team and partners in the coming weeks. Want to learn more about how GA can make a difference in your business today? Get in touch.