C-Suite Perspectives On AI: Felipe Zambrano Of Garrick Solutions On Where to Use AI and Where to Rely Only on Humans
An Interview With Kieran Powell
Data biases. It’s important to evaluate the data and understand if there can be any biases or disclination against sex, race, ethnicity, and other factors. If there is a large percentage of data that requires editing or changing the biases, then this dataset and project might not be a good fit for leveraging AI.
As artificial intelligence (AI) continues to advance and integrate into various aspects of business, decision-makers at the highest levels face the complex task of determining where AI can be most effectively utilized and where the human touch remains irreplaceable. This series seeks to explore the nuanced decisions made by C-Suite executives regarding the implementation of AI in their operations. As part of this series, we had the pleasure of interviewing Felipe Zambrano.
Felipe Zambrano is the President of Garrick Solutions, a management consulting firm focused on helping business owners gain time back and increase productivity by 30% through automation and process improvements. Felipe is an open-minded and driven leader with experience in managing companies, leveraging technology for efficiency optimization, and a passion for embracing challenges fearlessly.
Thank you so much for your time! I know that you are a very busy person. Our readers would love to “get to know you” a bit better. Can you tell us a bit about your ‘backstory’ and how you got started?
Sure. I believe my love for change started when I started working in Finance for Fortune 500 companies such as American Airlines. I loved being able to find new ways to solve problems or become more efficient. I liked driving change. Finance was a way for me to discuss with industry experts on operational subjects and be heard regardless of the difference in industry experience.
My passion has evolved and my love for automation started about now 4 years ago. My younger brother Daniel just mentioned the concept of Robotic Process Automation (RPA). As he described it shook and excited me. I had found a new way to drastically shift my work responsibilities to become more efficient, or as I see it “Gain back time”. I only have 24 hours in the day, and I need to focus on the important things, not on the tedious and low value tasks.
I began automating things such as creating folders and subfolders, and automating email blasts. I started taking a step back and writing down all my tasks and responsibilities and finding out ways to automate my job.
This was fascinating. I later supported a company that bought and sold commercial aircraft parts, and created an automated quoting system, that would quote incoming requests 24/7, every five minutes. This helped the business compete against larger players, without having the extensive manpower required to do so.
Automation helps makes companies more efficient, reduce their operating costs, and help leverage their resources better.
This journey has taken me on an exciting path of growth, and embracing new technologies to revolutionize how businesses operate. I’m very passionate about what I do, and I’m focused on driving exponential change in all businesses I interact with.
It has been said that our mistakes can be our greatest teachers. Can you share a story about the funniest mistake you made when you were first starting? Can you tell us what lesson you learned from that?
It’s funny now, not so much back then. When I worked for American Airlines earlier in my career, I oversaw a part the financial budget deck which consolidated the entire Latin American (LATAM) operations for headquarters. It was nicknamed “The Skinny”. Needless to say, it was far from skinny. It was made up of 3 large Excel files connecting to each other.
This presentation was to be presented by the managing director of LATAM to senior leadership in Dallas. As you can imagine, this presentation had been passed down several years, and I was now in charge of this large model. It had several pages, formulas, and queries. It also gave key talking points that were extracted from a database.
With a week left to go I was working on making the project more seamless, and then I broke “The Skinny”. In addition to that I was unable to access the stored information from the database.
I was staring at over 50 empty slides without comments and statistics. I hadn’t saved a backup copy.
Needless to say, some sleepless nights were about to come to my life. As I stared down this problem, I realized that I had to start from zero, and build my version of the financial model.
I first met with the IT developers which taught me how to pull data from the database, and then started using my very rudimental Visual Basic knowledge to put the pieces back together.
I was able to create copies of all the presentations and started to relink them to the database. What I quickly realized was that there was a lot of “excess coding”, of my predecessors trying to fix issues and make updates, and that none of my predecessors really took the time to restructure the file as it evolved.
Fortunately for me I was able to recreate all of the slides, and because I had started from scratch, I was able to retrieve the data in a faster manner. I also shrank the file to a single 15mb Excel file. I had the skinny on starvation! This was more of a personal lesson rather than an accomplishment for the department, as I had to get back to square one.
It taught me to be bold, but also cautious, and have a backup plan just in case. It taught me that challenges can be overcome if you push yourself and think beyond what you see in front of you. A famous quote comes to mind “Pressure makes diamonds”. Hard lessons make you learn and grow, and it helped me push in other times when I’ve experienced mistakes or setbacks.
Are you working on any exciting new projects now? How do you think that will help people?
I’m currently working with an Ophthalmology practice in South Florida, named American Vision Group, and I’m extremely excited to work with their senior leadership and managers. They are very forward-thinking, driven and determined to find better ways of doing things. We’re implementing automations and bridging systems to better utilize data.
It’s exciting to see a company push forward, break the mold, and challenge themselves to new heights.
I’m also working with a client in the aircraft space, that’s looking into creating a large language model, and a chatbot to better leverage data, to facilitate the flow of accurate information to all the stakeholders in a more conversational manner.
Aviation is an industry that’s always evolving, and it’s exciting to see companies embrace change, and continuously do things better.
Thank you for that. Let’s now shift to the central focus of our discussion. In your experience, what have been the most challenging aspects of integrating AI into your business operations, and how have you balanced these with the need to preserve human-centric roles?
I think the first step is to take away the notion that AI is here to take people’s jobs. I believe that human ingenuity and creativity is what got mankind to where we are today. I see AI more as how Microsoft defines it, “Copilot”. AI is meant to support us to do our jobs better, so that we gain more time to focus on the important things, and leverage technology to help us perform calculations faster, and get faster access to more accurate data.
I think educating people on the capabilities and strengths of AI opens people’s perspectives on their role. It’s important to work with the employees, managers, and human resources to understand how the roles can be restructured into what can businesses leverage AI or automation to take over, and what should the core functions of the employee.
I believe there will always be a place for AI, but ultimately the humans need to evaluate the output AI and large language models produce, and then make a final decision. Involving people early in the conversation will make the process of leveraging AI across businesses faster and will bring participation and involvement from the workforce.
There will always be a need for human intervention, to better train AI and large language models, to make sure the guard rails are in place, and make sure the output given is what was intended.
Can you share a specific instance where AI initially seemed like the optimal solution but ultimately proved less effective than human intervention? What did this experience teach you about the limitations of AI in your field?
I was doing consulting for a business in the commercial aircraft parts industry, and I was evaluating the use of AI to negotiate the initial pricing of units as the requests to purchase came in.
We had automated the entire quoting process, but I wanted to see if we could take it a step further. After evaluating the problem, we concluded that the market is too dynamic, and that a standard negotiated price across the board wasn’t going to work. Sometimes a company hasn’t received a lot of interest in a unit, so they’re more willing to discount it further, versus a unit that has a lot of inquiries. Unfortunately, in this case, because the activity was dynamic and always changing, AI wasn’t a good solution to implement.
It taught me that it’s not the Swiss Army knife that everyone thinks, and that it does have a right place in every organization, but it’s up to management to accept that not every problem can be solved with AI.
Another area where AI isn’t a strong contributor is in circumstances driven by emotions or interpersonal skills, such as counseling.
How do you navigate the ethical implications of implementing AI in your company, especially concerning potential job displacement, and ensuring ethical AI usage?
It’s always important to evaluate and test the use of AI, as well as create guardrails to prevent unethical or other unwanted biases. All AI models have biases, and the users and the development team must focus on making sure they are comfortable that the AI or large language model operates and responds based on the structure established by the business implementing the model.
I think it’s important to retrain your staff, and for them to move away from the “us versus them” mentality of AI taking employees’ roles. The idea is to work together, so that the business can be more efficient. There can be job displacement because of productivity improvements, but both employees and senior leadership need to allocate resources for the continuous development of its workforce.
There’s a famous phrase that goes “if you don’t evolve, you’ll get left behind”. The reality is that the world and technology evolve at an exponential rate. Employees must realize that they need to improve their skillset, in order to remain competitive and relevant in the business environment.
Could you describe a successful instance in your company where AI and human skills were synergistically combined to achieve a result that neither could have accomplished alone?
We leverage AI heavily in the sales and marketing departments. The use of large language models helps our sales team train to better handle objections and opposing points of view. For example, we use Chat-GPT to provide contradicting reasons for utilizing our products, or for customers choosing to decline our services.
We then take that information evaluate it, and tailor the responses and feedback to our industry and the specific customer we are addressing. This is an iterative process, where we are constantly evaluating and growing our points of view and identifying better ways of conveying the value add that Garrick Solutions provides.
Chat GPT is a great brainstorming tool, but the magic happens when you combine the data with human perspectives and skills. The best way to describe this collaboration is how Microsoft termed it, “Copilot”.
Leveraging technology to facilitate our everyday work environment, so that we are better prepared to accomplish more difficult tasks and projects.
I want to highlight that humans should always be in the loop to manage and make decisions for unforeseen circumstances.
Based on your experience and success, what are the “5 Things To Keep in Mind When Deciding Where to Use AI and Where to Rely Only on Humans, and Why?” How have these 5 things impacted your work or your career?
1. Evaluating if a process can leverage historical information to predict future outcomes. This question sets the stage for the level of contribution AI can make in a specific situation. One example in aviation is leveraging flights, delays, origin and destination, times of the year, and aircraft types to optimize flight paths, and how to better be prepared for disruptions due to weather or flight crew restrictions. This was my first step in identifying whether a process can leverage AI. The more we become educated on the subject the more we can identify AI opportunities.
2 . Structured versus unstructured data. In order to leverage AI and large language models, information needs to be able to be stored in a tabular format, whether it’s extracted from a database, or fed from Excel files. Structured and clean data is what differentiates whether one can leverage AI for a particular task. For example, responding to questions related to a product or service via email might be difficult to leverage AI in that context. Ten different people can ask the same question in ten different ways.
3 . Data biases. It’s important to evaluate the data and understand if there can be any biases or disclination against sex, race, ethnicity, and other factors. If there is a large percentage of data that requires editing or changing the biases, then this dataset and project might not be a good fit for leveraging AI.
4 . Financial viability. We all know that AI is the buzzword around the world, but companies must quantify and validate their return on investment. Developing a Large Language Model (LLM) can be a very costly and time-consuming task. Companies need to have a medium to long term approach for these types of projects, as development and testing can take several months. In addition to the cost, you must also take into consideration senior management’s time expectation of completion. The business might not have the time to dedicate to this project and might seek returns in a shorter time period.
5 . Evaluating the data set size. When developing an LLM it’s critical to review how much data is available to train a model on. Sure. Using a small dataset to train a Large Language Model may lead to incomplete learning, causing the model to struggle with understanding complex patterns and nuances in language. This can result in less accurate predictions and a limited ability to generate contextually relevant responses.
Looking towards the future, in which areas of your business do you foresee AI making the most significant impact, and conversely, in which areas do you believe a human touch will remain indispensable?
I think that all industries will continue to evolve, and as life has taught us, everything changes. With that said, human touch and rationale will continue to remain critical in areas where not all the data is readily available, and you must move forward and make a decision. I believe in the medical field human touch will continue to be an integral part of this industry, as the services provided go beyond tangible products. It’s about the interaction with the patient, their symptoms, and their dispositions towards or against specific medicines or treatments. These are complex scenarios that aren’t as straight forward.
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. 🙂
One thing I’m very passionate of is learning and growing, but also sharing experiences and moments with people that will help you develop far beyond your current way of thinking. I want to challenge everyone to become uncomfortable, envision the person you wish to be 5 years from now, and go start learning and associating with those individuals that will help you become what you wish to be.
Every day that passes we lose that day forever. I want to share one of my favorite quotes by Helen Keller that goes “Life is either a daring adventure, or nothing at all”. These words inspire me and touch me at my core of my soul and my being. I challenge everyone to be daring, to push further, to fly higher, and make this world a better place.
How can our readers further follow your work online?
Check out my YouTube channel where I post content on how to grow as an individual and as a business leader.
You can also check out our company website at www.garricksolutions.com.
I’m happy to answer and brainstorm with anyone that might be interested. I love learning and helping others grow and self-develop. My personal email es felipe@garricksolutions.com. Looking forward to hearing from you.
This was very inspiring. Thank you so much for joining us!
About The Interviewer: Kieran is the EVP of Channel V Media, a Public Relations agency based in New York City with a global network of agency partners in over 30 countries. Kieran has advised over 150 companies in the technology, B2B, retail and financial sectors. Previously Kieran worked at Merrill Lynch, PwC, and Ernst & Young. Get in touch with Kieran to discuss how marketing and public relations can help achieve your company’s business goals.
C-Suite Perspectives On AI: Felipe Zambrano Of Garrick Solutions On Where to Use AI and Where to… was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.