Achille Ettorre is an Analytics and AI Advisor, Harvard Business Review board member, and author of The Digital Advantage: Success Strategies for Harnessing People, Culture, and Technology.
See below for episode links, timestamps, contact info and transcript:
EPISODE LINKS:
Achille’s Website: https://achilleettorre.com
Achille’s Books: https://achilleettorre.com/book-launch/
CONTACT NATHANIEL:
Feedback – https://forms.office.com/r/mAHmTU1eeP
OUTLINE:
0:00 – Introduction
1:36 – Programming an IBM “Big Turtle”
3:01 – Achille’s defining moments
4:55 – Career transitions
6:43 – Achille’s definition and of AI
7:48 – Evolution of AI
10:36 – Four cases of AI use
12:00 – Enterprise AI
12:38 – Coffee shop drive through agent
13:45 – Supporting user experiences
18:33 – AI adoption steps
22:14 – Move fast versus move slow
24:03 – Summary of AI adoption steps
30:10 – DeepSeek and OpenAI Deep Research
32:39 – Protecting data
34:03 – Book launch
34:45 – Achille’s thought leadership origins revisited
PODCAST LINKS:
Apple Podcasts: https://podcasts.apple.com/us/podcast…
Spotify: https://open.spotify.com/show/5K5jj9n…
YouTube Playlist: https://youtube.com/playlist?list=PLvi4c35PeqnmiuDt4tycVE9FUnXLMt5IG&si=6QWqDuR9tpYJhARv
SOCIAL LINKS:
LinkedIn (Nathaniel Leies): https://linkedin.com/in/nathanielleies
LinkedIn (Predictum Inc.): https://linkedin.com/company/predictum
LinkedIn (CoBaseKRM): https://linkedin.com/company/cobasekrm
Episode Transcript
(0:00) Coming up on Knowledge Company Now
Nathaniel: What in your words is AI?
Achille: Well I would — you know — simply put from a definition perspective: All AI is, is a tool that—
Nathaniel: The big distinction between AI and other forms of automation is that AI has reasoning.
Achille: I think that’s table stakes now. With China coming in with DeepSeek there needs to be some type of governance and some type of measurement so you can have a human touch. That is gonna be the arms race in the future.
Nathaniel: The company is interested, but AI doesn’t exist yet. What steps should they take?
Achille: I typically break these things out into three themes: people, process, and the third one’s technology.
Nathaniel: Who do you think is an example of a person or a company doing AI really well?
Achille: [Laughs] That’s interesting… You know, I—without—
(0:55) Introduction
Nathaniel: Welcome to Knowledge Company Now, an interview series with experts in data analytics, AI, and experimentation. Over the course of the series, we explore what it means to be a knowledge-driven organization.
My name is Nathaniel Leies and I’m excited to welcome you on this journey. Our first guest is really someone special. His name is Achille Ettorre. He is not only a mastermind behind the analytics programs at some of the largest companies including Disney and Loblaws, the largest retailer in Canada, he’s also a friend of the show.
As we engage in this conversation, I hope you find it just as insightful as I did. So, without further adieu, let’s go ahead and get to the conversation.
(1:36) Programming an IBM “Big Turtle”
Nathaniel: I want to start with a question from your TEDx talk I’ve been dying to ask you. For viewers who have never heard of it before, what is a big turtle?
Achille: Listen, this was a long time ago, Nathaniel, and it’s interesting how much that question’s come up since my TEDx talk. You know, I’ll share with you this. I was in my elementary years. I really enjoyed mathematics. I was always very strong with numbers and really enjoyed math. And there was an enrichment program. And at that time, IBM came up with a program. I think it was Lotus.
It was Lotus, I think it was 123 or Lotus One, first generation. And I got selected to go out and learn how to program a turtle. And it’s amazing because I believe I was there no longer than a week. It was definitely the majority of the week, and we’d start our day and end the day. And it actually took a couple days to learn the programming to make it go a certain distance. It was even harder to make it do a circle. But by the end of the week, we were all professionals—or at least proficient—in programming that turtle. That was probably my start.
Nathaniel: The turtle, if I understand, is a robot that sits on the floor and can basically draw shapes just depending on how you program it. That had to have been mind-blowing, to go to IBM as an elementary school student and get to tinker with robots.
Achille: That’s exactly it.
(3:01) Achille’s defining moments
Nathaniel: Achille, you’ve had quite a few accolades. You’re a member of the advisory board for the Smith School of Business’s Master of Management in the Artificial Intelligence program at Queens University. As of last November, you’ve become a board member for the Harvard Business Review. You were an advisory associate to the International Institute of Analytics founded by Tom Davenport and Jack Phillips. You’re a TEDx speaker, and author of The Digital Advantage: Success Strategies for Harnessing People, Culture and Technology. I’m just curious to know, are there moments from your career that were particularly memorable?
Achille: It’s a loaded question there, Nathaniel. I can tell you this—in terms of defining moments in my career, I truly believe when I finished university and landed my first job at FANUC Robotics, I didn’t realize what I was getting myself into. I was in a manufacturing environment and learning how robotic arms are programmed… and me being from a finance background, that was something that really intrigued me.
I actually won a President’s Award for a programming event in a Honda plant in Aliston, Ontario. I was so amazed at how robots were actually able to take routine tasks and automate them. So that was the first. The second was working in supply chain—and this is what I fell into, this line of work, this industry—working specifically with Walmart Canada, and understanding the different automation systems within the distribution centers and how distribution centers actually work. I learned a tremendous amount working for people across the world, especially in Bentonville, about maximizing efficiencies in the network.
(4:55) Career transitions
Nathaniel: It’s interesting. You start with a background in finance, but you clearly had an interest in mathematics and robotics from your early years. You find yourself in an industry where you’re working with automation and robotics, with supply chains, and then, you become exposed to data in the process. It almost sounds natural, the way you describe it, then going on to your work with Loblaw, with Walmart. Doing loyalty programs and analytics. Did these transitions feel natural to you?
Achille: Yeah, I would say it was almost done… when I look back, you know, it was done in such a way where I just kind of grew into where I am today. But the next defining moment I would say was working with Loblaw companies, where I’d literally just finished my master’s program, and I remember being put into a room to discuss my next project, which was being part of a acquisitions team to make one of the biggest acquisitions that Canada’s ever seen.
And I would tell you that big data or the use of data was kind of formalized on that day for me. That would be my start. And from there, it evolved to a tremendous amount of different things, working with a lot of industry leaders, working with people that are more passionate about it than I am.
And I spent a number of years kind of creating that skill set and continuing to evolve as the industry evolved. Today we talk about AI. A lot of the science, though, Nathaniel, that’s involved in the problems that we solve are solving, they’re the same. mean, AI has been around since the 1950s and is just now getting embedded or getting into mainstream operations within all businesses across the world.
(6:43) Achille’s definition of AI
Nathaniel: Listening to your TEDx talk, one of the themes that really impressed me was that AI can help people to build a better life. You qualify that further by sharing that AI helped you to strike a balance between having a demanding career and also taking on the demanding role of being a father, being a spouse, being a family member.
You also have an interesting experience in data analytics, witnessing technologies develop, and now you are a top LinkedIn voice on the subject of AI. For someone who hasn’t followed AI closely, what, in your words, is AI?
Achille: Well, I would simply put from a definition perspective, all AI is a tool and it’s a tool that takes repetitive tasks, mundane tasks and automates them. It literally takes a process that requires a lot of human capital or a lot of labor and places a system, a program, a process within a model to automate whatever that task is. And AI is able to do it at scale.
(7:48) Evolution of AI
Nathaniel: I think that’s an interesting place to begin with AI… Taking a task that I as a human would have to do manually and just making it so that a piece of technology would do it instead, and at scale.
But to me, the big distinction between AI and other forms of automation is that AI has reasoning and logic capabilities. Its creators are pushing the boundaries of its ability to synthesize and create new ideas.
So my question is, would you agree and what would you say are the defining moments in the evolution of AI?
Achille: Well, listen, all of us have heard of ChatGPT and all of us have, you know, I hope to, I hope have at least experimented with it. And I would suggest that, you know, just at the start of ChatGPT when it was released, you know, a few years back, it’s evolved tremendously over the past few years. And it can do all the typical reasoning that would literally take a human hours, if not days to get to some type of situation or area where they can make an informed decision. I think that’s table stakes now. With China coming in with DeepSeek and with all these other models that are gonna surface over time, these models are only gonna get better. And I would say to you, will they work better than a human? Probably. Will they enhance our work? I think so.
But in that same breath, there needs to be some type of governance and some type of measurement so you can have a human touch so that biases don’t come into these reasoning models or decision-making processes. And that is gonna be the arms race in the future. More than that, I would say to you where we are today, with my company alone, I can tell you that we’ve created an AI agent that actually does interviews.
By working with a company called Bluebell. And what that does is it literally takes 800 resumes or a thousand resumes for a particular job description where I provide the criteria of the human and it will pick and rank based on what I provide as a team to say that these are the type of people and skill set that I want in my business. And it will give me the top 20 resumes. And then I only need to meet two or three people to build my team.
And the people part of it is significant because it literally gives me the opportunity to hire A players, but players that are not as strong, it literally lifts them up. And that lifts the entire performance of the whole organization. And that’s one example. I can definitely go into more, but I hope that answers the question in terms of where we’re at today. And again, I only see this getting better over time.
(10:36) Four cases of AI use
Nathaniel: Your hiring example brings up an interesting point. Everybody, I think, is at a different point in their journey with AI. For some people, it might just be using a chatbot in their daily life to help them with fairly common tasks, like drafting an email, or getting answers to a research question.
Other people might be piloting an AI adoption at a smaller scale in their organization. I think of it as a small team, maybe just exploring a chatbot, maybe exploring another tool. But they’re looking for ways to use that tool to bolster their work or their processes.
Then, there’s a little bit more of what I heard you describe, which is that you have—and please correct me— you’ve developed your own AI application. In this case, your application is ranking resumes for job applicants, to help save you time and hopefully find an optimal candidate for your hiring.
Achille: That’s a live example. I work with a company to build that agent out and I can definitely share it with the team. It’s done a phenomenal job in saving a tremendous amount of time for myself. And this is for my family business where I don’t need to sift through a whole bunch of different resumes and try and pick the right one. I can literally have an AI tool do that heavy lifting for me so then I can meet the people, interview them and make the best decision for our company.
(12:00) Enterprise AI
Nathaniel: That’s astounding. So yes, perfect example of a tailored AI application, and everything that goes along with that: user interface, software development to make your interaction with the model—you know—something you can actually do.
The last example of AI that I see is at a larger enterprise scale. The question of ‘how can we a a large – and in many cases – global company, use AI across teams, maybe even breaking divisions and unifying various teams.’
Achille, you have extensive experience in enterprise. I would be kind of curious to ask: when we talk about AI and enterprise, how are you seeing AI being rolled out?
Achille: It varies Nathaniel based on the maturity level of the organization. So, you know, we can certainly see how Google, Amazon, Apple and all these types of organizations are at a more advanced level. But I’m working with a client right now and you know, they’re looking to automate the drive-through experience with an AI agent. And if you think about that, yes, it is taking someone’s job with respect to taking an order, but the goal is to standardize the orders so that they can handle more volume and do more work. And then the work gets reassigned to actually procuring or quite frankly, getting the order ready.
Right now it’s with a coffee. It’s a coffee company. And they’re handling about 800 to 1000… anywhere from 800 to 1,000 orders a day. And by implementing this tool, they’re looking at minimizing the error rate or the shrinkage rate from like 10% to like five. And that’s a significant amount of money that’s saved, but it’s also a significant amount of investment that you can make in your people to either procure and do that type of work or reassign them to different parts of the business.
12:38 – Coffee shop drive through agent
Nathaniel: With this example of having AI help in collecting orders, and then what I imagine might be an existing analytics program— so things like looking at customer preferences… looking at order history… understanding what drives value for the customers – do you see an interplay between existing analytics, meaning the things that people are already doing, and then the things that people want AI to be helping them accomplish?
Achille: Well, listen, in my experience, there are larger retailers out there that really design their loyalty programs to help manage that type of behavior. You know, the automation or the analytics or the AI is a tool that gives the retailer the ability to understand not only the customer behavior by geographic, but it can make you understand what the banner purchasing behavior is. And so quite literally, you’re able to understand that if your customer journey is online, you’re going to develop a different fabric or a different offering in order for that customer to order, process whatever it is that they want within your company.
But there are customers that want to come into a particular location and they like the bricks and mortar. And those types of individuals are probably prone to different types of offerings or different types of experiences. And the AI, which is what we call it today, and the data that’s collected allows these larger organizations to provide that information at a geographical level that was never been able to be provided before. And they’re able to make adjustments on the go or more real time than week to week.
13:45 – Supporting user experiences
Nathaniel: Part of my background is doing user experience design. Something that we talk about in user experience is that different types of people might want different experiences. You might be totally correct to say somebody might want to go on their phone, open an app and order a coffee that way and to pick it up at a curbside parking. Other people might still enjoy the experience of walking inside a brick-and-mortar shop, having a personal interaction with the barista they see every week, and ultimately, getting their coffee that way.
It sounds to me as though being able to take those geographical preferences, those consumer preferences, and being able to help the product creators or the experience creators, AI could actually have a lot of value in that. For one thing, the people who are defining the customer experience for the product might not actually have to collect the data themselves. It’s already being provided by AI. It could also be that AI is helping them to create a stronger product. A much stronger experience in that data collection aspect. It could also reduce the delay between when then product is informed by the customer’s reaction and when the customer is actually having that reaction. So we don’t necessarily need to approach somebody who is talking with a customer and try to extract the knowledge that they have. I also think in this way, AI might give us more accuracy in what it’s able to communicate from an interaction with a customer.
Achille: Well, listen, I’ll build upon your comment there. And one more thing that would help that UX experience is other customers is — I look at my children, I mean, they’ll Uber their Starbucks to the house and I’m looking at them and it’s like, in my time, we would never do such a thing. And yes, they’re paying a premium for it. But if you think about it, they’re not the only ones. There is a huge demographic that literally Ubers anything to their home from a coffee to groceries.
And it’s not just Uber, it’s Instacart, there are different other companies that do this. But that last mile has changed over the course of time. And again, tools like AI give you the leverage or the competitive advantage not only to procure such things, but also understand the demographics so you’re able to deliver that AI time and time again.
(18:33) AI adoption steps
Nathaniel: So we aren’t just talking about chat bots anymore. We’re not just talking about a “software as a service” application.
We are talking about businesses and companies that are taking a very meaningful and deliberate approach to how they use AI to automate some part of their business. My question for you is very much this:
Let’s say you are working at a company, and it’s interested in piloting some sort of project using AI. Perhaps delivering significant customer value, or perhaps taking care of something else that needs to get done within the business or its processes. The company is interested but AI doesn’t exist yet, what steps should they take?
Achille: That’s a good question, Nathaniel. I’m going to point to a little bit of what I’ve written in my book with respect to execution of these types of programs and projects. In that instance and what you said, or any other instance, I typically break these things out into three themes. And the first one’s people, the second one’s process, and the third one’s technology.
And when I talk about this, depending on where you are at your journey, you’re gonna have to invest in processes to understand them and make sure you know what it is that you’re doing, because if you’re gonna automate something or put it to scale, those things need to be refined and defined. Your people need to be in a situation where they understand what it is that they’re doing if you’re purchasing some type of technology to manifest or deliver whatever it is that you want to solve.
And I would say that there’s no particular order, but in my experience, it’s been, you start with the process to get a gauge on where you’re currently at and what you want to deliver. Then you look at your people to see where they’re at.
And technology is not last by design, but at least you can make a concerted effort if you need to buy something vanilla, or you need to go with the Cadillac, or you need actually any type of customization. And along the way, the more you keep things standardized, the better it is. But that doesn’t fully answer your question.
If AI is not in the boardroom or is not part of the strategy of your particular organization, large or small, doing something like this is next to impossible because you definitely need senior leadership sponsorship and approval so that the journey is one that everyone understands. It’s not a success rate that you’re starting something and it’s gonna be done. There’s gonna be pitfalls. There’s gonna be things that go wrong. There’s gonna be failures, but you fail fast and then you learn how you wanna proceed moving forward.
And that’s where I go back to my three pillars on people, process, and technology. These are not things that you can just prescribe and pull out of a box. You actually have to go in and do the work. But if you do the work from the beginning and invest the time, your AI project will be delivered very successfully. If you try and skip some steps or try and save some funding, more likely than not, there’ll be some hiccups along the way.
(22:14) Move fast versus move slow
Nathaniel: Right. So that is a good point.
We don’t want to necessarily rush ahead. We do need to make sure that we have those foundational pieces in place before we take a substantial step on an AI project.
I guess I would just like to ask: I wonder if there are people who are feeling maybe that they are lagging alittle bit behind where others are in terms of their progress with AI.
Is it more important for businesses to ensure that their people and culture are right as the first step? Or is it more important to move fast, so to speak, and to harness the new technology?
There is kind of an argument that says — okay — if we adopt “move fast” mentality, it can create a culture of its own. On the other hand, without guardrails a “move fast” mentality might end up being destructive. I especially again think of people in critical roles — the research. The manufacturing. To move fast or move more deliberately. What’s your take?
Achille: Again, that’s a great question and it relies on where the company is in terms of their maturity level within tech or innovation and understanding what change means, adoption means, improving things within your organization. I will tell you my opinion though, for me, I definitely always move slow.
I believe and I’m a firm believer, sorry, I am a firm believer that the science around all this work, these AIs, these models, these optimizations that are being implemented, they’ve been around for years. The math has been there.
The arms race is understanding how you apply the math, how you apply the process and how you make a difference within your organization and choosing the tool that’s right for you. That tool could be using Excel. That tool could be using SAP HANA. Quite frankly, whatever the tool is, you have to sit down and make sure that when you productionalize the output, it’s standard and it’s able to scale.
In that, in what I’ve just shared with you, the company definitely needs a governance program so that no biases—or when biases do come into play—I shouldn’t say no biases because quite frankly, there always will be a bias. There are humans that are at each touch point that can make suggestions, interpretations, or even stop what’s occurring because moving at the speed of AI is great. But if you don’t understand the implications of what it is that you’re doing, you can cause more damage than good.
(24:03) Summary of AI adoption steps
Nathaniel: So Achille, I’m going to go ahead and recap. Number one, if I’m understanding, people and the culture have to be mentally primed for AI.
On one hand, I think we do need to have people who are open-minded enough or curious enough, to already be exploring AI or to take the leap and to do it. Then there’s the company culture that encourages this exploration.
Finally, it sounds like we need a program to audit where can AI be used. You’re auditing processes, you’re auditing existing technology, you’re auditing the value that you want to deliver to a customer.
We are looking at AI technology and we’re saying, “Is there a process where this technology can benefit us?” Maybe even we’re looking at a process that’s not working correctly and saying “Could we fix it…? Could the AI technology inspire a new process that could do better than what we currently have.”
So all of these things are sort of in play. If we have these sorts of thoughts— the people, the culture, the process audit, evaluatng technology, there’s at least a plan or some foundations of a strategy. There’s an agreed-upon end goal or vision of what this AI might look like.
Achille: Yeah, listen, you’re spot on.
Nathaniel: Let’s say that right now listening to us, there’s somebody who is saying, man, I’ve really been wanting to do these things for so long … I’m just not sure how I can take the first step to get people on board.
What would you recommend for this person when they go into work tomorrow that they might be able to generate some interest or take the first step?
Achille: Listen, for me, I’m a roll up your sleeves and dive in and start learning. And I would tell you that learning and making progress at 1% a day or moving slowly definitely builds a strong foundational understanding of what AI can do and what it can’t do.
Because quite frankly there are a lot of definitions or a lot of people suggesting or talking about the things that it can do and it just can’t today. I’m sure we’re getting there, by the way. And the more you sit down, whether it’s through an online course, looking at some YouTubes, going to some type of formal education to understanding the ability or what’s under the hood with respect to AI, you have a choice of becoming a generalist and going wide within different industries, or you can literally hone in on a certain piece—I’m going to say automation—and you can go really deep.
And that could be the suggestions that I’ve made today with respect to voice modeling, which is pretty hot right now. And I gave the example of doing interviews and taking orders for procurement, whether it’s a drive-through or a McDonald’s, frankly, or it’s a situation where you’re using ChatGPT and you’re automating tasks to understand who’s in your marketplace and if there’s anything that you could be doing to put your situation in a better place.
The possibilities are endless. My family uses it informally to help plan our vacations and it’s become quite the tool to give us suggestions on what would work best for us. So the possibilities—or the art of the possible—is endless right now. We are truly at the beginning of the next revolution on how things are gonna be done.
Nathaniel: Who do you think is an example of a person or a company doing AI really well? And on the flip side, who’s doing it fairly wrong?
Achille: That’s interesting. I think all the major tech firms are doing AI right. But I do think they have the budget and they’ve been thinking like this for a number of years. It’s just come to mainstream right now.
I think a lot of the investment firms and banks that have the funding are doing things right. I would suggest I think they have capabilities to implement AI tools within their businesses but quite frankly can’t because of regulatory issues. And that’s private business as well. So large enterprise I think have it right. I think there’s regulatory issues that hold people back in actually launching the technology or launching what it is that they want to do.
And that goes into things about the right to be forgotten or who owns the data and those type of issues that will probably take some time for society to figure it out.
Who’s gotten it wrong? That’s a tough question because I don’t necessarily know how many people would come out and say they’ve gotten it wrong. But I know that there are situations or there are instances of certain organizations being taken out or being put offline because some automations that they put in place aren’t working.
And some of that deals with situations where you have offshore call centers that are supporting businesses within North America. Others are hospital networks that bought the package, took it out of the box, implemented it, didn’t really think it through and haven’t really honed in on what that governance program looks like. Hard for me to name someone directly, but I would tell you that those are instances or examples that I’m familiar with, and I’m sure there’s many more.
(30:10) DeepSeek and OpenAI Deep Research
Nathaniel: Two lightning questions. I’m just going to fire them off at you, Achille. Give me your hot take, your personal opinion, whatever you’d like.
DeepSeek. What are your feelings about it? I know it’s been causing quite a lot of talk recently. I’d just like to know where you weigh in on DeepSeek.
And then number two, OpenAI has just launched Deep Research. How do you feel about Deep Research?
Achille: Good question, because I’m definitely working a little bit with both of them. And here’s my preliminary view. The world—the arms race—is looking at what tools can come into play within society so that they can make a better world for everybody.
And it’s interesting how DeepSeek has come out of nowhere and provided a similar, if not reliable solution, that OpenAI has provided. Whether the information within China is stored in China or whether the data is taken from a different country, I really can’t speak to that. But I will tell you, whatever tool you decide to use, you need to understand what the safety guardrails are.
And so there are things you can do to protect yourself so that you’re not putting yourself in a situation where whatever data you’re using—within your organization or personally—is put out there for people to do whatever it is that they would like.
Google’s research lab—I think it’s very powerful. I think the model is probably one of the most sophisticated ones that are out there. But I also believe that it’s only been released because of what DeepSeek has shown the world. And I think more of these innovations will come into play faster as time progresses to see who wins that arms race. So, exciting times.
I’m hesitant because I need to spend time learning the models and understanding the biases or what it is that they’re doing differently than the models that were published beforehand for my particular usage. Someone else may have a different experience and may need that level of sophistication, or that deep analytic view that, quite frankly, for my applications, I haven’t seen any.
(32:39) Protecting data
Nathaniel: When we talk again in the future, I’d like to learn more about your take on how users can protect data and apply what you call the safety guardrails.
Until that time, I’ll ask just this: Is there anything simple or anything that’s a no-brainer that AI customers can do to try to ensure at least a minimal level of data security or protection when they get started evaluating software?
Achille: Table stakes, you definitely would want some technology to help you manage the governance. Absolutely. But I also would suggest having a council or a few people around the table to review what the impacts are for the integration of data points from stack to stack. You may think something is flowing a certain way, and it may not.
And there are certain variables or there are certain changes within your current environment that may cause a change within the model that you may not be aware of. I’m not suggesting that, you know, the AI agent or the bots are going to be chatting with one another and conspiring. But if you are making decisions on revenue and optimization, or you’re making decisions that will impact the ROI within your business, you owe it to yourself to build out this governance fabric within your organization to protect yourself and those around it.
(34:03) Book launch
Nathaniel: As a final question: The Digital Advantage: Success Strategies for Harnessing People, Culture and Technology. Achille, this is your first book. How do you feel?
Achille: It’s been a long journey, Nathaniel. I’m pretty proud of the work that’s gone into it and excited to hear what other people think about the work that’s involved. I’ve been very fortunate within my career to work with some of the top thought leaderships around the world. And that’s kind of helped shape how I view things.
But this book is literally a tool for anyone to kind of look at how they want to apply better decisions within their organization.
(34:45) Achille’s thought leadership origins revisited
Nathaniel: As we say our goodbyes, I just wanted to share a quick story of appreciation from our Chief Marketing Officer at Predictum, Shawn Smith. Achille, you need no introduction to Shawn Smith. After all, the two of you have been close friends for quite a number of years. Shawn mentioned that he was proud to be by your side when you started this journey of being an AI and analytics thought leader. And of course, this was 12 years ago at the SAS Global Conference in Las Vegas.
He shared that the two of you were sitting together. You were watching the keynote speaker. And as you were watching, he mentions that the light went on for you. And you said, hey, I can do this.
I’m seeing, quite literally in real time, that your trajectory is going the way that Achille from 12 years ago maybe hoped it would. So I just want to say congratulations from me for everything you’ve accomplished so far, and ask you:
Since 12 years ago at the SAS conference, up until today, how has your path gone since them, and what’s ahead on the road for you?
Achille: It’s funny, I think I remember that day with Shawn and there were many more days. I think it’s gone very well. I’ve been doing this for quite a long time because it’s been a passion of mine. And I just find my skill set—or quite frankly, my curiosity—leads me to continue to learn and evolve on this path and build upon what I’ve done from a corporate level to where I am today.
I also think that there’s a lot more that I can learn and I think that there’s a lot more innovation that’s going to come across humanity over the next few years. I’m not only excited for me, but I’m excited for what my children will see or everyone within the next five to ten years because one thing I can guarantee is that it will be different. I’m excited to see it.
Nathaniel: Achille Ettorre, thank you so much.
Achille: Thanks for having me, Nathaniel. I really appreciate it. This was a lot of fun.
