How L&D Can Lead AI Implementation for Lasting Impact with Megan Torrance

Welcome to The Accidental Trainer, a podcast where you'll hear firsthand stories and tips on how to start and grow your training career Hello everyone, and welcome back to another episode of ATD Accidental Trainer Podcast. I'm your host, Alexandria Clap, and today we have with us the lovely Megan Torrence. Hello, Megan. Hello.

I don't often get described as lovely, so gee, thanks. Oh, you are the loveliest. It's such a pleasure to get to chat with you and to work with you, and I'm so happy to be talking today and celebrating a little bit of your book. But let me do a proper introduction.

For anyone who doesn't know who Megan is yet, she's the founder of TorrenceLearning, co-creator of our upcoming fall ATD AI Intensive, that's happening October and we'll probably talk about it later, and the author of the newly released book, AI Implementation Guide. That is just barely scratching the surface of your accomplishments, but hopefully gives folks an idea of who you are. And today we're gonna talk about your book. Congratulations again.

This is your third book? My third ATD Press book, yes. It's super exciting, and I love working with the team on this. It feels very collaborative.

Before we talk about that, let's make sure to talk about you a little. And you've been on this podcast before. You can tell us your origin story, how you kinda landed in your current role, how you got to be working with AI, as maybe a lot of people have found themselves in an accidental journey to get to that point, but you get to choose. What made it to this moment where we're here on the podcast?

I think like many people in our profession, certainly of my generation, I am accidentally an instructional designer. And yet it's not super accidental, and I think most of us who find ourself in this space, there were certainly indicators or signals early on, right? My undergraduate degree is in communication and org communication. I have a business degree where I spent even more time on organizations and how they work and a little bit on the business side.

I had a job actually while I was in college. It was like the most amazing college job. I had friends who were slinging french fries. I got to work in a nice office doing graphic design, and then they figured out I could write, so I was doing writing, and they're like, "Hey, if you put these two together, could you do the newsletter?

Just like do the newsletter?" And then the Americans with Disabilities Act and the OSHA Bloodborne Pathogens Act were coming out and for US folks, those both, generated the need for a lot of training. So they said, "Well, hey, you're already doing graphic design and writing in the newsletter. Could you write the," I'm making air quotes that maybe nobody else can see, "training," for how do we release this throughout 51 offices across the state of New York?

That was a fantastic experience and I got so much support from my bosses. I really owe so much to those early career managers who trusted me to do all sorts of things. When I joined what is now Accenture, and at the time it was Andersen Consulting, I was in the people practice, and back then the way we did people in consulting was early career people were instructional designers and making facilitator guides and e-learning. Mid-career people did change management, and leadership did org development.

Now, we know in 2026 that's not how it goes. But that made sense back then. So I got all sorts of early training in how to do this, and all of a sudden, because that business degree got thrust onto projects that had nothing to do with training. But I was sitting at this really interesting intersection of process design, change management, technology, and everything was a project.

That started pulling together all these pieces, and I'd been a big technology bug as a kid. Not so much gaming, but programming. And so this all started to make a lot of sense. I spent a lot of time making accounting centers , and thinking about accounting.

And the last one of those projects in which we were centralizing, standardizing, digitizing, and automating a process, the last one of those was, "Hey, let's put in a learning management system, and let's make a centralized learning or actually like a federated learning support team." And I'm like, " sure, that's just another process, and I happen to know a little bit about this one." So accidentally falling in or falling back in, but falling back in on the infrastructure and the technology side.

Content and the learning experience itself came later . That's probably a longer story than you wanted, but No, I loved it, and I heard a lot of things. I wanted to go back to one of these hidden superpowers that you mentioned that you got some early practice with, which is writing, and then that kind of recycled back into being really important for instructional design, and I think comes up a lot in conversations nowadays as we're dealing with AI of who's the better writer and what kind of skills do you need to be able to write anymore?

I know you talk about working with AI even when writing your book about AI. But also just wanted to mention to all of our listeners, we end up working at ATD Press with so many authors who are practitioners. They are instructional designers first or they're L&D professionals and they're like, " I'm not an author. I've never written a book."

But then they end up being pretty good at it because maybe they have some of these skills that you've talked about. I can manage a project and I can write. Those come in really handy when you write a book. Had to mention that, of course.

But I love the things that you're mentioning about process design. I mean, those just come back around for all of the work that you're doing right now, which is exactly what the question was. So yay, I'm excited to talk. I have to mention, when I started at ATD in January of 2020, I got access to all sorts of ATD books for the first time, so of course one of the first ones I remember reading was your first book.

Had a really long commute at the time, so I would take the Metro, and I think that you are so good at this in all of your books, weaving in lots and lots of stories about people doing the work, so you can really picture it right away. You mentioned you've written a book about project management and how to do that as a learning and development professional. So how did we get to this book right now? There's a lot of books about AI written by AI slop itself and others who are seemingly new AI experts on the scene.

So I'm sure that probably weighed into your decision-making process about whether or not you should add to the proverbial heap. How did you make a case? It's interesting because I didn't want to write something that would be ethereal, because writing a book's a lot of work. I wanted something that would be lasting.

I wanted something that would be a guide, and there's plenty of up to the minute I wanna know how to use Copilot in Excel, you know, YouTube's probably my best space for that or something that's less production intensive than writing a fully published book, right? Or at least the way book publishing is done right now. I had the Agile for Instructional Designers and Data and Analytics for Instructional Designers, and I remember a conversation with somebody on the ATD press staff at one point, and they're like, "Yeah, your niche is for instructional designers."

So I'm like, "Oh, that's interesting," right? But this one actually isn't just for instructional designers. It's actually it's not just for L&D, and it's really a guide to looking at how do we implement technology and large scale change of any sort across an organization in a thoughtful way, and it really, it's funny you mentioned superpowers, it really taps into the superpowers that learning and talent professionals have that we can bring to the table. So that's really where this fits.

It's funny, right? So the book title is AI Implementation Guide for Learning and Development, and Jack Harlow, who's the ATD press editor who worked with me on this and the title, we went back and forth. I mean, so many emails about book titles. And everybody's writing a book about AI, right?

At the end of the day, we just kinda got worn out. We're like, yeah, AI Implementation Guide for L&D, that works. Whatever. Let's just get back to the writing of the book.

And it's not a book just about AI and it's not a book just for L&D. It's really a book about any large change, and it's really for learning leaders. Which if you're an L&D professional, you're like, "Oh yeah, that's me." "I'm a learning leader."

But if you're a business leader in the organization who believes in the power of learning to do better. Even if you are in sales or payroll or manufacturing, this is for you, too, and that is where the book kind of fits in the space. "There is not a single your best prompting trick will be X, Y, and Z or use these six tools in your workflow in order to do something better." All of that is ethereal, and this book is not that.

I have to follow up on superpowers, 'cause you said that this book taps into L&D professionals or learning leaders taking advantage of the superpowers that they already have. So what does that mean? What superpowers do they already have? It's interesting, right?

So you mentioned writing, many L&D professionals take our ability to write and our visual communication, whether or not we're graphic designers, we have visual communication skills, we have multimedia principles, we get this. Some of us have tools and capabilities to make great things. Some of us just have simply great communication, right? We also understand that the goal of the work that gets done is not to increase understanding, it's to do more and better higher quality work, right?

So we have that work focus. But even when we step away from the creation of the things that we do, think about this we know how to facilitate group conversations and make sure everybody's voice is heard. We have relationships with people, our project sponsors high in the organization, our new hires who are just getting started. We have subject matter expert connections across the organization.

We probably have a better internal network in the organizations we work in than most people in other functions. And we are trained to ask questions and to test assumptions. So we have so many capabilities that we apply to making learning experiences that we can be applying to other things. There was a fantastic podcast I listened to the other day talking about how L&D professionals ought to be part of every project, not just to make the training that's required.

But because they bring that interesting skill set that's super useful. Oh, I love that idea. You have a project manager on every project, even if the project manager isn't a salesperson or technology person or operations person. Why not have a learning person, a skills person on every project?

Especially when we're moving away from things that are just formal courses. That could be a whole other conversation. But I want to ask, you start off the book with some lessons from technology adoption and what we have known to be true, what happens when any type of new technology comes on the scene, which is a really helpful way to ground conversations about AI. Maybe we forget to do that when the new shiny thing comes on the scene.

So I saved this quote. You said that, "Tech adoption lives at the intersection of psychology, strategy, and design." What are some of those components of that intersection, psychology, strategy, and design, that we as learning professionals can influence? There's a lot we can't influence, right?

But there is a lot that we can. Strategy is where's the organization going? Is L&D influencing strategy? Probably not.

We're certainly influencing learning strategy and program strategy, and but not necessarily organization strategy. But when I think about strategy and how our job is to help connect people to that strategy so they see that upward flow, it's in how we talk about and convey and represent the trust in our leaders to make good decisions, the trust in our peers to learn together and improve things together, the trust in our tools, which comes in part from having good skills to be able to use the tools in the first place, right?

a lot of places we could go from a strategy perspective, but I think trust is a big piece, and really important for adoption, right? Alexandra, if you and I are working together, and I trust you, and I care about you, and I know that you care about my best interests, and you're doing something cool, it might be scary and new to me, but it's worth looking at it because I trust you. And so that trust component is something that we can help build through the programs that we do and the work that we do, and the communications.

Psychology, I think of strategy, of like large groups and organizations. Psychology, it's what happens to us individually. It was really interesting, if you look at LinkedIn, it seems like everybody is using agentic, harmonized, orchestrated AI to do everything, and you're just sitting back eating bon bons and clicking a button here and there to check, say, "Yep, I humaned that loop." It's super fast.

Everybody's getting amazing results. And the reality of it is that many, many, many people are not using these tools. They're not able to use them well. They don't trust them.

They feel it's moving too fast. Both job-wise and society-wise we're not ready. The reality exists somewhere in between, and the reality exists differently for each individual and each organization. And so kind of these broad brush that are out there are really hard and don't connect with individuals.

I'm really advocating that psychology part of adoption is to connect with individuals. We can give everybody a tool. Woo-hoo, everybody logged into Copilot. Yay.

But are they using it? Do they trust it? Do they trust it appropriately? Do they use it well?

Those are different questions. Then design. That's a part where we play in, not just in great visual design or experience design, but that user experience of the design and the change management of the process. Do I have to Alt+Tab 16 times across a bunch of different tools in order to use something, or does it flow seamlessly within the work?

I'm talking not just about learning tools, but the tools of work that the people we support use. I love it. Of course, you know I'm gonna go back to that conversation about trust, because you have a really handy framework for tackling a lot of the concerns that you mentioned. I know it's a huge concern across our ATD community.

I'm proud that we have a lot of folks who are interested to use AI, but they're very cautious because they care about the ethics and the governance, and trying to understand how to operationalize human oversight. What does that look like? So how do you address that with your WIsE@AI framework? So WIsE@AI is really a space to have a conversation, right?

A reminder of the things to have a conversation about. We created this in early days of gen AI before there were very few policies. Now many organizations, medium and larger, have an AI policy, this becomes an easier way to remember it. WIsE@AI stands for wisdom in application.

Am I using AI for a good purpose? Is this something it's actually good at doing versus not? Inclusive and inclusion, thinking not only about inclusion from a historically, a belonging perspective, but also what data do I include? This is that garbage in, garbage out.

S stands for security, right? The data security that we look at for individuals, for our students, our participants, our learners, our customers, as well as the security of company secrets. E is for equity, so this kind of rounds out the WIsE part. E is for equity, and are we being fair to the people who are creators of content that is used by AI?

Are we being fair and equitable to the people whose lives are impacted by decisions made with AI? I might also add in, it's not in the book, but add in environmental concerns, right? How does that figure in our use of AI? We get to WIsE and then AT.

The A in AT is for accountability. Who's on the hook for decisions made? Then T is transparency, which is how do we talk about and be very, very frank with people about when AI is used and when it's not, and that will be a moving target. Because you know what?

In the old days of machine learning, we just called it good computing. We don't often realize how much of our lives is mediated or supported with AI tools. So on our team, we come right out and say, "Hey, this was generated or supported or edited after the use of AI." Probably in three years folks'll think that's pretty quaint.

There's new report that I saw, the Work AI Index. I think it's from Glean, AI institute. Have you seen that, where they talk about bot sitting? And when you talked about transparency that made me think about their report.

They made up a fun name of bot sitting , to describe what folks are doing with they're spending so much of their time, trying to sort of babysit what the outputs are of their AI tool, and then reworking it and like having to reshape it. This is where the transparency piece comes in of are you having trouble indicating what the real relationship is of how much work you did and how much work the AI did for all sorts of reasons why. Do you get rewarded with more work if you spent less time on it?

Do you get eliminated because AI did the work? I can understand why folks might be feeling, to go back to the word you were talking about trust. How do you have a safe, trusting environment that you feel like you can honestly share how much time you're spending like using these tools and who's doing what as the AI versus the human? Let's talk a little bit about your impact zones.

a really nice framing for talking about before we get into the nuts and bolts of how you take on implementation. What does that mean, and how did you make sense of that in the context of your book for framing AI? One thing as I was working on the book, it occurred to me that we often talk about AI as though it's one thing, right? The AI told me this as though there's only one, right?

And it's not even just there's machine learning, and there's generative AI, right? That's a distinction. But even when we look at how it impacts the learning and development professional, there are different ways in which it does, and having one monolithic conversation about it doesn't really do us service. I really thought there may be more impact zones, but by the time we put the book to bed, I found three AI impact zones and the first is in our own productivity as a learning professional.

So we've been talking about writing, right? Am I using tools to help me write? Am I using tools to help me generate media? Am I using tools to translate, or do voiceover.

All these things that help me as a learning professional create my outputs better, faster, stronger, leaping tall buildings, whatever that might be. In AI impact zone one, the learning team is making the decision of what tool to use, when to use it, how to use it. At the end of the day, the product that is created does not have to be an AI product. It was created with the AI, but at the end of the day, it's a Word document, an Excel file, a video, an MP3, a SCORM or an xAPI course, right?

It becomes the same medium and it's no longer dynamic. In impact zone two, this is when we're putting AI in front of the learner. L&D team is still making the decisions. We pick the tool, we decide when and how to use it, we craft the experience.

But now, the A, not I-ness of it is being interacted with a different set of humans that are outside the L&D team. That's our learners. So think about chatbot role plays, right? Super popular.

Chatbot performance support tools, particularly within the learning experience. This is a non-gen AI use, right? But an ML, a machine learning pathway, learning pathway that is determined and personalized based on your data, right? Using AI.

That's zone two. Again, those first two zones, learning and development is leading the charge, making the decisions and we start the conversation. Zone three is different. Zone three is when the business uses AI to change how it does business, how a process happens or a new offering or something.

And in that zone, L&D is not leading the conversation. We get brought into the conversation after it's already started, which is like just about every other change in an organization, right? L&D is not at the table first. That's just nothing new.

In this conversation though, the stakes are much higher. 'Cause we're now outside of the learning environment. We're dealing with actual customers, actual product, actual impact. Not that L&D doesn't have actual impact, but right?

The money is often much more significant than we're talking about in L&D products. L&D has an opportunity to engage and be very, very helpful in zone three, but sometimes I worry that we spend so much time talking about zone one and zone two, that we're not taking the time we save in zone one and zone two to go engage in zone three. I was just thinking as you were describing them, We were doing demos yesterday on different AI solutions, and there was one sponsor who mentioned that a report just came out for folks who, their sales training team were more likely to participate in role playing simulations when it was through this AI tool versus just in person with your peers.

Could not get anyone on board with that, even though it's so helpful. And then just how it was worth it for some of these clients because they saw just an immediate impact. This would be impact zone two, 'cause they get to interact with the AI, giving it feedback. I'm thinking about this but even yesterday, I don't think we covered things necessarily that would've been in impact zone three.

But I'm gonna stick in this question that I know I have asked you about before. I'm gonna go back to your audience of learning leaders. So there's more than just the instructional designer, the individual contributor. There's folks who have a bigger business impact, like influence and power maybe at this stage in their current role.

Why is impact zone three important for any L&D professional? Maybe I'm not phrasing that right, so you can help me reframe what I mean, 'cause I think you know that I wanna make sure people care about this impact zone, but maybe they don't see, "I'm not directly immediately doing that work, so why is that relevant to me?" The what's in it for me. Yeah, okay, so here's the WIIFM.

In impact zone three, so imagine you and I Alexandria, work for a manufacturing company, and we find out that there's a project in the quality control space, that watches products coming off of the conveyor belt, uses computer vision to do a QA assessment of that, and we are bringing in a new super powered, fancier version of this, and we get invited to the team. Now, here's what we can do on that team and the impact that's gonna have on us. We now may need to be training existing people to do something new.

We now need to train new people on a new process, and maybe we don't need all the same kinds of skills that we used to have as prerequisites because we now have this tool that will do more things. Or we're maybe adding additional tasks for the people to do because they have this AI vision tool that will take on a lot of the basic work. So the role itself is changed, and the people that we are bringing in change, and possibly the quantity of people that are doing that job change.

We may also be involved in teaching the tool how to work better. So there's some different things that we can be doing here that we absolutely have a role even before we get into the whole AI implementation canvas, which is a big part of the book too, in which we're actually facilitating the implementation. Even if we are just showing up as L&D professionals doing our L&D job, impact zone three will impact me. For example, pre-generative AI, one of my clients was an insurance company, and they brought in a machine learning algorithm to do near immediate underwriting of life insurance plans below a certain limit.

It was very carefully boxed, right? And we had a very large training program, went all around the country, teaching the sales force how to talk about, how to use, and how to set up for success this algorithm so that they could do the little tiny policies fast and spend more time selling the big policies. That's an AI impact zone three right there. I love it.

You just mentioned the implementation canvas. I just have a big bucket question there because I think maybe we do a little level setting. What is that? It's such a huge, powerful tool that you've created.

Tell us all about it. The AI implementation canvas really draws on the L&D's professional, the L&D professional's skill set in collaborating across functions. And so it's a set of 14 conversations to be had about any AI implementation at all, and it ranges from strategy to workforce impacts, to the data models and structures, to how we measure, how we experiment, how we scale, how we design a user experience. I think what's really important here, it's not just about the people parts or the talent development parts of an AI implementation.

It brings the people and the talent development parts alongside the data and the legal and the business parts of that implementation, and it encourages a cross-functional team to have this wide set of conversations. And the cool thing about the canvas is that there are no answers. It will not tell you anything other than guide you to ask questions, gives you some starter questions, but guide you to ask questions and then solve those questions together with the other people on your team.

It's a very human approach. I love it, Megan, because when, I'm doing exactly what you said that people do, they just refer to the big AI as like when AI came on the scene, but whatever, I'm gonna do it. Years and years ago when it's just like 2022, '23, those were the conversations we kept having internally is like, "Well, I just have these list of questions about AI." And I love that whether you're approaching it right now or in a year from now, they could come back to this.

It has more of a chance of being evergreen because you're still gonna need to ask those same questions, hopefully. You're still gonna need to do things that are ethical and you're considering the data elements and the legal elements. You've talked about some of the buckets. I'm remembering a version where it has colorful blobs around the different themes or criteria that you go into.

Can you give an example, maybe more of a concrete example where a team might all be coming together and they're using it to ask questions that might be different from maybe another project that's happening inside the organization. I know I'm starting to talk really abstractly here, but just how it could be used so differently but still for the outcome of putting AI into place in your organization. Let's kinda intersect this with the zones, right? So zone one implementation, this is a great place to practice using the canvas.

It gives you a bunch of questions. So say I'm gonna go buy a new AI course creation engine thingamabob, and I get this tool, I open up the canvas, and it sparks me to ask questions about, "Where's my data stored? Who gets to use my data? What is the workflow impact and the throughput impact of using this new tool?

Why am I using this tool in the first place? What are the things we're going to look at when we are piloting this? How are we going to pilot it?" Because as one of my colleagues the other day said, "Demo is demo, pilot is pilot, but reality is an entirely different thing altogether," right?

How do I pilot with something really close to reality without giving up my data to a company I haven't engaged with yet? Those are hard questions. And then when I do scale, how do I do that? What extra things have to be in place in order to scale?

What are the pricing models that are in place in order to scale? What people can't see on the podcast, I'm doing the interpretive dance of this and I'm working my way around the physical canvas, 'cause I got 14 different space. What is the human impact of doing this, of bringing in a tool like this? If I don't have to talk to my subject matter experts nearly as much anymore, does that change the nature of the job?

Does that make the job less nice for me? Does that isolate the subject matter experts from the results of the work? What does that mean for us? So there's so many different questions, and when we look at this on zone one, we can get our hands around a lot of these questions, and if we don't know the answers, we can go hunt them down.

On a zone three implementation, we may show up and say, "Hey, y'all have got a good start here. Can you fill me in? Where is the data for this? Which regulations are likely to be touched and impacted here?

What's the long-term scale impact of this and opening up new platforms and systems to our existing data sets?" So there's lots and lots of things. We just used it at a very large project. It was a zone two, so AI in front of the learner where our team mediated conversations with huge subject matter expert from a content and a validation and making sure the AI was staying on its rails, and the entire data security and infrastructure and scale on the inside.

And it was so interesting how the two of them, like our team was the team that held all those together, but those two groups didn't talk to each other on a regular basis at work. Wow. Really powerful. I want to jump to experimentation if we have time.

You have a lot about, recommendations about how to use the scientific method for helping to control experiments. There's so much to learn from failures, though. I'm curious if you have any failure stories. Maybe something just didn't work out the way that you thought it would when you tested it or you wanted it to or you needed it to.

Anything like that? At TorranceLearning, we have a regular cadence of just getting together and almost like a mini hackathon. Like, "What are we gonna make today?" And we make these little proof of concepts.

And our most recent question, and often a question is what is a tedious, onerous task that, we would love to offload, right? A task in which we take hours for all of the dozens of projects we have in flight at any one time, and we drop the last week's total hours onto a graph that gives us a… Well, it goes back to the Agile book. It gives us a burnup chart of how the project is progressing. And it's incredibly manual, like just super manual.

And we have reports, but the human is the interface in between. We thought surely there is a better way. Our, air quotes, "fail" around this, our discovery around this, is that we're not ready to automate this. We're not ready to use AI to just like, "Yeah, go update this," 'cause one, it has to be right.

Yeah. It has to be right. Two, every spreadsheet is stored in a slightly different place on the network. It's not consistent because some of our projects are six years old and some just started last week, and the actual field that we're dropping the data into might be different on one sheet than on another, and it was a really great recognition of interestingly something I learned back when I was doing those large shared service center re-engineering outsourcing projects, is that it will be incredibly difficult to centralize, digitize, outsource, offload a process that is not a good, organized process to begin with.

It was just one of these, like, "Oh, yeah, we need our ducks in a row if we're gonna hope to automate this, and then we have a different set of decisions to make." So it was one of those yeah, we failed at that kind of moment because everything else was starting to get to oh, that one's easy, that one's easy, that one's easy, and I think we were picking low-hanging fruit. And we grabbed this one, like, nope, not gonna happen. Yeah.

But you have to. You have to start to move to those harder to work on projects. Okay, I know we're running out of time, so I wanna do a quick plug for ATD Intensive. You are a co-creator for this, happening in October.

Anything that you wanna mention about focusing on AI for three weeks in October? I can't wait for this. There's a lot I like about this program. And not the least of which is I get to work with you in developing the program, that's been a fun springtime.

It's three Thursdays, and we're organizing it around the three impact zones for AI. So day one is all around your own productivity as a learning designer and a talent professional. Super cool stuff. Day two is around putting AI in front of the learners.

Day three is around what happens when the business brings in AI and you have to support that change. So we've got some maybe some non-traditional ATD speakers who'll be showing up for that. So I'm really excited there. Along the way, we will bring back our super popular hackathon from last year, which was so much fun.

It was so much fun to watch teams form and coalesce around a project and get to know each other and do something, make progress or not, right? just like the human dynamic of this is super fun but then the results and the things that people shared and the things that people made were absolutely astounding and hilarious in their half-baked states, because we only had two weeks. I'm really looking forward to the hackathon. Me too.

I loved the projects that we saw last year. So creative. We'll make sure to include a link to Intensive. We're, of course, ran out of time, didn't get to all of the fun questions about your book, but as we close, can we just focus on you one more time?

Do you have a learning goal, something that you're doing to get out of your comfort zone? I am always learning about things, but right now, actually, if I were to say my learning goal I might change it and be like my training goal is I am training for another long distance hike and just learning how the body builds muscle and builds endurance and how you have to pick and choose your data sources, like your learning sources. Super, super helpful. Good stuff and also good reminder to get away from screens and do something out in the real world.

I love it. Well, Thank you so, so much, Megan. It was a pleasure to chat with you today. As always, this has been awesome.

Thanks, Alexandria. Thanks for listening to this podcast from the Association for Talent Development. Be sure to check the show notes for resources mentioned in today's episode. And if you found the show useful or insightful, please be sure to like, subscribe, and share it with a colleague.