AI Advocacy in Engineering: Translating Tech into Business

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AI Advocacy in Engineering: Translating Tech into Business


In this article, we delve into the crucial role engineers play in advocating for AI projects within the organization and how such projects' benefits can be effectively communicated to the rest of the team. We'll explore how engineers can offer practical tools and strategies to pave the way for the success of AI practices. Specifically, we walk through three fundamental aspects:

  • Technical guidance
  • Safe use of AI
  • Teamwork
  • Being an AI Advocate

    Your Role as a Technical Expert

    As an AI engineer, you are expected to bring the technical expertise. Drive innovation, provide education and training on AI tools, and highlight both the benefits and limitations of AI. Ultimately, your team should have a better understanding of how AI can support your business's growth.

    Guiding Safe Use of AI

    Understanding the intricacies of AI, you have the responsibility to ensure your team adopts a safe framework for using AI. This includes dealing with the potential risks and biases that may arise from AI systems in your operation and aligning AI use with the company's values. Creating policies and setting best practice standards for AI usage in the company can prove invaluable.

    Promoting Teamwork

    The ability to work with others in the development and implementation of AI projects is essential. This includes providing technical guidance, offering aid to your team members, understanding the business side of the project, and helping team members understand the technical aspects. Equally important is having an effective change management process in place and a continuous feedback loop with stakeholders.

    How to Communicate the Benefits of AI

    As an AI engineer, one of your key responsibilities is to explain to the less tech-savvy members of the team how AI can benefit the business. This involves identifying opportunities in the AI landscape that align with the goals of the business, conducting feasibility assessments of potential projects, and clearly communicating the associated costs. Prototyping an AI solution is an excellent way to showcase its potential benefits.

    The Engineer’s Playbook

    The Engineer's Playbook offers strategies to enhance communication about AI projects in a way that non-engineering professionals can understand. These include simplifying technical jargon, using storytelling to elucidate complex ideas, and using visuals to aid understanding.

    The playbook also presents several tools that could promote the general understanding of AI in the team, including seminars, tutorials and workshops. Properly utilized, these tools can help lay the groundwork for a more welcoming environment for AI implementation.

    Owning the Title of AI expert

    Being constantly up-to-date with the latest trends and tools in the AI sector empowers you to lead AI projects more effectively. It allows you to apply what you've learned to the problems your company faces and drive innovation within the company.


    Promoting AI in your organization is a layered process. You must present yourself as a technical advocate, build on teamwork, show innovation, constantly learn and stay aware of the business needs of your company. By mastering these, you will not only become a powerful AI advocate in your company but also help drive its success in the long run.

    To achieve these, consider joining a company that fosters your skills, like Script that is currently seeking experts in several positions.

    Video Transcription

    So welcome to this talk. This is AI Advocacy in engineering, AKA Translating Tech into Business. This is a rough overview of what I'm gonna talk about today.Let's get into some of the details about this, though. So the first thing we're gonna talk about is how be an actual advocate as an engineer for your AI projects within your company. Then we're gonna get into how to properly communicate the benefits of AI projects. Which can be a little bit difficult when you're trying to translate, like, technical things into more business strategy, but there's actually really powerful ways to do it. And then I'm gonna dive into what I'm calling the engineers playbook, which will give, practical tools and strategies to help you, communicate better and we'll look at some case studies that have shown that AI practice can be very successful.

    And lastly, we're gonna look at how you can own the expert title of being, an AI expert at your me. So let's start with the first section on the AI advocacy. So there's 3 main areas that you can work with to become an AI advocate at your company. The first pillar is under the technical guidance. So you bring the technical expertise as an engineer. You can drive innovation. You can provide education and training. And then the second one would be I know there's a lot of, catches and risks and things that come with using AI. So you wanna make sure that you're providing a good, safe framework to work within it. And use it. And the last one is teamwork. You need to be able to work with other people at your company to make these projects happen.

    And it's very important that you play a role in So start with the first one, technical. You really wanna be the technical expert bringing, the AI like features and capabilities to life. You wanna make sure that you're highlighting not just the beautiful things it can bring, but also sort of some of the limitations that it can bring as well. You wanna be very transparent about that so that we can all work together to have a better understanding of how it can support the business. You wanna be a driver of innovation. There's a lot of, like, really cool stuff happening in the AI space constantly all the time always, and it's amazing to see. So bringing that into your company can be huge. It can make you know, make or break the next project that can be incredible.

    And lastly, you wanna bring education and training. I'll get into it a bit later about how you can, like, provide this for your company, but being able to do that is gonna be huge. So for governance, You wanna talk about the ethics of AI because it can be a little bit difficult. Sometimes to navigate these, you wanna work with other folks in your company as well to make sure that you're aligning with the the values of your company. You wanna be able to assess any risks, or, be aware of any biases that may be showing up in AI systems that you're building or using. You may not have the perfect solution for it, but at least bringing them to light can really make or break the difference between your project being successful.

    And you wanna be able to help alongside, your project with some policies around it. So you wanna be you wanna, like, help generate what best practices you can have, setting standards, that kind of stuff. And the last thing we wanna talk about is teamwork. So if you are working at a company as an engineer, you are most likely not working alone. You've probably got some other folks that you're working with, and it's important to work alongside them and, help them and have them help you. It's a it's a very symbiotic kind of thing. So you wanna make sure that everyone understands the engineering side and that you can work with them to understand the business sides as well.

    Change management can be really difficult. You wanna make sure that you're putting, good, like, guidelines and frameworks in place and steps to help people adapt to, like, new AI tools and processes, and you wanna have a continuous feedback loop as well. So you wanna check-in with stakeholders every once in a while. You wanna be able to check-in with users And, like, whatever you're using your project forward, you need to get feedback about it so that you understand how you can make it better. Or, if it's working perfectly, great. You can move on and do something else. So I'm gonna now talk about how you can be a liaison to the business, using So being able to communicate the benefits of AI is really, really critical.

    As engineers, we kind of understand right away that AI is very cool and powerful, but understanding, what that looks like for a business can be a little bit more difficult So you have to be able to sort of translate the really cool things that AI does into stuff like eyes or, how this looks for a user.

    It's, it's kind of a there's kind of an art to it when I get into that a little bit later. But it's very important to be able to do that. You wanna be able to identify the opportunities that come up. So looking at other trends in the AI landscape, and figuring out if any of those align with what your company is doing, any goals that they have, any like, problems that are currently occurring at your company and seeing if you can maybe use AI that you've seen at other places to fill in that gap and help give you an advantage.

    You can do a feasibility assessment. It's very important. You may see something in the AI landscape and be like, oh my goodness. I can't wait to bring this to my company and then you start getting into the the meat and potatoes of it. And it's way more difficult than you thought, and maybe it's not so feasible anymore. Or maybe you see something that you think is really difficult and then you start to get into it and you're like, wow, this is super easy. All I need to do is xyzandboomporton. And that's that's a good thing. Either way, it's good to know, and it's good to be able to communicate that to your stakeholders.

    Being able to speak about the cost of something is very important because a lot of these AI tools are not free, unfortunately. It's important to be able to say, we think it's gonna cost this much or based on, I don't know, the pricing model or based on this other company implementing this. We've seen that it costs this much. So being able to communicate that to your stakeholders will go a long way. So there's no shock value when they get the bill. And being able to prototype stuff is very important. Early in my career, I was given some advice, which was basically nothing speaks louder than working software. So if you can prototype something really quickly, even if it's using, like, fake data and prebuilt tools, That's perfect.

    Just to get a proof of concept in front of people's face so that they understand what you're trying to build and what you're trying to show. So we're gonna talk about the playbook now, which has some great tips and tools, and strategies for how you can get your get your point made with why AI projects are really cool. So the first topic is around strategies. Then tools, then the case studies. So the strategies really important is to simplify technical jargon. When I first learned about Vector embeddings, I would not shut up about them, but nobody really understood what I was talking about or cared. So being able to turn that into something like it makes lookups really easy and stuff go fast. It was much easier for a lot of the business minded folks to understand.

    Things like that, just translating a little bit or turning it into, if we if we build this little thing, Here's the output of what we get. It's just simplifying it into sort of, like, action and then output makes it much easier for people to understand storytelling is huge when communicating to other people. It's much easier for someone to, like, buy into an idea if you can place yourself in the shoes of, like, the person who made whatever your AI project is. It's much easier to, like, visualize it, to understand it, to get the empathy for it, and to to, get a real sense and feel for why it's an important thing to go and, you know, build this cool AI project. And lastly, visual aids will go a long way. There's a lot of people that are visual learners and people just tend to absorb information visually. I mean, I'm presenting slides. So, like, you kinda get it.

    It's much easier to see something than it is to just talk about it and sort of, like, conceptually visualize it. It's much easier to have something in front of you. So if you're able to make that happen that's incredible or maybe talk to, I don't know, a designer at your company or, you know, even just wireframe something, write it on a piece of paper and take a picture of it. Like, just doing those little things will go a long way for sure. Some tools that you can use. These ones, have worked really well for me personally. Doing seminars, like, just showing up and talking to people. Like, you can even pre record something and just play it in front of your company. In front of folks, you know, it's like a Friday afternoon or a lunchtime or something, people can get a sense of your excitement and your commitment to it.

    And it'll start to, you know, spin the wheels and get the cogs going, for their own ideas about innovation with AI projects. Tutorials very, very useful because you can, sort of follow stuff along step by step. It's a lot of fun to build tutorials as well because, like, you learn new stuff yourself that you may not have known before you started. It gives people a sense of like Uh-uh. It really makes them feel less going through it themselves. Workshops are another great way to get people to start using AI a little bit more hands on so that they're getting, like, more of a firsthand account of it. It can be a little intimidating if if you've never used any of those tools before.

    If you've never, like, thought about those kinds of projects, it can be very it can be like a big steep hill to climb. But if you get people into a workshop where they're just playing with something that you've put in front of them, it can be something really simple like a chatbot or, you know, it doesn't need to be it doesn't need to be huge, but just getting people to try it out will really, really help getting your idea across.

    I highly recommend a workshop. So the first case study I wanted to highlight is around getting a lot of data and then using AI to predict an optimal solution. So in this one, It's a these are kind of obstructed scenarios. I didn't wanna put, like, names and stuff in them, but There's a major construction company, and they had, project scheduling and resource allocation issues. There will be budgets, getting blown apart and there would be delays all over the place. So they decided to turn to AI. They took all of the data that they had about previous projects that they had done and all of the resources that they used to make those projects happen. And, like, what the cost of them was, what the success of them was, that kind of data.

    And then they used AI tools to use, to get you like, optimal amount of resources and time for the projects that they had. So it made it so much easier for this company to have their purchase completed on time and without that extra amount of resources being waste at the end of a project. So that was a very cool implementation case. I I love this one. A second one, There's a software project development company that struggled with communicating with different time zones. This is very common, I think, today in, like, our remote world where people are working on different coasts, different continents. It's a pretty big world. So sometimes it can be hard to communicate and, like, find the right slices of time where everybody can get into the same meeting. This project used an AI powered chatbot that people could just talk to and query because a lot of the information that people were trying to communicate to each other was actually very easy to provide asynchronously as in you could just, like, write it up and then paste it somewhere.

    And then people could go read it. But using, like, an AI powered chatbot to go and have that information ready so that when you ask it questions, it would be able to answer these questions. And then it would sort of I don't know. Make it so that you didn't need to have a meeting at all because you you already had the answers. It's just it's in the chatbot. It's amazing. So even though the team was still distributed, they were able to communicate more effectively. And they actually saw a decrease in meetings, which is fantastic. Because meetings are important, but too many of them. Not the best. Last case study here. There was an IT project that had a lot of delays because of technical challenges and, market changes.

    So the team decided to use predictive analytics to forecast and suggest mitigation strategies. They were able to come under budget because of the risks that they were able to see or four seats. Sorry. And then they didn't have those delays before we proceed forward. So the Last big topic I wanna talk about is owning the expert title as the AI representative in your company. If you are able to combine communication skills along with the technical know how to do stuff and translate that all into a way that business professionals can understand. It's Oh, so valuable. So so so important.

    The best way that you can stay relevant is to just continually learn about what the trends and tools are in the AI space. There's all kinds of aggregate blog sites and, courses you can take. There's so many things, so many ways that you can you can even use ai to teach yourself about more AI if you really want to. And then you wanna apply, like, lateral thinking or just seeing patterns in the AI space that you can then apply to your own company or business. This is where you can drive a lot of innovation and what is happening with your, users' issues or, company's goals. You can apply what you've learned, in in, like, the AI space to these problems. And then just, yeah, is making sure that you're working on AI projects.

    You're contributing to the design, contributing to the development of it, contributing to the governance, all that good stuff. So in summary, these are the key takeaways for this talk. You wanna make sure that you are acting as the technical advocate for AI projects. So purposefully show off what you can do. You wanna work with your team. Absolutely important for working at a company or business. Make sure that you include all the important stakeholders and keep them as informed as you can. Make sure that you're showing off innovation in a way that they're going to understand by using, like, storytelling, visual aids, that kind of stuff. We reviewed the engineers playbook. So you can use some of the tools I talked about, like workshops, seminars, There's a third one, and I forgot what it was. Man, I wrote these slides myself. I pushed up 7 hours and tutorials. I forget. But keep yourself empowered to keep others empowered. It's very important.

    And making sure that you're always relating all of this stuff to your business's needs because as cool as a lot of technical things are, they don't always apply to your business specifically. So you need to kind of, like, pick and choose the ones that are gonna be important so that you can get the right buy in. And then just being an expert in AI, you don't need to, like, have fancy accreditation or anything like that. But staying on top of the latest technology tools trends, will help a long way. Thank you very much for attending this talk. It's been fun. I hope you have a great day. The company I work for script. We're hiring for multiple positions. So you can check out our careers page by scanning that QR code, or typing in this link.

    I don't I don't know how to share slides beyond what I'm sharing here. So, you could probably put it at yourself, but, yeah, give it a give it a look, see if there's anything that too.