Designing a Technical Workforce That Adapts at the Speed of AI - with Carrol Chang of Andela

Daniel Faggella (0:12)

Welcome everyone to the Emerge AI in Business Podcast. Today's guest is Carrol Chang, Chief Executive Officer at Andela. Andela connects global technologists with Fortune 500 companies to help them build high-performing engineering teams for the AI era. In today's episode, Carrol discusses how AI is changing what technical talent needs to look like and why job titles alone no longer describe what a role actually requires. She walks through how leaders can redesign hiring and assessment to focus on judgment and problem solving, the skills that matter most once AI handles more of the hands-on technical work, and how mapping a workforce by task instead of title helps teams to stay flexible as roles keep changing. Today's episode is sponsored by Andela. If you offer AI products or services into the enterprise, you need to find enterprise leaders with relevance and readiness. Emerj attracts VP and higher-ranking enterprise audiences who are already convinced that they need to move beyond traditional IT. To learn the exact strategies we use to help leading AI brands and startups connect with their ideal enterprise AI buyers, visit emerj.com-ad1. That's emerj.com/a-d-1.

Now the conversation with Carrol.

Carrol, welcome to the show.

Carrol Chang (1:46)

Thank you for having me, Yolande.

Daniel Faggella (1:48)

I'm going to start with something that is probably very simple, but just so that our audience members get where we're coming from with this conversation. I think job titles used to tell us a lot about a person's job and their skills and what they were expected to do. But currently, I'm not so sure that it still works like that. I think a role can look totally different than it did two years ago. And same title, very different job description at the moment. And I think the reality of that is that most companies haven't realized that. You're still hiring and judging people the old way, thinking this was your title before at your previous company, so you would be a perfect fit for the title here too.

I want to hear you get your opinion on that, but also figuring out how is AI reshaping technical talent needs, role expectations, hiring trends, those things.

Carrol Chang (2:37)

This is a great question. To understand what is going on with AI and how big this shift is in knowledge work, I think that we should look at software engineering. And that's because software engineering is the first profession that AI has radically changed.

AI will do this to probably every other field of knowledge work, but the first one that it has done it for is software engineering. And software engineering is the profession and the job that Andela has specialized in for our entire decade plus history. And so we see and we feel this more so than a company that might work in another field. And so what's going on is that you can have a job title, let's say a full stack software engineer, and that job title alone is not going to tell you what that person is expected to do.

Right now, there are certain engineers who have led the pack. They are super AI fluent. They are early adopters and they are fully agentically coding. So what that means is that rather than handwriting code, writing every character, what they are doing now is they are giving directions and guidance and guardrails and some context to AI, and then coding agents are executing the work. Agents are writing the code, the person is not anymore. And then the engineer will go and review the outputs, give some feedback, ask the AI to give it another try, and that'll go back and forth a little while until there's finally a finished product that can be shipped into production. It can become live. And that is how some people are doing the job today, because AI has enabled that. The old version of doing software engineering is that you handwrite all the code.

It's almost this artisan craft of creating your work product. And there is a lot of expertise that has to have built in doing that. There's a lot of pride in that too. There's a lot of expertise and know-how.

But AI came onto the scene, and most people in the world know AI for, you know, I could ask AI a question about my vacation planning or, you know, some questions I have about, you know, what's going on in the world. But for actual knowledge work and for jobs like software engineering, AI is now capable of doing the job. And it's capable of doing the job not at a senior level and not at a perfect level, but it's capable of doing the job at a breathtaking level of competency that you would have typically thought of an entry-level worker needing to learn. And because of that, now, if you just see this job title, full stack, software engineer, and you don't know whether the expectations are, we want you to do this job fully identically and we want you to be totally fluent in knowing how to direct AI to do your job, versus we want you to be not using AI at all. I actually don't know any engineering leader who is on that end of the spectrum anymore. But actually where most of the world is right now is somewhere in the middle.