Why Every Company Is About to Hire an AI Manager (No Coding Required)

My thing is, the major job that's going to rule the market in the next two years is AI managers, like I said, like you said. So imagine this. A company built an AI system to help them hire faster. Applications were processed in seconds.

Hundreds of candidates screened automatically. It seemed to work. Then someone looked closer and realized that the system had been rejecting applicants based on race and demographics. not because anyone specifically programmed it to, but because nobody was watching it closely enough to notice.

And once you start learning guardrails, once you know what it is trained with and what happened with the AI, that's when you, like I said, right, you understood how many of us, but the moment I'm answering these questions, why are you having bias towards these people? So that's Sid Hardha Vangala of MassTech's Advanced Technologies. He's one of the new kind of professionals being hired specifically to make sure situations like that don't happen. They go by a few different names, but the one that's actually sticking the most is the term AI manager.

And if you're thinking about getting into this role, then you stumbled across the right video. 88% of organizations are expected to use AI this year. Most of them are buying tools before they understand how to even run them. That gap needs someone to fill it.

So there's a growing demand for this and that someone is starting to get a job title. But nobody handed the first people in this role a playbook. They basically just stepped in, figured it out. And in a lot of cases they still figuring it out We talked to two of them What they told us paints a clear picture of what this job actually involves and what skills you need to succeed in it I was hired He was telling me, Luis, we are struggling with AI because we don't know where to start.

We don't know what to do. Clients are asking for AI. Luis Escalante is an AI delivery manager at Guerrilla Logic. When he first stepped into this role, even his own company wasn't sure what it would look like.

But he quickly learned that while his technical skills would be valuable, his communication skills were even more vital in guiding companies and understanding what problem they are looking to solve before implementing AI. I'm here not to develop anything in terms of technology. It's more oriented to, you know, trying to diagnose before developing or deploying something with AI. Sid Bengala at MassTech describes the same gap from the engineering side.

Right now, how it is, is, you know, an engineer would know how to use an AI and the business would know, oh, we need AI. But then the missing part is where and how do we use it? That's where I come in. That middle layer between what a company wants and what AI can actually do, that's the job.

Not building the models, not selling the tools. It's closing that gap between intention and reality So what skill actually gets you there I think that the most important question to ask because actually it not related to AI at all It's about consultancy services. If you are good providing consultancy to others, you'll be able to identify where to start because that's the most important point that you can actually work with the different clients or in your company. it's to understand what is truly happening.

Because if you skip the diagnosis and go straight to deployment, you already heard what happens. Sid frames it as a simple decision filter. He runs every time someone says they need AI. Again, they wanted to incorporate AI where the workflows of theirs would make it easy.

Again, like I said, we identify where AI would fit in and what would be the correct use case for that AI in their thing. We would totally be honest. like, hey, I don't think you need any AI. That willingness to say no is not a weakness in this role.

It's what makes you credible, especially in a fragmented market where someone will offer you an AI solution you don't even need. Which brings us back to where we started. The bias story isn't an edge case. It's what happens when AI runs in a live environment without someone actively owning the guardrails.

The actual problem arises once we start incorporating it into what we do day to day when you need governance, when you need guardrails, when you need human oversight over what happening So that when you know AI starts behaving differently It understanding that governance isn the glamorous part of the job but it the part that separates the people who are managing AI from the people who are just watching it run. One of the hardest questions in this field is also one of the most important.

How do you know if it's working? How do you measure the value? Lewis thinks about ROI differently than most. We are talking about, for example, productivity gains.

That's important if we are talking about ROI with AI. How much savings am I having with automation, not only in terms of money. How much work are we getting done? Is it 2x or is it at least 1.

5x? The outcome, the deliverables that we are doing. The ability to prove impact, not just demonstrate potential, is what keeps this role trusted and growing. The people getting in now, while the role is still being defined, are the ones who will set the standard for what it looks like.

The skills that I mentioned, besides the technical part of AI, are actually in your job description as a consultant, or as you mentioned, delivery manager and other similar roles, because the first thing that you need to do is to ask questions. If you have the people skills, the judgment to ask the right questions, and the discipline to measure what actually matters, this role might just be for you.