For the past few years, much of the focus around AI has been centered around the engineers, founders and researchers building it. However, as AI takes on a more autonomous role, companies are discovering that managing the technology is just as vital as integrating it.
According to recent data, 88% of organizations are expected to use AI in 2026, with many opting to purchase agentic tools over developing in-house, and the numbers suggest that vendor solutions are succeeding at twice the rate.
The most forward-thinking AI companies have developed a role that can fall under many names, but can best be described as an AI manager.
Leaders tasked with deciding where AI belongs, what it should actually do, and how to keep it from creating bigger problems than it solves. A role that is much less about building models and more about managing them.
That job is still evolving, which is exactly what makes this moment so interesting for those who are either contemplating a career move or entering the workforce.
With no real playbook for what an AI manager looks like, the first generation stepping into this role is defining the tasks that shape how companies adopt AI in ways that are practical, measurable, safe, and successful.
What an AI Manager is tasked with
When Luis Escalante accepted a position as an AI Delivery Manager at Gorilla Logic, the assignment was not neatly packaged. The need was obvious, but the definition of what to expect was not.
“Clients are asking for AI,” Escalante recalled being told early on. “We are struggling with AI because we don’t know where to start.”
An uncertainty that is becoming a familiar starting point across industries. Companies know the value AI brings and that they should be expected to use it to stay competitive. But still don’t know where it fits, what problem it is solving, or whether they are about to spend heavily on tools that add noise instead of value.
As a result, Escalante described his work as far more diagnostic than developmental. “What I try to explain is that I’m here not to develop anything in terms of technology,” he says. “It’s more oriented to trying to diagnose before developing or deploying something with AI.”
That means running discovery sessions, identifying workflow bottlenecks, assessing how mature a company is in its AI adoption, and figuring out where AI can actually improve outcomes.
Sid Vangala, a senior AI applications developer at Mastic, described the role in similarly practical terms. “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 is critical. Because right now, much of the demand for AI is still driven by pressure more than precision. Leaders know they need a plan, but not always a reason. Many may aim blindly, in hopes the efficiency will follow. What we’ve seen as a result of this approach is that only 1% to 5% of enterprise generative AI projects achieve true measurable value, while the rest fail.
Today, one of the most important things these AI managers do is tell companies where not to use AI.
Vangala gave a simple example. If someone needs to create a support ticket, AI may add little if it is only going to ask the same information a form already requires. But it becomes useful when it can analyze ticket patterns, identify delays that happen, and help teams predict future slowdowns.
“We identify where AI would fit in and what would be the correct use case,” he says. “We would totally be honest. Like, hey, I don’t think you need AI here.”
That honesty may be one of the clearest markers of the role. The job is not to force AI into every process. It is to know where it belongs and where it does not.
The new skills required to lead in an AI-powered workplace
For anyone looking at this field and assuming it is purely technical, both Escalante and Vangala point in a different direction.
The most important skill, Escalante argues, is not coding. It is consultancy.
“If you are good at providing consultancy to others, you’ll be able to identify where to start,” he says. “It’s to understand what is truly happening.”
That means learning how to spot friction, ask better questions, and diagnose the real issue before AI ever enters the picture. A company may think it has an AI problem when it actually has a workflow problem, a communication problem, or a prioritization problem.
He put it bluntly; if you apply AI before understanding the bottleneck, “AI is going to accelerate things. By the end, it’s going to expose what is bad.”
Vangala framed the decision-making process around a different set of questions. “Can this problem be solved? Next question, can I solve the same problem using AI faster?”
That mindset is what separates AI management from AI enthusiasm. It demands judgment, not just technical literacy. A skill that is just as valuable for any type of engineer looking to solve a problem.
There is also the matter of governance. Once AI starts operating in a production environment, especially customer-facing ones, someone has to be responsible for the guardrails. Someone has to think about bias, privacy, oversight, hallucinations, performance, and cost.
“You need governance when you need guardrails, when you need human oversight over what’s happening,” Vangala says.
That makes the role part strategist, part operator, and part risk manager. It also explains why both emphasized that understanding AI is only one half of the equation. The other half is knowing how to integrate it responsibly into the real world, where mistakes are expensive and sometimes public.
Why roles like this are likely to become common across companies adopting AI
If the current phase of AI adoption has taught companies anything, it is that buying tools is not the same as using them well.
That gap is what will continue to drive the demand for roles like this. As AI becomesbecome more deeply embedded into operational workflows, companies will need people who can evaluate its impact, guide adoption, coach internal teams, and prove that it is delivering more than a flashy demo.
In some cases, both Escalante and Vangala have seen companies create these roles internally, understanding the value that they both bring not only during the beginning and end of the process, but also after the onboarding process is completed.
That includes measuring ROI, which both describe as one of the most challenging and most important parts of the job. Businesses are not just asking whether AI works. They are asking whether it saves time, improves productivity, and justifies the infrastructure costs that come with it.
For Vangala, the threshold is practical. “If it is like 1.5x, that is when it would be feasible for businesses to go forward.”
The title itself may evolve. The responsibility certainly will. But the underlying need is already here.
In some ways, the first generation of AI managers is doing the work that the broader AI boom initially skilled over. Not selling the fantasy or just building the technology. But translating it into something useful, governable, and measurable inside an actual team.
For professionals considering this field, that may be the clearest takeaway.
