A career in AI is often described as a destination where you learn tools, build a portfolio, and land a job. However, this view overlooks what is actually happening inside the company. Rather than creating a single new career path, AI is reshaping how work gets done.
People aren’t breaking into AI by starting over. They layer it on top of the work they’re already doing, whether it’s engineering, marketing, operations, or product, and use it to drive better outcomes. As a result, the definition of an “AI career” is also changing.
For years, the path to a career in AI was relatively narrow. It points to highly specialized roles such as machine learning engineers, data scientists, and researchers, which often require advanced degrees and deep technical expertise.
As the field advances, that model is starting to break down.
What does a career in AI mean today?
As AI moves beyond experimentation and into everyday workflows, it is increasingly being treated as a layer throughout an organization rather than a standalone feature. No longer limited to a small group of experts, AI is impacting the way teams build products, analyze data, and make decisions.
This shift is changing what it means to work in AI. Rather than following a single, well-defined path, many people enter the field by building on what they’re already doing, increasing their own value in the process.
Sumit Agarwal, deputy analyst at research and advisory firm Gartner, said this shift is forcing professionals to expand their skill sets, and as AI becomes part of daily work, even technical roles will require business understanding in addition to technical expertise.
This shift is also changing how value is defined in the role of AI.
“The market rewards applied ability, not theoretical knowledge. If you can show impact within your capabilities, you’re already in the field,” said Judah Phillips, chief AI and product officer at Market Holdings and co-founder of Squark AI, a no-code AI platform that Market Holdings acquired in 2024.
As companies move their AI efforts from experimentation to execution, applied capabilities are more important than credentials. The focus is shifting to results. It’s not about who knows the tools best, it’s about who can use AI to achieve measurable outcomes.
This shift is also reflected in the way analysts define the role of AI. Josh Builta, senior director of enterprise platforms and applications at research advisory firm Omdia, a division of Informa TechTarget, says AI is being incorporated beyond traditional roles. Professionals in fields such as software development, product and business analytics are increasingly using AI to enhance their workflows and decision-making, he said.
As a result, hiring practices are also changing. Rather than focusing on titles and qualifications, companies are looking for people who can apply AI to their daily work.
“They are looking for a combination of technical AI skills such as programming, data analysis, and machine learning, along with basic AI literacy and the ability to effectively apply these tools in a business setting,” said Greg Fuller, vice president of Codeacademy at Skillsoft, an interactive online learning academy.
However, technical ability alone is not enough. Employers are also placing greater emphasis on skills such as critical thinking, communication, collaboration, and ethical awareness, especially as AI plays a growing role in decision-making.
“AI-driven roles are constantly evolving and require continuous upskilling, so the most sought-after candidates are those who demonstrate adaptability and a commitment to continuous learning,” Fuller explained.
Real-world entry routes: How professionals can get started with AI
Currently, there is no one path to AI. Many professionals are starting by incorporating AI into existing jobs rather than defined roles.
Most people treat AI like a tool or a certification race when the results actually matter.
judah phillipsChief AI and Product Officer at Market Holdings and Co-Founder of Squark AI
Analysts are moving from descriptive to predictive insights. Engineers are building AI-enabled features into existing products. Operations and business teams use models to inform decisions and improve the way they work.
It’s more about expanding your career than changing it. “The best bet is to do what you’re already doing,” Phillips says. “People aren’t breaking into AI by starting over. They’re overlaying AI onto their current roles and increasing their own value.”
In this way, AI is becoming established within enterprises. It’s easier than ever to get started, but expectations have changed. It’s not about trying the tool, it’s about showing what the tool does.
This is also where many aspiring practitioners encounter challenges. “Most people treat AI like a tool or a certification race, when it’s actually results-oriented,” Phillips says. “We tend to stay at the demo level, which means building things that look impressive but aren’t actually used.”
The gap is often between experimentation and impact, he added. Building a model or prototype is only part of the process. What sets effective practitioners apart is their ability to connect their work to real business outcomes. They start by applying AI to existing work, such as improving campaign performance, streamlining work, and enhancing analytics, and gradually move on to take on more advanced responsibilities. Even within an existing role, the actual application is often more important than formal credentials or independent projects.
As companies expand their AI efforts, many are struggling to find people who not only have the technical expertise, but can apply AI to solve real-world business problems, Birta said. A common misconception is that AI’s role is primarily model building, he added. Equally important is the ability to link AI capabilities to specific business outcomes.
“Experts who can bridge the gap between technical capabilities and practical applications of AI are essential,” Builta explained. “To do that, these professionals need to understand the nuances and challenges of the industries they work in and be able to demonstrate how AI can address them.”
How leaders can develop and support AI talent
As more employees leverage AI in their daily work, companies are rethinking how they build and support that capability internally.
According to Randall Hunt, CTO of cloud services company Caylent, one common mistake is treating AI as a purely technical endeavor. “One of the biggest mistakes organizations make is treating AI adoption as a technical effort rather than an organizational effort,” he said.
In practice, this means extending AI beyond engineering to teams such as operations, finance, sales, and human resources. Keyrent has given employees different paths to using AI. This started with basic training for everyone and expanded to business and technical role tracks.
The results were clear. Adoption was fastest among non-technical teams when tools and training aligned with the way work was done. This broader approach shows how companies are starting to think differently about AI talent. Rather than relying on a small group of experts, many companies are distributing AI capabilities across teams and integrating them into the way they work.
Organizations that scale AI capabilities fastest are those that invest in internal enablement first.
Randall HuntKeyrent CTO
“There is a trend towards external hiring as a primary AI talent strategy,” Hunt said. “But the organizations that scale their AI capabilities the fastest are those that invest in internal enablement first.”
External hiring still has a role to play, especially when bringing in expertise to support more advanced initiatives. However, there is an increasing focus on enabling existing teams to effectively deploy and apply AI.
Gartner’s Agarwal said companies are taking a more balanced approach, investing in upskilling existing employees while selectively hiring external talent to support more advanced initiatives. Agarwal said a recent Gartner survey found that only 20% of executives believe their workforce is AI-ready, and 62% said their organizations lack the technology and talent needed to fully support AI implementation.
The bigger challenge comes after the initial experiment. Many companies have begun piloting AI, but few have succeeded in moving their efforts into production. “The gap between pilot success and production systems is where most AI initiatives quietly stall,” Hunt says.
Bridging this gap requires more than technical skills. It comes down to governance, a clear definition of success, and ensuring that AI is part of a real-world workflow and not just a standalone experiment.
Practical takeaways: How to get started with AI
For individuals and businesses alike, AI is changing what it takes to get started.
The method that works best for an individual is often the one that is most practical. That means starting with the work you’re already doing. Rather than just focusing on tools and certifications, the goal should be to identify where AI can improve existing processes, decisions, and outcomes. That could mean using AI to handle routine tasks, improve analytics, or build simple models to support daily tasks.
What matters is not the complexity of the AI use case, but whether the AI delivers value. “You have to close the loop from idea to result,” Phillips says. “The difference is not in intelligence but in execution.”
This means working with real data, testing ideas in real environments, and measuring effectiveness. The most meaningful learnings often occur when you move beyond prototype to production, even on a small scale.
For companies, the focus is on integrating AI into the way teams work, rather than building standalone AI teams. It starts with identifying where AI can have the most immediate impact and giving teams the tools, data, and support to experiment and build. It also means providing people with more than formal training. It’s about getting the job done, trying things out, collaborating with other teams, and applying AI to real-world workflows.
The most advanced organizations treat AI as a core capability that drives business outcomes, rather than an ancillary initiative. Building a career in AI and building a team to support AI is ultimately based on the same principles. That means connecting technology to real jobs and showing where it makes a difference.
Liz Hughes is an award-winning editor and writer covering AI and emerging technologies and a former magazine editor. AI business and Today’s IoT world.
