The race to adopt AI has created an unusual dynamic in the technology job market: some of the most valuable experience isn’t coming from enterprise deployments, but from professionals experimenting with new technologies before their organizations formally approve, purchase or standardize them.
With companies looking to move from AI pilots to production deployments, hiring managers are turning to IT pros who understand how to integrate AI into business processes, connect it to enterprise data and manage the operational realities that come with deploying it at scale.
That shift is changing how technology professionals think about career development.
“Because AI and automation is evolving rapidly, professionals who proactively develop skills ahead of the enterprise standardization are going to be rewarded because they are able to understand their potential and their limitations,” says Iris Adae, vice president of data and analytics at KNIME.
She points out that proactive learning encourages individuals to be curious, ask questions, and be invested in learning more.
According to Skillsoft’s Workforce Readiness Report, only 16% of employees say they receive training before new AI tools are rolled out, so in many cases professionals are building readiness before the organization has fully caught up.
Greg Fuller, vice president, technology skills suite, Skillsoft, says the pace of change is too fast for people to wait until every tool, workflow, and standard is fully settled.
“The professionals getting ahead are building capability in advance, especially skills that travel across platforms and use cases,” he says.
He adds the key is not chasing every new tool that appears--it is building transferable skills that stay valuable even when the tools change.
Successful Execution is the Differentiator
The trend extends beyond AI to cloud platforms, automation tools and infrastructure technologies, which are evolving quickly enough that waiting for a formal corporate training program may leave workers a step behind.
“Technology often moves faster than the formal adoption process inside a company,” says John Kim, AI engineer at Solidroad. “By the time a tool has gone through procurement, security review, policy writing, and training, the underlying technology may already be influencing how people work.”
Fuller notes the Skillsoft survey found just 11% of employees reported formal skills assessments or benchmarks.
“This means many companies are still making talent decisions without a clear picture of what people can actually do,” he says.
However, employers aren’t rewarding people simply for using the newest tools—execution is where the real value is.
Many organizations have employees who understand generative AI concepts, but fewer have employees who understand how to make those systems work reliably inside a business environment.
According to Adae, that requires a combination of technical and operational skills that extend well beyond prompting models.
“Successful practitioners are able to understand AI data readiness, workflow design, systems integration or access controls,” she says. “They know how to connect AI systems to trusted sources of data and are able to maintain auditability to make sure outputs can be trusted by the people that are using them.”
Focusing on Practical Experimentation
Kim argues that practical experimentation is often the fastest way to develop that understanding.
“My approach is usually to start small and get hands-on quickly,” he says. “I do not learn much from only reading product announcements or watching demos, because those rarely show where a system breaks.”
Testing tools against realistic scenarios often reveals limitations that aren’t obvious in vendor presentations.
“Getting a good answer once is easy,” Kim says. “The harder and more interesting part is understanding why it works, where it fails, and how much you can actually trust the output.”
There is also a growing recognition that employees experimenting with emerging technologies can create value for organizations long before a formal rollout begins.
Adae says early adopters often bring lessons that companies would otherwise spend months learning through trial and error. They also tend to develop habits of experimentation that remain valuable as technologies change.
“Someone who has already hit a technology’s limits saves the company months of figuring it out the hard way,” she explains. “They walk in with patterns and shortcuts the rest of the company can build on.”
Avoiding the Shadow IT Risk
It’s important to draw a clear line between responsible learning and unsanctioned use of enterprise data.
“There is a clear difference between responsible experimentation and shadow IT,” Kim said. He recommends learning through public datasets, synthetic examples, sandbox environments and approved tools rather than exposing customer information or proprietary data to unapproved AI systems.
The growing demand for AI, automation and cloud skills is also reshaping the role of IT itself.
Organizations increasingly expect technology teams to contribute directly to business performance rather than simply maintain infrastructure.
“IT is moving from being the team that keeps the lights on to being the team that decides which lights to switch on in the first place,” Adae says. “Cloud, automation and AI are what make that possible.”
That transition elevates a skill that is often overlooked in technical training: the ability to connect technology decisions to business outcomes.
“The skill that makes the shift work is translation,” Adae says. “IT people who can listen to a business problem and recognize where automation, AI or a cloud service genuinely helps, and where it would create more risk than value, become partners rather than service providers.”
Balance Emerging Tech with Foundational Skills
For professionals trying to position themselves for the next hiring cycle, it is important to balance emerging technologies with foundational skills. Security, governance, systems architecture and data management remain essential regardless of which AI platforms gain traction.
Ultimately, understanding how technologies work, where they break down and how they can be deployed safely may prove more valuable than becoming an expert in any single platform.
“I would focus on building judgment, not just collecting tools,” Kim says.
Fuller says he recommends starting with foundations that give IT workers the ability to troubleshoot, evaluate outputs, and stay flexible when the tools change, and then building exposure to emerging technologies in a way that keeps knowledge broad, not fragile.
He also advises against locking too tightly into one tool or one vendor too early.
“The better bet is to build the underlying skills that carry across platforms and use cases,” Fuller says. “Titles will change, tools will change, but strong capability tends to compound over time.”
