AI success depends on human skills like adaptability, judgment and collaboration -- not just technical expertise. Organizations must rethink hiring to find talent that can evolve.
By
Erin RushmanGuest Contributor
Published: 24 Sep 2026
AI is shifting the value balance between technical and human skills. That changes what organizations should look for when they hire, develop and promote AI talent.
Many companies, however, are judging a fast-moving field with a static definition of expertise. They're still using credentials and fixed technical specialization to measure capability in a discipline where the tools, workflows and knowledge required can change in months.
While technical expertise remains important, it is no longer enough on its own. These days, lasting value is created by professionals who can learn quickly, navigate ambiguity, communicate clearly and help others adapt.
As AI lowers the barriers between specialized functions, soft skills are becoming the new markers of success. And AI transformation is becoming a people-centered effort as much as a technology initiative.
That's why organizations that prioritize finding employees who demonstrate adaptability, sound judgment and collaboration will be better equipped to translate AI's potential into sustainable business value.
Adaptability is becoming more valuable than static expertise
The most effective AI professionals are not necessarily those with the longest list of credentials. They are the people who remain curious when the technology changes, open-minded when an approach fails and willing to revise their thinking as new information emerges.
Why? Because AI projects rarely move from experiment to implementation in a straight line. Teams discover limitations, change tools, revise assumptions and sometimes abandon an approach entirely.
Experience still matters. But in a technology environment that changes faster than job descriptions can keep up, the ability to acquire new expertise might matter more than the expertise someone already has.
I have seen this in practical delivery environments. During one daily scrum, a developer acknowledged that an AI prompt was not producing the expected result and asked the team for feedback. Rather than defending the original approach, the developer adjusted quickly based on the group's input.
That's the kind of behavior organizations should reward. Innovation depends on experimentation, and experimentation requires a willingness to be wrong without becoming defensive.
Judgment matters in ambiguous environments
Organizations are still determining where AI can create value, where it introduces risk and where human oversight remains essential. Teams must often make decisions without complete information or established precedent.
This doesn't mean passively accepting uncertainty. It means knowing how to ask better questions, identify weak assumptions and make thoughtful progress without manufacturing false certainty.
During a digital transformation initiative, for example, a client provided research intended to guide the project's strategy. After reviewing the information, the team determined it wasn't sufficiently reliable to support the decisions ahead.
Rather than proceed on a weak foundation, the project manager initiated a difficult conversation with the client, reassessed the inputs and reset the approach. That decision required judgment and confidence. It also protected the project from producing a polished answer to the wrong question.
The strongest AI professionals understand that moving quickly does not always mean rushing forward. Sometimes the most valuable contribution is knowing when to pause, challenge the premise and establish a stronger foundation for the work.
AI adoption depends on experimentation. But experimentation only works when people feel safe enough to test ideas, raise concerns and learn from failure.
Even high-performing teams don't get everything right on the first attempt. They succeed because people can share incomplete ideas, invite feedback and improve their work collaboratively. Iteration is treated as part of the process rather than evidence that someone has failed.
This was evident on a team developing AI prompts for tools intended to simplify video editing. The prompts improved through repeated collaboration and refinement. Because team members were encouraged to ask questions and pressure-test ideas, they could identify problems earlier and develop better solutions faster.
Psychological safety does not happen by accident. It must be reinforced through leadership behaviors, recognition and team norms. Organizations should value the people who build trust, listen carefully and create space for others to contribute, not just those who deliver the most visible final output.
When employees feel safe, they learn faster. When they learn faster, the organization becomes more adaptable.
Rethinking how we hire
What is the key to hiring for AI capabilities? Evaluate potential as carefully as experience.
Situational interviews can reveal how candidates respond when the work becomes difficult or uncertain. Asking someone to describe a time they received challenging feedback, for example, can uncover whether they seek input, experiment with new approaches and apply what they learn.
Communication is equally important. AI professionals must translate technical possibilities into business outcomes, explain tradeoffs and build alignment among stakeholders with different levels of technical fluency.
Strong candidates often demonstrate humility, curiosity and clarity. They can acknowledge what they do not know, explain what they do know without unnecessary jargon and remain engaged when challenged.
An AI talent evaluation framework can help organizations find people with the right skills for the current age by asking questions such as:
Adaptability. Is this candidate willing to change their approach when evidence changes?
Judgment. Does this candidate know when not to use AI?
Communication. Can this candidate translate technical capability into measurable business impact?
Intellectual humility. Can this candidate acknowledge uncertainty?
Collaboration. Will this candidate improve their ideas through feedback?
The goal is not to minimize technical expertise. It is to evaluate technical capability alongside the human qualities that determine whether that expertise can be applied effectively.
The future of AI belongs to adaptable teams
AI will continue to reshape how organizations operate, compete and create value. But technology alone will not determine which companies succeed.
Technical expertise will always matter. But in AI transformation, human capability is what turns potential into performance.
Success will depend on building teams that can learn faster than the environment changes. It will pivot on hiring people who are adaptable enough to evolve, thoughtful enough to navigate uncertainty and collaborative enough to create the trust innovation requires to create business outcomes for our clients.
Technical expertise will always matter. But in AI transformation, human capability is what turns potential into performance. That's why organizations that can turn technical capability into good decisions will be best positioned to make the most of the technology.
Erin Rushman is the vice president of strategy, experience and process automation atPerficient. With decades of experience working at the intersection of technology and business, Erin is passionate about creating transformative value for clients by aligning people, process and technology to business strategies and objectives. She has deep expertise across CX, digital marketing and organizational alignment of people, processes and technology. Named a Crain's Notable Business Leader in 2026, Erin leads Perficient's consulting teams and operations, and is a steward of Perficient's award-winning company culture.
