The “tech vs. non-tech” label oversimplifies diverse skill sets and is counterproductive
This habit of splitting people into “tech” and “non-tech” is lazy thinking. It’s inaccurate and doesn’t reflect how businesses actually operate. You don’t build enduring companies by marginalizing half the intelligence in the room. This division oversimplifies diverse capabilities across roles, cutting out people who have deep, specialized knowledge in areas that may not traditionally sit within IT or engineering.
In reality, great teams come from cognitive diversity. You want people who can combine software intuition with operational insight, or legal acumen with cybersecurity awareness. Categorizing people as either “tech” or “non-tech” hides that value. It’s reductive, and in leadership, reductive thinking leads to missed opportunities.
You’re not building stronger companies by slotting people into fixed categories based on incomplete snapshots of what they know. What matters is whether they’re solving real problems, scaling solutions, learning quickly, and getting the job done.
If you’re in the C-suite, this matters more than you might think. Every time you push strategy based on simple labels, you lose talent flexibility. You lose the chance to match people with stretch roles where they can create asymmetric returns. If your recruiting, team structures, or organization charts over-index on simplistic “tech capability” filtering, you’re probably misallocating talent.
Big outcomes tend to be driven by people who don’t fit conventional labels. Rethink how you internally define expertise. Don’t let your talent evaluation be sloppy just because the language you use is.
There is no universally agreed-upon definition of what qualifies as “technical”
Ask ten executives what it means to be a “technical” person, and you’ll get ten different answers. Coding in Python? Managing cloud infrastructure? Designing electronic circuits? Writing legislation for privacy laws? Troubleshooting a home network? The problem is, these definitions are subjective. So when companies use “technical” as a filter, whether in hiring, performance reviews, or project design, they’re applying different standards at different times. Inconsistent evaluation leads to inconsistent execution.
Worse, measuring people with undefined yardsticks creates instability. Talent doesn’t know what you’re expecting. Decision-makers don’t know what they’re evaluating. The result? Poor hiring decisions, missed internal promotions, and inefficient team assembly.
This ambiguity also feeds insecurity inside your organization. People will opt out of strategic conversations, not because they lack ideas, but because they’ve been convinced they’re not “technical enough” to contribute. That’s not a capability gap. That’s a systems error.
As a leader, your job isn’t to reinforce arbitrary gatekeeping. It’s to make sure the right people are solving the right problems, regardless of how their skill sets are labeled. You need precision in how you define competencies across functions. Push your teams to identify specific capabilities, not assumptions based on role titles or past experience.
Labeling someone “technical” only helps if you can explain what the label means, and why it matters for the business outcome you’re pursuing. If you can’t define it, don’t use it.
Undefined, shifting criteria for technical excellence contribute to impostor syndrome
It doesn’t matter how long someone’s worked in tech or what titles they hold, if the standard for being “technical” keeps shifting, people will always question whether they really belong. When success is judged by vague, moving benchmarks, it’s no surprise professionals, even highly capable ones, start to feel like they’re faking it. This is a widespread, structural issue, not a personal weakness.
Think about how often someone with years of experience feels out of place in a conversation because another person mentions a new AI tool or development stack. It’s not about lacking skill. It’s about a culture that signals, constantly, that being up-to-date on every trend is the only way to be “valid.” That’s not sustainable. That doesn’t scale. And for organizations, it’s a productivity and retention risk.
When teams are full of people questioning their worth, they start opting out of important conversations. They hesitate. They avoid risk. This kills velocity.
As a leader, you want decision-makers confident in their ability to act and staff who aren’t second-guessing their value. Avoid creating structures that drive impostor syndrome by rewarding only the most visible or bleeding-edge expertise. Instead, reward results, contributions, and practical competence.
Ensure that your people know exactly what’s expected of them and how their role connects to outcomes. Clarity is a competitive advantage when the alternative is self-doubt built on undefined standards.
Labeling fosters an “us vs. them” dynamic, impeding collaboration between IT and other departments
Technical teams often default to calling others “non-technical stakeholders”, usually to signal the need to simplify or explain. But this kind of shorthand does damage. Over time, it reinforces organizational silos. You end up with engineering not trusting marketing to handle data, IT hesitant to share automation tools with operations, and security blocking access instead of enabling smart oversight. The rationale? “They’re not technical.”
This isn’t about capability. It’s about control, and fear of accountability. Once that mindset embeds itself, it affects hires, processes, and architecture. You prevent employees from contributing, even when they have the right insight or experience, simply because their title doesn’t match the invisible bar set by IT or engineering. And then you wonder why collaboration feels strained.
Executives need to push back against this gatekeeping. If every function in your company relies on technology to scale, then every function should be seen as capable of contributing to it. If collaboration across departments feels like friction, examine the language your teams are using, and what assumptions sit underneath it.
Process fluidity between business and technical leaders should be non-negotiable. Stop measuring collaboration by who owns the codebase and start measuring it by who contributes to better outcomes.
Categorizing individuals as “tech” or “non-tech” ignores the potential for skills development
People can learn. When you define someone by what they’re not, especially using a vague label like “non-tech”, you limit their trajectory before it starts. This approach severely underestimates talent. Plenty of highly successful professionals, CISOs, cloud architects, AI leads, didn’t begin their careers in technical roles. They built those skills systematically, through exposure, training, and in many cases, by stepping into unknown territory and figuring things out fast.
Technical growth isn’t reserved for early-career engineers or developers. It’s accessible to anyone with the right support and mindset. When you ignore that, you build teams with limited flexibility. You miss the people willing to stretch their scope because they weren’t already experts when they walked in the door. Long term, that limits innovation, because you’re only sourcing problem-solvers from one narrow stream of talent.
Hiring purely on hard skills is an outdated tactic. If you’re focused on futureproofing your teams, look for indicators of curiosity, adaptability, and systems thinking, not just years of experience with a particular toolset. Technical skills change. Mindsets scale.
Executives who optimize for potential can move faster. By accelerating internal mobility and investing in cross-skilling early, you convert more employees into multi-skill contributors, and reduce your dependence on an exhausted hiring pipeline that chases the same over-farmed talent pools everyone else wants.
Technical skills have a limited shelf life due to rapid technological advances
Technical capabilities age quickly. The tools, frameworks, and stacks that were relevant two years ago may already be obsolete today. Cloud platforms change their configurations. Programming trends shift. AI frameworks evolve weekly. When companies treat technical fluency as a fixed milestone, rather than an ongoing commitment, they misread the pace of change.
This is where high-performing teams differentiate. They don’t just know things, they’re ready to unlearn and relearn under pressure. And the individuals who drive that behavior aren’t always the ones with traditional “tech” labels. They’re the ones who treat learning like a muscle, not a credential.
From a leadership standpoint, this matters because your organizational risk increases every time someone clings to outdated practices simply to seem competent. Teams that can’t pivot with the field will fall behind, quietly at first, then all at once.
If your team defines competency by knowledge that expires every 6-12 months, you’re incentivizing the wrong behavior. You need to value velocity of learning, not just historical expertise. That’s what sustains execution in a dynamic field.
Executives should budget for continual upskilling, build pathways for rapid tool adoption, and evaluate staff not by what they’ve already mastered, but by how they adapt when the ground shifts underneath them. That’s your actual competitive variable. Not stack familiarity. Not GitHub stars. Just raw learning agility.
The term “tech person” generalizes and dilutes the nuance of specific technical expertise
“Tech person” is vague. It collapses entire disciplines into a single, ambiguous label. Someone who builds APIs isn’t automatically skilled in threat modeling. A machine learning researcher probably can’t fix your router. Expecting cross-domain competence just because someone is labeled “technical” sets your teams up for confusion and inefficiency.
In most organizations, the range of required technical roles is broad and non-overlapping: developers, cybersecurity analysts, DevOps engineers, data scientists, IT administrators, each thinking and executing very differently. When you generalize outcomes under one label, you dilute clarity. Roles aren’t interchangeable. Teams only scale when responsibilities are clearly defined, skills are aligned to needs, and expectations match expertise.
C-suite leaders should avoid job classification models based on loose definitions. Precision here matters. Job titles should reflect the kind of contribution expected, not a generic skill tier. When you understand this, you hire and staff better. You avoid wasting internal time and reduce miscommunications between leadership and delivery teams.
At scale, clarity of role and technical scope directly improves team performance, and reduces burnout caused by unrealistic expectations. Being technical doesn’t mean knowing everything. It means knowing what matters in your specific mission and going deep where the business needs it most.
Replace generic “tech person” labels with specific, role-based descriptions
Most people labeled “tech” don’t need the label, they need recognition for what they know and do. Describing someone as a “cloud professional,” “security engineer,” or “platform architect” is more actionable than calling them a “tech guy.” It tells you what they’re good at, where they add value, and how they might integrate with other parts of the organization.
This shift in language matters. Specificity reduces assumptions. It builds trust. It also creates more manageable interfaces between teams, because expectations are clearer. You’re not just tasking “someone technical”, you’re partnering with a person who has defined expertise. That speeds up decision-making and lowers communication friction.
As an executive, you want your teams built around precision and accountability. When labels are specific, feedback becomes sharper, evaluations become fairer, and allocation decisions become faster. If you hear the term “tech person” in your organization, challenge it. Ask what that actually means in context, and insist on language that connects skills to real deliverables.
Generic makes people invisible. Specific makes them valuable. Your talent strategy should reflect that in every role profile, project setup, and performance review. Use language that acknowledges depth, not assumptions.
Negative framing, using terms like “not a tech person”, diminishes self-confidence and hinders growth
When people describe themselves, or others, as “not a tech person,” what they’re really doing is placing a ceiling above future growth. That language reinforces self-limiting beliefs, especially inside teams where technology plays a central role. It conditions individuals to define themselves by what they lack, not what they bring. And in business, that’s costly.
This kind of negative framing discourages curiosity. It trains people to disqualify themselves from learning opportunities before they’ve even started. That reduces adaptability across teams, slows innovation, and narrows your internal talent mobility. Most importantly, it affects how employees see their value in the organization. When perception erodes, so does performance.
As a leader, your words influence cultural momentum. If your teams regularly use the framing of “technical” and “non-technical” to gatekeep or self-identify, that signals a deeper issue. You need to push your managers to reinforce strengths, focus on what team members are capable of learning, and eliminate language that encodes professional limits.
You get better results when people step into projects because they feel empowered to contribute, not when they opt out because they’ve been told, directly or indirectly, that they don’t qualify. Avoid defining people by exclusion. You’ll unlock more of their capacity that way.
Broaden your understanding of what qualifies as “technical” to reflect varied expertise
Many roles that aren’t usually placed in the “tech” category require high levels of technical knowledge. Lawyers working in cybersecurity, project managers executing multi-system deployments, or teenagers resolving device issues, these are all technical acts. The problem isn’t people lacking skill; it’s organizations failing to recognize skill unless it fits a narrow mold.
“Technical” simply means applying specialized knowledge toward solving complex problems. By that definition, many people in your company are technical, they just haven’t been framed that way. Expanding this definition creates space for more contributors, more range in hiring, and broader cross-team effectiveness. Restricting it shrinks your operational reach.
C-suite executives should regularly re-examine how definitions like “technical” are used in hiring criteria, performance evaluations, and collaboration processes. The more inclusive and accurate your definition, the wider your field of execution becomes. People will step up when they see their expertise recognized. They’ll engage if they feel credentialed to contribute.
Hellen Patton, Cybersecurity Advisor at Cisco, addressed this directly in her RSA Conference talk, “Are You Technical? Proving Competency in Cyber.” She emphasized that many professionals, especially in cybersecurity, possess strong functional expertise despite lacking hands-on infrastructure skills. Her conclusion was clear: redefine competency in a way that reflects how modern roles actually operate.
Use that insight. Redefine “technical” based on capability and performance, not outdated assumptions or job titles. You’ll expand the boundaries of what your organization can accomplish.
Hire for potential and cultural fit, not just for static technical expertise
Relying exclusively on past technical experience to evaluate talent is a slow path to stagnation. Skills degrade fast in tech, and experience alone isn’t a reliable indicator of future contribution. What actually drives value is a candidate’s ability to adapt, absorb new knowledge quickly, and integrate themselves into a team effectively. Someone fluent in legacy systems won’t help you if they can’t evolve with your platform, or if they resist collaboration.
Hiring based on potential means identifying people with curiosity, adaptability, and a strategic mindset. These are the qualities that convert unknowns into skill. You bring in people who may not check every box of technical experience, but they’ll grow into roles fast and bring a fresh line of thinking. Sometimes, these are career switchers or bootcamp grads. Sometimes they’re internal candidates who’ve demonstrated consistent execution under pressure, just in a different business function.
At the C-level, this approach gives you a competitive hiring advantage. Everyone else is competing for the same small group of “ready-now” experts. If you shift focus toward people with learning velocity and strong cultural alignment, your pipeline becomes deeper, and more diverse.
It also generates long-term retention. People who feel invested in, given space to learn, perform for longer. They’re grateful, loyal, and often exceed expectations because someone gave them a shot. And those bets compound. One high-performing, cross-trained hire opens the door to ten more.
Accept multiple approaches to using technology, rather than judging based on one “right” method
There is no single correct tech stack or architecture path. Engineering, data science, infrastructure, these are all fields where multiple tools, frameworks, and approaches can lead to excellent results. Some organizations use the latest advancements. Others optimize reliability and operational simplicity. Neither approach is “less technical.” It’s a matter of context.
Judging someone based on whether they’re using the newest tool or cleanest implementation misses the point. Outcomes matter more than process. Evaluating rigor, efficiency, and value delivered is more important than obsessing over whether a team is using the same technology as your strongest engineer. Different problems require different tools. Different teams scale at different paces. Tech is not static.
Executives must set the tone here. Lead with outcome-based thinking. Don’t let internal culture devolve into elitism around tools or frameworks. Ask: did it work, was it reliable, is it maintainable, and does it scale? If yes, how it was done matters far less.
When you reward adaptability and execution over rigid tool conformity, you create an environment where people are encouraged to optimize based on the real constraints of the project, not just follow whatever is trending. That gives your teams speed and maturity, without sacrificing technical excellence.
Use the term “tech” judiciously, context and specificity are key
It’s not about eliminating the word “tech.” It’s about using it in the right context. Talking about “tech skills,” “tech sector,” or “technical teams” makes sense when it’s grounded in clear definitions. The issue arises when the term becomes a shortcut for vague assumptions, whether in hiring, team design, or execution. That’s when alignment breaks down.
Language influences how organizations make decisions. Loose terminology leads to miscommunication and misplaced expectations. Calling someone a “tech person” without specifying their actual skills creates ambiguity that shows up in project planning, team resourcing, and performance reviews. This slows teams down and creates friction between departments trying to collaborate on complex initiatives.
Specificity fixes this. Instead of saying “get a tech person on it,” say who you need, engineering lead, DevOps, product technologist. Labeling the skill functionally, rather than generically, makes everything around it more streamlined. It clarifies ownership, accelerates coordination, and increases accountability.
For C-level leaders, language alignment is operational leverage. It ensures strategy and execution sync with less overhead. Teams are faster, roles are clearer, and you avoid the cost of confusion while scaling.
Still, there are situations where using “technical” is practical and effective, as long as it’s attached to context. You’ll always need frameworks like “technical due diligence” or “technical documentation,” but they must tie back to defined scope, not subjective assumptions. Keep the term when it adds clarity. Cut it when it doesn’t. Language should serve your execution, not obscure it.
The bottom line
High-performing teams aren’t built on binary labels. They’re built on precision, adaptability, and the ability to recognize potential before it’s obvious. If you’re still dividing people into categories like “tech” and “non-tech,” you’re missing out on talent, blocking collaboration, and slowing innovation by design.
As a decision-maker, your language shapes your culture. How you define roles, how you frame expertise, how you evaluate contribution, all of it drives behavior. Generalizations lead to misalignment. Specifics drive performance.
The companies that win aren’t the ones that hoard technical knowledge behind walls. They’re the ones that democratize it, develop it, and expect it everywhere. If your hiring, leadership, or internal processes still rely on vague labels, now’s the time to rebuild them around clarity, not convention.
It’s not about being more technical. It’s about being smarter with what technical actually means, and getting serious about how your organization scales human capability.
