Candidates use AI, and recruiting teams use AI too

LinkedIn’s 2026 research found that 81% of job seekers had used AI or planned to use it in the search process, while 93% of recruiters planned to increase their use of AI. The technology is entering both sides of the hiring market.

Reducing friction does not automatically make selection easier. Application volume grows, polished language becomes more common and employers face more signals to evaluate.

The issue is not AI use itself. The value of the signal changes.

Using AI to improve writing cannot simply be treated as misconduct when employees may use the same tools after they are hired.

The structural problem is that many candidates can now produce similar levels of polish. A convincing sentence and a convincing work capability are no longer as tightly connected as they once appeared.

The resume survives, but it moves from final evidence to first claim

Resumes remain efficient summaries of industry, role and project history. What changes is how much confidence employers should place in prose and keywords alone.

A resume can be treated as a set of hypotheses to verify with structured questions, role-relevant work samples, actual outputs, follow-up reasoning and references, with stronger verification reserved for the capabilities where hiring error is most expensive.

Start by shrinking the list of things that actually need verification

Good assessment is not a longer checklist. It identifies the few capabilities that drive performance and assigns a fitting form of evidence to each.

For B2B sales that might be pipeline creation, stakeholder management, negotiation and repeatability. For engineering it might be problem decomposition, code comprehension, debugging and design trade-offs. Skills-first hiring does not erase experience; it asks what ability exists behind the label.

Instead of banning AI, design tasks where ability remains visible even with AI

If the job will use AI after hiring, a realistic assessment can allow the tool and then evaluate whether the candidate can review, modify and explain the result.

Ask why they chose an approach, what changes under a new constraint and which parts of the AI answer they distrust. Work samples plus structured follow-up and live reasoning can separate tool use from judgment.

Candidates need reproducible evidence, not merely better prose

‘Increased sales’ is weaker than explaining the market, role, actions and measurement behind the result. The same principle applies across functions.

Where portfolios are difficult, candidates can still specify project scale, responsibility, before-and-after metrics, failures and corrections. AI can polish the language; it cannot replace the ability to reconstruct the experience under questioning.

The search firm’s role shifts from introduction to verifiable context

As AI makes sourcing and document preparation faster, the value of simply forwarding a resume declines. Employers need compressed context: why this person fits the role, which facts are verified and which remain uncertain.

For executives and specialists, actual responsibility, organizational scale, decision authority, outcome conditions and motivation matter. Search quality increasingly depends on clarifying what can be trusted and what still needs to be tested.

BANSEOG VIEW

The scarce resource in hiring may become the trustworthy signal, not the application

When AI makes applications faster and more polished, employers should focus less on detecting AI users and more on defining the capabilities that matter and building low-cost evidence for them.

The faster search becomes, the more human judgment shifts from ‘who did we find?’ to ‘why should we trust this signal?’

SOURCES

Primary sources and references

LinkedIn and SHRM figures are primarily global or US-centered. They are used to compare the direction of AI adoption and hiring-signal verification, not as direct estimates for the Korean labor market.