The Algorithm Arms Race: How Australian Hiring Managers Can Separate Candidate Talent from AI Polish

In Australia, 65% of jobseekers now use generative AI to draft applications. The result is a hiring loop where software is effectively pitching to software. Based on Morgan McKinley’s 2026 Global AI Report, Sydney recruitment specialist Nick Largent explains why banning these tools fails. Instead of policing AI use, hiring managers need to set clear rules on misrepresentation and verify real candidate capability through practical assessments.

Recruitment across Australia, and worldwide, has arrived at a curious impasse: software is effectively pitching to software.

On one side of the hiring desk, candidates are using generative AI to draft spotless, keyword-optimised job applications. On the other side, hiring teams are relying on automated parsing tools to screen the influx, creating a cycle where algorithms are effectively evaluating algorithms.

Findings from Morgan McKinley’s 2026 Global AI Report highlight how rapidly this dynamic has unfolded across the Australian talent market:

• 65% of Australian candidates use generative AI to write or refine their CVs and cover letters.
• 41% of local jobseekers use AI tools specifically to optimise applications for applicant tracking systems (ATS).
• 14% of Australian employers automatically reject applications identified as AI-generated, seven times the global baseline of just 2%.

To understand what this digital standoff means for businesses, we spoke with Nick Largent, Principal Consultant for Projects and Change at Morgan McKinley Sydney. Drawing on more than twenty years of recruitment experience across transformation, project services and digital sectors, Nick shares how employers can cut through automated noise, separate productivity from dishonesty and keep human judgement at the centre of hiring decisions.

1. Move Beyond the CV as the “Primary Source of Truth”

With almost two-thirds of Australian candidates using generative tools to polish their resumes, well-written applications are now commonplace. However, leaning heavily on automated parsing or keyword density can lead hiring teams to make poor talent decisions.

"Employers need to move away from treating the CV as the primary source of truth," says Nick. "A CV should open the door for a conversation, not determine whether someone is capable of doing the job. When a CV created by ChatGPT, Gemini or Copilot is screened by an ATS, you have software talking to software. That dynamic dilutes the recruitment process. The danger is that we end up selecting people who are best at writing applications rather than those who are best at doing the job."

Nick’s recommendations for initial screening:

• Evaluate outcomes over keywords: Screen candidates against a clearly defined set of core capabilities and demonstrable business results rather than job titles or keyword matching.
• Revisit borderline profiles: Introduce intentional checkpoints to review non-traditional applicants with strong transferable experience that automated filters might overlook.
• Treat technology as an enabler: "Technology should help us find talent we might otherwise miss, not narrow the field unnecessarily," Nick explains.

2. Distinguish Between AI Assistance and Misrepresentation

As generative tools become standard workplace productivity aids, Australian employers are grappling with where smart assistance ends and candidate dishonesty begins.

"The distinction is between improving how you communicate your experience and misrepresenting what the experience actually is," Nick explains. "Using AI to improve grammar, structure a CV, tailor an application to a role or prepare interview questions is perfectly reasonable. Candidates have always sought help with their CVs through recruiters, mentors or professional writers. AI is simply another tool."

However, crossing into exaggeration or fabrication is an integrity issue rather than a technological one.

"The boundary is crossed when candidates use AI to invent achievements, exaggerate responsibilities or claim technical capabilities they do not possess," says Nick.

"That is not an AI issue, that is an honesty issue. Candidates must be able to substantiate what they have written. If someone claims to have led a major transformation, they should be able to explain their personal contribution, the challenges they faced, the decisions they made and the outcomes they delivered", Nick adds.

3. Avoid the Auto-Rejection Trap

The report reveals that 14% of Australian employers automatically reject applications identified as AI-generated, compared to just 2% globally.

Nick cautions that blanket bans create an organisational contradiction.

"Businesses are actively encouraging their own employees to become comfortable using AI to improve productivity," Nick points out. "Why are we then penalising candidates for using that exact same technology to communicate their experience more effectively?"

While heightened caution is understandable in heavily regulated sectors where accuracy is vital, auto-rejection mechanisms remain a blunt instrument.

"Automatically rejecting a candidate because AI helped produce an application does not tell you if they are capable, honest or a good cultural fit," Nick says. "In transformation and project spaces, you actively want people who know how to work smarter with emerging tools. The competitive advantage comes from identifying the best talent, not from identifying who did or did not use a writing tool."

4. Test Practical Ability Earlier With Real-World Scenarios

Because candidates can easily use chatbots to generate polished, rehearsed answers, traditional interview questioning is no longer sufficient to verify real-world competence.

Nick advises introducing practical, role-relevant exercises much earlier in the assessment cycle:

• Transformation & project management: In Projects, Strategy & Change, present a realistic scenario with delivery risks, competing priorities or difficult stakeholders, and evaluate how the candidate approaches the problem, makes decisions and communicates with teams.
• Business analysis: Run a concise requirements-gathering exercise to evaluate structural thinking.
• Finance: In Accounting & Finance, provide a raw financial data set and ask the candidate to interpret the key commercial insights for a non-finance stakeholder.
• Technology: Focus on live, unassisted problem-solving exercises in Technology rather than static take-home assignments.

"The key is to assess how someone thinks, how they prioritise and how they apply their experience, rather than just testing their ability to produce a polished answer," Nick explains. "You want to evaluate how someone operates under real conditions rather than how well they rehearse with a chatbot."

5. Upskill Hiring Teams: Probe for Evidence, Don’t Interrogate

Organisations also need to ensure hiring managers have the skills and confidence to evaluate AI-assisted profiles fairly without resorting to guesswork or suspicion.

"Hiring managers do not need to become AI experts, but they do need to recognise that a well-written application for ATS is not proof of competence, and AI involvement is not proof of dishonesty," Nick notes. "Training should focus on practical examples: how to identify claims that need verification, how to conduct structured interviews and how to probe for evidence without turning the interview into an interrogation about AI use."

"Recruitment is ultimately a people decision," he adds. "AI literacy should strengthen the judgement of hiring managers, but it shouldn't encourage them to outsource that judgement to another system.

6. Bridge the Corporate AI Policy Disconnect

Australia leads the world in formal corporate AI governance, with 86% of Australian organisations reporting formal AI policies in place compared to a global average of 48%. However, at the recruitment desk, hiring managers are often left to guess how to handle AI-assisted applications.

"Most corporate policies sit at the board level to protect company data and manage risk," Nick observes. "They do not tell a hiring manager what to do with a stack of CVs on a Monday morning. Two candidates applying for the same role should not face fundamentally different standards simply because one manager is comfortable with AI and another is not."

A major blind spot lies in candidate communication. When asked whether he currently sees Australian employers clearly spelling out their AI expectations in job advertisements, Nick’s assessment is candid:

"Very rarely—if ever," Nick admits. "And that is when it starts to dilute the recruitment process: when there is no transparency around how much AI is being used in the screening process, or what allowances are made for candidate applications. Possibly behind the scenes businesses have views, but organisations could be far clearer from the onset about what their hiring practices actually are."

Nick’s guidance for organisations:

• Build a practical recruitment playbook: Outline what candidates can use AI for, what constitutes misrepresentation and which decisions require human sign-off.
• Communicate expectations in job ads: State clearly whether AI assistance is acceptable for presentation, and outline upfront if unassisted technical testing will be required.
• Be transparent about employer AI screening: If automated tools are used to rank or filter candidates, be open about where human review steps in to maintain trust in your employer brand.

7. Focus On Potential for Early-Career Hiring

University graduates and entry-level applicants are among the most fluent users of generative tools, frequently turning to AI to translate academic work into commercial resumes. Nick warns against disqualifying junior applicants over automated formatting.

"Candidates entering the workforce have limited professional experience, so AI can help them articulate academic projects and internships," Nick says. "Automatically penalising them could significantly disadvantage people who have the potential to become outstanding employees."

Instead of scrutinising formatting, employers should evaluate learning agility.

"Place less emphasis on how professionally written an entry-level CV is, and focus much more on potential, curiosity, problem-solving and the ability to learn," Nick advises. "Ask candidates to talk through a project they contributed to: what went wrong, what they personally did and what they learned. The objective is to identify people who can grow into a role, rather than simply those who know how to present themselves as a finished product."

Evidence of capability is the ultimate standard

As generative tools continue to evolve, trying to eliminate AI from recruitment is an unsustainable approach. Instead, Australian organisations must anchor their hiring processes in verified capability and human connection.

"If I had to choose one essential standard, it would be to make evidence of capability the foundation for every hiring decision," Nick concludes. "AI can help make recruitment more efficient, but it should never replace the professional judgement needed to understand a person's experience, motivations and potential."

"Recruitment is ultimately a people decision. Organisations that get this right will use technology to improve the process while keeping people firmly at the centre of every decision. The objective has not changed: hire the person who can do the job, deliver the outcomes and make a positive contribution to the business. No AI can replace human judgement for that."

Want to learn more about navigating AI in Australian recruitment?

Download our 2026 AI Report to explore full regional insights or get in touch with our team to discuss your recruitment and talent solutions across Sydney and Australia.