AI is now a permanent part of the hiring process whether we like it or not. Candidates are using it to refine their applications, just as recruiters are using it to streamline the hiring process. For talent acquisition teams, the goal isn’t to police AI use, but to look past the polish to assess authentic capability, motivation, and alignment.
As ‘perfect’ answers become the standard, we need to ensure we’re hiring for impact, adaptability, and long-term fit. Let’s look at how to design a hiring process that surfaces true potential.
Why traditional hiring signals are breaking down
Candidates are using generative AI to write resumes, refine cover letters, prepare interview answers, and even rehearse responses in real time. Generative tools are exceptionally good at:
- Writing fluent, confident career narratives
- Translating vague experience into polished outcomes
- OptimisingOptimizing applications for keywords and job descriptions
What they are far less effective at is demonstrating judgment, decision-making, learning, and values in context. That is where modern hiring processes must focus.
1. Redesign applications to reduce AI optimisationoptimization
Generic application steps are now highly AI-friendly. To assess real intent and understanding, applications need to ask better questions. Instead of requesting lengthy cover letters, use short, targeted prompts tied directly to the role and business. Ask questions that require reflection, prioritisationprioritization, or a point of view rather than description.
For example, asking what excites a candidate about a specific challenge in the role reveals far more than asking why they want to work for your company. Well-designed application questions help surface:
- Whether the candidate understands the role
- How deeply they have engaged with the organisationorganization
- Early signs of motivation and alignment
This also improves candidate experience by reducing performative writing and brings to light more authentic responses.
2. Shift from word-based signals to work-based signals
Rather than relying on keywords or experience in a resume, work-based assessments are one of the most effective ways to assess authentic capability, especially when they are lightweight, relevant, and realistic. They also shift quality assessment earlier in the funnel. By introducing simple, role-relevant assessments before the interview stage, organisationsorganizations save time, reduce low-quality progression, and build a stronger, more credible candidate pool from the start.
The highest-signal tasks tend to:
- Reflect real work the role will actually do
- Include ambiguity or incomplete information
- Focus on decision-making, not just outputs
Asking candidates to critique an existing piece of work, respond to a real scenario your team is facing, or explain trade-offs under time constraints provides insight that is difficult to replicate in interviews alone. What matters most is not the final result, but how the candidate approaches the task. This creates a cleaner signal early and ensures interviews are spent on the right people, not just the best-written applications.
3. Structure interviews to surface thinking, not scripts
Unstructured interviews reward rehearsed answers – a risk most organisationsorganizations can no longer afford to take. Structured interviews with adaptive follow-up questions create space for real thinking to emerge. Instead of relying heavily on “tell me about a time” questions, interviewers should focus on how candidates reason, reflect, and learn.
High-signal prompts include questions about:
- What they would do differently next time
- The hardest trade-offs they faced
- What did not go to plan
Candidates who have genuinely lived the experience can reflect on experiences and explain their thinking. Those relying heavily on AI-generated narratives often struggle when asked to go beyond the first layer.
4. Look for consistency across the hiring journey
One of the most effective ways to assess authenticity is to look for pattern consistency rather than perfection. This means assessing the same capability in different ways across the process. Compare how candidates describe their experience in written responses, explain it verbally, and demonstrate it through tasks.
Minor inconsistencies are normal and often human. Overly consistent, generic narratives can be a signal of over-polished storytelling rather than lived experience. Using multiple interviewers to assess the same capability independently also helps reduce bias and surface richer insight.
5. Make values and mission alignment harder to script
Most candidates can generate convincing answers about values. Far fewer can explain how those values play out when things are difficult. To assess real alignment, explore tension rather than agreement. Ask candidates about moments where values were challenged, decisions were uncomfortable, or alignment was imperfect.
Discuss real scenarios that reflect your organisationorganization’s trade-offs. This moves the conversation from aspiration to reality and helps both sides assess fit more honestly. Ensuring authentic mission-assignment can drive engagement, reduce burnout, and lower turnover in the long term.
6. Be transparent about AI use and expectations
Trying to catch candidates using AI creates mistrust and rarely delivers better outcomes. Transparency is now critical. Be clear about where AI use is acceptable and where it is not. This could be included in your job advertisements or a dedicated page on your career site.
Ask candidates how they use AI in their work today and what judgment they apply when using it. This reframes AI literacy as a capability rather than a red flag and provides insight into ethics, maturity, and self-awareness. For many roles, responsible AI use is now part of being effective at work.
Remember, being transparent surrounding AI use is two sided. To build trust with candidates, it is important that your organisationorganization is clear about how you use AI throughout your hiring process too.
7. Train interviewers to assess substance over style
AI has raised the baseline quality of communication, and interviewers need new calibration. This means investing in structured training for recruiters and hiring managers so they can consistently spot green flags and red flags, not just polished answers.
That training should build the capability to:
- Listen for reasoning, not eloquence
- Probe beyond first answers
- Separate confidence from competence
- RecogniseRecognize rehearsed responses vs real understanding
- Identify risk signals like deflection, vagueness, or surface-level thinking
The mindset shift is simple but important. Great answers are no longer rare, but great thinking still is. OrganisationsOrganizations that invest in interviewer capability will consistently outperform those that rely on intuition alone, because they build consistent decision quality, not just better conversations.
The future of authentic hiring in the age of AI
You cannot out-detect AI, but you can out-design it. Hiring processes that prioritiseprioritize proof over polish, sense-making over storytelling, and real work over hypothetical answers will surface authentic capability, genuine motivation, and true alignment.
AI has not made hiring harder. It has made intentional hiring essential. For talent acquisition teams willing to evolve, this is an opportunity to build fairer, more predictive, and more human hiring experiences.
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