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Across job platforms and workplace studies, a consistent pattern has emerged: people are adopting generative artificial intelligence (AI) tools for career-related tasks, but adoption doesn’t translate into full trust or reliance. Data from Indeed shows 70% of job seekers already use generative AI for foundational tasks, from researching company cultures to drafting cover letters and interview talking points. Yet the relationship people have with these tools is best described as selective rather than confident – useful for speed, formatting and idea generation, but not for final judgment.
A study by KPMG and the University of Melbourne found that employee use of AI at work rose from 13% to 37% between 2022 and 2024. Despite this growth, the same study reported declining employee trust in AI alongside rising concerns about its use. People aren’t moving from skepticism to unquestioning acceptance; they’re moving toward a more normalized but selective form of engagement – a distinction that matters for career development, where AI is embraced for speed and information access but not yet trusted the way people trust a skilled human professional.
For most of the past two decades, career support was built on an assumption of information scarcity. That assumption is now outdated: job seekers can instantly access labour market data, skill requirements, salary ranges and career trajectories, and AI tools further accelerate this by packaging and personalizing that information.
AI handles the “what”, humans handle the “should”
AI already excels in areas that are structured and information-heavy: summarizing job descriptions, generating tailored resumes, suggesting career paths and helping users rehearse interview responses. But career decisions aren’t purely informational. They involve trade-offs that are harder to formalize – risk tolerance, identity, financial pressure, family expectations, geographic constraints and personal definitions of success – inputs that don’t map cleanly onto prediction models. This is where AI struggles: these dimensions of decision-making are context-dependent, subjective and often unstable even for the person making the decision. As a result, AI tends to expand the number of plausible options without necessarily helping people choose between them. More options don’t automatically produce clearer decisions.
This tension is captured in the concept of self-efficacy. A 2026 study in Technology in Society involving 183 university students found that while AI is highly effective at reducing “information deficits” by providing fast, factual answers, it fails to build the confidence required for high-stakes life changes. A large-scale randomized trial in Chile reached a similar conclusion: a generative-AI WhatsApp agent excelled at answering factual queries, but its impact on actual enrolment was statistically insignificant. Human counsellors, by contrast, significantly increased students’ likelihood of ranking a program as their first choice and led to higher enrolment. AI excels at the “what” – data and requirements – while humans remain masters of the “should”: vocational judgment and fit.
The bottleneck has shifted from information to interpretation
For most of the past two decades, career support was built on an assumption of information scarcity. That assumption is now outdated: job seekers can instantly access labour market data, skill requirements, salary ranges and career trajectories, and AI tools further accelerate this by packaging and personalizing that information.
As a result, the challenge has shifted from access to interpretation. People increasingly face situations where multiple paths are viable, but the right choice depends on personal constraints, uncertainty and long-term trade-offs that are difficult to evaluate objectively. Confusion emerges not from a lack of data but from too much of it, combined with uncertainty about what matters. AI can help structure information, but it doesn’t resolve the underlying problem of value-based decision-making. This is especially relevant as the labour market becomes harder to read: LinkedIn projects that 70% of the skills required for most jobs will change by 2030, while the World Economic Forum expects a 39% shift within a similar timeframe.
There is also a psychological threshold AI cannot cross, particularly when the stakes are emotional. AI is fluent and persuasive, often presenting recommendations with an air of authority even when the logic is flawed or incomplete – a risk of overconfidence that makes the human element irreplaceable. A 2025 study in BMC Psychology found that people show a clear preference for human interaction in social-emotional scenarios: even when AI’s answers were identical to a human’s, participants sought the empathy that only a human can provide. In career development, where decisions affect identity and financial security, people want to be heard and understood by someone who grasps the weight of the choice.
The rise of hybrid career development models
Two extreme narratives currently surround AI in professional services: one assumes it will replace career practitioners entirely; the other assumes it has little meaningful role to play. Neither reflects what’s actually unfolding. Career development is moving toward a hybrid model in which AI handles scalability and information processing while practitioners focus increasingly on interpretation, judgment and human-centred guidance.
If AI can instantly review a resume, identify transferable skills and summarize labour market trends, the value of practitioners will no longer rest primarily on access to information. Instead, it will come from helping clients make sense of that information within the context of their lives – deeper work around identity, uncertainty, motivation, belonging and meaning. Practitioners may also need to strengthen competencies AI struggles to replicate: ethical judgment, contextual understanding, trust-building, empathy and nuanced communication. Ironically, AI may push the field to become more human, not less.
Important risks remain. AI systems are not neutral – they inherit biases from training data, labour market inequalities and existing social structures, and may unintentionally reinforce gendered, racialized or class-based assumptions about work, or over-prioritize employability metrics while underestimating well-being, accessibility, discrimination or long-term satisfaction. AI also presents information fluently even when it’s incomplete or incorrect, and clients may mistake confident output for authoritative guidance.
This creates a new responsibility for career professionals: helping clients critically interpret AI-generated advice rather than simply consume it. Digital literacy, AI literacy and ethical interpretation may soon become core competencies in the field. We are moving away from the “first pass” – generating basic options and drafts – and toward a “second pass”: a higher-value tier of service defined by challenge, encouragement, reflection and depth.
Yet a significant readiness gap remains. According to a CERIC survey, only 14% of career professionals feel fully prepared to integrate AI into their practice – an urgent signal that the profession needs to evolve, or risk falling behind the technology meant to elevate it.
AI excels at speed and quantity – generating dozens of resume variations, summarizing thousands of job postings and producing instant career pathways – but career development has never simply been about producing more outputs. Its deeper purpose has always involved helping people build agency, self-understanding and meaningful direction in work and life, dimensions that cannot be fully automated, at least not yet. The future of the field will likely not involve choosing between AI and human practitioners, but understanding where each adds value. Practitioners who integrate AI thoughtfully may reduce administrative burden and expand access for clients – but their long-term relevance will depend less on competing with AI and more on offering what AI cannot replicate.
Conclusion: a sharper focus
AI is not a replacement for human judgment; it is a lens that sharpens its value. By automating routine work, AI forces us to focus on what cannot be reduced to efficient output: context, complexity and meaning.
Career guidance is not becoming obsolete. It is becoming more distributed and more dependent on how well humans and machines complement each other in practice, not just in theory. As we approach the next career crossroads, this generation will have more data at its fingertips than any before it. The question is: do you seek more data, or more meaning?
