Talent Acquisition Timeline: The Past, Present, and Future
Leadership
Executive search has undergone a profound shift—from a network-driven process based on visibility and access to a technology-mediated landscape shaped by digital profiles, algorithmic tools, and evolving expectations of leadership. In this article, Helen Mekonen draws on her experience as a search consultant to examine the past, present, and future of talent acquisition. She explores the barriers historically faced by underrepresented leaders, the need for greater discernment in an era of performative visibility, and the legal and ethical concerns surrounding AI’s expanding role in hiring. With practical guidance for search professionals and employers alike, this piece calls for a more human-centred, equity-conscious approach to identifying talent.
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Bata Shoe Museum, Canadian Council for the Arts, CEE Centre for Young Black Professionals, City of Toronto, David Suzuki Foundation, Fasken, Genome Canada, George Brown College, GTAA, Humber, IMCO, Kids Help Phone, Luminato, McMaster University, MLSE, OICR, Ontario Presents, ROM, Sankofa Square, Sick Kids, TD Bank, TTC, UHN Foundation, United Way Greater Toronto, University of Toronto, University of Waterloo, University Pension Plan Ontario, York University
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When I entered executive search, LinkedIn was still emerging, and the Rolodex was still king. Recruitment was a relationship-based business, and success often depended on proximity to power—who you knew, who knew you, and whether your name came up in the right conversations. It was a model that worked well for some, but left many others—especially women and racialized leaders—excluded or invisible.
Much has changed since then. Search has become more digital, more skills-oriented, and more data-driven. Candidates are increasingly evaluated not just on their credentials, but on their digital footprint—what they post, where they speak, and how they’re perceived online. Tools powered by artificial intelligence promise speed and efficiency, surfacing talent that might otherwise go unnoticed.
But these shifts bring new complexities. Visibility is no longer neutral. And efficiency is not always the same as fairness. In this piece, I reflect on how search has evolved—from the constraints of the past, to the pressures of the present, to the real risks of the future—and what we must hold onto as we move forward: discernment, ethics, and a commitment to seeing people in their full context.
1 The Past — When Networks Were Everything
Before LinkedIn—before AI-driven sourcing tools, and before talent pipelines became searchable—executive search operated within a narrower frame. Referrals and known networks were the cornerstone of recruitment, and ‘top candidates’ were often those already moving through elite professional circles and were already employed in top-tier organizations. The Rolodex wasn’t just a tool—it was a gatekeeper. This model, while efficient for those inside the circle, left many others invisible. Women—particularly racialized women—often faced a compounded challenge: not only were they underrepresented in leadership roles, but their access to the informal networks that led to those roles was limited or blocked entirely.
In their astute article Women and the Labyrinth of Leadership, Alice Eagly and Linda Carli argue that the popular ‘glass ceiling’ metaphor fails to capture the reality of these barriers. Unlike a single, ‘shatterable’ ceiling near the top of the hierarchy, the labyrinth represents a journey filled with twists, dead ends, and hidden passageways. Navigating it requires more than competence—it demands resilience, strategic insight, and often, support that candidates didn’t even know they were missing (Eagly & Carli, 2007).
Executive search processes of the past rarely acknowledged these complexities. The emphasis on ‘fit,’ ‘presence,’ and being ‘known’ often created blind spots—especially for candidates whose paths didn’t resemble the straight lines of traditional leadership trajectories. Too often, search processes reinforced what had already been seen and validated, rather than illuminating new kinds of leadership potential.
This is the context I stepped into as a new consultant in executive search. And while some of those dynamics still persist today, the present moment is defined by a powerful shift: from exclusion by obscurity to the possibility of being discovered.
2 The Present — Visibility, Skills, and the Digital Profile
For today’s clients and search partners, the challenge isn’t a lack of talent—it’s finding the right talent amidst noise, partial visibility, and evolving expectations. While digital platforms like LinkedIn have expanded the talent pool, they’ve also introduced new complexities. The rise of online branding, algorithmic search, and performative leadership can make it harder—not easier—to discern who is truly ready for a role.
It’s no longer enough to rely on who’s visible. The work now lies in distinguishing between candidates who are strategically ‘discoverable’ and those who are merely digitally loud. Search committees often say, ‘I just want to see the top four people.’ But who gets counted among those four? And based on what criteria? At BIPOC Executive Search, we believe visibility must be critically examined, not assumed to be a proxy for readiness or excellence.
Here’s a guide for organizations and recruiters who want to lead in this new era of talent acquisition:
1. Go Beyond the Usual Suspects
Don’t limit your initial pool to those who are already high-profile.
Ask your search partner: “Who’s being overlooked right now, and why?”
Consider how leadership may look different across sectors, geographies, or generational cohorts.
2. Scrutinize Digital Presence Thoughtfully
A polished LinkedIn page doesn’t guarantee depth.
Ask candidates about the ideas behind their posts, not just their follower count.
Prioritize substance over aesthetics: have they contributed meaningfully to real work or discourse?
3. Partner with Researchers Who Read Between the Lines
Good search professionals don’t just filter by keywords—they look for patterns of growth, moments of resilience, and contributions that may not be listed in job titles.
Ask about the research process: what nontraditional avenues are being explored? Who’s finding the “quiet excellence”?
4. Embrace Skills-Based Profiles
Many of today’s most promising leaders may have nonlinear career paths.
Prioritize transferable competencies, adaptability, and problem-solving ability over neat title progression.
5. Challenge the ‘Top Four’ Mindset
Rather than defaulting to a shortlist of high-visibility names, consider asking:
“Who would we miss if we only looked at the most visible candidates?”
“What would it mean to hire someone with high potential but lower current profile?”
“How can we lean into generosity—giving time to uncover potential, being open to gaps in ‘must-haves,’ and discovering unexpected add-ons in a candidate’s profile?”
6. Broaden the Definition of ‘Fit’
‘Fit’ shouldn’t mean familiarity. It should mean alignment with mission, values, and the evolving needs of the organization.
Create space for candidates who may lead differently, speak differently, or show up differently—but who bring exactly what the future demands.
This moment calls for discernment, not just reach. While AI and social platforms can aid discovery, they can also amplify bias—surfacing the same kinds of candidates, again and again. For organizations committed to inclusive leadership and innovation, the question must shift from “Who’s already visible?” to “Who should be visible—and how do we ensure we find them?”
3 The Future—Visibility, AI, and the Real-World Risks of Automated Hiring
We’re entering an era where artificial intelligence tools are quietly reshaping recruitment. From automated resume filters to predictive algorithms that analyze tone and language for ‘fit,’ AI is no longer just a novelty—it’s becoming a normalized part of how employers find, screen, and assess talent. But that normalization comes with serious consequences. I’ve spoken to candidates who wonder: Who is reviewing my application? Is it a person—or an algorithm? I’ve also heard search professionals ask: Can AI help us reduce bias? And my answer is always the same: only if we understand its limitations. Because what’s often marketed as ‘objectivity’ can, in practice, obscure the very biases we’re trying to dismantle.
A recent article by employment lawyers Andrew Shaw and Matthew De Lio outlines some of the legal risks emerging in Canada as AI becomes more embedded in hiring decisions(Shaw & Lio, 2025). In Ontario, for example, Bill 149 will require employers to disclose the use of AI in job postings beginning January 1, 2026. The intent is transparency—but what’s striking is how unclear the requirements still are. The law offers a broad definition of AI but doesn’t specify which tools actually trigger the disclosure requirement. This leaves a dangerous grey area: employers might unknowingly omit disclosure, believing a filtering or screening tool doesn’t count.
Even more concerning, as Shaw and De Lio point out, disclosure doesn’t equal fairness. Knowing that AI is being used doesn’t mean a candidate understands how it works—or how their data is being processed, stored, or interpreted. And as of now, there are no mechanisms in Ontario or federally that require employers to audit for algorithmic bias or explainability in the hiring process.
What does this mean for search professionals? It means we must proceed with caution—and with context. AI may help with efficiency, but it cannot assess human potential the way a thoughtful, informed, and equitable process can. At the moment, AI can’t interpret a non-linear career path or understand how caregiving, community organizing, or navigating systemic barriers shapes leadership (although perhaps it will be able to in the future). It also can’t account for privacy. Where does candidate data go when entered into third-party AI tools? How long is it stored? Who else sees it? The federal government’s Artificial Intelligence and Data Act (AIDA)—intended to address these questions—was stalled due to prorogation. In the meantime, organizations are left without clear national guardrails and must navigate compliance risk across multiple jurisdictions, including the EU’s new Artificial Intelligence Act, which already impacts Canadian employers operating abroad.
AI is here to stay—but it must be human-led, context-aware, and ethically scrutinized. Here’s what responsible use may look like in the hiring process as we move into this new age:
Human Oversight: Use AI to assist, not replace, the judgment of experienced professionals. Every AI-informed decision should be reviewable—and challengeable—by a human being.
Impact Assessment: Before using AI, evaluate what data it draws from and whether it risks embedding prohibited characteristics (e.g., gender, ethnicity, disability) into decision-making processes.
Transparency with Care: Disclose AI usage to candidates clearly and early—but also explain what it does and doesn’t do.
Bias Testing: Actively test AI tools for discriminatory patterns, especially in how they rank or discard applicants.
Candidate Consent: Ensure candidates know how their data will be used, where it’s stored, and for how long.
In the rush to optimize hiring, we risk outsourcing one of the most human-centred decisions an organization can make. But leadership is not a data point. And recruitment isn’t just about selection—it’s about interpretation. The future of search won’t be decided by who has the flashiest tools. It will be shaped by who uses them with discernment, care, and a willingness to look deeper than an algorithm ever could.
Conclusions
Executive search has always been about more than identifying talent—it’s about understanding it. In the past, that understanding was limited by networks and narrow definitions of leadership. Today, it’s complicated by digital noise, algorithmic tools, and incomplete visibility. And tomorrow, it will be shaped by how thoughtfully we integrate technology with human insight. As we move into an AI-mediated hiring landscape, the question isn’t whether we use these tools—but how. Are we using them to amplify what’s already been seen, or to uncover potential that has gone unnoticed? Are we using them to streamline decisions, or to deepen them? Are we prepared to explain—not just rely on—the outputs they generate?
Leadership is not a keyword. It’s not a data point. And it’s certainly not something that can be reduced to an automated score. As search professionals and hiring leaders, we have a responsibility to resist easy answers. To challenge bias, not replicate it. To ask deeper questions. To remain human in the most human of decisions: choosing who will lead. Because the future of talent acquisition won’t be shaped by who we can find—it will be shaped by how we choose to see.
Bibliography
Eagly, A., & Carli, L. L. (2007). Women and the Labyrinth of Leadership. Harvard Business Review. Retrieved from https://hbr.org/2007/09/women-and-the-labyrinth-of-leadership
Shaw, A., & Lio, M. D. (2025). Artificial Intelligence, Real Consequences? Legal Considerations for Canadian Employers Using AI Tools in Hiring. Retrieved from https://www.labourandemploymentlaw.com/2025/02/artificial-intelligence-real-consequences-legal-considerations-for-canadian-employers-using-ai-tools-in-hiring/
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