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The speaker is rarely junior. More often, they are a Managing Director, a senior partner, a regional CIO, or a transformation lead with decades of delivery behind them. They have led multicounty programmes, signed off seven figure budgets, and survived more reorganisations than they care to remember.

And yet here they are. Out of role. Searching. Waiting for calls that do not come…

What surprises them most is not the job loss itself. Senior leaders understand restructures, politics, and cycles. What unsettles them is what follows: recruiters are suddenly elusive, roles appear oddly narrow, and conversations that once began easily now end with courteous silence.

The market, it seems, has moved on while they were busy running it.

The market that has changed its mind

Across financial services, technology, and consulting particularly, the senior leadership market has not collapsed. It has narrowed.

According to CIPD data, employer hiring intentions remain subdued while redundancy planning continues at scale. Fewer organisations report difficulty filling vacancies, an unglamorous but telling signal: supply is no longer scarce, even at senior levels.

At the same time, automation has ceased to be aspirational. McKinsey estimates that existing technologies could automate activities accounting for 60–70% of the time spent in today’s jobs, with significant implications for organisational design and leadership density.

Gartner’s HR research shows organisations deliberately reducing management layers while expanding individual spans of control. Fewer leadership seats exist, and those that remain are broader, more demanding, and less tolerant of prolonged ramp up.

Mercer characterises the current period as one of value extraction rather than experimentation. Boards want returns on past investment, not additional complexity.

None of this is dramatic. It is structural.

There is an irony many leaders only recognise in hindsight.

For years, they simplified operating models, automated processes, standardised platforms, and removed duplication. They did precisely what boards asked.

In some cases, that success reduced the need for their own role.

Once a transformation is embedded, fewer people are required to oversee it. When budgets tighten, organisations rarely replace senior leaders unless the business case is immediate and unmistakable. No one describes this as redundancy caused by competence; it is handled politely, efficiently, and with carefully chosen language.

Why the phone is quieter than expected

When senior leaders ask why recruiters appear less engaged, the reasons are rarely personal.

First, the market now rewards precision. Profiles written in internal grade language, “strategic leadership,” “enterprise transformation,” “business partnering”, do not translate cleanly into today’s narrower mandates. Recruiters are searching for outcomes, not abstractions.

Second, compensation history matters more than it once did. With senior pay increasingly compressed, organisations become cautious about candidates whose previous packages imply negotiation, precedent, or internal tension.

Third, many roles that once justified permanent executive appointments are now delivered differently: interim, fractional, contract, or absorbed into existing leadership portfolios.

And finally, there is timing. Leaders who spent years declining recruiter calls missed the chance to build market equity when conditions were favourable. Relationships, like pensions, tend to work best when funded early.

The age question (never asked, always present)

By the early to mid-fifties, a quiet recalibration often occurs.

Not because capability declines experience is often at its peak but because organisations increasingly favour either long run succession bets or short-term delivery specialists. The space in between has narrowed.

Some leaders will return to corporate roles at the same level. Others will not.

Not because they lack ability, but because the organisational mathematics has changed.

This is not failure. It is alignment with a labour market that has adjusted its preferences.

A word of caution on “stepping down”

Stepping down a level is often offered as pragmatic advice. In practice, it is one of the most misunderstood and frequently unsuccessful moves senior leaders make.

From the organisation’s perspective, an overqualified hire creates unease. There is a persistent concern that the individual will leave as soon as a more senior opportunity appears, or that the role is merely a holding position. Even when unspoken, this doubt influences hiring decisions and internal trust.

From the individual’s perspective, the experience can be equally problematic. Initially, the role feels reassuring: structure, colleagues, familiarity. Over time, however, many leaders find themselves underused. Decision rights are narrower, influence is constrained, and the very experience that once differentiated them now sits politely on the sidelines.

Boredom follows. Frustration rarely lags far behind. And eventually, so does another exit often within twelve to eighteen months.

The issue is not ego. It is misalignment.

Stepping down works only when the role is explicitly framed as timebound, problem specific, or a bridge to a clearly defined next phase. Absent that clarity, it tends to disappoint everyone involved.

What tends to work better

For many senior leaders, the more durable recalibration is not a lower title, but a different engagement model.

The more useful question is often not “What role should I accept?” but:

“What problem am I best placed to solve right now?”

This shift opens more credible paths:

  • defined scope mandates where experience is required, not tolerated

  • interim or transformation leadership with explicit outcomes and endpoints

  • portfolio careers combining advisory, fractional leadership, and delivery work

  • contract operating roles tied to integration, remediation, or turnaround

In these models, seniority is not something to downplay. It is the product.

Practical adjustments that help

Leaders who navigate this phase well, tend to do a few deliberate things early:

They decide what they are selling, corporate re-entry, interim delivery, portfolio work, or reinvention rather than presenting optionality as flexibility.

They rewrite their narrative in commercial terms: costs removed, risks reduced, revenue protected, time saved. The CV becomes an argument, not a chronicle.

They treat the search like a pipeline, applying the same discipline they once expected of their teams: target lists, warm introductions, consistent visibility, and measured follow-up.

They engage a small number of recruiters properly, with clarity on scope, geography, and trade-offs, rather than dispersing energy widely.

And when contracting is the answer, they professionalise it, clear offers, defined outcomes, credible pricing rather than treating it as a pause.

Coaching, when used well, is not about reassurance. It is about perspective, decision-making, and reframing identity beyond job title.

Corporate life has always been conditional. What has changed is the speed with which conditions are reviewed.

Many senior leaders were insulated for years by momentum and title. That insulation has thinned.

Those who come through this period best, are not necessarily the most decorated. They are the ones willing to reposition deliberately, resist false humility, and deploy their experience where it is genuinely needed, rather than shrinking themselves to fit roles that no longer require them.

After all, transformation was never meant to stop at the organisation. It has a habit of continuing, personally, long after the programme has closed.


I spend a significant amount of time speaking with senior leaders across financial services, technology, and consulting who find themselves navigating this exact transition often for the first time in their careers.

These conversations are rarely about CVs alone. They are about positioning, timing, market reality, and how to deploy experience without diminishing it.


If this resonates, I share further insights on executive transitions, leadership recalibration, and market dynamics at C Graham Consulting:

#ExecutiveLeadership#SeniorLeadership#CareerTransitions#FinancialServices#Consulting

Careers#TechnologyLeadership#ExecutiveSearch#LeadershipAdvisory#FutureOfWork

 

 
 
 
Christopher Graham, Founder & Managing Partner, CGC
Christopher Graham, Founder & Managing Partner, CGC

Recruitment is hard.

Getting it right requires clarity, leadership, time, and intent. So naturally, if your goal is to attract the weakest possible candidates, frustrate the good ones, and burn your employer brand to the ground, there are some proven best practices you should absolutely follow.

Below is a helpful guide on how to fail spectacularly at hiring, a checklist observed far too often in the wild.


1. Pay Low Recruitment Fees

Nothing signals “we value talent” quite like paying bottom-of-the-market fees.

This ensures recruiters:

  • Prioritise other clients who pay properly

  • Allocate junior consultants or automated sourcing

  • Give your role just enough attention to tick a box

Recruitment is a sales role. Shocking, I know. Pay peanuts → get monkey-level commitment.


2. Cut Off Your Recruiters and Use Third-Party Vendors Instead

Why work with people who understand your business when you can add layers of confusion?

By routing everything through third-party suppliers:

  • No one knows what’s really going on

  • Candidates receive vague, inaccurate briefs

  • Consultants can’t represent your firm properly

The result? Maximum noise, minimum insight, zero accountability.


3. Avoid Briefings at All Costs – Palm It Off to TA

Hiring managers briefing recruiters is wildly overrated.

Instead:

  • Delegate everything to your TA team

  • Ensure multiple rounds of “Chinese whispers”

  • Share no insight into team dynamics, succession plans, or business priorities

TA teams are excellent, but they cannot know every role, function, stakeholder, and political nuance in a complex organisation.

If you’re a manager (at any seniority level) and you’re not involved in hiring, expect it to fail.


4. Recruit for Politics, Not Talent

Always prioritise:

  • Internal optics

  • Who won’t upset anyone

  • Who fits existing power structures

This guarantees:

  • Team disruption

  • Poor performance

  • Quiet quitting or loud resignations

Nothing kills momentum like hiring someone because they were “safe.”


5. Demand Unicorns… on a Hamster Budget

Ask for:

  • World-class skills

  • Blue-chip pedigree

  • 20 years’ experience

  • Multiple languages

  • Leadership gravitas

Then offer:

  • A below-market salary

  • Limited scope

  • Zero upside

When no one applies, act surprised.


6. Oversell the Role Shamelessly

Describe the role as:

  • “Fast-paced”

  • “Transformational”

  • “Highly strategic”

Even if it’s actually:

  • BAU

  • Under-resourced

  • Decision-light

This ensures your new hire leaves within probation, and you get to start the process all over again. Fantastic for KPIs, terrible for teams.


7. Rely Entirely on AI to Hire

AI is brilliant, when used properly.

So naturally, use it:

  • As a blunt filtering tool

  • To auto-reject unconventional but high-potential candidates

  • Without understanding context, career arcs, or transferable skills

AI can’t join the dots. It doesn’t understand nuance, trade-offs, or leadership judgement. But by all means, let it decide your future hires.


8. Stretch the Process to Six Months

A slow process tells candidates:

  • You’re disorganised

  • You can’t make decisions

  • You don’t value their time

Top candidates will:

  • Lose interest

  • Accept other offers

  • Disappear politely (or not so politely)

And then, you guessed it, you start again…


9. Add Lots of Late-Stage Testing

Psychometrics. Case studies. Presentations. Preferably after eight interviews.

Then:

  • Over-index on the test results

  • Ignore lived experience and judgement

  • Outsource the decision to a spreadsheet

Isn’t hiring meant to be your job as a leader?


10. Offshore Recruitment to Cut Costs

To really elevate the experience:

  • Offshore your recruitment team

  • Remove all local market understanding

  • Spam candidates with irrelevant roles

Bonus points if candidates join your “talent pool” and immediately regret it.


11. Ignore Context, Training, and Experience

Assume:

  • Skills are static

  • Learning curves don’t exist

  • Onboarding is optional

Remember: Be clueless, it's ok.

 

12. Change the Role Mid-Process Without Telling Anyone

A classic.

  • Shift priorities

  • Add responsibilities

  • Remove budget

  • Keep recruiters and candidates in the dark

Confusion builds character. Or resentment. Mostly resentment.


13. Treat Candidates as Commodities

No feedback. No updates. No respect.

This ensures:

  • Brand damage

  • Glassdoor reviews

  • Long memories

Senior talent talks. A lot.


14. Finally, Blame the Recruiter

When it inevitably goes wrong:

  • Blame external partners

  • Blame TA

  • Blame “the market”

Anything except the process.


The Real Point, (Because Yes, This Is Satire)


Great recruitment requires:

  • Leadership involvement

  • Clear priorities

  • Realistic expectations

  • Mutual accountability


Context matters. Experience matters. Training matters.

And above all people matter.


If you want to attract top talent, the opposite of everything above applies.

If you want help fixing it, well, that’s where we come in.


C Graham Consulting

 

 
 
 
By Christopher E.D. Graham FCIPD — C. Graham Consulting - CGC
By Christopher E.D. Graham FCIPD — C. Graham Consulting - CGC

Artificial intelligence has moved from experimental HR technology to a central component in recruitment. Automated sourcing systems, résumé screening tools, large language model assessors, and AI-driven ranking engines are now standard features in many talent acquisition functions. The promise is consistent: speed, efficiency, fairness, and the removal of human bias.

But this narrative is incomplete.

In reality, AI in hiring is not neutral. Research from MIT, Oxford, Stanford, the OECD, and NIST demonstrates that AI systems in recruitment often reflect and amplify the assumptions built into their data, design, configuration, and operational context. Instead of eliminating bias, AI reorganises it and often does so more consistently, more quietly, and at far greater scale.

For organisations hiring at senior and executive levels, this distinction matters profoundly. Leadership appointments shape culture, capability, and commercial outcomes for years. These decisions cannot be delegated to automated recruitment systems that lack global context, cultural understanding, and intuitive judgement.

What follows is an expanded analysis of how bias enters AI-driven hiring, why it affects leadership selection disproportionately, and why senior recruitment still requires human discernment, rather than algorithmic pattern matching.

AI begins with human inputs: How search parameters shape outcomes

AI recruitment tools only operate within the parameters set by the humans who use them. This makes user inputs the first and often the strongest source of structural bias.

Recruiters routinely apply search filters relating to:

·       required locations

·       specific companies or industries

·       rigid years of experience

·       academic institutions

·       titles containing exact keywords

·       gender or diversity balancing

·       “must-have” sector exposure

While these criteria may appear rational individually, when combined they often constrain the search so tightly that entire categories of strong leaders are excluded before the AI begins its work.

For example:

·       A “25 years minimum” requirement can exclude high-performing leaders with accelerated careers.

·       A strict industry filter can eliminate strong operators from adjacent markets who bring fresh perspective.

·       A narrow location filter may ignore internationally mobile talent.

·       Keyword-driven searches miss senior leaders whose CVs reflect unconventional or non-linear career paths.

These restrictions are not malicious but they signal to the AI what counts as acceptable. Once those instructions are entered, the algorithm optimises within those constraints without question.

Global talent hubs amplify input bias

Many global companies centralise sourcing in low-cost talent hubs (e.g., India, the Philippines, Poland). These TA teams are highly capable, yet they naturally rely on familiar CV formats, universities, and career structures.

Their interpretation of what “strong experience” looks like is influenced by their domestic market.

AI then amplifies these interpretations at scale.

This is one of the reasons why global hiring funnels often skew toward particular regions or backgrounds not because of deliberate preference, but because the search begins in a narrow lane and AI is designed to stay inside it.

 

Training Data and Machine Learning Design: How AI Learns Bias

Even when search parameters are fair, AI systems inherit bias from the data used to train them. Machine learning models recognise patterns, not principles and if the pattern is incomplete or skewed, the model will misinterpret reality.

One of the clearest examples is the “Gender Shades” study (Buolamwini & Gebru, MIT). Commercial facial-analysis systems misclassified darker-skinned women up to 34.7% of the time, compared with 0.8% for lighter-skinned men. The flaw was simple: darker-skinned women were severely under-represented in the training data.

The same principle applies to AI hiring tools.

Many résumé classifiers and relevance scorers are trained on:

·       American or Indian CVs

·       technology-sector profiles

·       English-language datasets

·       linear career trajectories

·       job descriptions from specific markets

When these systems encounter different leadership models for example, European generalists, Middle Eastern strategy leaders, or Asian cross-sector executives the algorithm may not understand the career signals.

It may mis-rank profiles simply because they do not match the patterns it has learned.

AI can infer identity even when not instructed to

Oxford’s Sandra Wachter has shown that AI systems often infer sensitive characteristics indirectly through non-sensitive data, including:

·       names

·       geography

·       writing style

·       education patterns

·       employment gaps

·       job title conventions

This means algorithmic bias can occur even when a system is explicitly designed to avoid demographic variables. The model reverse-engineers identity from patterns in the data.

The implication is clear:

If the training data is not truly global, the AI cannot evaluate global leadership talent correctly.

 

Corporate Priorities, Diversity Policies, and Regional Norms Influence AI Behaviour

AI-driven hiring does not occur in a vacuum. Recruitment systems are shaped by organisational objectives, corporate culture, diversity strategies, and local expectations.

Western markets: Diversity-led configurations

In the UK, Europe, and North America, many organisations incorporate:

·       gender or ethnicity targets

·       ESG-linked hiring mandates

·       balanced shortlists

·       intervention mechanisms favouring under-represented groups

To support these imperatives, vendors sometimes adjust model behaviour, for example, boosting visibility for certain demographics or reducing over-represented profiles in early filtering.

This does not mean AI is “biased”, rather, it reflects a corporate decision to correct systemic imbalance. But it still means AI is intervening in the ranking process.

Asian markets: Credential-heavy models

Across much of Asia, hiring priorities differ. Employers often emphasise:

·       academic pedigree

·       tenure and loyalty

·       technical qualifications

·       hierarchical progression

·       cultural fit

AI systems configured for these environments reproduce those preferences reinforcing credential-first selection, which can unintentionally filter out globally minded or cross-sector leaders.

Multinational organisations: Conflicting influences

A large organisation might:

·       design its hiring systems in the US,

·       configure diversity settings in Europe, and

·       operate sourcing out of India.

Each region brings its own assumptions. AI attempts to reconcile those assumptions, but the end result can be inconsistent shortlists or confusing ranking behaviour.

The OECD, NIST, and the World Economic Forum all emphasise that AI must be governed, not left to drift, because competing priorities make neutrality impossible.

Talent Acquisition Culture: AI Reflects Human Judgement; It Does Not Replace It

Although AI is marketed as objective, it remains deeply influenced by the people who operate it. Talent acquisition teams with their own cultural norms, professional backgrounds, and regional understanding guide how AI interprets and evaluates talent.

Interpretation matters more than automation

Recruiters decide:

·       how to structure Boolean and AI-enhanced searches

·       which CVs “fit” the brief

·       what career progression “should” look like

·       how to weigh different industries

·       when to override or trust algorithmic recommendations

AI tools simply magnify these judgements.

Centralised sourcing teams create consistent patterns not necessarily global ones

When a large proportion of global hiring is conducted from a single region (e.g., India), the early funnel will often reflect:

·       local familiarity

·       local career logic

·       local CV structures

·       local interpretation of “strong experience”

This is not a criticism of regional TA teams it is a predictable structural outcome.

AI learns from the culture around it. If one region dominates the process, the system reflects that region’s worldview.

A 2023 multidisciplinary study confirmed that AI does not correct human bias; it scales it. Recruitment automation increases the reach of the assumptions held by the people using it.

 

Salary Alignment: The Most Misunderstood Filter in Senior Hiring

In senior and C-suite hiring, compensation expectations are not merely a preference they determine feasibility.

A candidate earning USD 1.5m will not accept a role paying USD 500k, regardless of brand or opportunity. Leaders at this level typically have:

·       significant financial commitments

·       established living standards

·       family obligations

·       long-term incentive vesting

·       multi-year compensation structures

Ignoring salary alignment leads to wasted effort and failed searches.

Why salary filtering is legitimate but can still introduce bias

Salary data becomes problematic when:

·       models infer compensation from incorrect assumptions

·       AI misinterprets global pay structures

·       TA teams use salary as a proxy for seniority or capability

·       regional compensation norms distort international hiring

This is particularly evident when AI designed in the US or India is used to evaluate European or Middle Eastern executive candidates.

Salary alignment must therefore be guided by actual market intelligence, not AI predictions.

Company Expectations and Internal Assumptions Shape the Search Before AI Even Begins

Recruiters search for what the business asks for.AI then amplifies those requirements.

Internal hiring briefs may include:

·       preferred competitor backgrounds

·       specified leadership styles

·       desired personality traits

·       preferred nationalities or cultural backgrounds

·       rigid academic prerequisites

·       specific sector-only experience

Some of these expectations are sensible. Others are unexamined assumptions that narrow the pool unnecessarily.

Advisory capability is essential in executive search

At the senior end, recruiters must act as advisors, not order-takers. Effective executive search requires pushing back when criteria are:

·       too narrow

·       not aligned with the market

·       exclusionary without reason

·       based on personal preference rather than business need

AI cannot question a flawed brief it reinforces it.

This is why the value of a strategic search partner increases at senior levels: the partner helps the organisation distinguish between true essentials and assumed essentials.

When the brief is biased, the entire search becomes biased no matter how advanced the algorithm.

Bias Is Global, Not a “White Male” Issue

A persistent misconception is that algorithmic bias stems exclusively from “white male developers” or Western institutions. This narrative oversimplifies the issue and obscures the structural reality.

Bias emerges wherever:

·       datasets are incomplete

·       cultural assumptions dominate

·       algorithms learn from limited perspectives

·       talent acquisition is regionally concentrated

This happens in:

·       the United States

·       India

·       China

·       Southeast Asia

·       Europe

·       the Middle East

·       Africa

Bias is not tied to one demographic, country, or cultural group.

Bias is created whenever a single worldview shapes the system regardless of who or where it comes from.

Understanding this is essential for building responsible, globally informed hiring processes.

Why AI Cannot Be Considered Neutral in Senior Hiring

When user inputs, training data, corporate expectations, TA culture, salary feasibility, and internal assumptions intersect, AI hiring systems cannot produce neutral outcomes.

AI appears objective, but in reality, it:

·       extends the worldview of its operators

·       reflects the structure of its data

·       responds to organisational priorities

·       reinforces hiring norms

·       misinterprets unfamiliar career paths

·       struggles with global leadership diversity

For senior and executive hiring, where stakes are high, this creates risk. Misaligned leadership appointments affect strategy, culture, performance, and stability.

Institutions including MIT, Oxford Internet Institute, Stanford HAI, OECD, NIST, UNESCO, SHRM, and CIPD all emphasise that AI-driven hiring must be governed carefully.

Automation can support decisions, but it cannot replace strategic judgement.

Why Executive Search Still Matters in an AI-Driven World

AI is helpful in volume hiring and administrative workflows. But senior leadership hiring is fundamentally human. It requires qualities that AI cannot evaluate:

·       judgement

·       adaptability

·       emotional intelligence

·       cultural fluency

·       ambiguity tolerance

·       strategic foresight

·       character and integrity

AI recognises patterns it does not understand nuance.

What executive search adds that AI cannot:

·       global reach beyond automated sourcing funnels

·       deep market insight across regions and sectors

·       evaluation of non-linear or unconventional careers

·       the ability to interpret cultural and organisational fit

·       balanced assessment of leadership capability

·       robust challenge to narrow or biased hiring briefs

·       advisory support to boards, CEOs, and CHROs

At C. Graham Consulting, we view AI as a useful tool but not a decision-maker. Leadership appointments require experience, contextualisation, and human discernment.

The rise of AI makes expert executive search more important, not less.

Selected Global References

Academic & Research Institutions

·       MIT Media Lab — “Gender Shades” (Buolamwini & Gebru)

·       Oxford Internet Institute — Algorithmic fairness research (Wachter et al.)

·       Stanford HAI — AI Index Report

·       Brynjolfsson, Li, Liang — AI labour-market impact studies

·       The Alan Turing Institute — Responsible AI guidelines

Global Governance Bodies

·       OECD — Principles on AI; Employment Outlook

·       UNESCO — Recommendation on the Ethics of AI

·       NIST — AI Risk Management Framework

·       World Economic Forum — Responsible AI in HR Toolkit

Professional & Industry Bodies

·       CIPD — Fairness and AI in people management

·       SHRM — Studies on AI-driven recruitment

·       ILO — Labour market and technology reports

Technical & Multidisciplinary Studies

·       “Fairness, AI & Recruitment” (2024)

·       “Multidisciplinary Survey on Algorithmic Hiring” (arXiv, 2023)

·       Partnership on AI — Responsible hiring frameworks

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