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.
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.
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
White-Collar Work in Financial Services & Consulting: A Clearer, More Positive Outlook for 2026
As we reach the end of another demanding year, many people in financial services and consulting are trying to make sense of the constant changes around them. Headlines about automation, restructuring and artificial intelligence can sometimes suggest that professional work is disappearing. Yet the evidence from the most respected research firms tells a very different story.
Across major global hubs New York, Boston, Miami, Texas, London, Paris, Zurich, Singapore, Dubai, Hong Kong and Sydney white-collar work is not collapsing. It is being reshaped. Jobs are moving, responsibilities are evolving, and skillsets are changing, but the overall picture is far more encouraging than the noise suggests.
1. A Market in Motion: Jobs Lost, Jobs Created, and Jobs Transformed
One of the strongest perspectives comes from McKinsey Global Institute, which tracks structural changes in employment across the United States and Europe. They report that the US saw 8.6 million occupational transitions between 2019 and 2022, with a further 12 million expected by 2030.
These are not simple job losses; they reflect a shift in the nature of work. Roles declining most sharply tend to be routine: administrative support, basic customer service and repetitive middle-office tasks.
Meanwhile, opportunities are increasing in areas such as:
data and digital product leadership
risk management and regulatory strategy
legal and governance functions
transformation and change
technical and STEM roles
senior leadership and oversight
As McKinsey put it:
“High-skill employment continues to rise, even as routine white-collar roles decline.”
This helps explain why markets such as New York, London, Singapore, Dubai and Sydney continue to hire senior specialists even when junior or transactional roles are trimmed.
2. The Technology Shift: Automation Is Changing Tasks, Not Replacing People
Gartner’s work provides a grounded view of automation and AI adoption. Their research shows that organisations combining automation, AI and process redesign may reduce operational costs by around 30%, often by improving accuracy and removing duplication.
Crucially, Gartner’s deeper findings emphasise:
Automation replaces tasks, not entire roles.
Efficiency gains come from reducing administrative burden.
Demand is rising for oversight, interpretation and judgement.
As firms adopt more technology, they invest more in experienced leadership.
A Gartner finance survey also notes that 58%of finance functions now use AI or machine-learning in at least one process up sharply from the year before.
This raises the strategic importance of skills related to:
model governance
data quality
operational risk
AI ethics
digital product ownership
While some functions in operations, clerical work and basic research are diminishing, the surrounding ecosystem of higher-responsibility roles is expanding.
3. Human Expectations: Burnout, Stress and the Search for Better Work
Technology is not the only force reshaping the workforce. Mercer’s Global Talent Trends report highlights a growing human factor: burnout and financial stress.
Mercer reports that:
82% of employees globally are at risk of burnout.
In Asia, 83% reported experiencing burnout in the past year.
Employees lose approximately six working hours per month worrying about finances.
These pressures are particularly pronounced in professional and financial services, where intense workloads are common and market cycles unpredictable.
Burnout does not simply reduce productivity. It leads people to reconsider their long-term career direction, prompting many mid-career and senior professionals to become more open to movement. This is contributing to the increasing fluidity in talent pipelines across global hubs.
4. Confidence Amid Change: Workers Are More Mobile, Not More Fearful
PwC’s Workforce Hopes and Fears Survey shows an interesting paradox: although many feel overwhelmed by rapid technological change, a growing number are optimistic about their future.
Across 56,000 respondents in 50 countries:
Workers believe change may bring new opportunities.
Those who use AI daily are significantly more confident about skills and salary progression.
In Singapore, 34% expect to change employer in the next year higher than during the “Great Resignation”.
This increased mobility benefits organisations that invest in leadership, capability and culture. It also opens the door to strategic senior hiring.
5. How Organisations Are Responding: From Productivity to Human Performance
Deloitte’s Human Capital Trends report illustrates how organisations are adapting to this shift. Many are moving away from narrow concepts of productivity and towards a broader focus on human performance.
Key themes include:
a shift to skills-based work models
deeper investment in leadership capability
redesigning teams rather than merely upskilling individuals
recognising that technology adoption often outpaces workflow redesign
In financial services and consulting, where professional judgement and trust are central, this alignment between human capability and digital tools is rapidly becoming a competitive advantage.
6. Organisational Structure: The Compression of Middle Management
Korn Ferry’s research highlights one of the most important structural developments: the flattening of organisations.
Their global survey of 15,000 professionals shows that many companies have removed one or more layers of middle management in recent years. This has created:
fewer traditional mid-management roles
a shortage of experienced team leaders
increased responsibility at senior levels
faster progression for those with leadership capability
Korn Ferry also found that 67% of employees would stay with their employer if given a clear development pathway even if they were dissatisfied with aspects of their role. This underscores the strategic value of career clarity and progression frameworks.
7. Hiring Trends: More Selective, More Strategic, Still Active
CIPD’s Labour Market Outlook provides additional insight into the UK and European markets. Their findings include:
17% of employers expect AI to reduce headcount over the next year.
Reductions are concentrated in administrative, clerical and junior managerial roles.
Overall employment expectations remain mildly positive, though more cautious than in previous years.
Wage growth has stabilised at around 3%.
Michael Page’s Talent Trends reports, particularly from Asia, reinforce this. Professionals increasingly expect:
clearer compensation frameworks
flexibility
transparent progression
supportive leadership
This mirrors what CGC observes: although many firms are reducing general headcount, they continue to invest in senior and specialist appointments, particularly in roles tied to transformation, regulation, technology, governance and digital capability.
8. Looking Ahead to 2026: A More Balanced and Encouraging Landscape
Across the major global financial and consulting centres, a consistent pattern is emerging.
Roles experiencing the sharpest decline:
administrative and clerical work
processing and transactional tasks
some middle-office functions
basic research or analysis roles
traditional layered middle management
Roles growing strongly:
data science, analytics and digital leadership
governance, audit, compliance and risk oversight
AI model governance and technology ethics
transformation, programme leadership and operational redesign
wealth and asset management leadership
sustainability, infrastructure and energy transition advisory
senior and executive management, including Partner-level roles
This shows that work is not disappearing it is evolving. Many of these emerging roles are more intellectually engaging and more strategic than the roles declining.
The future of white-collar work centres on capabilities such as judgement, interpretation, communication, ethical decision-making, and the ability to lead people through complex change. Technology acts as an enabler, not a replacement.
A Positive Note to End the Year
As we close 2025, the evidence offers reassurance. The world of work continues to change, but the opportunities for skilled professionals, thoughtful leaders and experienced advisers remain strong.
In financial services and consulting, the most valued capabilities are still profoundly human.
At CGC, we see daily how organisations are seeking leaders who can bring clarity to complexity, create momentum and inspire confidence as they navigate transformation. These are not roles that automation can replace.
If you would like to discuss your 2026 leadership plans, explore insights for your sector or consider how to refine your talent strategy, CGC is here to support you across Europe, the USA, the Middle East and Asia.
What This Means for Employers in 2026
Hire strategically, not broadly; focus on roles that drive transformation.
Position your EVP around clarity, flexibility and development.
Build teams that support human performance, not only productivity.
Use AI to empower people, not replace them.
Strengthen leadership benches early, especially in global hubs.
What This Means for Professionals in 2026
Develop skills in AI literacy, governance, data interpretation and transformation.
Seek employers that invest in your growth and well-being.
Build leadership capability early—middle-management layers are thinner.
Stay open to mobility; global markets are receptive to seasoned, adaptable talent.
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