For years, breaking into investment banking or top-tier consulting in the UK required a familiar formula for high-flyers: academic excellence, technical mastery, polished storytelling, and relentless preparation. Challenging and competitive enough - but in recent months, a new variable has entered the equation.
How AI is Reshaping Junior Finance and Consulting Recruitment
AI is now firmly integrated into both sides of the junior recruitment process. Candidates are using it to prepare and apply, while businesses and recruiters are using it to screen candidates. The interaction between these two forces is transforming how capability is assessed.
For banks, consultancies, and candidates alike, the implications are profound. In this article, we share some of our findings from candidates who have recently applied to graduate schemes, surveyed by Freshminds in November 2025.
AI as the Candidate Preparation Co-pilot
Freshminds’ proprietary research into 2025 investment banking applicant behaviour shows a clear pattern: AI is no longer a niche tool; it has become a standard preparation companion.
Candidates surveyed by Freshminds - mostly undergraduates from leading UK universities - overwhelmingly reported using AI across nearly every stage of preparation. This included:
CV and cover letter drafting: tailoring language to job descriptions, improving clarity and structure
Technical drilling: practising Discounted Cash Flow (DCF) models, comparables, accretion/dilution, case frameworks
Interview simulation: mock Hirevue interviews, preparing structured behavioural answers, timed responses
Firm research: summarising recent news or deals, extracting insights from annual reports, tracking sector trends
Many candidates emphasised that AI improves efficiency, but that judgment and originality remain their own. Others admit to using it at “just about every stage”.
Furthermore, at the interview stage, junior applicants are arriving more rehearsed, more consistent, and technically sharper - especially in the early rounds.
For firms looking to identify future banking and consulting stars, this means first-round performance may be less predictive of underlying capability than it once was. And with applications looking increasingly similar and the volumes being much higher, it’s becoming a lose-lose for both candidates and businesses.
“I used AI to summarise financial news, clarify complex concepts, and check the clarity of my written answers. However, I always made sure to verify the information myself and adapt the outputs to reflect my own understanding and personality.”
Freshminds candidate, Nov 25
For a deeper look at how candidates specifically are adapting, see how AI is changing graduate recruitment and internship applications.
AI in the Funnel: Perception and Reality
At the same time, the candidates in our survey widely believed that AI is embedded in early-stage screening.
The most commonly cited touchpoints include:
Applicant Tracking Systems (ATS) filtering CVs
AI-scored psychometric tests
One-way video interviews (for example, Hirevue-style platforms)
Online assessments with automated proctoring/supervision
Whether or not AI is always used to score responses, candidates believe that it is. Some firms explicitly state that AI does not score one-way interviews, while others provide limited transparency.
Where signalling is unclear, anxiety rises - particularly around fairness, bias, and false negatives. Some candidates report avoiding AI in applications and assessments entirely, fearing penalties for “cheating,” even when their fellow applicants relied heavily on it.
For employers competing for top UK talent, inconsistent messaging in this area may quietly erode brand trust.
The Problem of Homogenisation
One consequence of AI-assisted preparation is “answer convergence”. When thousands of candidates use similar tools to structure responses, generate examples, and refine delivery, patterns tend to emerge:
Highly polished but formulaic answers
Identical framing of answers to “Why this firm?”
Overly structured case approaches
Technically correct but mechanistic explanations
Traditional scoring systems may inadvertently reward polish over depth, but polish is now commoditised. This presents a new assessment challenge. If everyone sounds prepared, how do you detect genuine understanding? Firms must be able to probe for immediacy, creativity or cognitive robustness:
Transfer learning under unfamiliar scenarios
Multi-step reasoning with changing assumptions
Live recalculations in dynamic modelling tasks
Reflection questions (“Why this approach over alternatives?”)
Some organisations have developed their own AI-proof systems for assessments. For example, McKinsey has developed Solve, described as “a gamified assessment created to showcase your problem-solving abilities”. This cannot be used with AI assistance, and McKinsey states that “we are committed to providing a fair and objective assessment experience for every candidate.”
Similarly, Macfarlanes, a London-based law firm, has introduced a case study-type assessment for potential trainees, which is designed to reflect the real-life experience of working in a client-facing role in a fast-paced environment. Described as an immersive experience, it asks candidates to interact with and react to work tasks, testing applied judgment, decision-making and tech literacy.
Ethics and Fairness
AI’s dual presence in preparation and screening has created a subtle ethical tension.
Candidates can be segmented into roughly three behavioural groups:
Augmenters – These use AI to accelerate research, improve clarity, and generate practice questions, but they take time to verify outputs and maintain authorship.
Dependents – Rely heavily on AI to shape narratives and structure responses.
Abstainers – Avoid AI entirely, often due to fairness concerns or fear of penalties.
For employers, this segmentation is revealing. It may correlate with an individual’s future working style, appetite for risk, and judgment maturity.
More importantly, fairness signalling has become a competitive differentiator.
Junior talent, particularly from diverse or non-traditional backgrounds, is sensitive to perceived bias in algorithmic screening. If AI is involved in filtering, firms must demonstrate:
Human-in-the-loop decisions and safeguards
Clear evaluation criteria
Transparent communication of AI usage
Bias auditing processes
Confidence in fairness increasingly influences offer acceptance rates.
“AI has been increasingly present in recruitment processes, particularly through automated CV screening and online assessments. I’ve noticed its role mainly in the initial stages, helping firms manage large volumes of applications efficiently. However, I still believe that human interviews remain the most decisive part, as they allow candidates to demonstrate motivation and cultural fit.”
Freshminds candidate, Nov 25
From Output Evaluation to Process Observation
If AI improves output quality, assessments must shift to allow for this. Process visibility is becoming more important.
Banks, businesses and consultancies can respond by:
Using adaptive, evolving case prompts that resist rote preparation
Embedding live problem-solving elements with time pressure
Introducing assumption sensitivity testing
Requiring candidates to explain trade-offs and decisions in real time
Scoring reasoning articulation, not just final answers
In consulting interviews, this may mean deliberately shifting case variables mid-discussion. In finance, it may involve adjusting modelling assumptions on the fly.
AI can help candidates prepare for frameworks but it is less effective at simulating genuinely dynamic uncertainty, and recruitment design must reflect that distinction.
Is AI a Responsible Upgrade or a Threat?
It would be reductive to view AI purely as a distortionary force. Used responsibly, AI can improve recruitment quality and accessibility. In February 2026, it was reported that Mishcon de Reya is trialling Bright Apply, an AI-powered candidate screening tool for its 2026 graduate recruitment season. The tool uses information from applications to conduct a tailored interview, a transcript of each one is passed to the early careers team to review. The firm states that feedback has been positive, and that the tool “puts candidates at the heart of the process”.
For firms, opportunities include:
AI summarisation tools that support - but do not replace - human evaluation
Intelligent insights extraction from long-form answers
Structured feedback generation to improve candidate experience
Data-driven analysis of pass-through rates to identify bias
The principle should be augmentation, not automation, and human judgment must remain decisive, particularly at the final stages of a process.
This sits within a broader wave of business transformation — explore the key trends shaping organisations in 2026.
Implications for Junior Candidates
For aspiring analysts and associates in the UK, the new landscape requires recalibration. AI can accelerate learning, improve structure, help with blind spots and simulate pressure. But it cannot substitute for deep conceptual understanding, original thinking or commercial instinct.
Candidates who use AI well - verifying outputs, stress-testing assumptions, and retaining ownership of insight - are likely to outperform both over-reliant peers and abstainers.
The strongest signal in 2026 may not be whether AI was used, but how thoughtfully it was used.
“I used AI as a study partner. It helped me review technical topics (DCF, trading comps, accretion/dilution), brainstorm practice questions, and tighten wording. I wrote every answer myself, verified information with primary sources and avoided sharing any personal or confidential data. AI improved efficiency, but judgment and originality were mine.”
Freshminds candidate, Nov 25
What Employers Should Be Factoring In
If AI makes candidates better prepared, and AI makes screening more efficient, does the recruitment process become more meritocratic - or more mechanistic? The answer may depend upon design.
If firms overweight automated early filters and reward surface polish, they risk selecting the most AI-optimised applicants. If they redesign assessments around reasoning, adaptability, and ethical judgment, AI may elevate the entire talent pool.
For UK investment banks and consulting firms, the competitive advantage for early careers hiring will lie not in avoiding AI - but in governing it intelligently.
A New Equilibrium
AI has become both a preparation co-pilot and a gatekeeper.
It has raised the baseline quality of applications while compressing preparation cycles. It has introduced fairness concerns. And it has forced employers to rethink how potential is measured.
The firms that respond thoughtfully - by increasing transparency, strengthening human oversight, and redesigning assessments for depth rather than polish - will potentially attract stronger talent and protect their employer brand.
For candidates, the message is equally clear: use AI to accelerate capability, not to replace it.
AI in Graduate Recruitment: FAQs
How is AI changing graduate recruitment in the UK?
AI is now present on both sides of the process - candidates are using it to prepare and apply, while firms are using it to screen and assess. This is transforming how capability is measured and raising new questions about fairness and meritocracy.
Are UK graduates facing a tougher jobs market as AI transforms recruitment?
Yes - while AI has raised the baseline quality of applications, it has also made the process more competitive. With higher application volumes and increasingly polished candidates, it's becoming harder for graduates to stand out. Firms are having to rethink how they identify genuine talent beyond surface-level polish.
What is "answer convergence" and why does it matter?
Answer convergence is when large numbers of candidates produce very similar responses due to using the same AI tools. It makes it harder for recruiters to differentiate between applicants based on traditional scoring methods.
How can firms assess genuine capability if AI improves everyone's answers?
The focus should shift to process observation - live problem-solving, adaptive case prompts, assumption sensitivity testing, and asking candidates to explain their reasoning in real time.
What are the ethics of AI in recruitment?
AI's dual role in both preparation and screening creates ethical tensions around fairness and bias. Firms should ensure transparency about how AI is used, implement regular bias auditing, and keep human judgment decisive - particularly at the final stages of hiring.
Will AI make recruitment more or less fair?
It depends on how firms design their processes. If automated filters are overweighted, the risk is selecting the most AI-optimised candidates rather than the most capable. Thoughtful design could, however, raise the quality of the entire talent pool.
