AI for Hiring - how AI is transforming talent acquisition

This is the first of a three-part series focused on the use of AI in corporate environments.

This first piece focuses on AI for hiring, the second will focus on AI for training and the third will focus on AI for enhancing productivity.

We will introduce the key areas of innovation in each article, before presenting a market map inclusive of differentiation angles and opportunities for startups.

This article works through:

• A framing of AI for hiring

• An overview of trends and the impact of AI in hiring

• A market analysis, including a 'living draft' market map

To jump ahead, click the links above!

Without further ado...

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The rise of AI in hiring

AI is fundamentally reshaping the hiring landscape.

AI-powered solutions are capable of offering unprecedented efficiency gains, reducing human biases, and enabling a shift from traditional, formal credential-based recruitment to a more dynamic, skills-focused approach.

As is frequently discussed, workers are participating in several mini-careers throughout their working lives, so employer demand for formal credentialing is being overtaken by demand for provable skillsets that can be applied to a number of career opportunities.

However, this transformation brings challenges, namely in ensuring transparency, fairness, and the role of human judgment in evaluating candidates.

Extensive research from leading institutions underscores the increasing role of AI in modernising hiring processes - including McKinsey’s Global Institute, World Bank, and European Union. We reference this work in this analysis. Such is the pace of adoption that national governments are finding themselves needing to regulate this burgeoning use of tech as it penetrates many key decision-making processes and begins to play a growing role in shaping organisations.

AI's influence permeates various facets of recruitment, from talent identification to onboarding, with significant potential to redefine workforce dynamics. This said, we have not considered the evolving development, placement and hiring of agents/ bots for roles i.e. advertising of roles designed to be entirely undertaken by agents.

An analysis of recent studies and reports highlights AI's transformative impact across four critical sub-areas of hiring which we introduce below and then dive into in more detail in subsequent sections:

1. AI’s role in identifying and assessing talent

   AI has revolutionised the process of matching candidates to job roles by analysing not only resumes but also inferred skills and potential. The McKinsey Global Institute reports that organisations leveraging AI-driven assessments have seen a 35% improvement in aligning candidates with appropriate roles, credited with enhanced productivity and reduced turnover. This evolution is particularly vital as the labour market increasingly demands adaptable, cross-functional skill sets. Organisations are hiring for roles they anticipate being dynamic, rather than static.

   
   

2. AI-powered screening and bias reduction

   The potential of AI to minimise biases in hiring decisions is a focus of extensive research. While algorithms can be designed to emphasise competency, studies from the European Commission and others caution that if these systems are trained on biased historical data, they may actually perpetuate existing prejudices. This said, AI has demonstrated promise in structuring more objective evaluations than traditional human-led screenings- arguably, it has potential to further promote diversity and inclusion...

   
   

3. AI in onboarding, including early productivity

   AI is redefining onboarding processes, primarily by focusing on expediting the time it takes for new employees to reach ‘full’ productivity (i.e. the levels expected and beyond). Reports from Deloitte indicate that AI-driven onboarding solutions can accelerate learning curves by 20-30% through personalised training programmes, plus bringing a focus on necessary information rather than bombardment (as is typically the case when starting a new role!). AI-powered digital assistants and chatbots offer real-time support to new hires, enhancing knowledge retention and engagement during the critical initial phases of employment and during moments in which new joiners ‘require’ new information. We refer to this as learning in the flow of work.

We do not have a standalone ‘challenges’ section but have instead opted to weave potential concerns and drawbacks into each of the three sections. At a high level, it’s important to acknowledge that the integration of AI in hiring raises ethical concerns, particularly regarding transparency and fairness. Findings from the World Bank emphasise that a lack of ‘explainability’ in AI-driven hiring decisions can pose regulatory and reputational risks for organisations- this refers to the fact we don’t have a case-by-case description of all ‘decisions’ taken by AI systems and do not consistently check and update the assumptions behind key decisions.

Implementing AI necessitates rigorous oversight, including regular audits and bias detection mechanisms, to ensure fairness and compliance with AI governance frameworks, as they are formalised. Not only this, tech can be limited by what is measurable and observable, as opposed to the factors that shape an employee’s likely success in a role.

As organisations increasingly incorporate AI into their recruitment processes, balancing automation with human judgment becomes imperative. AI should serve as a tool to enhance, not replace, human decision-making in hiring. Its responsible application will determine whether it acts as a catalyst for fair, efficient, and inclusive workforce development.

Part 1 - trends overview

We will now dive into each of the three topics above in more detail, including comments from leaders in the space, taken from direct interviews we undertook as part of this project.

Click to expand the below - this section includes expert comments:

AI's role in identifying and assessing talent

AI technologies are transforming both i) the process of acquiring talent for hiring managers by automating repetitive tasks and analysing extensive datasets to identify suitable candidates, as well as ii) the nature and profiles of the talent being hired. On the latter point, the McKinsey Global Institute suggests that AI and automation are poised to significantly shift labour demand, particularly in STEM fields, necessitating an increased reliance on AI-driven hiring tools able to predict skills paths for individuals that map with organisations’ priorities.

Michelle Connon-Roodt (Global People Consulting at EY) commented:

“I see a lot of value in AI’s ability to map skills and aid skills-based hiring. The success of this kind of work is largely dependent on the accuracy of organisations’ central skills intelligence (i.e. knowledge of what skills they need, already have and how to plug gaps). Organisations are shifting their hiring processes, hiring for skills required now but also with an eye to the future on the skills likely to be required in that role in the period ahead. They’re no longer hiring for static jobs. They are hiring into positions that will be dynamic, evolving, with varied skills paths.”

Further research underscores AI's impact on recruitment efficiency. A study by James Wright and Dr. David Atkinson highlights that traditional hiring processes, relying heavily on CVs and interviews, have been found to be only 16% effective in identifying the right candidate for a role. The integration of AI can enhance this effectiveness by automating the sourcing and screening of candidates, thereby improving the quality and speed of hires. If an AI solutions promised that it could make hiring decisions 25% effective, you’d think that sounded bad or not worth the investment, but it would still be a 9% improvement on human-driven processes, based on this data… This is to say that our baseline is pretty low.

Additionally, AI's ability to analyse vast amounts of data enables it to identify patterns and predict candidate success more accurately than traditional methods. This data-driven approach allows organisations to move beyond subjective assessments, focusing instead on empirical evidence of a candidate's potential fit and performance in the role. This will vary by role and by industry so models will need to be tailored accordingly, to make sure AI is prioritising the right aspects of candidates’ profiles.

By leveraging AI, companies can also potentially tap into a broader talent pool, including passive candidates who may not have been actively seeking new opportunities but align well with the organisation's needs. We revisit this further on in this blog when we explore opportunities for startups within the AI for hiring space.

AI-powered screening and bias reduction

As referenced elsewhere in this blog, AI's ability to minimise bias in hiring decisions remains the subject of extensive study - it will be difficult to eradicate fears of bias, simply because of humans’ varied roles in the loop. While algorithms can be programmed to focus on competency-based assessment, research from the European Commission highlights the risks of algorithmic bias if AI systems are trained on flawed or historically biased datasets, hence the need for frequent information and data auditing, outlined above. Nevertheless, AI has shown promise in structuring assessments in a more objective manner than traditional human-led screening, helping to enhance diversity and inclusion in hiring practices.

Roxana Dobrescu (Chief People Officer at commercetools) on AI and bias:

“AI is a powerful enabler in hiring, but it’s not a magic wand. Where it truly shines is in efficiency- automating repetitive tasks, sourcing candidates at scale, and moving from analysing large data sets to surface useable, useful insights. At commercetools, we see AI making a difference in resume screening, reducing bias in structured assessments, and even improving candidate engagement through personalised communication.”

However, Roxana also warns that AI is not a substitute for human intuition:

“But where does it fall short? Context and nuance. Hiring isn’t just about matching keywords to job descriptions- it’s about understanding potential, team dynamics, and cultural fit. AI can’t (yet) replace the human ability to read between the lines, challenge assumptions, and make judgment calls that go beyond data points.”

With this in mind, AI-powered solutions should be augmenting rather than replacing HR professionals, ensuring that hiring decisions account for soft skills, cultural fit, and personal motivations, which AI struggles to assess effectively.

There is a strong level of startup activity in this space, serving both candidates and recruiters, some of which is focused on interview and assessment practice.

Michelle Connon-Roodt (Global People Consulting at EY) on AI Transparency:

"There is of course a real need to understand desirable skills that you should look for when building your team, but the AI solutions have not yet managed to capture motivation, energy, and passion. They cannot, for example, give a good sense of how much a candidate wants to achieve."

Intriguingly, ‘bias’ is both a cause for concern and optimism. Indeed, a key advantage of AI-driven hiring tools is their ability to minimise unconscious bias, a common issue in traditional recruitment. A 2023 study by Piotr Horodyski found that AI-powered hiring platforms significantly improved hiring diversity by removing names and demographic details from applications before initial screening, allowing recruiters to focus on skills and experience alone.

This technology has been adopted by companies such as Unilever, which implemented an AI-powered hiring system to reduce bias. The AI-based screening process helped the company achieve a 16% increase in hiring diverse candidates while also cutting hiring time by 75% (James Wright & Dr. David Atkinson, 2019).

AI can also identify bias in human-led processes. Dr Fabian Stephany and Dr Johann Laux (Oxford University) have suggested that AI can be used not only for candidate screening but also for auditing historical hiring decisions, flagging inconsistencies or patterns of bias that may have gone unnoticed. It is not clear whether many organisations have conducted this retrospective analysis of their hires - we’re not sure they’d publish the results, for obvious reasons but this subject remains of interest all the same.

While AI presents a powerful opportunity to create fairer hiring processes, challenges remain. AI models trained on biased data can perpetuate discrimination rather than reduce it. Xinyu Chang's 2023 paper highlights an example in which an AI hiring system for a major tech firm automatically downgraded female candidates based on historical hiring patterns favouring male applicants.

To mitigate such risks, we need to pursue “explainable AI” in recruitment (also discussed in the introduction), ensuring that HR professionals can audit and understand why an AI has made a specific hiring recommendation. We should also pursue real-time AI auditing tools to flag potentially biased decisions.

Ultimately, AI’s potential to reduce bias depends on continuous monitoring, frequent recalibration, and human oversight.

AI in onboarding and early productivity

Beyond candidate selection, AI is playing a crucial role in accelerating onboarding, helping new hires integrate faster into the workforce. AI-driven onboarding programmes have the potential to drastically reduce time-to-productivity by tailoring training to individual needs and streamlining repetitive administrative tasks. This is a substantial improvement. Indeed, it could actually lead to more effective utilisation of probation periods - probation periods typically last 3-6 months but it’s often the case that a person is not properly integrated within the team and processes until the end of the period…what if their suitability could be well understood within the first 1-2 months, instead?

AI-powered onboarding solutions personalise learning by adapting to a new hire's experience level, learning preferences, and skill gaps. This approach ensures that employees receive highly relevant and effective training - this reduces inefficiencies associated with generic onboarding programmes. There is of course still an important place for cultural integration and not a fully automated/ arms-length onboarding programme.

Real-World Impact of AI Onboarding Solutions

Raf Guper (Co-founder at Ujji AI) on AI-powered onboarding:

"Slow time to value is one of the biggest pains leaders struggle with, especially as teams expand rapidly. It takes 6 to 12 months on average for new joiners to achieve full productivity. By answering certain questions provided by Ujji AI (via text, audio, or video), our tech gathers inputs to automatically structure customised onboarding playbooks. Adding internal resources- such as presentations, documents, call recordings- helps the AI capture further knowledge and build bite-sized video lessons, quizzes, and real-life tasks."

He explained AI’s impact with a real-world case study:

"AyaData saw their training NPS soar to 9.5 following onboarding training powered by Ujji AI. One employee stated: ‘It has affected my perception of Aya in a positive way because now I know the company is helping me grow and acquire knowledge, and it’s a good thing.’”

Similarly, 2023 research from Piotr Horodyski, referenced above, suggests that AI-based onboarding reduces knowledge gaps by 40% in the first 90 days of employment.

AI-Enhanced Coaching and Performance Monitoring

One of AI's biggest advantages in onboarding is its ability to provide real-time coaching and performance feedback. AI-powered platforms with coaching facilities provide interactive learning modules, assess employee progress in real-time, and offer instant feedback based on task completion and knowledge retention.

AI-driven coaching tools are making professional development more dynamic and adaptive, adjusting content in real time to align with industry trends and specific job functions. This means that employees receive ongoing, personalised support, allowing them to develop critical skills much faster. And employees are warming to AI coaches…

Michelle Connon-Roodt (Global People Consulting at EY) commented:

"Employees are increasingly comfortable with AI coaching, which can provide instant feedback and skill-building recommendations from the point an individual joins a team. AI feels like an ally that’s working on your side, making it easier to be honest about skill gaps without fear of judgment."

Automation of Repetitive Onboarding Tasks

AI also improves onboarding efficiency by automating repetitive administrative tasks, allowing HR teams to focus on strategic initiatives. According to McKinsey’s AI Report, companies that have implemented AI-based HR chatbots and automation tools have reduced onboarding-related paperwork time by 50%. 

For instance, AI-driven systems can:

• Auto-generate onboarding schedules based on role and experience.

• Automatically assign mentors or peer buddies based on skill needs.

• Analyse employee responses to onboarding surveys to improve programmes continuously.

• Provide new hires with AI-driven FAQ chatbots, reducing HR workload while ensuring 24/7 support.

The potential is enormous, but there are some drawbacks:

• AI cannot fully replace human mentoring, which remains critical for cultural integration and team dynamics.

• Some employees resist AI-driven onboarding, particularly those unfamiliar with digital learning platforms- this is likely to be sector- and possibly role-specific.

• Privacy concerns may arise as AI collects and analyses employee data.

Dr Fabian Stephany's (Oxford University) previous comments on AI’s Role in Workforce Learning:

"AI can enhance the onboarding experience, but companies must ensure it remains a tool for efficiency - not a substitute for human connection. Mentoring, collaboration, and cultural onboarding still require personal interaction."

Despite these challenges, AI-driven onboarding is rapidly becoming the norm, providing structured, scalable, and efficient learning that ensures employees hit the ground running.

Conclusions from this analysis for corporates

AI is undeniably transforming hiring by making talent acquisition more efficient, reducing biases, and shifting the focus toward skills-based hiring. However, as experts highlight, AI is not a standalone solution- it must work alongside human recruiters to ensure a fair and ethical hiring process.

On reviewing the analysis, we believe that organisations looking to adopt AI-driven hiring solutions should:

1. Ensure AI is transparent and explainable.

2. Use AI as an assistive tool, not a replacement for human decision-making.

3. Continuously audit and refine AI models to prevent bias.

4. Align AI-powered hiring strategies with long-term workforce planning.

5. Implement robust data privacy measures to comply with global AI governance standards.

With responsible implementation, AI has the potential to create a more inclusive, fair, and effective hiring process- one that benefits both businesses and job seekers alike.

Part 2 - market activity

Exploring (startup + scale-up) activity in this space

We dove into the market to understand where startups and incumbents are focusing their efforts. As such, we defined two axis for the map, which dictates internal sub-categories within quadrants.

The X-Axis

1. Automation vs. Augmentation

1. Automation – i.e. AI replaces human processes (e.g. fully automated resume screening, chatbot-driven interviews).

2. Augmentation - i.e. AI enhances human decision-making (e.g. AI-assisted candidate ranking, interview intelligence platforms).

The Y-Axis

2. Candidate-Centric vs. Employer-Centric

1. Candidate-Centric- i.e. Tools improving candidate experience (e.g., AI career coaching, AI-driven personalised job matching). Example types of solutions, for reference:

   1. AI-powered job matching

   2. Resume & interview optimisation

   3. Career guidance & upskilling

   4. AI-enhanced networking & referrals).

2. Employer-Centric- i.e. Tools optimising hiring for employers (e.g. AI-based ATS, predictive hiring analytics).

🚧 This market map is in draft and will be finalised by the publication of the final piece on 19th March. If you have comments, feedback or would like to be added, please get in touch with rs@brighteyevc.com! 🚧

We opted to keep the buckets relatively broad, because startups and scale-ups in the space tend to be solving for more than one of the sub-categories - for example, companies providing interview assistants for recruiters tend to also support with aspects of application tracking systems.

The sub-categories observed include but are not limited to:

• Interview assistants for recruiters

• Interview trainers for candidates

• Skills assessments for recruiters

• Skills assessment training for candidates

• Job marketplaces with application assistance

• Re-skilling and career pivot platforms

• And several more…

We have intentionally not included companies focused exclusively on the hiring of agents.

Avenues of Differentiation

We reviewed the companies in the map and considered the following avenues of differentiation for companies in the space.

Given the high density of employer-centric hiring solutions in the AI recruitment market, differentiation is key.

Below are five ways / areas that companies can begin to develop their moats:

1. Technical moats- AI capabilities and proprietary data

• Better AI models → The ability to leverage more advanced NLP and deep learning for talent acquisition (e.g., adaptive interview AI).

• Proprietary data → Access to unique candidate and hiring data (e.g., job history, skill assessments, behavioural analysis) that other platforms cannot replicate.

• Real-time learning AI → AI that continuously refines its job-matching algorithms based on hiring outcomes to improve predictions.

2. Workflow & integration moats

• Deep ATS and CRM integration → Offering native integrations with enterprise hiring stacks (for example, with Workday, SAP, LinkedIn Talent Hub, etc.).

• End-to-end talent lifecycle management → Expanding beyond hiring to internal mobility, reskilling, and talent retention.

• Cross-functional AI → Hiring tools that also enhance workforce planning, not just recruiting.

3. Business model moats- unique pricing and go-to-market strategy

• Freemium models → Giving candidates free AI-powered career coaching and monetising employer-side features.

• Pay-per-hire vs. Subscription → A contingency-based AI hiring model could disrupt traditional ATS pricing.

4. Candidate-centric AI- untapped personalisation & career ownership

• Personalised job search → AI that adapts to a candidate’s long-term career goals, not just immediate job matches.

• AI-powered personal branding → AI that creates candidate portfolios (LinkedIn-enhanced resumes, AI-generated introductions, etc.).

• Transparency & trust → Explainable AI in hiring to reduce bias and ensure fair evaluation (a major concern for AI in recruitment).

5. ‘White-glove’ AI & augmented recruiting

• AI + human hybrid models → AI assists recruitment agencies rather than replacing them.

• Deep vertical specialisation → AI hiring solutions specific to one industry (e.g., AI-driven hiring for legal, healthcare, or deep tech).

• Global hiring intelligence → AI models trained to understand regional hiring trends, visa requirements, and cross-border talent pools.

Opportunities for new startups – where is there some white space?

Having considered the market dynamic, directions of travel and existing differentiation angles, we consider there to be a number of underdeveloped and overlooked areas where new startups can create category-defining companies.

Here’s where we see white space:

1. AI for passive talent

Most AI hiring tools focus on active job seekers. The biggest talent pools are passive candidates who aren’t actively looking but open to the right opportunity.

Opportunity:

• AI-driven talent scouts that monitor passive candidates and suggest the best timing for outreach.

• "Always-on" AI networking that connects candidates to hidden job opportunities before they even apply.

Potential Model: AI-driven LinkedIn for passive talent pipelines.

2. AI hiring for SMBs & decentralised teams

AI hiring is dominated by enterprise-focused solutions. But millions of small businesses and remote teams struggle with hiring.

Opportunity:

• AI recruiter-all-in-one → A GPT-powered virtual recruiter for startups & SMBs that handles sourcing, screening, and candidate messaging.

• AI for gig & contract hiring → Tools that help startups build flexible, AI-matched contract teams instead of traditional hiring.

Potential Model: AI-powered fractional hiring platforms for lean, remote teams.

3. AI hiring that prioritises skills over CVs

Most AI hiring still relies on resumes & job descriptions. The future is skill-based hiring where AI evaluates competencies, projects, and real-world performance.

Opportunity:

• AI-powered skill assessments & job matching that bypass traditional resumes and ATS systems.

• Portfolio-first hiring → AI-driven candidate matching based on real work samples, open-source projects, and case studies instead of job history.

• Skill verification through AI-powered challenges & micro-certifications.

Potential Model: "GitHub for all professions"—an AI platform where professionals showcase work instead of resumes.

4. AI-powered career agents for candidates

There are AI hiring tools for employers, but not for job seekers. Candidates still navigate job searches alone.

Opportunity:

• AI career agent that finds & negotiates jobs for candidates automatically.

• AI-powered networking assistant → Scans a candidate’s LinkedIn, GitHub, and portfolio to generate warm introductions to hiring managers.

• Automated job applications → AI that applies to relevant jobs on behalf of the candidate and personalises outreach.

Potential Model: AI "headhunter for every job seeker", completely flipping traditional recruiting.

5. AI ethics & bias reduction in hiring

AI hiring tools face growing scrutiny over bias and fairness. There’s no gold standard for ethical AI hiring yet.

Opportunity:

• Bias-detection AI for hiring teams → AI that audits employer hiring patterns and flags biased decision-making.

• "Explainable AI" recruitment → AI that provides clear reasoning for why a candidate was selected/rejected.

• AI-driven DEI (Diversity, Equity & Inclusion) hiring platforms that optimise for fairness & representation.

Potential Model: AI-powered "Fair Hiring Auditor" that ensures AI recruitment decisions are transparent and unbiased.

We're keen to talk to startups building in this space, so if this is you, please reach out to the team!