How startups can drive innovation in recruitment, career navigation and pre-hire skills assessment

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Emerge Edtech Insights

12 min readFeb 28, 2024

Recruitment and pre-hire assessment market map, by Emerge Education.

We’re building our annual list of the top emerging edtech companies in workforce development for 2024 with our Workforce Development board chaired by Donald H Taylor. As we do, we’re diving into the trends and opportunities for innovation along each step of the workforce development journey, for individuals and for organisations → from job discovery, pre-hire assessment and interviews to onboarding, upskilling, talent management and reskilling for the future of work.

In this first article, we’ll look at how edtech startups can help organisations and individuals solve their biggest challenges in career navigation and accessing talent.

Read on for:

Challenges, trends and opportunities, including our predictions for the transformative impact of genAI
Views from sector experts, plus tips for founders
A mini-market map of key players and top emerging startups in this space

Keywords: career navigation, recruitment, application, job board, hiring, interview, candidate assessment, job description, pre-hire

💡 Why it matters

More than 100m low-wage workers globally may need to find new jobs in the next decade. On top of this, McKinsey estimates that by 2030 another 100m workers in developed economies will need to switch occupations — the majority of this growth in labour demand is expected to occur in high-income jobs, especially technology.

How will these organisations and job seekers find each other?

🏈 State of play

For decades, organisations have relied heavily on selection tools like psychometrics (such as ability tests, personality assessments, work preference inventories), competency-based assessments (e.g. situational judgement tests), and behavioural-based interviews to find the perfect candidate for a vacant position. However, these tools fail to capture a complete, unbiased view of our available talent pool and they are ineffective at predicting who will actually thrive in a job. The era of these traditional assessments is coming to an end.
After years of steady progress, AI is poised to massively disrupt the talent assessment space. One recent survey found that 55% of HR leaders in the US already use predictive algorithms in hiring.
There are now AI tools available for almost every stage of the employment process. Candidates can find job openings and recruiters can find candidates through sourcing platforms such as LinkedIn, Monster and Indeed. Many companies employ algorithms to automate many of the manual tasks involved in recruitment, such as resume screening and interview scheduling, while another set of companies, such as JobScan and VMock, use algorithms to help candidates improve how their resumes appear to other algorithms. Other companies, such as Pymetrics and PredictiveHire, create specialised questionnaires and assessments as inputs to AI to predict or analyse for job performance. Firms may use AI to transcribe recorded statements to text, then analyse those textual responses with natural language processing. Some vendors, such as HireVue and TalView, have used facial analysis in interviews.

🚨 Challenges

One of the biggest recruitment challenges currently facing employers is that of hiring today for the skills needed tomorrow. Although not a new challenge per se, the rapid adoption of emerging technologies, the accelerated pace of change in organisations’ operating models and the emergence of new sources of competition are increasingly compelling employers to select candidates on the basis of their learning agility regardless of the relevancy of their current skillset. In other words, recruitment is more than ever an exercise that rewards those who hire for the unknown unknowns.
Susan Steele, chief people officer, Ebiquity and Emerge VP

Public anxieties about the “dawn of robot recruiting” — AI-driven hiring is seen as something of a black box and many are sceptical of claims that algorithms can be a quick-fix for persistent problems around bias in recruiting. Press focus at the moment is more on how AI might “stop you getting hired”.
Efficiency comes at a cost — A team led by Harvard Business School professor Joe Fuller surveyed more than 2,250 business leaders in the US, UK and Germany. Their motives for using AI tools were efficiency and saving costs, but 88% of executives said they know that their tools reject qualified candidates.
The wealth of skills data isn’t meaningfully connected yet — LinkedIn (job/candidate discovery) and Oracle (CV screening) are leading HR behemoths that sit on a ton of skills data but have not done much yet to connect it. IBM Watson, Burning Glass and EMSI have done incredible work to demystify skills taxonomies and to understand macro skills trends to inform staffing plans, but gaps persist on a micro level in building truly skills-based pre-hire assessments. Data alone is not enough. There is a pressing need to find ways for high-quality data analytics to assist jobseekers and hiring managers with effective decision-making.
The nature of work is changing fast — McKinsey estimates that the majority of jobs that will emerge by 2030 in Europe don’t yet exist. How can data help prepare for this uncertain future?

🔥 Trends

AI will revolutionise the entire talent assessment, development and management value chain.
Low-hanging fruit here include using AI to automate CV screening, competency mapping and existing personality assessments, and to streamline workforce planning by continuing to refine predictive analytics in assessments. This means organisations can expect more accurate identification of high-potential candidates based on historical data and patterns, empowering HR professionals to make data-driven hiring decisions.
Looking further ahead, realistic AI avatars will redefine talent assessments by assessing capability through natural conversations, as well as levelling the playing field by enabling realistic interview practise. To reduce stress and help candidates master responses in order to present the most authentic vision of their capabilities, this might involve users uploading company and role specifics to create simulated AI assessors that interview them using that organisation’s actual language patterns and culture aspects.
AI will also extend beyond the recruitment process itself, offering customised developmental feedback and digital coaching for candidates using language tuned to individual learning styles, development levels and personal contexts.
But it’s not just AI — companies like PwC have already started using virtual reality to immerse candidates in real-world scenarios for evaluation. This immersive approach provides a deeper understanding of candidates’ abilities and decision-making skills.
Until these tools are better understood (and potentially, regulated), job seekers will need strategies to pass through AI-powered processes and many will turn to techniques informed by AI itself to help optimise for hiring algorithms. Using software like Jobscan and VMock can help check and enhance your resume for AI, all before you submit.

🌍 Key players

🔭 Who is getting ahead?

SkilledHuman is working to democratise access to the latest skillsets. For founder Martin Harwar, the problem is that any top-down definition of roles in an organisation typically looks like the mirror image of a CV: a list of academic qualifications and previous experiences. But those are the two worst predictors of success, unlike prior knowledge of how the organisation works. Attitude, aptitude, a growth mindset and a willingness to learn proactively are almost always going to beat an Oxbridge degree. A better approach, far removed from dusty skills taxonomies, is top-down control of bottom-up input on skills. And it’s faster — SkilledHuman’s approach to building a tailored skills framework takes a week, not a year.

FutureFit, a Canadian AI-powered career transition GPS, enables individuals to ‘explore’ the world of skills and careers, allowing them to set a ‘destination’ of where they want to go next, ‘locates’ their starting point of capabilities and experiences, and recommends ‘pathways’ for how to get there, while providing human supports along the way. The platform uses AI to provide recommendations based on labour market information. According to founder Hamoon Ekhtiari, career progression is primarily a navigational problem — and one that’s not getting enough attention.

🔮 Predictions

Organisations will stop asking for CVs and start asking for portfolios, shifting the focus away from qualifications and towards competencies. The state of job descriptions is dismal. A lack of high-quality skills data means organisations don’t understand the skills and competencies required to successfully perform a given role. Inadequate job descriptions lead to poorly targeted adverts, inappropriate automated resume screening, a defective interview process, inefficient onboarding and poor talent mobility. Recruiting on skills, rather than past experience, is necessary to get more diverse people into posts.
Chatbots will provide candidates with immediate help and answer their questions about the job or application process. This allows recruiters to engage with candidates more effectively and save time by automating routine tasks.
Portability will be vital, so that learners can own their own learning and experiences, and transact with many organisations on this basis. This learner-centric approach emphasises flexibility, agility and seamless interaction of resources and platforms. What is important is how the skills development and training ecosystem flexes to the needs of learners and employers in order to open up access opportunities — and how different platforms communicate with each other.

📈 Risks

It is well known that traditional recruitment methods rely on subjective measures such as resumes, interviews and reference checks. These methods are prone to bias and can lead to poor hiring decisions. The utopian vision is for AI-driven pre-hire assessments to use objective data to predict job performance and minimise the impact of discrimination, leading to a more diverse and inclusive workforce. This is not the reality — yet.
The risk is that not only does AI reflect human biases, it can amplify them by feeding non-representative data to algorithms which then are used to drive apparently objective decisions. Between 2014 and 2017, Amazon tried to build an Automated Applicant Evaluation system that would screen job applicants to determine those with the most potential for success. Amazon stopped using the algorithm in 2018 after discovering that it favoured applicants based on words like “executed” or “captured” that were more common on men’s resumes versus women’s; the algorithm was trained on 10 years of its own hiring data and resumes of current employees, who skewed male, reflecting a gender disparity in many tech fields. Amazon’s story has been a wake-up call and companies who want to embrace this approach in future without the proper safeguards could face legal action for an algorithm they can’t explain.
Lack of transparency means it can be challenging to understand how AI algorithms make their decisions, making it hard to assess their fairness or diagnose problems. AI-driven pre-hire assessments can significantly impact diverse and underrepresented candidates. Researchers from NYU recently found that AI systems built to measure personality were not reliable testing instruments — and while not all companies use personality assessment tools in their hiring processes, there’s concern about when and how tools are integrated into hiring without the full understanding of recruiters or applicants. If HR teams and jobseekers don’t know how AI is affecting the evaluation process, the algorithm could be amplifying bias and discriminatory practices without anyone noticing at first.

In the last 10 years, startups have transformed the world of work. AI and a fully remote workforce stand to bring about even more changes in the next 10. We now know the implications of building massive tech companies and platforms without controls or infrastructure in place to grow safely. Thus, anyone paying attention should care deeply about the leading companies and founders making key decisions on workforce development, as most of our jobs will be impacted.
Christina Sass, founding partner, Dive In and Emerge VP

🎯 Opportunities for startups

Engines of opportunity for workforce development, by Emerge Education.

In this category, we see particular opportunities for AI-driven solutions that offer:

Aggregators of skilled manual labour → Problem: Chronic shortage in Europe and US of manual skills, which is a big blocker for green energy transition. Solution: Bootcamps, migration solutions and staffing providers that increase the net supply of manual skilled labour and connect it with high-value jobs.
AI-powered interview AND Real world job simulations to generate candidate pipeline → Problem: Interviews are time-consuming and inefficient. Solution: 1) Employer-tech-stack-linked lite bootcamps. 2) Scalable online mini-internships as a way of building talent pipeline. 3) OPM but with the employer as the brand rather than the university.
Verticalised job boards → Problem: Generic job boards are not tailored enough for specific verticals’ needs (e.g. green tech, freelancers, sales). Solution: Next-gen job boards will be verticalised and own more of the value chain including vetting candidates and supporting better matches.
Recruiter co-pilots → Problem: HR teams want to serve as many people as possible but it’s hard to scale given time constraints. Solution: Infrastructure to scale high-quality HR capabilities at zero marginal cost.
Job simulation assessment → Problem: Interviews are bad at predicting performance in role. Solution: A world where hiring is based on someone’s true skills and potential, not their CV and experience.

💎 Tips for founders

AI-driven pre-hire assessments should not be used as a barrier to entry for candidates. Ultimately, employers are risk-averse; often, they’re looking for reasons to disqualify candidates, such as long stretches of unemployment, to narrow down their options from large pools of applicants. Instead, they should be viewed as an opportunity for candidates to showcase their skills and receive valuable feedback. Ensure that assessments are user-friendly, transparent and prioritise candidate privacy to build trust. Be clear about accountability, including who is responsible for the decisions produced by an AI algorithm.
AI-driven pre-hire assessments are more than just a one-time solution. Organisations must continuously monitor and improve their approach to stay ahead of the curve. This might involve updating the algorithms used, incorporating new data sources or adjusting the types of assessments offered.
Regulation is on the way. In 2023, a New York City law restricting the use of AI tools in the hiring process went into effect, although it’s still unclear how regulators will be able to enforce it. Other states in the US are doing the same — Illinois passed a law requiring companies to disclose their use of similar AI tools — and congressional lawmakers have introduced bills that would regulate AI in hiring at a national level, including the Algorithmic Accountability Act of 2022 but have faced hurdles getting them passed. International regulation may also be on the way, with a European Union proposal that could also limit the use of AI technology. The UK government is also planning new regulation. Audits of algorithms may become standard practice.

🔗 Read on

Read more news, views and research from the only fund backed by the world’s leading education entrepreneurs, in Emerge Edtech Insights.

📣 Call to action

We are now building our list of the top emerging edtech companies in WD in 2024.

👇 If you have seen an exciting company in this space, please tell us in the comments 👇

Our list analyses 100s of companies operating worldwide, using public and private data — it is crowdsourced, and voted on by our Workforce Development edtech action group, led by Donald H. Taylor.

Please share companies you think we should consider in comments 👇 and join us on 3 July to discover who has made the final list!

🙏 Thanks

At Emerge, we are on the look-out for companies (existing and new) that will shape the future of workforce development over the coming decade.

If you are a founder building a business addressing any of these challenges in L&D we want to hear from you. Our mission is to invest in and support these entrepreneurs right from the early stage.

So if you are looking for your first cheque funding do apply to us here: https://lnkd.in/eWi_9J5U . We look at everything as we believe in democratising access to funding (just as much as we believe in democratising access to education and skills).

Emerge is a community-powered seed fund home to practical guidance for founders building the future of learning and work. Since 2014, we have invested in more than 80 companies in the space, including Colossyan, FutureFit AI and Skills Trust.

Emerge Education welcomes inquiries from new investors and founders. For more information, visit emerge.education or email hello@emerge.education, and sign up for our newsletter here.

Thank you for reading… I would hugely appreciate some claps 👏 and shares 🙌 so that others can find it!

Nic