What 110 fast-growing companies reveal about hiring in the AI era

Lepaya analysed hiring activity across 110 high-growth companies in finance, professional services, and technology. The pattern for the future of work is consistent across all three sectors: technical skills are opening doors, but human skills are deciding who gets hired. Entry-level hiring has shrunk dramatically with just 12% of consulting openings and 18% of tech openings as AI absorbs routine work. Mid-level professionals who can combine technical depth with strategic influence, ethical judgment, and cross-functional collaboration are the most-hired group across every industry studied.

Finance: navigating regulation and AI transformation

The finance sector is solving two problems at once. Regulatory demands are tightening. Legacy infrastructure is being modernised with AI. The hiring data reflects both pressures simultaneously.

• 60% of roles focus on AI-linked risk management, compliance frameworks, and data infrastructure
• 30% of roles are senior-level: directors, heads of function, and VPs steering AI adoption and regulatory strategy
• 50% target mid-level professionals who can execute complex regulatory and digital mandates

(Percentages overlap because some roles count in multiple categories; e.g., a senior AI compliance director appears in both "senior-level" and "AI-linked" buckets.)

The skill profile finance is hiring for has shifted. The traditional domain specialist, deep in regulation but light on technology, or strong in data but unfamiliar with compliance no longer matches the job description.

What's now expected:

• A risk analyst who can interpret AI-driven fraud detection models and explain them to regulators
• A data engineer who builds machine learning systems and translates technical insights to non-technical executives
• A compliance officer who understands both the regulatory framework and the AI models being deployed within it

The hiring pattern reveals something most workforce planning conversations miss: finance is no longer hiring for technical specialisation. It's hiring for the ability to operate at the boundary between technical systems, regulatory environments, and senior stakeholder conversations.

Professional services: when experience becomes the baseline

The consulting industry has undergone the most visible shift in talent demand of any sector studied.

• Entry-level positions make up just 12% of openings
• Mid-level positions (Associate / Manager level) account for 55%, leading client delivery and implementation
• Senior roles (Director / Partner-track) represent 28%, focused on business development and client relationships

The 12% entry-level number is the most striking. For decades, consulting's economic model was built around large pyramids of junior analysts doing the bulk of the research, modelling, and slide production. AI has compressed that work to near-zero marginal cost. The pyramid is flattening.

What firms are now hiring for:

• Experienced professionals who can lead organizational change inside client companies
• Trusted advisors who can build credibility with C-suite executives
• Strategists who can translate AI capabilities into concrete client implementation plans

The traditional grunt work: market research, SWOT analyses, slide production, is no longer the path into consulting. The new path requires showing up with experience, judgment, and the ability to deliver outcomes that AI can't produce on its own.

This is the broader pattern likely to reshape every knowledge-work industry: the entry-level rung gets shorter, the mid-level expands, and the skill bar to clear the first hire rises significantly.

Technology: where engineering meets business impact

Even in the most technical industry studied, hiring priorities reach well beyond engineering skills.

• 18% of roles are entry-level: higher than consulting but lower than historical norms
• 52% target mid-level professionals responsible for product delivery, engineering, and customer success
• 32% target AI/ML engineers and data scientists building next-generation systems
• 22% focus on product managers translating AI capabilities into customer value

Two patterns stand out.

First, technical depth is necessary but not sufficient. The hiring data shows a clear demand for engineers and product managers who can connect technical work to business outcomes. Pure technical specialists without communication, collaboration, or strategic context are losing out to candidates who combine both.

Second, product management is being hired more aggressively than its volume suggests. 22% of roles in a heavily engineering-led industry going to product managers is high. The reason: AI raises the value of the people who can translate technical capability into something customers will pay for. Building AI is now table stakes. Productising AI is the differentiator.

The cross-industry pattern

The three sectors look different on the surface but share the same underlying logic. Across all 110 companies, the same dual requirement emerges:

The traditional binary, technical or human, hard or soft, STEM or non-STEM, is breaking down. Across every sector studied, companies aren't choosing between the two. They're demanding both.

The four capabilities driving growth across all three industries

Looking at the job descriptions in the 110-company dataset, four capabilities show up consistently across sectors and seniority levels.

1. Strategic influence

The ability to translate complexity for non-technical stakeholders and drive alignment across functions. In every sector, the people getting hired are the ones who can take dense technical or regulatory information and shape it into decisions executives can act on.

2. Ethical judgment

The ability to make decisions where data provides guidance but not answers. As AI takes over more pattern-matching work, the remaining decisions tend to be the ones where the data is incomplete, the trade-offs are real, and human judgment is non-negotiable. Companies are hiring for the people who can navigate that territory.

3. Cross-functional collaboration

The ability to bridge teams with competing priorities and different expertise. AI-augmented organizations need people who can pull engineering, product, compliance, sales, and customer success into aligned execution. The collaboration skill is no longer "nice to have", it's the throughput constraint on most growth initiatives.

4. Business acumen

The ability to understand how technology serves broader organizational goals. The engineers who get hired aren't only the best engineers, they're the ones who can articulate why their work matters to the business. Same pattern in every other function.

What this means for HR and L&D leaders

The hiring data has hard implications for how organizations should be designing capability strategies right now.

• Stop building entry-level pipelines designed for the old work. If 12% of consulting openings are entry-level, the traditional grad scheme model is being structurally disrupted. Investing in capability development for junior talent now means betting on the work AI doesn't do, not the work it now handles.
• Over-invest in mid-level capability building. Mid-level professionals are the largest hiring group across all three sectors. They're also the population most exposed to AI-driven workflow change. Capability investment here generates the highest retention return.
• Develop combined skill profiles intentionally. The data shows companies are hiring people who can hold both technical and human capability at once. Most internal development programs still separate them. Combining "tech upskilling" and "soft skills" into integrated programs is a competitive move.
• Track the four capabilities (influence, judgment, collaboration, business acumen) as deliberately as you track technical skills. Most L&D dashboards still only measure technical training. Measuring these four capabilities through manager observations, peer feedback, and applied work is a leading indicator of who will be hireable, promotable, and retainable in this market.

The strategic point

The narrative that AI is replacing knowledge work is partially right and largely misleading. AI is replacing the routine layer of knowledge work: the analyses, the synthesis, the drafting, the formatting. What's left is the work that requires judgment, influence, and cross-functional integration.

Companies are hiring for that work aggressively. They're paying for it. And they're competing for the people who can do it well.

The strategic question for HR leaders is not whether AI will reshape hiring. The data shows it already has. The question is whether your workforce strategy is preparing people for the jobs that exist now, or for the jobs that existed five years ago.