Future-Ready Talent: Designing National Skills Programs for an Entrepreneurial, AI-Enabled Economy

Every government in the India–Gulf corridor is running a skills program, and most of them are counting the wrong thing. Learners enrolled, courses completed, certificates issued: these numbers rise reliably, cost little to produce and say nothing about whether anyone was hired, paid more or started a business. Meanwhile the target itself is moving — generative AI has, within three years, changed what an entry-level worker is expected to do — and programs designed for the last decade’s job descriptions are being scaled up for the next decade’s. This article sets out what the evidence says about skills programs that change outcomes, how AI alters the design brief, and what a ministry should specify when it commissions a future-skills program for a young, entrepreneurial economy.

The idea in brief. The evaluation literature on training and active labour-market programs is large and its lessons are consistent: short, generic, supply-driven training produces little; programs that build real human capital, involve employers directly, include work-based learning and are judged on placement and earnings over two to three years produce measurable, lasting gains — especially for those who start furthest behind. AI does not change those lessons; it changes the content and raises the value of two things — foundational skills that do not decay and the personal-initiative, entrepreneurial habits that let a worker use new tools before the curriculum catches up. A future-skills program should therefore be employer-anchored, behaviour-based, AI-native in delivery, and measured at 6, 12 and 24 months on the outcomes that matter.

The demand side: what the labour market is saying

The World Economic Forum’s Future of Jobs Report 2025, drawing on a survey of large employers worldwide, projects that roughly 170 million jobs will be created and about 92 million displaced by 2030 — a net gain of around 78 million — while close to two-fifths of existing skill sets are expected to be transformed or become outdated over the same period, and a majority of the workforce will need some form of training. The exact figures are employer projections rather than forecasts, but the structure of the message is what matters: net job growth alongside enormous churn in what jobs consist of.

The corridor’s two sides face that churn with very different demographics. India’s median age is in the late twenties and roughly two-thirds of its population is under thirty-five; its challenge is absorbing an enormous annual cohort of young workers into productive, formal employment, and its skills programs operate at a scale — tens of millions of trainees over a decade under national missions — that no other democracy attempts. The Gulf states have small national populations, historically absorbed into the public sector, and large expatriate workforces; their challenge is moving nationals into private-sector and entrepreneurial roles that their diversification visions require, which is why their skills agencies — Bahrain’s Tamkeen, Saudi Arabia’s Human Resources Development Fund and their peers — pair training with wage support and employer incentives rather than running training alone.

What both share is a demand-side signal that has been stable for a decade and is now sharpening: employers report shortages not primarily of credentials but of the ability to learn, communicate, solve problems and take initiative — the skills that David Deming’s 2017 analysis in the Quarterly Journal of Economics showed have been growing in labour-market value for a generation, and that AI is now making more valuable still.

What works: the evidence on training and labour-market programs

Skills policy is unusually well studied, because active labour-market programs have been evaluated with experimental and quasi-experimental designs across dozens of countries for decades.

The most comprehensive synthesis is David Card, Jochen Kluve and Andrea Weber’s 2018 meta-analysis in the Journal of the European Economic Association, covering hundreds of program estimates. Its findings are worth stating carefully. Average effects of active labour-market programs are small in the short run and larger two to three years after the program. Programs that build human capital — genuine training — have larger and longer-lasting effects than job-search assistance alone, but they take longer to show them. Effects are larger for women and for the long-term unemployed, and larger in recessions. Public-sector job-creation schemes show the weakest results. The message for a ministry is double-edged: training works, but not on a quarterly reporting cycle, and judging a program at six months will systematically under-count its value.

Developing-country evidence sharpens the picture. Orazio Attanasio, Adriana Kugler and Costas Meghir’s randomised evaluation of a Colombian vocational-training program for disadvantaged youth, published in the American Economic Journal: Applied Economics, found substantial gains in employment and earnings — particularly for women — from a program that combined classroom training with an on-the-job component in firms. Livia Alfonsi and colleagues’ 2020 experiment in Uganda, in Econometrica, compared vocational training with firm-provided apprenticeships and found both raised employment and earnings, with vocational training producing more portable, certifiable skills and firm-based training producing faster initial placement — a trade-off that program designers should choose deliberately rather than stumble into.

The design features that recur across the programs with lasting effects:

• Employer involvement in the curriculum and in delivery — the program teaches what a named set of employers will hire for.
• A work-based component — internships, apprenticeships or live projects, not simulations.
• Sufficient duration and intensity to build real capability, typically months rather than days.
• Soft skills taught explicitly — communication, teamwork, initiative — rather than assumed.
• Placement services and follow-up as part of the program, not an afterthought.
• Outcome measurement at 12 to 36 months on employment, earnings and retention.

Entrepreneurial skills as employability

The corridor’s governments increasingly frame skills programs around entrepreneurship, and the evidence supports a specific version of that framing. As article 31 discussed, the Togo experiment reported in Science in 2017 found that a psychology-based personal-initiative curriculum — proactive, self-starting, persistent behaviour — outperformed traditional business training substantially in raising firm profits. The same trait profile is what employers describe when they say they cannot find graduates who take ownership. Personal initiative is not a founder-only skill; it is the employability skill most in demand, and it responds to training.

That reframes the entrepreneurship component of a national skills program. Its purpose is not primarily to produce founders — most participants will be employees — but to build the habits of experimentation, customer thinking, ownership and resilience that make a worker valuable in a firm and, for a minority, capable of starting one. A skills program that includes a serious action-learning component (of the kind article 34 describes for campuses) produces a workforce that employers notice and a founder pipeline as a by-product. HexGn’s Future Proof program was designed on exactly this logic: six modules built around high-growth industries, microlearning that fits around study or work, and a career toolkit that treats entrepreneurial habits as employability rather than as an alternative to employment.

AI changes the target, not the method

Generative AI arrived in the middle of this policy conversation and has produced two reactions — that everything about skills must change, and that nothing has. The early evidence supports neither.

Three studies from 2023 set the terms. Shakked Noy and Whitney Zhang’s experiment in Science found that access to a large language model made professionals substantially faster at writing tasks and raised the quality of their output, with the largest gains for the weakest performers. Erik Brynjolfsson, Danielle Li and Lindsey Raymond’s field study of customer-support agents, published as an NBER working paper, found productivity gains averaging around 14 per cent from an AI assistant — concentrated among novice and lower-skilled workers, who effectively absorbed the tacit knowledge of experienced colleagues through the tool. Fabrizio Dell’Acqua and colleagues’ study with consultants, published through Harvard Business School, found large gains in speed and quality on tasks inside the tool’s capability frontier and worse performance on tasks just outside it — the “jagged frontier” that makes judgement about when to trust the tool a skill in itself.

For skills policy, three implications follow, and they are consistent with the older evidence rather than contradicting it. First, the novice uplift compresses onboarding: AI tools narrow the gap between new and experienced workers in many tasks, which means entry-level programs can target higher-value work sooner — and that the value of getting people into work quickly rises. Second, foundational skills matter more, not less: judgement, domain knowledge, communication and the ability to evaluate a tool’s output are what separate a worker who is amplified by AI from one who is replaced by it. Third, tool-specific skills decay faster than ever, which shifts the curriculum from mastering a particular tool to learning how to learn tools — the personal-initiative habit again, in a new costume. Article 30 in this series examines the same shift from the employer’s side.

The chart is an illustrative model of the point: foundational skills — reasoning, writing, numeracy, domain understanding — lose relevance slowly; tool-specific skills lose it fast, and the pace is accelerating. A program that spends most of its hours on this year’s tools is building an asset that depreciates before the certificate arrives. A program that spends most of its hours on foundations and initiative, and teaches the current tools as instances, builds an asset that compounds.

The Indian and Gulf program architectures

India’s architecture is the largest in the world. The Ministry of Skill Development and Entrepreneurship and the National Skill Development Corporation have run national skilling missions since 2015 that have trained well over ten million people across successive phases, through a network of training partners, sector skill councils and assessment bodies; the FutureSkills Prime platform, a partnership between the electronics and IT ministry and the industry association, targets digital and emerging-technology skills specifically; the National Education Policy 2020 integrates vocational education into mainstream schooling and higher education; and the IndiaAI Mission, approved in 2024 with a multi-year budget in the region of ₹10,000 crore, funds compute, datasets, AI education and skilling. The strengths are scale and institutional depth; the persistent challenges, well documented in the government’s own reviews, are placement rates, wage outcomes and the gap between certification and employer demand.

The Gulf’s architecture is smaller and more integrated with the labour market. Bahrain’s Tamkeen, established in 2006, combines training with wage subsidies and enterprise support for nationals in one agency, so that the incentive to train and the incentive to hire sit in the same budget; Saudi Arabia’s Human Resources Development Fund plays a similar role at larger scale within Vision 2030’s nationalisation targets; the UAE’s national AI strategy — the country appointed the world’s first minister for artificial intelligence in 2017 — pairs AI adoption with skilling for nationals; and Qatar, Oman and Kuwait run national programs on the same model. The strengths are integration and funding; the challenge is pipeline — the number of nationals entering the programs, and the depth of the private-sector demand they are trained for.

The two architectures are complementary in a way that corridor programs (article 36) can exploit: India’s scale in training design and delivery, and the Gulf’s integration of training with employment incentives, are each what the other lacks.

Design principles for a future-skills program

The measurement principle deserves emphasis because it is the one most often specified and least often delivered. A ministry that wants outcomes should write into the program’s contract: consent at enrolment for follow-up; a standard survey at 6, 12 and 24 months; verification against payroll, provident-fund or tax records where the legal framework allows; and reporting of response rates alongside results. The cost per placed learner at twelve months — total program cost divided by learners in verified employment or self-employment — is the single number that most changes provider behaviour, in the same way that cost per operating venture does for founder programs (article 31).

Sequencing a national program: from pilot to scale

The evaluation literature’s most practical lesson is about sequence. Programs that were scaled nationally before they were shown to place learners have, across countries, produced the certification statistics this article opened with; programs that were piloted with a handful of employer clusters, measured at twelve months and scaled only where they placed have produced the earnings gains the meta-analyses record. The sequence that works is unglamorous: two or three sectors where named employers commit to hire; a first cohort small enough to run well; twelve-month placement and earnings measured against a comparison group drawn from oversubscription; replication in a second region only for the trades that placed; and provider tiering, so that partners with verified outcomes receive the growth and partners without them receive a year to produce some. Scale, in this design, is the reward for evidence rather than a substitute for it — and a ministry that follows the sequence reaches national scale later than one that announces it, with a program that survives the review.

A composite case: fifty thousand trained, and then the question

A composite from several engagements; details altered.

A state skills mission had, over three years, trained around fifty thousand young people through a network of training partners paid per certified learner. Certification rates were high. A legislative review asked how many of the fifty thousand were employed a year later and at what wage, and the mission could produce only partner-reported placement figures that nobody had verified and that collapsed under sampling.

The redesign changed what the mission paid for. Partners were paid one part on certification and a larger part on verified employment at six and twelve months. Curricula were rebuilt with clusters of named employers who committed to interview graduates, and every program acquired a work-based component. Foundational and personal-initiative modules were made compulsory across trades, and AI tools were introduced into delivery with explicit training in evaluating their output. Enrolment consent, a follow-up survey and verification against provident-fund records were built in. The number trained fell in the first year, because weak partners left when the payment changed. The number verifiably employed at twelve months rose, the cost per placed learner fell, and the mission’s next report to the legislature led with a number it could defend.

What could go wrong

• Certification as the target. Providers optimise for certificates. Antidote: outcome-linked payment.
• Tool-chasing curricula. This year’s software fills the hours. Antidote: foundations-first design with tools as instances.
• Employers consulted, not committed. Advisory boards without hiring obligations. Antidote: interview or hire commitments in partner agreements.
• Judging at six months. Training effects are under-counted early and the program is cut. Antidote: horizons in the charter; leading indicators reported honestly.
• AI as a module. AI taught as a separate course rather than used in learning. Antidote: AI-native delivery with judgement training.
• Follow-up collapse. Response rates fall and only the employed respond. Antidote: consent at enrolment, verification against administrative data, response rates reported.

Questions ministers actually ask

“Which skills should we train for?” The ones named employers will hire for next year, taught on top of foundations that will still matter in ten. Let employer commitments, not forecasts, choose the trades.

“Will AI make our training obsolete?” It will make tool-specific training obsolete faster than before. It makes foundational and behavioural training more valuable, and it can compress onboarding so that entry-level workers become productive sooner.

“Why should entrepreneurship be in a skills program?” Because the traits that make founders — initiative, customer thinking, persistence — are the traits employers say they cannot find, and they respond to training. Founders are the by-product; employable workers are the product.

“How do we know a provider is good?” Verified placement and earnings at twelve months for previous cohorts, with response rates. A provider with only certification data has no outcome data.

“What is the one number?” Cost per learner in verified employment or self-employment at twelve months, by cohort.

Methodology & data notes

World Economic Forum figures are employer-survey projections from the Future of Jobs Report 2025 and are approximate; they describe expectations, not outcomes. Findings from the evaluation literature are summarised approximately and readers should consult the papers for effect sizes and conditions; the AI productivity studies cited are early, task-specific and should not be generalised beyond their settings. The skill half-life chart is an illustrative model, not a measurement. Program statistics for India and the Gulf are as published by the respective agencies and change continuously. The composite case combines several engagements with details altered. Companion articles cover founder programs (article 31), campus programs (article 34), assessment science (article 22) and AI’s effect on capability-centre jobs (article 30).

References & further reading

• World Economic Forum — Future of Jobs Report 2025
• Card, D., Kluve, J. & Weber, A. (2018) — What Works? A Meta Analysis of Recent Active Labor Market Program Evaluations, Journal of the European Economic Association
• Attanasio, O., Kugler, A. & Meghir, C. (2011) — Subsidizing Vocational Training for Disadvantaged Youth in Colombia, American Economic Journal: Applied Economics
• Alfonsi, L. et al. (2020) — Tackling Youth Unemployment: Evidence from a Labor Market Experiment in Uganda, Econometrica
• Deming, D. (2017) — The Growing Importance of Social Skills in the Labor Market, Quarterly Journal of Economics
• Campos, F. et al. (2017) — Teaching personal initiative beats traditional training in boosting small business in West Africa, Science
• Noy, S. & Zhang, W. (2023) — Experimental evidence on the productivity effects of generative artificial intelligence, Science
• Brynjolfsson, E., Li, D. & Raymond, L. (2023) — Generative AI at Work, NBER Working Paper 31161
• Dell’Acqua, F. et al. (2023) — Navigating the Jagged Technological Frontier, Harvard Business School working paper
• International Labour Organization — youth employment and skills research
• Ministry of Skill Development and Entrepreneurship, NSDC, FutureSkills Prime and IndiaAI — India’s skills and AI programs
• Tamkeen and the UAE AI strategy — Gulf skills and AI programs; Saudi Arabia’s Human Resources Development Fund publishes its programs on its own portal

HexGn designs and delivers future-skills programs for education and labour ministries — employer-anchored, foundations-first, AI-native in delivery, with entrepreneurial habits built in and outcomes measured at 6, 12 and 24 months.