The Ai Education Market Is Crowded With Certificates But Industry Wants Builders

. Yet employers continue to report a shortage of AI-ready talent.Over the past two years, the AI education industry has expanded at a pace rarely seen in professional learning. Universities have launched AI specialisations, online platforms have rolled out thousands of new courses, and bootcamps promising rapid career transitions have proliferated across markets. Coursera reported more than eight million enrolments in generative AI courses within a year of ChatGPT's launch, while LinkedIn continues to rank AI among the fastest-growing skill categories globally. Meanwhile, Grand View Research estimates that the global AI education market will grow at an annual rate of more than 30% through the remainder of the decade.On the surface, the numbers suggest that the talent pipeline is thriving. The reality inside many organisations looks different. As enterprises accelerate AI adoption, employers increasingly say the market is producing certificate holders faster than it is producing builders. The timing is significant.According to McKinsey's latest State of AI research, nearly 80% of organisations now use AI in at least one business function, while generative AI adoption has more than doubled over the past year. The next wave of adoption is expected to move even faster. According to Gartner’s 2026 Hype Cycle for Agentic AI, more than 60% of organisations expect to deploy AI agents within the next two years.Yet despite the rapid growth of AI learning and hiring demand, a significant gap persists between the talent available in the market and the talent companies are actually willing to hire. According to recent TeamLease report, AI and Generative AI roles face the sharpest talent mismatch in the market, with only one qualified engineer available for every 10 open GenAI positions and a projected 53% talent shortfall by 2026. The challenge is not a lack of people learning AI. It is a shortage of professionals who can demonstrate the ability to build, deploy, and operationalise AI systems in real business environments. As organisations move AI initiatives from experimentation to production, implementation capability is emerging as one of the most valuable and scarce skills in the market.Why employers are prioritising builders over certificate holders? In the early days of the generative AI boom, familiarity with AI tools and prompt engineering was enough to signal future readiness. Today, employers are looking for something different: People who can build and deploy. They need professionals who can create AI agents, develop RAG-based applications, integrate APIs, manage data pipelines, and deploy AI systems in production environments.This shift is changing hiring priorities. As AI certifications become more common, employers increasingly value proof of execution through project portfolios, deployed systems, and real-world problem-solving. The rise of agentic AI is accelerating this trend. Building AI agents that can execute tasks, coordinate workflows, access tools, and make decisions requires capabilities that extend beyond traditional coursework, including software engineering, data infrastructure, workflow orchestration, and model evaluation. As a result, AI education is shifting from knowledge acquisition to building systems that deliver measurable outcomes.A growing number of programmes are moving away from certificate-led learning and toward project-led learning. Rather than treating practical work as a supplementary component of the curriculum, these programmes are placing product development at the centre of the learning experience. The objective is straightforward: If employers hire builders, education must produce builders. Masters' Union's PGP in Applied AI and Agentic Systems is one example of this emerging model. Designed around the growing demand for implementation-focused AI talent, the 15-month, on-campus programme requires students to build six production-grade AI systems across six academic terms. Students progress through AI foundations, machine learning, AI agents, RAG systems, Agentic systems, and enterprise AI deployment, while simultaneously creating working applications that form part of their GitHub portfolio.The structure reflects a broader market reality. Organisations increasingly want proof of work. A deployed AI assistant, an automation workflow, a machine learning application, or an enterprise knowledge platform often communicates more about a candidate's readiness than a list of completed certifications.Another trend reshaping AI education is the growing role of industry in curriculum design. As AI tools, models, and deployment practises evolve rapidly, institutions are increasingly partnering with industry practitioners who are actively building AI products and systems.Masters' Union follows this approach through collaborations with organisations such as PwC, Ola Krutrim, and Rabbit AI, alongside practitioner-led learning experiences involving CTOs, founders, and AI experts from Google, Amazon, Microsoft, IBM and PayPal. The focus is not just on understanding AI but on learning how it is deployed in real business environments.This shift mirrors broader changes in the labour market. The World Economic Forum identifies AI and Big Data among the fastest-growing skill categories globally, while LinkedIn research points to skills becoming a stronger hiring signal than traditional credentials. As AI adoption accelerates, the challenge is no longer access to knowledge. It is the ability to apply that knowledge to build products, systems, and business outcomes. The first wave of AI education expanded awareness. The next will be defined by execution. Certificates may signal interest, but builders create value. Institutions that can bridge that gap are likely to shape the future of AI talent.(The views expressed are personal)This article is authored by Kushal Vijay, AI software engineer, Microsoft.