As artificial intelligence redefines industries, education is emerging as one of its most transformative frontiers. From adaptive learning platforms and AI-powered tutors to predictive analytics and automated administration, AI is enhancing how students learn, how educators teach, and how institutions operate. But while the potential is vast, successful implementation requires more than technology — it demands strategy, governance, and a human-centred approach. In this article, we explore how organisations across the public and private education sectors are leveraging AI to personalise learning, improve outcomes, and streamline operations. Drawing on success stories from global institutions and insights from McKinsey, Deloitte, PwC, KPMG, and Accenture, we highlight the strategic imperatives and governance principles that underpin responsible, scalable AI adoption in education.
Artificial Intelligence (AI) is transforming the education landscape in profound ways. From personalised learning pathways to intelligent tutoring systems and administrative automation, AI is not just enhancing how education is delivered — it’s redefining what’s possible. At a time when global education systems face mounting pressure to be more inclusive, efficient, and future-ready, AI offers both a challenge and an unprecedented opportunity.
However, the path to implementation is not without complexity. Integrating AI into education systems requires thoughtful planning, robust governance, and alignment with broader institutional and policy goals. As management consultants, our role is to guide education leaders, edtech providers, and public sector organisations in harnessing this potential — not just as a technology upgrade, but as a strategic enabler of lasting impact.
One of AI’s most promising contributions lies in personalisation — tailoring content, pace, and support to the unique needs of each learner. AI-powered platforms can assess a student’s strengths and weaknesses in real time and adapt instruction accordingly.
Case Study – Carnegie Learning (McKinsey & Company)
Carnegie Learning, a pioneer in AI-driven education, uses machine learning algorithms to personalise math instruction for secondary students. A McKinsey study found that students using its platform progressed 30–50% faster than peers in traditional classrooms, particularly in low-income districts. (McKinsey, 2023)
AI-powered tutors can simulate human-like guidance, providing real-time feedback, answering queries, and offering encouragement. These systems work 24/7, scaling individualised support that would otherwise be cost-prohibitive.
Case Study – Squirrel AI and Deloitte
In China, Squirrel AI developed an adaptive learning system that mimics the role of a live tutor. Partnering with Deloitte to scale operations and refine analytics, the system demonstrated improved performance outcomes for over 2 million students, especially in rural areas. (Deloitte China Education Insights)
AI enables scalable, formative assessments that can be administered and analysed instantly. More importantly, it supports richer feedback — guiding learners on how to improve, not just what went wrong.
Case Study – Pearson and Microsoft Azure (PwC)
Pearson leveraged Microsoft’s Azure AI to build a next-generation assessment engine capable of natural language analysis, improving the accuracy of essay marking and student feedback. PwC supported the integration and regulatory alignment across EU markets. (PwC UK EdTech Outlook, 2022)
Teachers are at the heart of education systems — and AI can significantly empower them. Predictive analytics and early warning systems can help educators identify struggling students and tailor interventions earlier.
Case Study – New York City Schools and IBM Watson (KPMG)
KPMG collaborated with NYC’s Department of Education and IBM to pilot Watson AI in identifying at-risk students. The initiative resulted in a 16% improvement in retention among targeted cohorts and helped educators allocate resources more efficiently. (KPMG Education Insights)
Beyond the classroom, AI is streamlining administrative functions such as enrolment processing, financial aid, and curriculum management. Chatbots, document scanning, and scheduling systems powered by AI are freeing up institutional bandwidth.
Case Study – Arizona State University and Accenture
Accenture worked with ASU to deploy AI-driven student service bots, automating over 100,000 student inquiries per semester. The result was a 60% reduction in response times and a measurable uplift in student satisfaction scores. (Accenture Education Thought Leadership)
While the benefits are clear, AI in education must be approached with caution. Issues such as data privacy, algorithmic bias, and transparency require robust governance frameworks and policy oversight.
Thought Leadership – BCG & World Economic Forum (WEF)
A joint report from BCG and WEF outlines governance principles for AI in education, including equity-by-design and continuous bias monitoring. Institutions that adopted these practices saw higher adoption rates and fewer legal risks. (BCG x WEF Report, 2023)
AI is not a passing trend in education — it is an enabler of systemic transformation. But its success depends on thoughtful implementation, stakeholder alignment, and an unwavering focus on human outcomes. Our role as leaders is to bridge the gap between vision and execution — helping public institutions, universities, and edtech firms embed AI in a way that is scalable, ethical, and impactful.
By combining deep educational insight with advanced technology strategy, we empower our people to unlock new models of teaching, learning, and operational delivery. As AI continues to evolve, so must the institutions it serves — toward more adaptive, inclusive, and data-informed education systems.