Widespread adoption of artificial intelligence could unlock as much as €15 billion ($17.42 billion) in productivity gains across Hungary's economy by 2030, according to analysis released by McKinsey on Tuesday in Budapest. The consultancy's findings underscore both the substantial opportunity and the pressing urgency facing Hungarian businesses and policymakers as they navigate the AI revolution reshaping global markets. The potential windfall represents a significant opportunity for a Central European economy seeking to close productivity differentials with wealthier Western European nations, yet also carries an implicit warning: failing to embrace AI technologies at pace risks deepening existing competitiveness gaps.

The McKinsey report frames AI deployment as a critical tool for Hungarian firms to address long-standing structural challenges. The productivity gap between Hungary and its more developed neighbours has persisted for decades, rooted in lower capital investment, skills disparities, and industrial composition differences. AI presents a potential lever to compress this gap relatively quickly, enabling businesses to do more with existing resources and freeing human workers for higher-value activities. Yet this optimistic scenario depends heavily on rapid, consistent adoption across sectors — a condition far from guaranteed in an economy where digital transformation uptake varies widely.

Leading Hungarian executives who participated in a roundtable discussion convened around the McKinsey findings offered nuanced perspectives on how AI will reshape their respective sectors and the broader competitive landscape. Their remarks reveal that while AI's transformative potential is genuine, the path to realising it is neither straightforward nor cost-free, requiring substantial capital investment and organisational restructuring before efficiency gains materialise.

Andras Becsei, deputy chief executive of OTP Bank, Hungary's largest financial institution, cautioned that AI's impact on corporate finances will be more complex than simple headcount reduction. Although artificial intelligence systems can diminish demand for certain human resources functions, they simultaneously drive up operating costs and capital expenditure requirements. Banks must invest heavily in new infrastructure, staff retraining, and system integration to deploy AI effectively. The net outcome, Becsei suggested, will not be cost reduction in the traditional sense but rather a fundamental transformation of how banks allocate resources and structure their operations — a transition requiring patient capital and long-term vision.

Peter Nagy, deputy chief executive of Magyar Telekom, Hungary's leading telecommunications company, offered more concrete evidence of AI's near-term productivity improvements in his sector. Artificial intelligence agents are currently handling approximately 20 percent of inbound customer service calls, a proportion Nagy expects to rise substantially. Beyond customer service efficiency, AI has dramatically compressed the product development cycle at Magyar Telekom, cutting the time required to launch new services to roughly 30 days from a previous 90-day timeline. The company has also reallocated approximately half its network monitoring workforce to more intellectually demanding roles, demonstrating how AI can eliminate routine tasks whilst elevating human workers to positions requiring greater expertise and judgment.

Gabor Orban, chief executive of Richter, Hungary's leading pharmaceutical manufacturer, injected a note of healthy scepticism into the discussion. The pharmaceutical industry has repeatedly witnessed the emergence of transformative technologies that initially generated enormous excitement but ultimately failed to deliver on their lofty promises. Genomics and digital transformation initiatives, despite revolutionary rhetoric, have not yet fundamentally restructured pharma economics in the ways proponents originally envisioned. Orban's caution reflects a legitimate concern that AI may follow a similar trajectory — delivering genuine but incremental improvements rather than the paradigm-shifting productivity gains some enthusiasts project. Pharmaceutical development timelines, regulatory requirements, and the fundamental complexities of drug discovery may prove resistant to AI acceleration.

Gergely Bacso, chief executive of Allianz Hungary, introduced a crucial dimension often overlooked in productivity discussions: global competitive dynamics. Cost savings achievable by American multinational corporations through AI adoption may be several multiples larger than those available to Hungarian firms, simply because U.S. companies operate at vastly greater scale and have higher baseline labour costs to reduce. This asymmetry creates a troubling scenario where AI adoption becomes an imperative not because it unlocks transformative gains for Hungarian businesses, but because failure to adopt means losing market share and investment to better-capitalised foreign competitors for whom AI adoption yields far greater returns. Hungary risks becoming a passive consumer of AI-driven products and services developed elsewhere rather than an active participant in the technology's development and deployment.

The competitive pressure Bacso identified extends beyond individual firms to the national level. Countries that successfully integrate AI across their economies gain structural advantages in attracting foreign investment, retaining talented workers, and developing indigenous technology capabilities. Hungary's position as a Central European nation with relatively strong engineering and technical education traditions gives it advantages in the AI era, yet these advantages risk atrophy if adoption lags. The window for Hungarian businesses to position themselves as serious players in AI-driven sectors may be narrower than publicly acknowledged.

The McKinsey projections and executive commentary together sketch a portrait of an economy at a critical juncture. The €15 billion productivity opportunity is real, and the pathway to capturing it is reasonably well-understood through examples like Magyar Telekom's success. Yet realising this potential requires sustained investment, acceptance of disruption and transition costs, and a sophisticated understanding that AI is not simply a technology to be deployed but a force requiring wholesale organisational reinvention. For Malaysia and other Southeast Asian economies following Hungary's trajectory, the Hungarian experience offers instructive lessons about both the scale of opportunity and the organisational and competitive challenges inherent in the AI transition.