World Models: The Next Horizon in AI for Predictive Enterprise Intelligence

Industries exposed to AI disruption are already achieving 3x higher revenue per employee growth, according to PwC’s 2025 analysis. Now imagine multiplying that advantage through AI that doesn’t just recognise patterns but actually predicts the future. This is the promise of world models, an emerging AI paradigm that transforms reactive operations into anticipatory strategy.

World models represent AI’s evolution toward true predictive intelligence. Whilst current AI excels at analysing what has happened, world models simulate what will happen next. Yann LeCun, Meta’s former chief AI scientist and one of the field’s pioneering voices, continues championing this approach through his Joint Embedding Predictive Architecture (JEPA). His vision, gaining momentum as organisations seek competitive advantages beyond conventional AI, points toward systems that learn to anticipate reality rather than simply process it.

DeepMind demonstrates this potential through research systems mastering diverse domains via prediction, whilst OpenAI’s Sora explores how video generation could become world simulation. Current predictive AI already delivers impressive results with aviation pioneers reporting 20-30% improvements in operational efficiency, including disruption reduction, according to BCG’s AI-First Airline report. World models promise to amplify these achievements, representing not incremental improvements but strategic leaps that will redefine what’s possible.

For Boards tracking the evolution of AI capabilities, world models represent the next frontier in transforming uncertainty into strategic advantage. Understanding this emerging paradigm now — before it reaches commercial maturity — positions organisations to capitalise when these technologies become accessible. The organisations building foundational predictive capabilities today will be ready to adopt world models tomorrow, shaping competitive landscapes whilst others scramble to catch up. This exploration through early research and emerging applications reveals not just future technology, but how businesses must prepare now for a fundamental shift in how they’ll anticipate and respond to change.

Understanding world models: Building tomorrow’s predictive advantage

World models give AI something remarkable: the ability to imagine. Where current AI recognises patterns in historical data, world models build internal simulations of how reality works, enabling them to predict futures that haven’t happened yet. This represents what I’ve previously referred to as predictive indicators in my work on Board decision analytics. Where I advocated for Boards to embrace indicators that actively model multiple possible futures, world models are the technical realisation of that vision. Whilst leading indicators signal potential directions and lagging indicators confirm past outcomes, predictive indicators go further by anticipating what will happen next. World models take this concept to its ultimate expression, not just modelling scenarios but actually simulating reality to generate insights.

LeCun’s research provides the conceptual breakthrough. Rather than processing information sequentially like current systems, world models create hierarchical predictions, understanding immediate consequences whilst maintaining awareness of long-term scenarios. This mirrors human strategic thinking: we don’t just recognise that supply shortages increase prices; we anticipate how price increases affect demand, which influences production decisions, which shapes market dynamics months ahead.

This capability transforms AI from a powerful analyst into a strategic advisor. Current AI applications excel within defined boundaries — customer service chatbots, fraud detection systems, content generation tools. World models promise something far more valuable: holistic prediction that navigates uncertainty by simulating multiple possible futures and identifying optimal paths forward.

In my previous exploration of decision analytics, I introduced how Boards could move beyond backward-looking metrics to embrace predictive indicators that model potential futures. World models represent the technological maturation of this concept. Rather than simply processing historical data to suggest probabilities, they create dynamic simulations that anticipate how situations will evolve. For Boards that have already begun implementing predictive indicators — from competitor innovation pace to supply chain bottleneck probability — world models will offer the next evolutionary step: comprehensive simulation rather than isolated prediction.

The technical architecture unlocks new possibilities through self-supervised learning. Models train on vast streams of unlabelled data — video feeds capturing real-world dynamics, sensor networks monitoring operations, transaction logs revealing business patterns. They learn underlying principles rather than memorising examples. This shifts the economics of AI development: whilst requiring investment in data infrastructure and computational resources, world models reduce dependency on expensive human labelling, making sophisticated AI accessible to more organisations.

The path to reality: Barriers and timelines

Whilst world models promise transformation, significant technical barriers must fall before they reach commercial viability. Understanding these challenges helps boards set realistic expectations and time their preparations appropriately.

Computational constraints represent the primary barrier. Current computing architectures struggle with the simultaneous processing required for comprehensive world simulation. Whilst today’s AI models process sequences, world models must simulate multiple parallel realities, each with cascading possibilities. This exponential computational demand may require quantum computing breakthroughs to become practical at enterprise scale. Without quantum’s ability to process multiple states simultaneously, world models remain constrained to narrow domains rather than comprehensive business simulation.

Data requirements pose another challenge. World models need vast streams of high-quality, real-time data from sensors, systems, and environments. Most organisations lack the infrastructure to capture, process, and feed such data volumes into learning systems. The shift from labelled training data to self-supervised learning helps, but the sheer scale of observational data required remains daunting.

LeCun himself acknowledges these “huge practical impediments.” Energy consumption alone could make large-scale world models economically unviable with current technology. These energy demands echo the energy sovereignty challenges I’ve discussed, where Boards must view power constraints as strategic imperatives amid global disparities. Training costs that already reach millions for large language models would multiply for systems simulating entire realities. The environmental implications of such energy demands create additional governance challenges.

Timeline expectations must remain grounded. Based on current trajectories, narrow-domain world models may emerge within 2-3 years for specific applications like autonomous vehicles or weather prediction. Enterprise-ready systems capable of simulating business operations likely remain 5-7 years away, contingent on advances in quantum computing, neuromorphic chips, and energy-efficient architectures. Comprehensive world models matching human-level prediction could take a decade or more.

This timeline suggests boards should pursue a dual strategy: building foundational predictive capabilities with current AI whilst monitoring world model developments for strategic timing. Organisations that wait for perfect world models risk falling behind; those that invest prematurely risk wasting resources. The sweet spot involves systematic capability building that creates readiness without overcommitment.

World models in action: Envisioning transformation across industries

The transformation from reactive to predictive operations shows us what becomes possible with world models. Where today’s predictive indicators forecast customer churn probability or regulatory change velocity as discrete metrics, world models would simulate entire business ecosystems, modelling cascade effects and interdependencies that Boards must navigate.

Imagine walking into tomorrow’s command centre where executives gather around a digital twin of global operations. Real-time data would create a living simulation — not merely displaying current performance but predicting multiple futures based on emerging patterns. Market shifts in Asia, weather systems forming over shipping lanes, equipment showing stress signatures, competitor moves in key segments — all synthesised into scenarios that inform immediate decisions whilst positioning for long-term advantage.

Aviation could lead this transformation. Current predictive systems already achieve improvements in operational efficiency, but world models would go further — simulating entire flight networks to predict cascade effects: how weather delays in Singapore affect crew availability in London, passenger connections in Dubai, and maintenance windows in New York. Airlines wouldn’t just respond to problems; they’d prevent them days in advance.

Financial services would gain unprecedented foresight. Whilst today’s AI detects 200% more fraudulent transactions, world models could simulate entire market ecosystems, predicting not just individual fraud attempts but systemic risks and market movements before they materialise.

Manufacturing would transform from reactive to anticipatory. Current predictive maintenance achieves up to 70% reduction in equipment breakdowns. World models would simulate entire production ecosystems, predicting how equipment degradation affects supply chains, workforce allocation, and customer deliveries months ahead.

Governance that enables predictive advantage

World models create fascinating governance challenges. Where boards currently govern discrete predictive metrics — ensuring accuracy in customer sentiment shifts or market demand elasticity calculations — world models would require governance of entire simulated realities. This isn’t about controlling AI; it’s about channelling predictive power that encompasses complete operational scenarios rather than individual indicators.

Essential governance starts with transparency around predictions. Unlike black-box AI that simply outputs answers, world models can show their reasoning of the simulated scenarios, probability distributions, and key factors driving predictions. This transparency requirement builds on the foundation I established for predictive indicators, where Boards need to understand not just what AI predicts but why. World models extend this need — instead of explaining a single prediction like “30% recession probability,” they must reveal the logic behind entire simulated scenarios. The governance frameworks I’ve advocated for decision analytics — from indicator validation to scenario testing — become even more critical when predictions cascade through simulated business operations.

Yet significant challenges remain. LeCun himself acknowledges the “huge practical impediments” to achieving human-level world modelling, including computational requirements that could exceed current capabilities and energy costs that raise sustainability concerns. These constraints make phased implementation through minimum lovable governance even more critical.

Risk management becomes more sophisticated and valuable. When world models predict equipment failure, they also simulate consequences — production impacts, customer effects, financial implications. This enables proportional responses: critical predictions trigger immediate action whilst minor issues queue for scheduled maintenance. Predictive risk assessment transforms governance from reactive compliance into strategic advantage.

Strategic horizons: Compound advantages through prediction

World models will offer something extraordinary—the ability to compound advantages over time. Organisations that eventually master predictive simulation won’t just operate more efficiently; they’ll see opportunities others miss, avoid problems others hit, and adapt to changes others resist. This will create strategic gaps that widen with each predictive success.

When world models mature, organisations simulating environmental changes months ahead will secure resources whilst competitors scramble. Supply chain fragility becomes manageable through multiple scenario predictions. Geopolitical uncertainty transforms from threat to advantage. Innovation accelerates as experimentation costs plummet—simulating products without prototypes, testing markets without campaigns, evaluating strategies without commitment.

The strategic implications are profound. Customer relationships deepen through anticipation—informing them of issues before they occur, suggesting solutions before needs arise. Partnership opportunities multiply as ecosystem simulations identify mutual value creation. Market positioning strengthens as predictive capabilities become visible differentiators.

This virtuous cycle accelerates once it begins. Better predictions improve decisions, which generate superior outcomes, which provide richer data, which enhance predictions. Early movers preparing now for world models will find it increasingly difficult for followers to catch up once these technologies mature.

Preparing for the predictive future

The three-dimensional value framework of leading, lagging, and predictive indicators won’t be replaced by world models—it will be supercharged by them. Leading indicators become richer when systems identify subtle signals across multiple variables. Lagging indicators gain context through simulation of alternative histories. Predictive indicators evolve from probabilistic forecasts to dynamic simulations that boards can interrogate and adjust.

Which operational predictions would transform your competitive position when world models arrive? The answer determines where to build foundational capabilities now, whilst monitoring technological progress for optimal timing. The dual strategy remains clear: develop predictive capabilities with current AI whilst preparing for the world model revolution.

For boards ready to prepare for predictive intelligence, understanding world models today offers more than operational insight—it provides a pathway to shape tomorrow’s competitive landscape. The organisations building predictive foundations now will be ready when the technology matures, transforming uncertainty from threat to opportunity in an increasingly complex world.

About the Author

Mario Thomas is a Chartered Director and Fellow of the Institute of Directors (IoD) with nearly three decades bridging software engineering, entrepreneurial leadership, and enterprise transformation. As Head of Applied AI & Emerging Technology Strategy at Amazon Web Services (AWS), he defines how AWS equips its global field organisation and clients to accelerate AI adoption and prepare for continuous technological disruption.

An alumnus of the London School of Economics and guest lecturer on the LSE Data Science & AI for Executives programme, Mario partners with Boards and executive teams to build the knowledge, skills, and behaviours needed to scale advanced technologies responsibly. His independently authored frameworks — including the AI Stages of Adoption (AISA), Five Pillars of AI Capability, and Well-Advised — are adopted internationally in enterprise engagements and cited by professional bodies advancing responsible AI adoption, including the IoD.

Mario's work has enabled organisations to move AI from experimentation to enterprise-scale impact, generating measurable business value through systematic governance and strategic adoption of AI, data, and cloud technologies.