Why Data and Context are the Keys to AI Success in 2026

2025 saw a global surge in AI investment, with leaders urgently advancing initiatives, often driven by a fear of being left behind. While this accelerated adoption, results remained inconsistent because organisations pushed for rapid outcomes without strengthening the foundations beneath them. 

Consequently, many AI initiatives have stalled. Research highlights this significant gap between ambition and reality. Lenovo and IDC estimate that about 88% of AI proofs of concept fail to reach production, with data readiness as a primary barrier. Similarly, BCG reports that 74% of companies across more than 20 industries struggle to achieve and scale value from AI.  

Despite massive investment, the failure to deliver outcomes created intense pressure from boards, regulators, and customers seeking accountability. 2026 now stands as a decisive moment for aligning ambition with operational reality. Progress depends on applying 2025’s lessons to build stronger data foundations and guide AI development with discipline and mature context. 

How Overconfidence Creates Blind Spots 

One common challenge enterprises face is overconfidence in AI. Many companies fall into the trap of assuming that investment in models, platforms or data science teams alone will generate success. Leaders often begin with advanced analytics or generative AI while overlooking the condition of the information that fuels these systems. This leads to pilot projects often performing well in controlled settings, yet outcomes shift once models interact with large and complex production environments. 

This pattern appears in multiple sectors. In telecommunications, churn prediction models often perform well in controlled tests but decline in accuracy when exposed to inconsistent legacy data. Retail organisations face similar obstacles. Analysts at McKinsey report that AI-driven demand forecasting in global retail frequently struggles due to siloed regional systems and non-standardised data, which leads to significant gaps between forecasted and actual demand.  

These outcomes reveal a recurring challenge. Executive confidence in AI capability often exceeds the organisation’s readiness to support it with the right data foundations. That gap creates delays, rework and operational risk. 

A strong and well-connected data environment improves AI performance. Enterprises that invest care and attention into the structure, clarity and consistency of their data create a powerful foundation for every stage of AI development. When information reflects the true state of customers, suppliers, operations and partners, AI systems learn with accuracy and provide insights that support confident decisions. 

Why Data Is the Key to AI Success 

It’s all about the quality of the data. Organisations maintaining clear, consistent records across platforms create smooth pathways for precise AI interpretation. Aligned customer identities, current supplier information, and uniform operational standards help models recognise patterns accurately. 

Integrated data environments offer further strength. Combining internal records with external sources creates a complete view of organisational context, allowing models to draw from diverse sources to identify emerging issues early and reveal previously obscured patterns. 

Strong foundations ensure AI systems, including large language models, generate accurate outputs based on business-specific grounding. When supported by organised, structured data, models provide insights reflecting real-world conditions. This solid base strengthens user trust and supports reliable decision-making across areas like customer service and operational planning. 

Recent examples from industry highlight the impact of strong data foundations. Financial institutions that improved the consistency and integration of customer and transaction data reported far more accurate risk assessments and smoother review processes. A global logistics provider that created alignment in its inventory data across regional hubs generated more precise forecasts and achieved stronger operational stability. These examples indicate that better data environments often lead to better AI performance. 

Enterprises that commit to high-quality, integrated and well-understood data build the environment required for AI success. This approach creates clarity, supports responsible decision making and allows AI to realise its full potential within the organisation. 

Why Trusted Data Forms the Basis of Trusted AI 

A high-performing AI system requires a unified source of reliable data. This principle applies across all sectors. Once an organisation builds this foundation, it improves quality, consistency, lineage and governance. The result is a platform that supports transparent and auditable decisions. 

This environment creates several advantages: models learn from accurate and consistent information, and business teams can trace data origins and understand how outcomes arise. Regulatory teams gain clarity over compliance controls, and operational teams can reduce time spent on reconciliation and manual review. When these conditions are in place, AI becomes a dependable part of the decision process. 

Research lends support to this approach. A study from McKinsey in 2025 found that enterprises with mature data practices were far more likely to scale AI successfully across business units. The key difference lies in the investment made in data quality, metadata management, lineage tracking and cross-functional ownership. 

The Essential Role of Context 

High-quality data creates stability and context delivers intelligence. Context reveals how people, organisations, events, locations and transactions relate to one another. This perspective helps enterprises understand patterns that would remain hidden within isolated sets of information. 

Following major energy supply disruptions in Europe in 2023, several energy companies successfully used contextual analysis to understand how suppliers, brokers, trade routes and policy changes interacted. As a result, leadership teams had a clearer view of risk and resilience planning. Moreover in the manufacturing sector, a global electronics company used relationship-based analysis to map supplier dependencies across multiple tiers. This helped identify hidden vulnerabilities during geopolitical disruption and supported more resilient procurement strategies. 

These examples show why context is increasingly important for effective AI. Models that receive relationship-aware data produce richer insights, clearer reasoning and more accurate recommendations. They also support greater human oversight, since contextual information helps analysts and decision makers trace how outputs arise. 

Rebuilding Confidence Through Trust 

The next phase of enterprise AI will centre on trust. Trust arises when data is reliable, when context clarifies relationships and when outcomes can be traced back to transparent sources. It is trust that allows leaders to scale AI from pilots to production environments. Trust enables regulators to accept automated decisions, and shapes customer confidence in digital services that rely on advanced analytics. 

Trusted AI systems generally have three characteristics; clear underlying data structure creates explainability, unified inputs deliver consistency, and context reveals the full landscape behind predictions or recommendations, delivering a holistic approach to operations. 

Enterprises with these characteristics are better equipped to scale AI in 2026. 

A Clear Path Forward 

AI continues to advance into critical areas like risk assessment, fraud detection, and operational forecasting. While enterprises explore these broad applications, effective progress begins with a strong foundation. Focusing on data quality, structure, and lineage creates an environment where AI operates with accuracy and accountability. 

This foundation is enriched by context. Mapping relationships across people, places, and events reveals patterns for deeper insight, turning isolated data into a coherent picture of real-world business conditions. When trusted data meets contextual understanding, AI becomes a reliable decision-making partner offering transparent insights. This confidence allows organisations to move AI from isolated experimentation to practical, high-value deployment. 

2026 will be a pivotal moment representing record AI spending and rising regulatory expectations. Early challenges have highlighted the need for discipline; organisations now realise meaningful progress depends on strong data foundations rather than rapid experimentation alone. Those applying these lessons enter 2026 prepared to build AI systems that deliver value with trust, clarity, and consistency.