Running AI at scale: Why 2026 demands new data foundations

In 2026, organisations will progress beyond experimenting with AI, to relying on it to enhance various areas of business operations. The adoption of AI agents in the workplace will become a natural part of how work gets done by fast-tracking decision making, strengthening teamwork, and coordinating activities across multiple platforms. Every digital action executed in daily business operations will contribute to building a layer of intelligence that can push organisations to work more efficiently and operate with greater certainty and control.

However, as the usage of AI grows, so do expectations and stakes. Organisations can no longer depend on fragmented data practices, informal guidelines, or weak governance practices. They will require clear guardrails that explain how automated choices are made, what data they use, and how accuracy and dependability are maintained when humans and AI work together in tandem. The harsh reality at the core of this problem is that AI can’t scale reliably if it is based on data foundations created for yesterday’s technology.

Designing architectures fit for autonomous intelligence 

For years, organisations have operated with separate operational and analytical environments, relying on distinct systems to manage transactions on one side and insights on the other. This made sense when data volumes were modest and decision-making cycles allowed for delay. Today, it is one of the most significant barriers to scaling AI.

The longstanding operational systems (OLTP) / analytical systems (OLAP) divide creates duplication, latency, and fragility. Data moves back and forth between systems that were never designed to work together, introducing inconsistencies at the very moment organisations need absolute reliability. AI agents, which rely on the ability to reason over live organisational data, are constrained by architectures that cannot provide the immediacy or coherence they require.

This is why more organisations are now challenging the old model and moving toward data architectures where operational and analytical workloads share the same underlying data foundation. Enterprises will start dismantling legacy structures and adopting modern, consistent foundations that bring both workloads together. The result will be reduced complexity, stronger governance, and far more reliable data flows into AI systems. Most importantly, unification gives AI agents the low-latency, high-quality data they need to operate safely and autonomously. This is not an optimisation exercise. It is a prerequisite for AI that can deliver real time operational intelligence at scale.

Turning AI agents into everyday operations 

With these foundations in place, AI agents will move beyond experimental pilots and become embedded into everyday operations. They will support complex, multi-step tasks, reasoning across organisational data and interacting with systems in ways that feel increasingly natural. For many organisations, agents will become trusted participants in routine processes, augmenting human expertise.

This shift is becoming clear in data intensive industries where accuracy is critical. In life sciences, AI agents are being applied to large volumes of unstructured information that were previously difficult to analyse at speed. AstraZeneca has shown how agent-based approaches can be used to parse over 400,000 clinical trial documents, converting complex scientific data into structured inputs for analytics and downstream AI. The value lies not in automation alone, but in making trusted data usable without compromising rigour or control.

As AI becomes more entwined with core decision making, high accuracy and governance is critical. Errors in these environments are not far-fetched concepts — they carry operational, regulatory, and ethical consequences. Looking ahead, effectiveness will be shaped less by model size or sophistication and more by the ability to combine high-quality data, unified governance across data and AI, deep domain understanding and systems designed to prioritise accuracy over convenience.

Reskilling the workforce for AI-driven operations 

As AI systems take on greater responsibility, reliability will become the defining measure of success. Models that perform well in controlled environments can degrade quickly when exposed to live data and changing conditions. Without continuous evaluation and the ability to improve accuracy, trust in automated intelligence erodes.

In response, leading organisations are adopting evaluation first approaches, where AI agents are assessed continuously against real tasks and real feedback. New classes of agent development tools are emerging to support this shift, enabling teams to define an agent’s purpose and quality expectations in natural language, automatically generate task-specific evaluations and improve performance over time using enterprise data. This reduces reliance on trial and error – moving AI beyond one off deployment towards systems that can be monitored, refined, and continuously aligned with business needs as conditions evolve.

Alongside this, one of the most important shifts in 2026 will be the democratisation of AI education. The organisations that excel will not be those with the most complex models, but those with workforces prepared to collaborate confidently with AI. Most AI related roles of the future will be existing roles, reshaped by real time intelligence and automation, requiring practical, contextual training rather than deep technical expertise.

As unified architectures reduce the operational burden on data teams, employees across the business will find it easier to experiment, iterate and innovate. Upskilling goes beyond being a technical initiative, but also a cultural one that empowers people to work more effectively alongside intelligent systems.

Rebuilding organisations for speed, trust and autonomy 

The growing interplay between humans and AI will reshape how organisations operate. Bloated, disconnected SaaS stacks will begin to recede as companies move towards simpler, more unified platforms that offer clarity as well as efficiency.

Cloud strategies will evolve in parallel, with greater emphasis on control, agility, and transparent governance.

Organisations will start to resemble connected networks rather than rigid hierarchies. Small, focused teams supported by AI enabled systems working on reliable data, will move faster and solve problems more effectively. Decision making will become more distributed, and innovation cycles will shorten as AI is trusted not just as a tool, but as an active contributor to operational success. 

Why reliability, not scale, defines success 

In 2026, AI deployment will be the easy first step; the real difficulty will be maintaining its reliability as it becomes the focus point of everyday operations. Businesses will require intelligence that utilises real-time data and operates with clear governance to become systems that people can depend on, rather than experiment with.

Success will be determined by operational discipline rather than model ambition. Established foundations, regular evaluation in real-world settings, and people with the practical abilities to direct, question, and improve automated judgements as circumstances change are all vital for AI to deliver value.

With these aspects in place, AI begins to function as infrastructure rather than as a collection of projects. In a world that is becoming more and more agent-driven, decision-making accelerates, organisations adapt to change more rapidly, and intelligence becomes the durable advantage.