Contextual AI has emerged as a defining framework for improving model performance. It focuses on the premise that by responding to data such as user history, environmental factors, and real-time conditions, AI can deliver more personalised results to users.
Yet much of the work on contextual AI to-date has overlooked a substantially larger optimisation opportunity. A lot of enterprise efforts have been placed into contextualising singular models rather than asking whether the problem is actually insufficient context, or whether the models themselves are even the right tool for each job.
This article examines the current framing of contextual AI in the market and assesses why we need to move away from isolated optimisation of models to a full AI system approach whereby specialised models are dynamically chosen based on user context, device constraints, and environmental or computational demands.
Current approaches to contextual AI
The premise for contextual AI makes logical sense: more context delivers better AI performance. This rhetoric spans customer experience platforms, enterprise software vendors, and security solutions, all promoting the same fundamental premise that if AI can understand completely user history, environmental factors, and situational conditions it will be able to generate optimal results.
The industry has latched onto this development, crystallised around familiar phrases: context-aware AI, contextual understanding and richer prompts. Customer experience platforms emphasise hyper-personalisation at scale and unified customer profiles, while enterprise vendors promise solutions that understand the why, where, and when of every interaction.
But the definition of contextual AI is evolving constantly. It spans both more simplistic approaches like singular model contextualisation, and is also beginning to enter into the art-of-the-possible of intelligent and adaptive AI systems.
A lot of enterprise implementation at scale has been focused on the former definition thus far. This approach resonates because it feels both rational and achievable, incremental improvements through enhanced data utilisation, and for the most part building on existing infrastructure rather than architectural redesign.
But the focus on optimising context for singular models sidesteps more critical questions around resource allocation and whether the model architecture itself is fit for purpose. This narrow framing leads organisations down expensive paths with diminishing returns.
Why this framing is limited
This focus on contextualising singular models has led the industry to focus on feeding better information into existing architectures without questioning whether those architectures actually suit the task at hand. A customer service query about account balance requires totally different computational resources than complex technical troubleshooting, yet current approaches handle both with the same system enhanced by different context.
The implications of this narrow outlook are significant. Expanding context windows and comprehensive data integration requires substantial computational overhead. Organisations run increasingly powerful models to process larger context payloads, even for simple queries that smaller systems could handle efficiently. And while costs scale exponentially, the performance improvements remain marginal. Compounding this, as foundation models hit data ceilings with publicly available training data largely exhausted, these diminishing returns will only accelerate.
The result is systematic misallocation whereby computational resources are deployed without regard to whether the model architecture actually suits the task at hand. While context undeniably improves individual model performance, this single-layer optimisation cannot address the fundamental mismatch between fixed architectures and diverse task requirements. A general-purpose model, regardless of how much customer history it receives, may still be poorly suited for specialised domains. Context provides important information but cannot transform a model’s core capabilities.
Why this matters now
The urgency for this shift to a full-system approach stems from converging pressures around scale, regulatory demands and market competition.
As organisations scale their AI deployments the limitations of more simplistic contextual AI approaches are becoming increasingly acute. Scale inevitably exposes inefficiencies that remain hidden in pilot projects and when organisations deploy more common approaches to contextual AI across thousands of daily interactions, the computational waste becomes impossible to ignore. Running enterprise-grade models for routine queries creates unnecessary energy consumption and infrastructure costs that compound rapidly.
Regulatory pressure around AI energy usage intensifies this challenge. Over-provisioned AI systems are no longer justifiable as regulatory frameworks are scrutinising AI energy consumption, making efficiency not just an economic consideration but a compliance one.
These pressures coincide with shifting competitive dynamics. The organisations gaining advantage are no longer the ones with the most sophisticated models, but those deploying AI most strategically. When every competitor has access to similar foundation models and contextual data, the differentiator becomes architectural sophistication, not simply which models they use.
Single-layer optimisation made sense when AI was experimental. At scale, with regulatory scrutiny and commoditised access to models, system-level thinking becomes imperative.
A new way of thinking
System-level optimisation approaches AI architecture differently. Rather than feeding comprehensive context to a singular model, intelligent systems dynamically route queries to specialised models based on computational requirements, task complexity, and user constraints. A routine account query reaches a lightweight model optimised for efficiency while complex domain-specific problems are routed to specialised architectures designed for that purpose. The real-time orchestration of this selection matches each request to the most appropriate resource, through adaptive allocation that responds to actual conditions.
The new questions surrounding context become: How do you distribute computational resources efficiently when centralised context processing creates bottlenecks at scale? How do you ensure appropriate AI capabilities reach users regardless of their infrastructure constraints or geographic location? How do you maintain system performance when different regions, devices, and use cases require completely different computational approaches?
Crucially, these challenges are not hypothetical. As access to foundation models and contextual data commoditises, the differentiator shifts from what models you have access to, to how intelligently you allocate them.
At Stelia, we engineer AI systems to operate intelligently as a whole, rather than in disconnected parts. Singular model contextualisation is a prime example of how isolated thinking can have significant consequences for businesses trying to optimise their AI outputs.
We look forward to sharing our upcoming deep research in the coming months which assesses the next phase of contextual AI advancement. This addresses the computational challenges of real-time model selection including how systems determine which specialised model to deploy based on user behaviour, device capabilities, and contextual demands, all without sacrificing performance.
