The AI landscape faces a number of big shifts in 2026. Agentic AI will undergo a reality check as enterprises discover the gap between marketing hype and actual capabilities, while organisations will go through a mindset change from treating AI hallucinations as crises to managing them, acknowledging the inherent limitations of the technology. There will also be a shift in how data will be structured in AI systems, to help the move from just finding facts (“what”) to understanding reasons (“why”). Middleware application providers will face new challenges, as those vendors controlling both platforms and data will become more influential. Finally, standardised AI chat interfaces will evolve into smarter, dynamically generated, task-specific user experiences that adapt to immediate needs.
Agentic AI Reality Check
2026 is the year when agentic AI will get a reality check, as the gap between marketing promises made in 2025 and their actual competencies will become starkly visible. As enterprise adopters share the mixed successes of agentic AI, the market will begin to differentiate between true autonomous agents and the clever workflow wrappers.
Currently, many products promoted as AI agents are, in reality, rigidly programmed systems that simply follow predefined paths. They cannot independently plan or adapt in real-time to accomplish tasks. The current evolution of AI agents closely resembles the development of autonomous vehicles: early self-driving cars could only maintain lane position by relying strictly on preset instructions, and likewise, today’s AI agents are limited to executing narrowly defined tasks within established workflows. True autonomy, where AI agents can dynamically perform and solve complex problems better than humans and without human intervention, remains, for now, an aspirational goal.
AI Hallucination Goes from Crisis to Management
In 2026, the AI hallucination crisis will reach a critical juncture as organisations realise they must learn to coexist with the current fundamentally imperfect technology – until a new technology comes into play that can effectively address the issue. The focus will shift from AI hallucination ‘crisis’ to management.
As the industry deliberates who carries the liability for AI’s mistakes and inaccuracies – the tool makers or the users – enterprises will stop waiting for vendors to solve the problem and take matters into their own hands. They will adopt a variety of pragmatic risk mitigation strategies – from double and triple-checking work, and enforcing human oversight for high-stakes decisions, to taking hallucination insurance policies.
Major model builders acknowledge that current foundational LLM technology cannot eliminate hallucinations and ambiguity through incremental improvements alone. New technology is needed. Until then, and perhaps with the realisation that a technological breakthrough is years away, users will start driving the hallucination conversation – both by building systematic defenses within how they use AI, and forcing vendors to accept shared responsibility through better documentation and clearer model limitations.
The Next Evolution in AI Data Architecture Lies in a Shift from “What” to “Why”
There will be a fundamental shift in how data is structured for AI systems, driven by the limitations of current approaches in answering complex questions. While Retrieval Augmented Generation (RAG) has proven effective at locating information and answering “what” questions, it struggles with the deeper “why” and “how” inquiries.
This limitation stems from RAG’s flat-file architecture, which excels at locating information but fails to capture the complex interconnections and relationships that underpin meaningful understanding and knowledge, especially in specialised domains like legal and professional services information.
The solution lies in AI-driven autonomous structuring of data. These systems will be better placed (than humans) to reveal critical relationships across multiple data points at scale, also highlighting the contextual dependencies essential for answering the “why” and “how” questions effectively.
Consequently, in 2026, with machines taking the lead, the method of structuring data will undergo a complete transformation, gradually eliminating the human role in creating structure, to reveal the business-critical interconnections across multiple data points.
Middleware AI Apps Squeeze
Given the essential link between data and AI, middleware companies that specialise in building custom applications layered on top of data platforms will begin to get pushed to the margins, forced to compete on niche features – while the core value of data and insight is captured by the platform owners. The true leaders will be those organisations that both own and manage their data, while also offering an AI-powered interface that enables users to interact with their data securely and efficiently, fully leveraging the capabilities of modern AI technology.
Shift to AI-generated, Task-Oriented User Interfaces
In 2026, the current traditional vendor-designed, standard AI chat-based user interfaces will transition to dynamically AI-generated task-specific user interfaces that adapt to users’ immediate needs. This represents a fundamental shift from standardised software – for example, where everyone uses identical Microsoft Word or SharePoint interfaces – to personalised, short-term user interfaces that exist only as long as the user requires them for a specific task.
This transformation will also address the critical pain point that users typically have – i.e, the crushing cognitive load of navigating bloated, feature-rich software. Instead of searching through endless menus in an overstuffed application like Excel, the user will simply state their goal – “Compare the Q3 and Q4 sales figures for our top 5 products and show me a chart” – and the AI will instantly generate a temporary, purpose-built interface – a “micro-app” – solely designed for that one single task.
In the context of dynamically generated user interfaces, both data storage and the creation of bespoke interfaces will be managed by AI. The AI organisations that will truly lead in providing such bespoke user interface-generating capability are those that possess and control their own data.
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- Artificial Intelligence in FinTech
- Data & AI
- Digital Strategy