1. Model Context Protocol (MCP) Will Become the Standard for AI-Native Applications

In 2026, Model Context Protocol (MCP) will emerge as a foundational layer for AI-enabled software. Rather than relying on fragile prompt engineering and bespoke integrations, MCP provides a consistent way for applications to share structured context, tools and live data with AI models.

This shift will significantly reduce integration complexity and allow AI systems to operate with far greater awareness of enterprise data, permissions and workflows. Much like early web standards enabled the internet to scale, MCP will quietly become essential infrastructure for AI-native applications.

There is still some work to be done with MCP and its scalability (memory footprint) for very large systems, but we fully expect this to be resolved in 2026.

2. Agentic and Multi-Agent AI Systems Will Become Autonomous Teammates

Agentic AI will move beyond early experimentation and into meaningful production use in 2026. These systems will no longer act purely as assistants responding to prompts, but as autonomous teammates capable of planning tasks, coordinating actions and validating outcomes.

We will also see the rise of multi-agent systems, where specialised agents collaborate to retrieve information, reason over it and execute actions. For search and knowledge work, this marks a shift from simple information retrieval to AI systems that actively help complete complex workflows.

Agentic AIs are still maturing in the way that they work and also heavily rely on MCP servers, but we fully expect these new technologies will solidify in 2026.  

3. Multimodal Search Will Become the Default Interface

By 2026, search will no longer be primarily text-based. Advances in multimodal models mean users will naturally combine text, voice, images, documents, charts and video in a single interaction.

Search systems will be expected to interpret and reason across these modalities seamlessly. Whether analysing a diagram, summarising a video, or answering questions about a report, multimodal search will become the default way people interact with information across both consumer and enterprise environments.

4. AI Governance, Provenance, and Trust Frameworks Will Become Mandatory

As AI systems are embedded deeper into business processes, trust will become non-negotiable. In 2026, governance will shift from high-level policy discussions to enforceable, auditable systems.

Organisations will need to demonstrate where information comes from, how answers are generated and what data and tools were used along the way. Provenance, explainability, and controlled behaviour will be essential not just for compliance, but for building confidence in AI-powered search and decision-making.

5. The Great AI Reality Check Will Drive a Focus on Measurable ROI

Following years of hype and experimentation, 2026 will bring a sharper focus on return on investment. Enterprises will increasingly prioritise AI and search initiatives that deliver clear, measurable outcomes over those that simply showcase technical capability.

This shift will favour practical use cases embedded into core workflows, with close attention paid to cost, performance and business impact. AI adoption will continue to grow, but tolerance for inefficiency and poorly defined value will diminish rapidly.

6. Small, Specialized Models Will Overtake General-Purpose LLMs

While large, general-purpose language models will remain important, 2026 will see growing adoption of smaller, specialised models tuned for specific domains and tasks. These models often deliver higher accuracy, higher speeds, lower costs, and better governance than their larger counterparts.

Alongside this trend, open-source LLMs are likely to see broader adoption, particularly in scenarios where organisations require greater control over data, deployment and fine-tuning, while balancing innovation with governance and risk.

For enterprise search and RAG systems, this shift will result in AI that behaves less like a generalist and more like a true subject-matter expert.

7. RAG Will Evolve from Simple Retrieval to Agentic and Knowledge-Driven Architectures

Retrieval-Augmented Generation (RAG) will continue to mature in 2026, evolving well beyond basic vector search. RAG systems will increasingly incorporate structured data, knowledge graphs and multi-step reasoning to improve accuracy and trust.

Agentic RAG architectures will plan retrieval journeys, cross-check sources and reason over relationships before generating answers. The result will be AI systems that resemble skilled researchers rather than simple search tools, capable of delivering more reliable and explainable insights.