Your enterprise AI must understand your business, not grow

By-Nick Eayrs,

Vice President, Field Engineering, Asia Pacific and Japan, Databricks

AI has reached a point where it can meaningfully accelerate enterprise productivity. The central challenge for organisations is no longer gaining access to more powerful models, but refining and deploying AI in ways that reliably automate routine, time-consuming work. Done well, this frees teams to focus on higher-value, creative and strategic tasks.

Closing the gap between AI’s potential and its reliability will define 2026. Rather than chasing ever-larger models, enterprises are beginning to demand smarter, contextual systems that align closely with their specific needs. Six shifts are emerging as critical: building agents that reason over proprietary data, operate collaboratively, are evaluated continuously, work across modalities, integrate seamlessly into workflows, and are supported by a workforce trained to work alongside them.

From generalised to domain-specific AI agents

General-purpose models trained on public internet data often struggle with the complexity of enterprise environments. They lack deep organisational context and are poorly suited to navigating internal processes, exceptions and compliance requirements. At the same time, enterprises face growing expectations around data and AI sovereignty, driven by regulatory, geopolitical and privacy considerations.

Domain-specific agents, grounded in proprietary data with governed lineage, are better equipped to interpret internal rules, edge cases and regulatory constraints. They also support sovereignty requirements by ensuring data privacy, security and jurisdictional compliance. This level of control reduces risk, meets legal and ethical obligations and helps preserve competitive advantage.

Examples from industry show that organisations focusing on data quality, domain depth and governance achieve better outcomes than those prioritising model size alone. The direction of travel is clear: success in the next phase of AI adoption will depend less on scale and more on secure integration, robust governance and contextual understanding.

From single agents to multi-agent orchestration

Enterprise workflows are rarely linear, and AI systems must reflect that reality. Real-world processes typically involve multiple stages, including data retrieval, validation, approvals and decision-making across different systems and teams. These workflows exceed what a single agent can handle reliably.

Multi-agent orchestration addresses this complexity by assigning specialised agents to discrete tasks such as compliance checks, reasoning or data access, while a supervising agent coordinates their activity. This supervisory layer sequences tasks, delegates responsibilities and synthesises outputs in natural language, enabling AI systems to operate at scale within governed, auditable and adaptable workflows.

From one-off testing to continuous evaluation

As AI systems move into production, continuous evaluation becomes essential. Models that perform well during development often degrade when exposed to live data or shifting conditions. Without ongoing assessment, reliability can erode quickly.

In response, enterprises are adopting evaluation-centric practices, where AI agents are measured continuously against real tasks, real feedback and changing operational environments. This approach allows organisations to detect drift early, reduce uncertainty and improve performance over time. Continuous evaluation also enables AI systems to learn from outcomes, ensuring they adapt more closely to enterprise requirements.

From text-based AI to multimodality

AI has historically been text-first, but enterprise communication increasingly spans voice, video, images, sensor data and messaging platforms. Multimodal AI reflects this reality by combining and interpreting diverse inputs, significantly expanding the scope of automation.

In practice, multimodal systems augment human interpretation at scale. In customer service, AI can analyse written queries, tone of voice and visual evidence simultaneously. In healthcare, models can combine patient records, medical images and sensor data to support diagnosis and treatment decisions. In retail and e-commerce, multimodal agents can process reviews, images and usage videos to better understand customer preferences and identify fraud.

From visible features to invisible integration

The most effective AI systems do not demand attention. Instead, they are embedded seamlessly into workflows, improving productivity without adding friction. This “invisible AI” becomes part of the operating environment rather than a tool employees must consciously engage with.

When AI is integrated in this way and evaluated continuously, collaboration between humans and machines becomes more natural. Productivity gains follow not from novelty, but from consistency, reliability and ease of use.

Sustained focus on skills and capability

As AI agents become embedded in daily operations, organisations must continue investing in people. This means equipping employees not only to build AI systems, but to manage, guide and collaborate with them.

Crucially, benefiting from AI does not require deep technical expertise. A marketer automating reporting tasks, for example, primarily needs skills in prompting, workflow design and oversight. Developing these capabilities across the workforce will be essential to realising AI’s full value.

2026: When AI truly understands the business

Taken together, these six shifts redefine enterprise AI adoption. Domain-specific and sovereign agents make AI business-aware; orchestration, continuous evaluation and multimodality make it dependable at scale; and invisible integration combined with skills development ensures it fits naturally into everyday work.

The organisations that succeed in 2026 will not be those deploying the largest models, but those that build strong data and AI governance, treat domain-aware agents as trusted collaborators, and continuously evolve how people and systems learn from one another.