Five hard truths about enterprise AI heading into 2026

In 2026, APAC IT leaders will move beyond debating AI’s relevance to confront a tougher, more urgent question: how can we operationalise AI without breaking the systems we rely on underneath? After two years of experimentation, proofs of concept, and rapid model innovation, enterprises across the region are now under pressure to convert AI from pilot projects into production-grade capabilities that deliver measurable outcomes.

What’s changed is the context. Budgets are tightening, regulatory expectations are rising, and the early excitement around generative AI is giving way to a more pragmatic demand for stability, governance, and RoI. Across the region, AI adoption is also being shaped bottom-up by developers and platform engineering teams. This shift matters because AI is no longer theoretical. Based on what I’m seeing across the region, here are the five hard truths that will define the year ahead:

1. AI becomes the core of IT strategies, not just an innovation side project

AI has already escaped the lab and will become inseparable from the core IT strategy. Across APAC enterprises, CIOs and CTOs are moving away from isolated pilots and towards full-scale reinvention of how work gets done. This isn’t about chasing the next shiny use case; it’s about how enterprises can embed AI directly into operational processes that keep the business running.

With budgets under pressure, AI adoption will be driven by its ability to optimise existing operations, not just enable new ones. Leaders who treat AI as an optional innovation will fall behind those who are categorising it as an essential infrastructure. AI strategies will only succeed where developers can turn intent into production using platforms that fit existing architectures, skills, and workflows.

2. Agentic AI emerges as the most practical form of automation

For many organisations, agentic AI will become the fastest way to reclaim time, free up skills, and redirect budget toward higher-value initiatives. Rather than replacing people, these agents will take on the repetitive, low-value tasks that consume time and attention across IT teams—provisioning infrastructure, managing backups, allocating resources, and handling routine remediation.

Agentic AI will succeed in environments where platform teams integrate agentic capabilities directly into their existing operational tooling without requiring major rewrites to systems or complex integrations. The real transformation isn’t technical, it’s operational.

3. AI inference shifts decisively to the edge

While hyperscalers will continue to dominate AI model training, inference, where AI delivers decisions and actions, will move increasingly closer to where data is being generated. In 2026, latency, resilience, and operational simplicity will outweigh centralisation. Across a range of functions—from retail analytics, micro-banking, healthcare diagnostics to mining

operations—all require real-time decision making in environments with limited or no onsite IT expertise.

This shift will force organisations to rethink how they deploy and manage AI at scale. Platforms that run AI reliably at the edge, with minimal local skills and consistent performance, will determine whether AI succeeds outside traditional data centres.

4. Hybrid infrastructure remains the foundation for modernisation

Despite predictions that cloud would replace everything, virtual machines and hyperconverged infrastructure remain the backbone of enterprise IT. That won’t change. Organisations across APAC are doubling down on hybrid models that let them modernise incrementally: keeping core systems stable while building new cloud-native and AI-driven workloads alongside them.

Hybrid infrastructure has become the practical path to modernisation, allowing organisations to keep core systems stable while introducing cloud-native and AI-driven workloads in parallel. This incremental approach reduces risk, protects prior investment, and gives IT teams the flexibility to modernise at a pace the business can absorb.

5. Developers become the true gatekeepers of AI platforms

One of the most underestimated shifts will be where influence sits. Increasingly, technology decisions are being made bottom-up by developers and platform engineering teams. Developers are choosing tools that integrate naturally with Kubernetes, open-source ecosystems, and existing workflows. Platforms that fail to align with these realities simply won’t be considered.

The future belongs to providers that speak in developers’ language—clear, technical, and practical—rather than enterprise jargon. Vendors that don’t demonstrate relevance risk being excluded from the next generation of platforms, no matter how strong their technology is.

The next phase of AI adoption won’t reward the loudest voices. It will reward the most disciplined ones. Enterprises that focus on modernising foundations, reclaiming operational time, and empowering developers will be best positioned to turn AI from promise into performance. In 2026, the biggest risk won’t be adopting AI too quickly. It will be standing still.

(The author is CTO and VP of Solutions Engineering, Nutanix APAC. Views are personal.)