In 2026, 89% of CIOs plan to increase their AI budgets, but IDC expects that 70% of G2000 CEOs will shift their ROI expectations towards revenue growth.
This aligns with the conversations we are having across the industry, with AI-forward enterprises coming to us to architect scalable, market-differentiated capabilities that directly impact their bottom line.
The performance gap
But what makes this moment so striking is that while organisations at the forefront of the industry are charging at large-scale efficiency and revenue-focused innovations, others remain trapped in a cycle of failed pilots.
On the efficiency side, companies like JPMorgan Chase are saving 360,000 hours of legal work annually through its Contract Intelligence (COIN) platform.
And Walmart eliminated over $900 million in logistics costs through AI-powered route optimisation.
From a revenue standpoint, reports indicate that 35% of Amazon’s e-commerce revenue comes from its AI-driven product recommendation engine.
Companies like Sephora are improving average-order-value with virtual makeup try-on, and Nike is boosting per-user revenue with AI-powered hyper-personalised campaigns.
But most enterprises aren’t there yet. Many are still producing demos that don’t progress forward, or proofs of concept that stall as soon as they hit deployment because they weren’t built for production-readiness.
The real divide is not the technology itself but rather how it’s being architected, how it’s being integrated at scale, and how the initiatives are being led within the organisation.
Architecting for success
Moving into 2026, as competition heats up and leaders become less tolerant of immeasurable pilots, the organisations that succeed will be those developing AI systems intelligently architected for production at scale, integrated within operational workflows, and designed from the outset to deliver measurable returns.
Organisations need to:
Start with the business problem, beware of false illusions of progress
Organisations need to start by thoroughly understanding the core business challenge they are trying to solve, what success looks like in terms of outcomes, and then carefully architect an AI solution that aligns with their unique operational context. There is no one-size-fits-all approach. From the outset, this requires robust tracking mechanisms to be put in place to monitor continuous performance over time and identify uptick in value.
And organisations need to beware of pitfalls relating to false illusion of progress. In a previous article we highlighted some surprising study results whereby developers believed AI had made them faster, despite that not being the case. This reinforces just how costly poor AI integration can be if adequate measures are not in place to determine results.
Architect for real-world use
Systems architected for production from the outset – with robust data pipelines, monitoring capabilities, scalability, and considered integration – can be deployed broadly and deliver enterprise-wide value more rapidly. Achieving such production readiness demands more than isolated technical capability alone, it requires a system-aware approach that accounts for data quality at scale, integration with existing workflows and the architectural expertise to anticipate performance challenges before they constrain business impact.
Future-proof their architecture for long-term value
Building on the last point, having architectural foresight that enables adaptability well into the future needs to be a fundamental part of an organisations’ AI strategy. Full-stack AI expertise, from infrastructure to application, is a non-negotiable when it comes to building AI systems that are built for lasting value, highly performant, cost-efficient, governable, and protected from future market developments which could cause a costly re-build and negate any additional value first created.
2026 outlook
As AI investment accelerates into 2026, enterprises are going to be held more accountable than ever for the ROI. And the divide between leaders and those being left behind will only widen.
The complex juggling act of showcasing measurable results, while managing the underlying complexities surrounding system integration, performance, cost efficiency, governance and future adaptability is why organisations are turning to partners like Stelia who can help them move to production-grade systems that deliver lasting impact.
Success will belong to companies that combine architectural discipline with clear commercial intent. For those that can master this, 2026 is the year of operationalising AI at scale and delivering tangible business outcomes that significantly increase your competitive stance.
