Most enterprises are still interacting with AI by prompting it—a surprisingly primitive approach for technology this sophisticated. The true frontier is imperceptible AI: autonomous orchestration that predicts work, executes tasks, and resolves exceptions before humans notice friction. Three structural blockers prevent enterprises from getting there, and only one C-suite role is positioned to solve them. A framework for the orchestration era.
For all the extraordinary progress we’ve made with AI, most enterprises are still interacting with it in a surprisingly primitive way: by prompting it. We ask for answers, re-prompt when it gets things wrong, and manually stitch its outputs back into our workflows. It’s clever, but it’s far from transformational. The true frontier of enterprise AI is not smarter prompts or more capable copilots, but AI so deeply embedded into workflows that it becomes imperceptible, a silent orchestrator that predicts work, executes tasks, coordinates systems, and resolves exceptions before humans even register the friction.
This is the world enterprises are racing towards. And as we head into 2026, AI-native workflows will shift from experiments to business-as-usual. But the organisations that get there first won’t be the ones with the most powerful models; they’ll be the ones that redesign their foundations for orchestration at scale.
Why We haven’t Arrived at Imperceptible AI Yet
Even the most ambitious enterprises are still stuck with fragmented, semi-manual processes. The blockers aren’t about AI capabilities which in reality have progressed faster than anyone expected. The blockers are structural.
First, enterprise stacks remain too fragmented and too brittle. Years of bespoke applications, legacy infrastructure, and multi-cloud sprawl mean workflows don’t flow; they zig-zag between systems created decades apart. Modernising this isn’t optional but rather, it’s the prerequisite for AI that can operate autonomously rather than sit politely on the sidelines.
Second, governance has lagged behind ambition. AI cannot become autonomous inside workflows without enterprise-grade control, transparency, auditability, and safe failover paths. Most organisations simply haven’t rewritten their governance models to accommodate adaptive, self-acting systems.
And finally, IT is still the bottleneck. Not because IT isn’t capable, but because even the best teams cannot keep pace with the volume of custom workflows required to operationalise AI across every business unit. It is mathematically impossible for centralised IT to build the number of automations the business will demand.
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Wasn’t Low Code Supposed to Fix This?
Low code has certainly brought enterprises closer to their goals. It has matured into a foundational tool for modernisation, with CIOs using it to upgrade legacy systems, integrate multi-cloud environments, reduce technical debt, and build applications in a fraction of the time traditional methods require. In many parts of the world, development cycles of six months or more remain the norm, and low code is often the only approach able to compress that timeline into a few weeks.
But low code alone hasn’t delivered the organisational transformation many expected—and that’s not a failing of the technology. It’s a limitation in how companies use it. Too many enterprises still treat low code as a faster app-dev tool under IT ownership, rather than the framework through which the entire organisation orchestrates work. If that mindset doesn’t shift, AI will never move from “prompted” to autonomous.
What Needs to Change: Low Code Must Become the Enterprise Orchestration Layer
To make imperceptible AI a reality, low code must evolve from simply building applications to orchestrating the end-to-end flow of work across the enterprise. This is the real inflection point.
Workflows can no longer be static sequences of forms, rules, and conditional logic. They need to become dynamic systems where AI agents, human employees, enterprise applications, and live data streams interact continuously. Low-code platforms are emerging as the only realistic orchestration layer capable of managing this complexity. Today, they connect systems, enforce governance, and enable workflows to adapt as the business does.
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In many ways, 2026 will mark the moment when enterprises start treating low code as a strategic backbone, much like how they once treated ERP. The question is shifting from “How fast can we build applications?” to “How do we orchestrate work across every function, with humans and AI collaborating fluidly?”
And as enterprise IT inevitably moves towards multi-agent environments (where specialised AI agents interact with one another and with core systems) the importance of getting this orchestration layer right becomes even more critical. Without it, the future of AI-enabled enterprises simply won’t scale.
Who Should Lead This Transformation? The CDO, Not the CIO
This is the bold shift many still underestimate – the evolution toward AI-native workflows will be championed not by CIOs, but by Chief Digital Officers.
CDOs are already leading nearly 60% of citizen development initiatives, and for good reason. They operate at the convergence of business goals, data strategy, and process redesign. Their mandate is inherently cross-functional, making them uniquely positioned to drive the kind of workflow reinvention required for imperceptible AI.
The CIO will remain essential, particularly for security, infrastructure, and governance. But ownership of how work itself is redesigned is moving closer to the business. And in the era of AI-native operations, business context is what unlocks value. This is why the CDO becomes not just an enabler of transformation, but the architect of the next-generation enterprise operating model.
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New Metrics for a New Era
The metrics used to measure success in low code and automation up until now were shaped by a different era that was focused on speed of development, application volume, or reduction of manual tasks. These indicators no longer reflect where AI and low code create value.
What matters today is how much of a workflow can operate autonomously from end to end, and how reliably it can do so. It’s about how little human intervention is needed for routine processes, and how quickly workflows can evolve when external conditions change. It’s the degree to which citizen-built solutions are adopted across functions, and the maturity of governance frameworks ensuring that AI operates transparently and safely.
Ultimately, success in this new landscape is measured in adaptability: how quickly an organisation can redesign its operations, not just its applications.
The Bottom Line: Prompting Will Fade, Orchestration Will Define the Leaders
By the time we look back at the end of 2026, prompting inside enterprises will feel like a stepping stone, useful in its time, but nowhere near the final destination. The organisations that pull ahead will be those that build AI-native workflows so well-orchestrated, so intuitive, and so safely governed that the presence of AI becomes almost invisible. Employees won’t “use AI”, rather they’ll simply experience work that moves fluidly around them.
Low code will quietly serve as the orchestration layer behind this shift. AI agents will increasingly take on the mechanics of work. And the CDO will shape how these elements come together to form the next generation of enterprise agility.
The transformation has already begun. The advantage will go to those who choose to operationalise it now, before the gap becomes too wide to close.
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