Autonomous: The Word That Might Define 2026

From AI Assistance to Autonomous Execution

I’m going to make a prediction. AI was the word of last year and autonomous might well be the word of this one.

We are only a few weeks into February but something already feels different. Over the past twelve months, most of us have been learning how to use AI by prompting it, testing it and embedding it into parts of our workflow. That phase was necessary because it gave us familiarity, reduced the fear factor and moved AI from novelty to utility. Here’s the thing though: that was only the first chapter.

What is changing now is that AI is no longer just responding to us. It is beginning to act on our behalf and that is a very different proposition.

The Waymo Moment: Building Trust Through Performance

I remember feeling this shift very clearly last year in San Francisco when I experienced Waymo for the first time. Sitting in a fully autonomous vehicle without a driver is not a normal starting point for trust. In the first few minutes you feel the nerves because your brain is still trying to reconcile what is happening with what it has always known. You ask yourself whether you really trust the system and whether it can genuinely make better decisions than a human in real time.

Then something interesting happens. You start to observe the quality of the decision-making and you see the system scan the environment, anticipate movement and react with consistency. On one journey a car suddenly pulled out from a parked position into our path. The Waymo vehicle saw it before I did and reacted faster than I would have. There was no panic and no hesitation, just execution. By the end of that first ride the nervousness had disappeared and trust had been built through performance.

That chain of trust matters because autonomy does not win through theory. It wins through experience.

Building My Own Autonomous Agent

Last weekend I decided to test this in a digital context by building my own autonomous agent. I have not coded properly in almost twenty-five years so this was not a routine project for me. It started as curiosity because I wanted to understand what autonomy really meant beyond the headlines. If systems are going to operate with agency then what does that look like in practical day-to-day terms?

Design Decisions Become Serious Decisions

Fast forward to the build itself and curiosity quickly became architecture. The moment you give an agent the ability to operate a browser, send emails and execute tasks, design decisions become serious decisions. I bought a Mac mini specifically to give the agent its own machine and operating context. I did that because experimentation without boundaries is careless and this kind of experimentation deserves boundaries.

The Importance of Sandboxing

I am not a security specialist but I do read widely and I respect what is emerging. We are seeing increasing discussion around agents interacting with one another, negotiating tasks and showing emergent behaviour in controlled research settings. Open-source frameworks such as AutoGPT have shown how quickly systems can set sub-goals and continue operating with surprising independence. We have also seen testing scenarios where systems attempted to preserve objectives when constraints were introduced. None of this is alarmist in my view but all of it demands discipline.

If I was going to experiment with autonomy, I wanted it sandboxed with clear isolation and clear guardrails.

Model Fit Matters

The build process also gave me a useful reminder about model fit. ChatGPT 5.2 remains my daily go-to for strategy, thinking and problem-solving but during this coding process it repeatedly took me into loops where fixes created new issues. Out of curiosity I switched to Claude and the difference was immediate. It acknowledged what I had already tried, traced the logic, identified the root failures and patched them cleanly. Within minutes the system behaved as intended. For someone who has not coded in decades, that felt less like prompting a tool and more like working with a technical partner who could see the whole architecture.

The Result?

By the end of the weekend I had an autonomous agent operating inside WhatsApp on my phone, which is exactly where I wanted it. There was no additional dashboard to manage and no new interface to learn. I could simply issue instructions in a familiar channel and get execution.

I can now take a photograph of a business card and the agent extracts the details, asks me for context and drafts both a personalised email and a WhatsApp message for approval. A task that might previously have sat in my backlog for days now takes seconds. I asked it to research flights one evening and woke up to a structured comparison of routes, pricing and stopovers with clear recommendations.

This is the distinction I believe leaders now need to internalise. AI assists while autonomous systems execute.

The Enterprise Imperative for 2026

That is why the enterprise conversation has to mature in 2026. Last year the question was whether organisations were using AI and most are in some form. The more strategic question now is whether organisations are architecting for autonomy. There is a meaningful difference between teams using AI tools in isolated pockets and an enterprise designing workflows where autonomous systems handle repeatable research, reporting, qualification and coordination tasks.

One improves productivity and the other reshapes operating structure.

When execution becomes partially autonomous, speed changes structurally and cognitive load reduces. Human effort can then move up the value chain toward judgment, creativity and leadership rather than coordination and repetition. This is not about replacing people. It is about elevating people into higher-value work where human context and decision quality matter most.

Current State: The NEXA AI Lab Perspective

Three weeks ago we hosted a NEXA AI Lab Enterprise Leadership Breakfast in Dubai where around twenty C-suite leaders shared where they are on their AI journeys. The room was thoughtful, practical and engaged. Everyone was experimenting and nobody was ignoring the shift. What was clear, however, is that many organisations are still formalising their AI frameworks, ownership models and governance structures. That is completely natural at this stage.

What may not remain natural for long is the pace of change.

The Gap Appears in Sequence

If autonomy is the next layer, the speed of execution accelerates again. Organisations with clear AI architecture will find it far easier to layer autonomy onto existing systems. Organisations still deciding where AI fits may not feel an immediate shock, but the gap tends to appear in sequence. It shows up first in speed, then in consistency, then in margin and eventually in customer experience.

Infrastructure rarely announces itself loudly. It simply changes how things move.

The Leadership Question

So the most useful question for leadership teams today is not whether they are using AI. It is how autonomous they are prepared to become. Have friction points been identified and prioritised? Is governance clear? Is ownership defined? Are sandboxed environments in place before autonomy scales? Are teams building trust through performance, just as we do in a driverless car?

Autonomy is not a feature. It is a capability shift.

If AI defined the beginning of this decade, autonomy may define the next phase of it. And once trust is built through consistent execution, there is rarely a return to the old way of working.