The future of AI isn't more agents, it's more impact

Dubai: Every major technological shift creates a temptation to focus on the technology itself. With artificial intelligence (or Super Intelligence as President Trump says), we talk about the size of our models, the number of tokens and agents, and the amount of compute power at our disposal. By these measures, the pace of progress has been extraordinary.

But for those of us deploying AI in the real world, the technology is not the mission. The mission is the outcome we are trying to create.

Worryingly, a December 2025 BCG report found 60% of companies globally are not generating any material value from AI despite substantial investment.

AI is now moving beyond assistance and recommendations towards systems that can increasingly act on our behalf. That makes it even more important to be clear about what we are trying to achieve.

What matters is whether AI can create genuine value, earn and scale responsibly.

Across conversations with government leaders, technologists, investors and businesses, I see these questions becoming increasingly urgent. The ongoing debate around AI safety reflects a broader reality: as AI becomes more capable, governance and accountability must evolve with it. The answer is not to slow progress, but to make sure safeguards advance alongside capability.

That starts with purpose.

For governments and businesses alike, AI should be judged by whether it helps deliver the outcomes they already care about.

Governments still exist to serve people well, improve quality of life and make everyday interactions as simple and seamless as possible. Businesses still want to serve customers better, make stronger decisions and create sustainable value.

AI gives us new ways to deliver on those goals.

Success lies in whether the technology helps us fulfil the mission better. This is why we should not confuse the model with the mission.

Models will change. Architectures will change. New agents will emerge. The most advanced technology today may not be the most appropriate technology for every use case tomorrow.

That focus is also the basis of future-proofing.

The goal is to build an AI foundation that can adapt as technology changes.

For me, a future-ready AI architecture is one that preserves choice: the ability to adopt a different model, work with a different partner or respond to new requirements without rebuilding the system from the ground up.

That means fit-for-purpose intelligence: the right technology, in the right environment, with the right controls, for the problem at hand.

The visible AI layer is often the easy part.

Beneath a seamless agent sits the harder work: connecting data, integrating systems, orchestrating services and making rules and information machine-readable.

What will matter is the strength of the foundations beneath those systems.

This becomes more important as AI grows more agentic.

As systems move from recommending to acting, the relationship between people and machines changes too.

We need clear limits around what these systems can do, what information they can access and when humans must intervene. Above all, accountability must remain with humans.

These are operational questions, not abstract ones.

Greater autonomy must be matched by governance, safeguards and defined human oversight.

That brings us to trust.

Trust is not the antithesis of innovation. Trust is what allows innovation to scale.

I do not believe responsible AI requires us to slow progress. As systems become more capable, governance, transparency and accountability must advance with them. Progress and safeguards should reinforce each other, not compete.

Organisations will give intelligent systems greater responsibility when they understand how those systems behave and where accountability sits. The same is true for governments and citizens.

Trust is not a constraint on adoption. It is what makes deeper adoption possible.

So is sovereignty.

Sovereignty in the AI era should not mean technological isolation or building every layer of the stack independently. It means retaining control over critical decisions around data, infrastructure, models and partners.

The ability to choose is itself a form of resilience and sovereignty

AI ecosystems will become more diverse by design. Different agents will use different models, and sectors and jurisdictions will require different levels of control.

The aim is to preserve enough flexibility to adapt without losing sight of the mission.

And that brings us to scale.

Building an AI application is one challenge. Operating nation-scale intelligent systems across large organisations, critical infrastructure or multiple markets is another.

At scale, technology meets reality: regulation, cybersecurity, sovereignty, infrastructure and human behaviour.

Those constraints determine whether a system can operate reliably beyond a controlled environment.

Scale is the real stress test.

I have seen this journey first-hand. Technology does not simply move from one market to another; it has to adapt to different regulatory environments, infrastructure and national priorities.

The real test is not where an innovation was created, but how widely its value can responsibly travel.

Capability must be translated.

The next era of AI will not be defined by how much technology we deploy, but by how well we use it to serve a clear purpose.

Technology will continue to evolve, and we should expect that change.

Our responsibility is to build in a way that allows us to keep moving with it; with the right safeguards, the right foundations and the freedom to make different choices as circumstances change.

That is what future-proofing really means.

It means using AI ambitiously and responsibly, while retaining control over the decisions that matter.

Because the model is not the mission. The mission is the outcome we are trying to create – for the goals we have always had; and our technology choices should continue to serve that purpose, not define it.