I was at the Annual Meeting of Global Future Leaders 2026 in Dubai, organized by the World Economic Forum in collaboration with the UAE Government. There were a lot of discussions on technology, economic resilience, global cooperation, and leadership, and one question stayed with me: how will our increasing technological capability translate into prosperity?
My central takeaway: AI leadership means pursuing three ambitions together. Build competitive economies, transform how companies create value, and develop the human capabilities that underpin both.
For leaders in business, public and private sectors, the challenge is deciding where to invest, how much authority to delegate and how to prepare institutions, organizations, and people for the changes ahead.
Build competitive economies with lasting capability
AI offers countries an opportunity to improve public services, accelerate discovery, and develop new sources of growth. Capturing it takes coordinated investment in infrastructure, institutions, enterprise, and talent.
Discussions on AI infrastructure reinforced how closely computing capacity, energy, water, and skills are connected. You must plan them together, with economic resilience and environmental sustainability in mind.
The investment case should also account for what host countries and communities gain. Data centers can support a wider economy of technical expertise, local suppliers, research and, new businesses. Those benefits don’t happen by default. They come from deliberate partnerships and investment in local capability.
International cooperation matters most here. Countries need domestic strengths alongside dependable relationships that support trade, scientific discovery, education, and trusted standards. The strategic task is to understand those dependencies and build partnerships that remain useful through disruption.
Transform business value and earn trust in deployment
For the organization, the opportunity is across customer experience, decision-making, product development, and operations. The leaders need to work out where AI can change the way businesses create value and then rework the way they do things around that opportunity.
That’s about autonomy. As AI moves from answering questions to acting, organizations must define what decisions systems can make, what decisions require human approval and who is ultimately accountable.
Oversight should scale with the consequences of error. Automating a routine administrative task carries different implications from delegating a decision affecting a customer’s finances or an employee’s livelihood.
My view is that autonomy should grow as reliability is demonstrated, boundaries are established, and people can still intervene. The performance measures should include business outcomes, decision quality, and the effectiveness of the controls.
Trust becomes tangible when customers and employees experience systems that work reliably and have a clear route to resolution when they do not.
Develop the talent that sustains progress
The workforce question deserves a central place in the AI strategy.
One concern I find particularly pressing is developing early-career professionals. Entry-level tasks have traditionally helped people learn a profession, understand context, and build judgment. If autonomous systems absorb that work, organizations must create other ways to develop expertise.
Otherwise, we risk improving efficiency today at the expense of the talent pipeline we need tomorrow.
AI investment should therefore include redesigned roles, mentoring, practical learning and pathways into new responsibilities. Leaders need to ask whether the experience will be gained as the work changes, and how the organization will go about preserving and renewing its expertise.
Prepare the next generation
My interest in borderless education is in this broader context. Access to quality learning is fundamental to national competitiveness, to business innovation, and to individual opportunities. Governments, educators, and employers can play a part by linking up the learning pathways and making skills easier to recognize across institutions and borders.
Affordable access, capable teachers, and trusted credentials remain essential. AI can extend personalized and multilingual support, with success measured by understanding and capabilities learners can apply.
Children’s adoption of AI should be viewed with the same long-term perspective. The experience of social media should make us question the incentives that products have to encourage prolonged engagement, dependency, and instant gratification.
AI learning experiences should encourage children to act, reason and reflect. Age-appropriate safeguards, privacy protections and guidance for parents and teachers must accompany adoption. Success is measured by children’s growing ability to think, learn, and act independently.
Turning ambition into accountable delivery
These discussions result in tangible decisions, which are worth investing in. What needs to change in the operating model, where human judgment will still be required, and how workforce capability will evolve as we go forward.
Progress needs clear ownership and evidence. AI programs should demonstrate customer and business value. Workforce initiatives should show that people gain useful capabilities and career opportunities. Infrastructure investment should demonstrate its contribution to the wider economy.
Dubai has cemented my view that these responsibilities go hand in hand. Competitive economies need innovative companies; innovative companies need capable people, and sustained adoption depends on trust.
The leadership challenge is to ensure that AI investment reinforces all three elements, so that the value it creates is both sustainable and broadly shared.
