Brendan Hooft, CEO & Managing Partner of ESPER.
What should we teach our children? UNESCO reports that “over two-thirds of secondary-school students in high-income countries“ now regularly use AI, while the OECD’s latest survey of teachers found only about a third using it in their teaching. Our children are learning the tools faster than our schools are learning to teach them.
Stanford found that since generative AI became widespread, employment for workers aged 22 to 25 in the most AI-exposed occupations has fallen 13% relative to their peers. Gallup reports that only one in five employees worldwide is engaged at work, yet engagement tells us how people feel about their work, not whether they are equipped for what it is becoming. Gartner predicted that by the end of this year, “20% of organizations will use AI to flatten their organizational structure, eliminating more than half of current middle management positions.”
For the first time, every stage of a working life is being rewritten at once. The question is no longer whether we must change but what we are changing into, and almost no parent, school, employer or government can yet answer it with precision. Without that answer, people cannot shape their own future, and nations cannot build theirs.
At LEAP 2026, one of the world’s largest technology conferences, held in Saudi Arabia, a 14-year-old boy asked me how he should future-proof himself. It reminded me of my son, who is also 14. Both deserve to know what to learn and to what standard, and to be enabled to get there. Answering that question with precision is what we set out to do.
The Talent Gap
Over the past 12 months, my firm has worked with a GCC national government to build the AI extension of its national digital skills standard, now under review. It draws on more than 40 reference frameworks, from ISO, NIST and the OECD to UNESCO, the EU and China.
Alongside it, our own research across seven government and private sector entities found people operating at about half the proficiency their roles require, in both professional AI skills and behavioral competencies. The constraint is readiness, not ambition, and it limits careers before it limits national plans. Not one entity had defined what being AI-native means for an individual, so hiring, training and promotion were each aiming at a different target.
Character Over Capability
The behavioral gap was as wide as the technical one, and that should not surprise us. When I was at Gartner, interviews focused on traits and behaviors as much as on the CV because they predict growth. The World Economic Forum expects 39% of workers’ existing skills to “be transformed or become outdated” by 2030. Skills can be relearned; character is what determines whether someone relearns them.
AI does not correct character; it amplifies it. Sound judgment becomes value at scale, and flawed judgment becomes error at the same speed. Five competencies matter wherever someone sits:
1. Curiosity: A genuine hunger to learn and explore, well beyond AI
2. Judgment: Knowing when to accept, override or escalate what the machine proposes
3. Systems Thinking: Seeing how people, processes and technology connect and what shifts when one of them changes
4. Agency: Taking accountability for every outcome you and your AI produce and taking ownership of your own future
5. Adaptability: Continuing to perform as tools, roles and rules change
Five Profiles
If character is the constant, skills are what change from one stage of life to the next. Build these now, knowing each will need renewing.
The youth thinks with AI, never through it. At a foundation level, they should learn to ask precise questions (context engineering), identify problems worth solving (use-case framing), tell real content from synthetic (content provenance) and test ideas through small experiments (experiment design).
The graduate supervises the work AI does. Their first direct report may well be an agent. At the Apply level, they must instruct AI precisely (context engineering), test output before relying on it (evaluation and benchmarking), confirm that data is fit for purpose (data readiness) and run agents within a defined mandate (agent operations).
The employee redesigns the work before someone else does. At the Design level, they must rebuild workflows for people and agents together (process redesign), direct agents (agent operations), resolve what they escalate (human review) and keep current the knowledge they rely on (knowledge operations).
The founder builds from zero, with a small human team running an agent workforce. At the Set Direction level, they must anticipate where AI will create new markets (strategic foresight), turn AI into products customers will pay for (AI product management), decide which work is done by people and which by agents (operating model design) and keep automating the next costly process (process redesign).
The executive: transforms what already exists while it runs. At the Integrate level, they must fold AI into strategy (strategy and roadmap development), decide what to fund and what to stop (portfolio prioritization), move people into AI-enabled roles (workforce and role transition) and decide how material AI risks are treated (risk treatment).
The more senior the role, the less a person does with AI and the more they decide about it. That is what conducting looks like.
From Score To Symphony
Together, these form a score, and a score changes nothing until it is played. When individuals, organizations and nations work from the same one, each can see the distance between where they are and where they need to be and close the gap deliberately. People can plan careers rather than guess at them. Organizations can hire, develop, promote, redeploy and source talent against one standard. Nations can align schools, universities and employers around a single definition of AI-native, as I set out in “The Human-AI Symphony Playbook.”
AI succeeds or fails on people. Without the talent to conduct it, it will be impossible to achieve what AI makes possible. The boy at LEAP and my son both need to know which part is theirs, and so do the rest of us.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?