“At a time where answers are really cheap and readily available, asking the right questions becomes the premium leadership and professional skill.” – Laura Fuentes – CHRO of Hilton
When information can be generated in seconds, professional value moves away from retrieving answers and towards defining the problem those answers should solve. That shift changes what leadership looks like. The decisive advantage is no longer simply knowing more facts than colleagues, but recognising which facts matter, which assumptions require testing and which consequences deserve attention before action is taken. In a hotel business, where decisions connect commercial performance, guest experience, employee judgement and operational detail, the quality of the initial question can determine the quality of the entire chain that follows.
The distinction is between information and judgement. A system can summarise customer feedback, compare performance across properties or draft a workforce plan, yet it cannot independently decide whether a decline in satisfaction reflects staffing levels, a change in guest expectations, a flawed measurement method or an isolated local problem. That requires context. It also requires the confidence to challenge a convenient explanation. Questions such as ‘What are we assuming?’, ‘Who benefits from this decision?’, ‘What evidence would change our mind?’ and ‘What happens next?’ turn passive consumption of information into disciplined inquiry.
The economics of abundant answers
Generative artificial intelligence has lowered the cost of producing first drafts, summaries, options and apparently plausible recommendations. The consequence is not that expertise becomes irrelevant, but that low-quality reasoning can now be produced at greater speed and scale. A weak question may yield an elegant answer to the wrong problem. A narrow prompt may reinforce a flawed premise. A confident output may conceal uncertainty that should have shaped the decision. The premium therefore shifts to framing: specifying the objective, identifying constraints, defining the relevant stakeholders and setting a standard for evidence.
This creates a new form of managerial leverage. A leader who asks precise questions can make technology more useful without treating it as an oracle. Instead of requesting a generic plan, the leader might ask for competing explanations, missing data, likely unintended effects and a comparison of short-term and long-term trade-offs. The interaction becomes less like searching a database and more like conducting a structured review. The machine supplies speed and breadth; the human supplies purpose, accountability and discernment.
Why the capability is especially relevant to people leadership
People decisions are rarely reducible to a single metric. Hiring, development, reward, retention and organisational design involve incomplete evidence and competing definitions of fairness. A workforce dashboard may show absence, turnover or engagement patterns, but interpretation depends on how data was collected and what is happening locally. A leader must ask whether a correlation reflects causation, whether a measure captures the experience it claims to represent and whether an intervention could create new burdens for the very people it aims to help.
That questioning discipline also improves conversations. In performance management, asking what support would remove an obstacle may be more productive than asking why a target was missed. In talent reviews, asking which capability the organisation will need next may prevent a narrow focus on current visibility. In inclusion work, asking who is absent from the decision and whose experience is being treated as typical can expose blind spots that polished analysis leaves untouched. Questions are not merely requests for information; they are instruments for distributing attention and power.
Evidence from the changing skills market
The wider labour market supports this emphasis. The World Economic Forum’s Future of Jobs 2025 report identifies analytical thinking as the most sought-after core skill, with seven out of ten employers considering it essential.1,2 The report also places AI and big data among the fastest-growing skills, alongside technological literacy, while retaining leadership, resilience, creative thinking and curiosity as important capabilities.2,3 This combination matters because questioning is where technical fluency and human judgement meet. Employees need enough understanding to interrogate an output, but also enough curiosity to look beyond the first plausible answer.
The pattern challenges the idea that automation creates a simple divide between technical and human work. As routine research and drafting become easier, analytical work may become more consequential rather than less. Employers still need people who can interpret systems, weigh competing interests, communicate decisions and learn when circumstances change. The World Economic Forum reports that half of employers expect to reorient their businesses in response to AI, while two-thirds plan to hire people with specific AI skills and 40 percent anticipate workforce reductions where automation can perform tasks.2 The practical implication is a rising premium on workers who can connect tools to meaningful business problems.
The objections and the limits
There are reasonable objections to placing so much emphasis on questions. Endless inquiry can become avoidance, allowing leaders to postpone difficult decisions behind another round of analysis. Questions can also be performative: a manager may invite challenge while rewarding agreement, or demand evidence selectively when a preferred conclusion is threatened. Good questioning therefore needs boundaries. A decision should have a clear owner, a defined timeframe, an explicit evidence threshold and a willingness to act despite residual uncertainty.
Another limitation is that not every answer is cheap. Reliable answers may still require fieldwork, specialist expertise, careful data governance and time with customers or employees. Artificial intelligence can reduce the cost of generating hypotheses, but verification remains expensive in many settings. It can also reproduce biases in the material used to produce its outputs, expose confidential information or create false confidence through fluent language. The right question is consequently not only ‘What can the system tell us?’ but also ‘What does it not know, and how could we find out?’
Turning questioning into an organisational practice
Organisations can make this capability visible by changing how meetings and decisions operate. Before reviewing a recommendation, teams can separate facts, assumptions, interpretations and choices. Before approving a project, they can identify the customer problem, the measurable outcome, the principal risk and the condition that would justify stopping. After implementation, they can review not only whether the target was met but also whether the target encouraged the right behaviour. These routines make critical thinking practical rather than inspirational.
Leaders can also model intellectual humility. Asking for a dissenting view, inviting the person closest to the work to challenge a senior assumption and acknowledging uncertainty all signal that inquiry is valued more than theatrical certainty. Training should include prompt design and AI literacy, but it should also cover source evaluation, data limitations, ethical judgement and the human consequences of operational choices. The strongest professionals will not be those who ask the most questions indiscriminately. They will be those who ask questions that improve the decision, reveal the trade-off and lead to responsible action.
The strategic issue is therefore not whether answers remain useful. They do. The issue is whether an organisation can distinguish rapid output from genuine understanding. In an environment saturated with generated material, leadership depends on setting direction, testing relevance and accepting responsibility for consequences. That makes questioning a form of professional capital: it determines where attention goes, how tools are used and whether speed produces progress or merely accelerates error. The organisations that develop this capability will treat intelligence not as the possession of answers, but as the disciplined pursuit of better decisions.
References
1. Future of Jobs 2025: Skills Outlook – https://www.weforum.org/publications/the-future-of-jobs-report-2025/3-skills-outlook
2. The Future of Jobs Report 2025 | World Economic Forum – 2025-01-07 – https://www.weforum.org/publications/the-future-of-jobs-report-2025
3. Future of Jobs Report – https://reports.weforum.org/docs/WEF_Future_of_Jobs_Report_2025.pdf
4. 3. Skills outlook – The Future of Jobs Report 2025 – https://www.weforum.org/publications/the-future-of-jobs-report-2025/3-skills-outlook?47a05339_page=2&47a05339_page=2&47a05339_page=9&47a05339_page=10
5. 3. Skills outlook – https://www.weforum.org/publications/the-future-of-jobs-report-2025/3-skills-outlook?47a05339_page=3&91a7963e_page=4
6. Future of Jobs Report 2025: The jobs of the future – and the skills … – 2025-01-08 – https://www.weforum.org/stories/jobs-and-the-future-of-work/future-of-jobs-report-2025-jobs-of-the-future-and-the-skills-you-need-to-get-them
7. New Economy Skills: Unlocking the Human Advantage – https://reports.weforum.org/docs/WEF_New_Economy_Skills_Unlocking_the_Human_Advantage_2025.pdf
8. Analytical thinking remains top core skill for employers – 2025-02-18 – https://www.fm-magazine.com/news/2025/feb/analytical-thinking-remains-top-core-skill-for-employers/
9. Insights from the Future Jobs Report 2025 – 2026-04-23 – https://interoperable-europe.ec.europa.eu/collection/employment-and-working-conditions/news/insights-future-jobs-report-2025
10. Future of Jobs Report 2025: 78 million new jobs by 2030 but urgent … – 2026-10-29 – https://www.eco-business.com/press-releases/future-of-jobs-report-2025-78-million-new-jobs-by-2030-but-urgent-upskilling-needed-to-prepare-workforces/
