The competitive advantage: Why ‘slow and steady’ matters in the age of AI
Dr Rita Fontinha, Director of Flexible Work, World of Work Institute, discusses why slow and reflective work is crucial in the AI race.
The use of AI is typically associated with speed. Speed of work, speed of outcome, speed of process. This has led to a powerful narrative that faster is better. But as organisations look towards AI as a way to improve efficiencies, this is having an impact on entry-level roles which are being replaced by automated systems. Figures from the World Economic Forum’s Future of Jobs Report 2025 found that 40% of employers expect to reduce their workforce where AI can automate tasks.
But in the race to remain competitive, are we losing the much valued ‘slow and steady’ aspect of roles where knowledge is built and critical thinking skills acquired? In fact, the very capabilities that AI accelerates may make slow, reflective, human‑centred work more valuable than ever. AI learns from the information it is fed, but people still need to be able to assess and evaluate the outcomes for accuracy, anomalies and also opportunities.
Why the future needs slower thinkers
Worker expectations are growing and diversifying. Flexible working is no longer the preserve of younger generations. Older workers facing rising retirement age, increasingly value reduced working time and the chance to enjoy life while still healthy. This shared desire for balance creates unexpected bridges across generations and brings a different kind of productivity.
Rather than equating speed with efficiency or AI with the loss of roles, slow and steady work can actually lead to working better. AI can produce outputs at the click of a button, but the real value lies in how humans interpret and apply it.
Jobs of the future will rely heavily on the ability to analyse AI‑generated information. The technology might be able to generate answers instantly, but what it can’t do is judge whether those answers are appropriate, fair, or aligned with organisational values. That responsibility still falls to humans and it demands time and the kind of deep thinking that cannot be automated.
This is where intergenerational and interdisciplinary teams become essential. Knowledge transfer between experienced workers and new entrants helps build the judgement required to work effectively with AI. Instead of removing entry‑level roles, organisations may need to redesign them to cultivate the reflective skills that AI cannot replicate. Pairing junior roles with structured mentoring may be the key to preserving mastery in an AI‑driven workplace.