In my recent blog on task decomposition as a key AI skill, I highlighted how breaking down complex problems enables effective human-AI collaboration. Yet before we can decompose a task, we must identify which problems are worth pursuing - a skill that takes on new dimensions in the age of AI.
This ability to recognize solvable problems expands dramatically with AI tools at our disposal. Tasks once considered too time-consuming or complex suddenly become manageable. The cognitive offloading that AI enables does not just help us solve existing problems - it fundamentally reshapes our understanding of what constitutes a tractable challenge.
Consider how VisiCalc transformed financial planning in the early 1980s. Initially seen as a mere automation tool for accountants, it revolutionized business planning by enabling instant scenario analysis. Tasks that would have consumed days of manual recalculation became instantaneous, allowing professionals to explore multiple strategic options and ask "what if" questions they would not have contemplated before. Similarly, AI prompts us to reconsider which intellectual tasks we should undertake. Writing a comprehensive literature review might have once consumed months; with AI assistance, scholars can now contemplate more ambitious syntheses of knowledge.
This expanded problem space creates its own paradox. As more tasks become technically feasible, the challenge shifts to identifying which ones merit attention. The skill resembles what cognitive psychologists call "problem finding," but with an important twist. Traditional problem finding focuses on identifying gaps or needs. Augmented problem finding requires understanding both human and AI capabilities to recognize opportunities in this enlarged cognitive landscape.
The distinction becomes clear in professional settings. Experienced AI users develop an intuitive sense of which tasks to delegate and which to tackle themselves. They recognize when a seemingly straightforward request actually requires careful human oversight, or when an apparently complex task might yield to well-structured AI assistance. This judgment develops through experience but could be taught more systematically.
The implications extend beyond individual productivity. Organizations must now cultivate this capacity across their workforce. The competitive advantage increasingly lies not in having access to AI tools - these are becoming ubiquitous - but in identifying novel applications for them. This explains why some organizations extract more value from AI than others, despite using similar technologies.
Teaching augmented problem finding requires a different approach from traditional problem-solving instruction. Students need exposure to varied scenarios where AI capabilities interact with human judgment. They must learn to recognize patterns in successful AI applications while developing realistic expectations about AI limitations. Most importantly, they need practice in identifying opportunities that emerge from combining human and machine capabilities in novel ways.
The skill also has ethical dimensions. Not every task that can be automated should be. Augmented problem finding includes judging when human involvement adds necessary value, even at the cost of efficiency. It requires balancing the technical feasibility of AI solutions against broader organizational and societal impacts.
As AI capabilities evolve, this skill will become increasingly crucial. The future belongs not to those who can best use AI tools, but to those who can best identify opportunities for their application. This suggests a shift in how we think about AI literacy - from focusing on technical proficiency to developing sophisticated judgment about when and how to engage AI capabilities.
The automation paradox that Lisanne Bainbridge identified in her 1983 analysis of industrial systems points to an interesting future. As we become more adept at augmented problem finding, we discover new challenges that merit attention. This creates a virtuous cycle of innovation, where each advance in AI capability opens new frontiers for human creativity and judgment.
Perhaps most intriguingly, this skill might represent a distinctly human advantage in the age of AI. While machines excel at solving well-defined problems, the ability to identify worthy challenges remains a uniquely human capability. By developing our capacity for augmented problem finding, we ensure a meaningful role for human judgment in an increasingly automated world.
