The Fastest Growing Problem You Can’t See

Modern work and life are built around information. New data, emails, dashboards, guidelines and posts arrive every hour, each promising that “more information” will finally give clarity and control. Yet, after years working in healthcare and public service, listening to clinicians on night shifts, managers in crisis meetings and patients trying to navigate fragmented systems, the opposite often happens: the more information appears, the more confusing, anxious and polarised everything can feel.

I often ask a deceptively simple question: what if one of the fastest growing problems in the world is not a disease, a technology or a new crisis, but the way we think about problems themselves? When attention is constantly pulled towards what is wrong, missing or risky, people can slide into “problem thinking”, a mindset where deficits dominate and possibilities barely get a look in. This mindset is then amplified by feeds and recommendation engines that keep serving us more of what we already believe, deepening echo chambers, stoking polarised debates and making it easier to dismiss anything that does not fit our existing story.

At the same time, there is a quieter paradox that rarely makes headlines. Professionals drown in metrics and reports, yet often lack the most meaningful information of all: what matters to people at the front line and how they actually experience services and systems. In hospitals and community teams, staff describe “death by report” while still feeling unheard about the basics such as unsafe staffing, broken processes, or moments of good practice that no dashboard ever captures.

Information overload exists alongside a more profound lack of meaning and care in how we relate to ourselves, each other and the world. Despite the flood of data and the growth of AI tools that can generate fluent summaries at speed, many organisations still fail to listen systematically to frontline staff and service users. This is one reason for an interest in tools and approaches, such as Insight‑Genie and similar diagnostic platforms in healthcare, that collect and synthesise insights from people closest to the work without adding to their burden.

When they are used well, these tools can surface patterns in workforce experience, communication and decision making that are invisible in routine performance reports, and can do so with a small number of focused questions rather than yet another long survey. In this framing, information overload is indeed a huge and growing challenge, but an even deeper problem is the absence of coherence, connection and care in how information is used. What many people lack is not more data, but guidance on what really matters: the minimum information needed to make a safe, values based decision, whose perspective is missing from the room, and how information, including AI generated summaries and dashboards, can serve meaning rather than drown it.

Tools that surface real world stories, patterns and feedback from staff and service users are part of this solution only when they help to distil complexity rather than gloss over it with generic, polished but empty language. The invitation is not to switch off from information, or to reject every new technology, but to become more intentional about how attention is spent. In practice, this can mean asking, alone and in teams: how much of the day is spent scanning for problems, faults and risks, and how much is spent noticing what works and what could be expanded?

It can also mean pausing to check whether the material in front of you, whether a briefing, a dashboard or a neatly written “AI summary” at the top of a search page, reflects real experience, cites its sources and is open about uncertainty. A further question is how even a small part of attention can be shifted towards meaning, relationships and possibility. By making these shifts, individuals and organisations can begin to loosen the grip of problem thinking, address information overload more wisely and create more hopeful, humane ways of working.