Behavioural Economics
Most CMOs cannot trace marketing spend to commercial outcomes. Budgets flow toward activity – content, channels, campaigns – without a strategy that connects them to growth. Marketing’s credibility problem in the boardroom is largely a competence problem in the marketing department.
Capital allocation decisions sit at the centre of every senior leadership agenda. Yet the boards and committees making them are rarely staffed by finance specialists. The frameworks they inherit were built decades ago, and the assumptions inside them still shape how institutions measure investment risk today.
Sales and marketing teams spend billions every year on messages that fail to move buyers. The reason is structural. Most purchasing decisions happen in parts of the brain that traditional research cannot reach. Customer surveys and intuition-based campaigns keep producing the same disappointing returns.
Most organisations are better at deploying AI than at using it. The workflows, decision habits, and cultural defaults of the existing organisation stay intact long after the new tools arrive. That gap between technical implementation and behavioral adoption is where most transformation investment is quietly lost.
Most organisations are spending heavily on AI without a clear view of which decisions the technology is actually supposed to improve. Models get shipped, dashboards proliferate, and senior leaders still cannot tell whether any of it is changing the quality of the choices the business makes. The missing layer is not more data or better algorithms, it is a disciplined way to connect AI outputs to the decisions a company is trying to get right.
Uncertainty is now the steady state, and most leadership teams are still managing it as a temporary disruption. Composure, judgement and the willingness to commit are degrading under that load, and the cost shows up in slower decisions, narrower thinking and quiet disengagement. The question is no longer how to remove uncertainty from the operating environment, but how to make the people running the business measurably better at working inside it.
Leadership systems built for one era are now managing a workforce shaped by another. Younger professionals are leaving organisations that cannot offer autonomy, purpose, or flexibility, not because they lack ambition, but because the structure no longer matches how they want to work. The retention and innovation cost of that mismatch is rising faster than most organisations are willing to acknowledge.
Most inclusion work in firms is built on good intentions and weak evidence. Leaders spend heavily on training, charters, and targets, then cannot show which actions moved hiring, promotion, or retention. The gap between stated commitment and measurable progression is where credibility, talent, and money quietly leak away.
Marketing budgets are under sharper scrutiny than at any point in a decade, and the old assumptions about how brands earn attention have stopped holding. AI has reset what creative, media and customer experience teams are expected to produce, and most organisations are still reasoning about it as a tool rather than a structural change to how brands compete. The commercial question is which parts of the marketing operation get rebuilt around AI, and which parts get protected because they still depend on human judgement.
Most organisations overestimate risk in markets they do not understand and underestimate opportunity in ones they have already written off. The problem is not missing data – experienced leaders tend to hold shared, systematically incorrect assumptions about how the world has developed. When those assumptions go unexamined in strategy sessions, they shape investment, market entry, and risk decisions in ways that better analysis alone cannot fix.
Attention is degrading inside organisations and the usual wellbeing programmes are not stopping it. Smartphone reflex, screen saturation, and chronic dopamine spikes are quietly reshaping how people focus, recover, and connect with colleagues. Leaders see the symptoms in productivity, engagement, and mental health metrics; they need an explanation that holds up scientifically and a set of habits people will actually adopt.
Mariano Sigman is an Argentine physicist and neuroscientist who helps organisations understand decision-making, learning, and communication through insights from cognitive science and neuroscience.