At yesterdays Think Women in Digital Government event, I raised a question that rarely appears in discussions about diversity in engineering: not simply who works in technology, but how engineers think.
Modern computing has been built through a very specific intellectual tradition. Engineers are trained to break problems into components, abstract complexity into models, and optimise systems through precise analytical reasoning. This approach has delivered remarkable results. It built the internet, modern software architecture, and the digital infrastructure that underpins contemporary life.
But analytical reasoning is only one way the human mind understands the world.
The psychiatrist and philosopher Iain McGilchrist argues that human cognition operates through two broad modes of attention. In The Master and His Emissary (2009), he describes how the brain’s hemispheres process reality differently. The left hemisphere focuses narrowly. It analyses structures, categorises objects and manipulates abstractions. This is the mode of thought that allows mathematics, engineering and formal logic to exist.
The right hemisphere processes the world differently. It understands context, relationships and ambiguity. Rather than isolating parts, it recognises systems.
Both forms of thinking are necessary. Healthy cognition requires their interaction. McGilchrist’s concern is that modern institutions increasingly favour the analytical mode at the expense of the contextual one.
Technology is perhaps the clearest example.
Software engineering rewards optimisation. A system is considered successful when it performs its defined function efficiently and reliably. But many of the most significant failures in technology do not arise from poor code. They arise from systems that were designed correctly in a narrow technical sense but were blind to the wider environment in which they would operate.
Cyber security provides a familiar illustration. Many breaches occur not because cryptography is broken or software fails to execute properly, but because engineers optimise technical architecture while overlooking how human beings interact with the system. Phishing attacks, identity misuse and behavioural manipulation exploit precisely this gap.
Artificial intelligence exposes the same issue at scale. Machine learning models can perform impressively in controlled environments while producing unpredictable results when deployed in real-world settings. The models optimise for mathematical performance metrics, yet struggle when faced with the complexity of human behaviour.
These problems reflect a broader intellectual pattern that philosophers recognised long before modern neuroscience.
Martin Heidegger warned that modern technological civilisation increasingly privileges what he called “calculative thinking”. In his essay The Question Concerning Technology (1954), Heidegger argued that technological reasoning treats the world as something to be optimised and controlled. This mode of thought is extraordinarily powerful, but it risks narrowing human understanding of reality.
He contrasted calculative thinking with what he called “meditative thinking”, a form of reflection that considers meaning, context and relationships rather than merely efficiency.
In other words, the challenge is not technology itself but the dominance of a single way of thinking about it.
This is where the question of women in technology becomes particularly interesting.
Neuroscience research suggests that, on average, men and women show different patterns of brain connectivity. A large study led by Ragini Verma at the University of Pennsylvania analysed diffusion MRI scans of nearly one thousand individuals. The results suggested that male brains tend to show stronger connectivity within each hemisphere, while female brains tend to show stronger connectivity between hemispheres (Verma et al., 2013).
These differences are statistical tendencies rather than rigid biological rules. Human cognition varies enormously between individuals. But the findings raise an intriguing possibility: women may integrate analytical and contextual processing differently.
If so, the implications for technology leadership are significant.
The digital systems that now shape society are no longer purely technical artefacts. They are socio-technical systems embedded in human environments. Designing them requires more than optimisation. It requires anticipating unintended consequences, understanding behaviour and recognising how technology interacts with the wider world.
Homogeneous engineering cultures tend to optimise for the cognitive style that dominates them. Historically, the technology sector has been shaped by analytical thinkers who excel at abstraction and reduction. That cognitive tradition built the modern digital infrastructure.
The next generation of challenges is different.
Artificial intelligence governance, cyber security resilience and the safety of large digital platforms require engineers who can combine analytical precision with contextual awareness. These are problems that sit at the boundary between technical systems and human systems.
Increasing the number of women in senior technical roles therefore has a practical dimension that goes beyond representation. It introduces cognitive diversity into the leadership of systems whose complexity increasingly exceeds purely technical reasoning.
The future of technology will not depend solely on building powerful systems. It will depend on understanding the environments in which those systems operate.
And that requires more than one way of thinking.
